A method for constructing an electricity price optimization model taking into account electricity retailers and prosumers

Abstract

The present invention discloses a method for constructing an electricity price optimization model taking into account electricity retailers and prosumers, including: obtaining basic parameters, the basic parameters including electricity price types, the electricity price types including fixed electricity prices, real-time electricity prices, and time-of-use electricity prices; using the strong duality theorem of linear optimization to establish a first model and a second model for electricity price optimization taking into account electricity retailers and prosumers; substituting the basic parameters into the first model, solving the first model with the goal of maximizing the profit of electricity retailers, and outputting a plan for electricity price selection; substituting the basic parameters into the second model, solving the second model with the goal of maximizing the profit of prosumers, and outputting a plan for the output of each unit and the type of electricity price among prosumers. It can be seen that the present invention introduces a flexible electricity price policy into the model, which can give electricity retailers the opportunity to increase their revenue by providing different electricity price policies, and can also enable prosumers to shift electricity consumption to a period when the overall sales electricity price is lower, thereby increasing the profits of prosumers.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a method, device and computing equipment for constructing an electricity price optimization model taking into account power retailers and prosumers. Background Art

[0002] Prosumers can be understood as consumers who participate in production activities, so prosumers can be understood as energy consumption systems. Figure 1 As shown, it includes distributed wind turbines, gas boilers, heat pumps, heat storage equipment, cold storage equipment, and power storage equipment. Prosumers are energy consumers of electricity retailers and energy suppliers to the next level of prosumers. Figure 1 The prosumer shown can obtain energy from the upper-level grid, and can also serve as an energy supplier for energy users. Retailers can be understood as the upper-level grid of the prosumer, providing energy to the prosumer. Retailers can include grid companies, which can also purchase energy from their upper-level grid.

[0003] In recent years, renewable energy and distributed generation systems have grown rapidly around the world. Smart meters combined with production, storage and smart grid technologies will greatly broaden the possibilities for prosumers to optimize their electricity consumption, production and storage. In terms of buildings, the heat generated by electricity (such as heat pumps) and heat storage become more important. At present, most energy consumption methods are mainly: prosumers purchase energy from electricity retailers and pay energy fees. The purchased energy can be consumed by themselves or resold, and electricity retailers make profits by selling energy to prosumers. It can be seen that reducing the costs of prosumers and increasing the profits of electricity retailers are worth exploring. Summary of the invention

[0004] To this end, the present invention provides a method, apparatus and computing device for constructing an electricity price optimization model taking into account electricity retailers and prosumers, in an effort to solve or at least alleviate the above problems.

[0005] According to one aspect of the present invention, a method for constructing an electricity price optimization model taking into account electricity retailers and prosumers is provided, which is suitable for execution in a computing device, and the method comprises: obtaining basic parameters, the basic parameters including electricity price types, and the electricity price types including fixed electricity prices, real-time electricity prices, and time-of-use electricity prices; using the strong duality theorem of linear optimization to establish a first model and a second model for electricity price optimization taking into account electricity retailers and prosumers, the first model including a first objective function and a first constraint condition, and the second model including a second objective function and a second constraint condition; substituting the basic parameters into the first model, solving the first model with the goal of maximizing the profit of the electricity retailer, and outputting a scheme for electricity price selection; substituting the basic parameters into the second model, solving the second model with the goal of maximizing the profit of the prosumer, and outputting a scheme for the output of each unit in the prosumer and the type of electricity price;

[0006] Wherein, the first objective function includes:

[0007] Fixed electricity price scenario:

[0008] Real-time electricity price scenario: maxf RTP =ρ RTP

[0009] Time-of-use electricity price scenario:

[0010] In the formula, maxf Conv represents the maximum profit of the electricity retailer under the fixed electricity price scenario, t represents the time of importing electricity from the main grid, γ base Indicates the unit price of purchasing electricity in a fixed electricity price scenario, represents the spot market electricity price, represents the amount of electricity purchased by the prosumer from the electricity retailer during time t, maxf RTP represents the maximum profit of the electricity retailer under the real-time electricity price, ρ RTP represents the one-time payment made by the prosumer to the electricity retailer for purchasing electricity. The one-time payment is determined in advance through negotiation between the prosumer and the electricity retailer. maxf TOU represents the maximum profit of the electricity retailer under the TOU electricity price scenario, γ ret represents the electricity price during off-peak hours under the TOU electricity price scenario, γ tou represents the surcharge of peak-hour electricity price in the time-of-use electricity price scenario, T i,peak Indicates peak hours of the day;

[0011] Wherein, the second objective function includes: In the formula, maxf c represents the maximum profit of the prosumer, f rev represents the revenue from the sale of solar power generated by the prosumer, f tax represents a fixed amount that prosumers must pay to the authority, Represents the cost of purchasing electricity from electricity retailers.

[0012] Optionally, the second objective function includes:

[0013] The second objective function under the fixed electricity price scenario:

[0014]

[0015] The second objective function in the real-time electricity price scenario:

[0016]

[0017] The second objective function under the time-of-use electricity price scenario:

[0018]

[0019]

[0020] In the formula, They represent the maximum profits of producers and consumers under the fixed electricity price scenario, the real-time electricity price scenario, and the time-of-use electricity price scenario, respectively. represents the cost of purchasing electricity from electricity retailers at a fixed price, represents the cost of purchasing electricity from electricity retailers at real-time electricity prices, represents the cost of purchasing electricity from electricity retailers at time-of-use electricity prices, express, It represents the time of inputting electricity from the main grid in the TOU electricity price scenario, mod represents the modulo operator, and N represents an integer.

[0021] Optionally, the first constraint condition includes: a period constraint of the time-of-use electricity price, a time interval for the repetition of the time-of-use electricity price, and a time constraint for each T i The peak and off-peak time constraints, electricity price constraints and each T i The second constraint condition comprises one or more of the price increase constraints during the peak period in the model, wherein the second constraint condition comprises one or more of the constraints of photovoltaic power generation group, electric energy storage equipment, heat pump, thermal energy storage equipment, electric energy balance constraint of the second model and thermal energy demand constraint.

[0022] Optionally, the period constraint of the time-of-use electricity price includes: T = 1 / year, and the repetition time interval of the time-of-use electricity price includes: T i =1 / day, each T i The peak and off-peak time constraints in T include: i,peak ∪T i,off-peak =T i , the electricity price constraints include: γ ret ∈R≥0,γ ret ≤γ base , each T i The price increase constraints during peak hours in include: tou ∈R≥0, the competition constraints include: In the formula, T represents the period of time-of-use electricity price, in years, T i Indicates the time interval for the repetition of the time-of-use electricity price, in days, T i,peak 、T i,off-peak Respectively represent each T i In the peak period and non-peak period, R represents a natural number. represents the profit of the prosumer at a fixed electricity price, It represents the profit of prosumers in time-of-use electricity price.

[0023] Optionally, the photovoltaic generator group constraints include:

[0024] The power generation of the photovoltaic generator set in the time period t∈T:

[0025]

[0026] Output probability constraint of the nominal capacity of photovoltaic generators:

[0027]

[0028] The power generation constraint of the photovoltaic generator set input into the main grid in each time period t∈T is:

[0029]

[0030] In the formula, represents the power generation of the photovoltaic generator set in the time period t∈T, represents the maximum power generation of the photovoltaic generator set, τ represents the maximum power generation time of the photovoltaic generator set, represents the photovoltaic power input into the main grid, and t represents the power input time from the main grid.

[0031] Optionally, the electric energy storage device constraints include:

[0032] Charging power constraints:

[0033]

[0034] Discharge power constraint:

[0035]

[0036] Energy balance constraints for electrical energy storage devices:

[0037]

[0038] Capacity constraints of electrical energy storage equipment:

[0039]

[0040] The charge state constraints of the electric energy storage device at the beginning and end of the time range t are:

[0041]

[0042] In the formula, represents the charging energy of the energy storage device at time t, Indicates the maximum discharge capacity of the electrical energy storage device, E t represents the amount of power stored at the beginning of the peak power consumption period or the non-peak power consumption period t, αbat Represents the self-discharge rate of the electrical energy storage device, E t-1* The amount of power stored at the end of the peak or off-peak period t, represents the discharged energy of the energy storage device at time t, η bat-d Represents the discharge efficiency of the electrical energy storage device, η bat-c Represents the charging efficiency of the electrical energy storage device, represents the charging energy of the energy storage device at time t-1, Indicates the maximum capacity of the electrical energy storage device, Indicates the initial electrical energy of the energy storage device. represents the initial and minimum final energy in the electrical storage device, represents the energy stored in an electrical storage device, Represents the electrical energy discharged from an electrical storage device. Indicates the maximum capacity of an electrical storage device.

[0043] Optionally, heat pump constraints include:

[0044] Output power constraints of heat pumps:

[0045]

[0046] In the formula, represents the energy required to operate the heat pump, represents the maximum output power of the heat pump, and t represents the time of electricity input from the main grid.

[0047] Optionally, thermal energy storage device constraints include:

[0048] Energy storage constraints of thermal energy storage equipment:

[0049]

[0050] Charging constraints for thermal energy storage devices:

[0051]

[0052] Energy balance constraints for thermal energy storage equipment:

[0053]

[0054] The charge state constraints of the thermal energy storage device at the beginning and end of the time range t are:

[0055]

[0056] In the formula, H Indicates the minimum energy level of the thermal energy storage device, H trepresents the thermal energy stored in the thermal energy storage device at time t, represents the maximum energy level of the thermal energy storage device, α hsu Represents the self-discharge rate of thermal energy storage equipment, H t-1 represents the thermal energy stored in the thermal energy storage device at time t-1, represents the heat energy released by the thermal energy storage device at time t-1, η hsu-d represents the discharge efficiency of the thermal energy storage device, η hsu-c represents the charging efficiency of the thermal energy storage device, represents the thermal energy stored in the thermal energy storage device at time t-1, express, represents the initial and minimum final energy in the thermal storage device, Represents the thermal energy stored in the thermal energy storage device, represents the thermal energy charged to the thermal energy storage device, represents the heat load of the electricity retailer during time period t.

[0057] Optionally, the power balance constraints of the second model include:

[0058]

[0059] In the formula, represents the total electricity demand in time period t, Indicates the amount of electricity consumed by the heat pump. Indicates the amount of electricity charged by the energy storage device. Indicates the amount of electricity discharged by the energy storage device. represents the power generation of the photovoltaic generator set input into the main grid in each time period t∈T, Represents the power generation of the photovoltaic generator set in each time period t∈T.

[0060] Optionally, the heat demand constraints include:

[0061]

[0062] In the formula, Represents the thermal energy charged by the thermal energy storage device, Represents the heat energy released by thermal energy storage equipment.

[0063] Optionally, the basic parameters include the basic parameters of the first model, and the basic parameters of the first model include: a one-time payment basic price, taxes, electricity demand of prosumers, unit cost of purchasing electricity from the upper-level power grid, electricity purchase volume from the upper-level power grid, effective period of time-of-use electricity price, repetition time interval of time-of-use electricity price, peak hours and non-peak hours in each repetition time interval, fixed electricity price, base electricity price for time-of-use electricity price, and surcharge for peak hours for time-of-use electricity price.

[0064] Optionally, the basic parameters also include basic parameters of the second model, and the basic parameters of the second model include: electricity purchased from electricity retailers, electric energy discharged by electric energy storage devices, thermal energy in thermal energy storage devices, thermal energy stored in thermal energy storage devices, thermal energy released by thermal energy storage devices, energy required to run heat pumps, nominal capacity of photovoltaic generator sets, electricity generated by photovoltaic generator sets, grid-connected electricity prices of photovoltaic generator sets, charging efficiency of electric energy storage devices, discharging efficiency of electric energy storage devices, maximum charging capacity of electric energy storage devices, maximum discharging capacity of electric energy storage devices, maximum capacity of electric energy storage devices, self-discharge rate of electric energy storage devices, and thermal energy storage devices. One or more of charging efficiency, discharging efficiency of thermal energy storage equipment, coefficient of performance of heat pump, self-discharge rate of thermal energy storage equipment, initial and minimum final heat in thermal energy storage equipment, initial and minimum final energy in electrical energy storage equipment, minimum energy level of thermal energy storage equipment, maximum energy level of thermal energy storage equipment, maximum output power of heat pump, taxes, basic price, electricity demand, fixed electricity price, one-time payment basic price, effective period of time-of-use electricity price, repetition time interval of time-of-use electricity price, peak hours and off-peak hours in each repetition time interval, fixed electricity price, base electricity price during time-of-use electricity price, and surcharge during peak hours during time-of-use electricity price.

[0065] According to one aspect of the present invention, a device for constructing an electricity price optimization model taking into account electricity retailers and prosumers is provided, which is suitable for execution in a computing device and includes: a parameter acquisition module, which is suitable for acquiring basic parameters, wherein the basic parameters include electricity price types, and the electricity price types include fixed electricity prices, real-time electricity prices, and time-of-use electricity prices; a model construction unit, which is suitable for establishing a first model and a second model for electricity price optimization taking into account electricity retailers and prosumers using bilinear and mixed integer theory, wherein the first model includes a first objective function and a first constraint condition, and the second model includes a second objective function and a second constraint condition; a model solving unit, which is suitable for substituting the basic parameters into the first model, solving the first model with the goal of maximizing the profit of the electricity retailer, and outputting a scheme for electricity price selection, and is also suitable for substituting the basic parameters into the second model, solving the second model with the goal of maximizing the profit of the prosumer, and outputting a scheme for the output of each unit in the prosumer and the type of electricity price; wherein the first objective function includes:

[0066] Fixed electricity price scenario:

[0067] Real-time electricity price scenario: maxf RTP =ρ RTP

[0068] Time-of-use electricity price scenario:

[0069] In the formula, maxf Convrepresents the maximum profit of the electricity retailer under the fixed electricity price scenario, t represents the time of electricity imported from the main grid, γ base Indicates the unit price of purchasing electricity in a fixed electricity price scenario, represents the market price of electricity unit price, represents the amount of electricity purchased by the prosumer from the electricity retailer during time t, maxf RTP represents the maximum profit of the electricity retailer under the real-time electricity price, ρ RTP represents the one-time payment made by the prosumer to the electricity retailer for purchasing electricity. The one-time payment is determined in advance through negotiation between the prosumer and the electricity retailer. maxf TOU represents the maximum profit of the electricity retailer under the TOU electricity price scenario, γ ret represents the electricity price during off-peak hours under the TOU electricity price scenario, γ tou represents the surcharge of peak-hour electricity price in the time-of-use electricity price scenario, T i,peak Indicates peak hours of the day;

[0070] Among them, the second objective function includes: In the formula, maxf c represents the maximum profit of the prosumer, f rev represents the revenue from the sale of solar power generated by the prosumer, f tax represents a fixed amount that prosumers must pay to the authority, Represents the cost of purchasing electricity from electricity retailers.

[0071] According to one aspect of the present invention, there is provided a computing device, comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be suitable for being executed by the at least one processor, and the program instructions include instructions for executing the method as described above.

[0072] According to one aspect of the present invention, a readable storage medium storing program instructions is provided. When the program instructions are read and executed by a computing device, the computing device executes the method described above.

[0073] According to the technical solution of the present invention, a method for constructing an electricity price optimization model taking into account electricity retailers and prosumers is proposed, and the model includes a first model and a second model. In the first model, with the goal of maximizing the profit of electricity retailers, a variety of electricity price mechanisms are introduced into the objective function of the electricity retailer model, the scheme of the electricity price mechanism is determined, and the determined electricity price mechanism scheme is sent to the second model. In the second model, with the goal of maximizing the profit of prosumers, the electricity price scheme determined by the first model and the actual situation of each unit of the prosumer are considered when using energy, and the operation of each unit of the prosumer and the electricity consumption of the prosumer are output.

[0074] Introducing a variety of electricity price mechanisms, that is, introducing flexible electricity price policies into the system of electricity retailers and prosumers, can give electricity retailers the opportunity to increase their revenue by offering different electricity price policies, and can also enable prosumers to shift electricity consumption to periods with lower overall sales electricity prices, thereby reducing prosumers' electricity costs and thereby increasing their profits. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 A schematic diagram of a prosumer according to an embodiment of the present invention is shown;

[0076] Figure 2 shows a structural block diagram of a computing device 200 according to an embodiment of the present invention;

[0077] Figure 3 A flowchart of a method 300 for constructing an electricity price optimization model taking into account electricity retailers and prosumers according to an embodiment of the present invention is shown;

[0078] Figure 4 A schematic diagram shows a structural diagram of a device 400 for constructing an electricity price optimization model taking into account electricity retailers and prosumers according to an embodiment of the present invention;

[0079] Figure 5 is a schematic diagram showing energy balance, cost and profit of prosumers in a fixed electricity price scenario according to an embodiment of the present invention;

[0080] Figure 6 A schematic diagram showing energy balance, cost and profit of prosumers in a real-time electricity price scenario according to an embodiment of the present invention is shown;

[0081] Figure 7 A schematic diagram showing energy balance, cost and profit of prosumers in a time-of-use electricity price scenario according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0082] The present invention provides a method for constructing an electricity price optimization model taking into account electricity retailers and prosumers, and introduces a flexible electricity price mechanism. In some real-time modes, the flexible electricity price mechanism includes three types, namely fixed electricity price, real-time electricity price and time-of-use electricity price. These three types of electricity prices are stipulated in the agreement signed between the electricity retailer and the prosumer, and the above three types of electricity prices will not change during the validity period of the contract. In practical applications, one of the three electricity prices can be selected to maximize the profit of the electricity retailer.

[0083] The fixed electricity price is the basic unit price for electricity consumption specified in the contract signed between the electricity retailer and the prosumer within a set time frame, measured in units of per kilowatt-hour.

[0084] Unlike fixed electricity prices, in real-time electricity prices, the real-time electricity price is a one-time payment stipulated in the contract signed between the electricity retailer and the prosumer within a set time frame. For example, the one-time payment is the fee that the prosumer agrees to pay directly to the electricity retailer. At this time, the profit of the electricity retailer is only the one-time payment. Since the electricity retailer only resells the electricity to the prosumer at the spot market price and does not charge any additional fees except the one-time payment, the entire profit of the electricity retailer is composed of the one-time payment paid by the prosumer.

[0085] The time-of-use electricity price is to divide the set time range into time-repeating intervals, and then divide each time-repeating interval into peak power consumption period and non-peak power consumption period. The set time range and each time-repeating interval can be set according to the actual application scenario, and the present invention is not limited to this. For example, the set time range can be one year, and the time-repeating interval divided within one year is one day. Taking 365 days in a year as an example, the number of time-repeating intervals divided within one year is 365.

[0086] In some embodiments, each time repetition interval includes a peak power consumption period and a non-peak power consumption period, and the present invention does not limit the specific time periods of the peak power consumption period and the non-peak power consumption period. For example, 9 to 18 o'clock is the peak power consumption period, and 19 o'clock to 8 o'clock the next day is the non-peak power consumption period.

[0087] In some embodiments, during the off-peak period, the electricity price of the prosumer is a fixed price, and during the peak period, the electricity price of the prosumer is the sum of the fixed price and the surcharge. It is worth noting that the fixed price, the real-time price, and the surcharge in the time-of-use price are fixed and unchanged during the validity period of the contract signed between the prosumer and the electricity retailer.

[0088] The present invention provides a method for constructing a comprehensive energy system scheduling model taking into account a carbon trading mechanism and demand response, which is suitable for execution in a computing device. Figure 2 FIG. 2 shows a block diagram of a computing device 200 according to an embodiment of the present invention. Figure 2 As shown, in a basic configuration 202, computing device 200 typically includes a system memory 206 and one or more processors 204. A memory bus 208 may be used for communication between processor 204 and system memory 206.

[0089] Depending on the desired configuration, the processor 204 can be any type of processor, including but not limited to: a microprocessor (μP), a microcontroller (μC), a digital information processor (DSP), or any combination thereof. The processor 204 can include one or more levels of cache such as a primary cache 210 and a secondary cache 212, a processor core 214, and registers 216. An example processor core 314 can include an arithmetic logic unit (ALU), a floating point unit (FPU), a digital signal processing core (DSP core), or any combination thereof. An example memory controller 218 can be used with the processor 204, or in some implementations, the memory controller 218 can be an internal part of the processor 204.

[0090] Depending on the desired configuration, system memory 206 may be any type of memory, including but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. System memory 206 may include an operating system 220, one or more applications 222, and program data 224. In some implementations, application 222 may be arranged to operate on the operating system using program data 224.

[0091] The computing device 200 further includes a storage device 232, which includes a removable storage 236 and a non-removable storage 238, and both the removable storage 236 and the non-removable storage 238 are connected to the storage interface bus 234. In the present invention, data related to each event occurring during program execution and time information indicating the occurrence of each event can be stored in the storage device 232, and the operating system 220 is suitable for managing the storage device 232. The storage device 232 can be a disk.

[0092] The computing device 200 may also include an interface bus 240 that facilitates communication from various interface devices (e.g., output devices 242, peripheral interfaces 244, and communication devices 246) to the basic configuration 202 via the bus / interface controller 230. Example output devices 242 include an image processing unit 248 and an audio processing unit 250. They can be configured to facilitate communication with various external devices such as a display or speakers via one or more A / V ports 252. Example peripheral interfaces 244 may include a serial interface controller 254 and a parallel interface controller 256, which may be configured to facilitate communication with external devices such as input devices (e.g., keyboards, mice, pens, voice input devices, touch input devices) or other peripherals (e.g., printers, scanners, etc.) via one or more I / O ports 258. Example communication devices 246 may include a network controller 260, which may be arranged to facilitate communication with one or more other computing devices 262 via a network communication link via one or more communication ports 264.

[0093] A network communication link can be an example of a communication medium. Communication media can generally be embodied as computer-readable instructions, data structures, program modules in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium. A "modulated data signal" can be a signal in which one or more of its data sets or its changes can be carried out in a manner that encodes information in the signal. As non-limiting examples, communication media can include wired media such as a wired network or a dedicated line network, and various wireless media such as sound, radio frequency (RF), microwave, infrared (IR) or other wireless media. The term computer-readable medium used herein can include both storage media and communication media.

[0094] The computing device 200 can be implemented as a server, such as a file server, a database server, an application server, and a WEB server, etc., and can also be implemented as a part of a small-sized portable (or mobile) electronic device, such as a cellular phone, a personal digital assistant (PDA), a personal media player device, a wireless network browsing device, a personal head-mounted device, an application-specific device, or a hybrid device that can include any of the above functions. The computing device 200 can also be implemented as a personal computer including a desktop computer and a notebook computer configuration. In some embodiments, the computing device 200 is configured to execute the method 300 according to the present invention.

[0095] Figure 3 A schematic diagram of a method 300 for constructing an electricity price optimization model taking into account electricity retailers and prosumers according to an embodiment of the present invention is shown. The method is suitable for residing in Figure 2The computing device 200 is shown in FIG. Figure 3 As shown, the method 300 includes steps S310 to S330.

[0096] In step S410, basic parameters are obtained. Basic data is used as input data of the model, and the basic data includes basic parameters of the first model and basic parameters of the second model. The basic parameters of the first model include: one or more of the following: one-time payment basic price, taxes, electricity demand of prosumers, unit cost of electricity purchased from the upper-level power grid, electricity purchased from the upper-level power grid, effective period of time-of-use electricity price, repetition time interval of time-of-use electricity price, peak time and non-peak time in each repetition time interval, fixed electricity price, base electricity price during time-of-use electricity price, and surcharge during peak time during time-of-use electricity price.

[0097] The basic parameters of the second model include: electricity purchased from electricity retailers, electric energy discharged by electric energy storage devices, thermal energy in thermal energy storage devices, thermal energy stored in thermal energy storage devices, thermal energy released by thermal energy storage devices, energy required to operate heat pumps, nominal capacity of photovoltaic generator sets, electricity generated by photovoltaic generator sets, grid-connected electricity prices of photovoltaic generator sets, charging efficiency of electric energy storage devices, discharging efficiency of electric energy storage devices, maximum charging capacity of electric energy storage devices, maximum discharging capacity of electric energy storage devices, maximum capacity of electric energy storage devices, self-discharge rate of electric energy storage devices, charging efficiency of thermal energy storage devices, and energy consumption of thermal storage devices. One or more of discharge efficiency, coefficient of performance of a heat pump, self-discharge rate of a thermal energy storage device, initial and minimum final heat in a thermal energy storage device, initial and minimum final energy in an electrical energy storage device, minimum energy level of a thermal energy storage device, maximum energy level of a thermal energy storage device, maximum output power of a heat pump, taxes, basic price, electricity demand, fixed electricity price, one-time payment basic price, effective period of a time-of-use electricity price, repetitive time interval of a time-of-use electricity price, peak hours and off-peak hours in each repetitive time interval, fixed electricity price, base electricity price during time-of-use electricity price, and surcharge during peak hours during time-of-use electricity price.

[0098] Subsequently, in step S420, a power price optimization model taking into account power retailers and prosumers is established using the strong duality theorem of linear optimization, where the model includes an objective function and constraints.

[0099] The model in the present invention includes a first model (i.e., a power retailer model) and a second model (i.e., a prosumer model). The first model is applied to power retailers, and the first model corresponds to a first objective function and a first constraint condition. The second model is applied to prosumers, and the second model corresponds to a second objective function and a second constraint condition. The first objective function and the second objective function are collectively referred to as objective functions, and the first constraint condition and the second constraint condition are collectively referred to as constraint conditions.

[0100] The first model aims to maximize the profit of electricity retailers. The first model is a model based on a flexible electricity price policy. The objective function of the first model includes:

[0101] Fixed electricity price scenario:

[0102] Real-time electricity price scenario: maxf RTP =ρ RTP

[0103] Time-of-use electricity price scenario:

[0104] In the formula, maxf Conv represents the maximum profit of the electricity retailer under the fixed electricity price scenario, t represents the time of importing electricity from the main grid, γ base Indicates the unit price of purchasing electricity in a fixed electricity price scenario, The market price of electricity per unit price, represents the amount of electricity purchased by the prosumer from the electricity retailer during time t, maxf RTP represents the maximum profit of the electricity retailer under the real-time electricity price, ρ RTP represents the one-time payment made by the prosumer to the electricity retailer for purchasing electricity. The one-time payment is determined in advance through negotiation between the prosumer and the electricity retailer. maxf TOU represents the maximum profit of the electricity retailer under the TOU electricity price scenario, γ ret represents the electricity price during off-peak hours under the TOU electricity price scenario, γ tou represents the surcharge of peak-hour electricity price in the time-of-use electricity price scenario, T i,peak Indicates peak hours of the day.

[0105] The first constraint conditions corresponding to the objective function of the first model include: the period constraint of the time-of-use electricity price, the repetition time interval of the time-of-use electricity price, and each T i The peak and off-peak time constraints, electricity price constraints and each T i One or more of the price increase constraints during peak hours. Specifically,

[0106] 1) The periodic constraints of time-of-use electricity prices include:

[0107] T = 1 / year

[0108] 2) Time-of-use electricity price repetition time interval includes:

[0109] T i =1 / day

[0110] 3) Each T i The peak and off-peak constraints in include:

[0111] Ti,peak ∪T i,off-peak =T i

[0112] 4) Electricity price constraints include:

[0113] γ ret ∈R≥0,γ re t≤γ base

[0114] 5) Each T i The peak-hour price increase constraints in include:

[0115] γ tou ∈R≥0

[0116] 6) Competition constraints:

[0117]

[0118] In the formula, T represents the period of time-of-use electricity price, in years, T i Indicates the time interval for the repetition of the time-of-use electricity price, in days, T i,peak 、T i,off-peak Respectively represent each T i In the peak period and non-peak period, R represents a natural number. represents the optimal solution of the fixed electricity price model, Represents the optimal solution for real-time electricity price.

[0119] The second model aims to maximize the profit of producers and consumers, and the objective function is:

[0120]

[0121] In the formula, maxf c represents the maximum profit of the prosumer, f rev represents the revenue from the sale of solar power generated by the prosumer, f tax represents a fixed amount that prosumers must pay to the authority, Represents the cost of purchasing electricity from electricity retailers.

[0122] In some implementations, the flexible electricity price includes three types: fixed electricity price, real-time electricity price, and time-of-use electricity price. In this case, the objective function of the second model specifically includes:

[0123] The second objective function under the fixed electricity price scenario:

[0124]

[0125] The second objective function in the real-time electricity price scenario:

[0126]

[0127] The second objective function under the time-of-use electricity price scenario:

[0128]

[0129]

[0130] In the formula, They represent the maximum profits of producers and consumers under the fixed electricity price scenario, the real-time electricity price scenario, and the time-of-use electricity price scenario, respectively. represents the cost of purchasing electricity from electricity retailers at a fixed price, represents the cost of purchasing electricity from electricity retailers at real-time electricity prices, represents the cost of purchasing electricity from electricity retailers at time-of-use electricity prices, express, Indicates the time of the time-of-use electricity price, mod indicates the modulo operator, and N indicates an integer.

[0131] In the real-time electricity price scenario, the retailer’s only decision variable is the one-time payment ρ RTP , which constitutes the profit of the electricity retailer and balances the profit of the prosumer in the real-time electricity price compared with the fixed electricity price. The one-time payment appears in the lower electricity price range, just like a constant term added to the objective function of the prosumer. Therefore, due to the simple price transfer method and the lack of other electricity price mechanisms, the electricity retailer cannot influence the behavior of the prosumer, which means that the lower-level solution (i.e., the fixed electricity price solution) is independent of the higher-level decision (i.e., the real-time electricity price solution). Therefore, it can be solved in the following order. First, optimize the lower-level problem to obtain the optimal solution based on and the optimal solution of the fixed electricity price model Compute the optimal lump sum that satisfies competitive constraints as follows:

[0132]

[0133] Then, when the retailer maximizes the optimal lump sum payment, the objective function of the second model in the real-time electricity price scenario includes:

[0134]

[0135] In the formula, represents the revenue from the sale of solar power generated by the prosumer, represents the tax paid by prosumers to the authorities, Indicates the power imported from the main grid, Represents the optimal lump sum payment to the electricity retailer.

[0136] The second constraint conditions corresponding to the second objective function include one or more of photovoltaic power generation group constraints, electric energy storage device constraints, heat pump constraints, thermal energy storage device constraints, electric energy balance constraints of the second model, and thermal energy demand constraints.

[0137] 1) Assume that the electricity production of the prosumer is carried out by a rooftop photovoltaic (PV) generator set. The power generation of this PV generator set in time period t∈T is For this power generation, the prosumer can consume it directly, store it in an energy storage device, or supply it to the main grid. At the same time, the power output upper limit of the nominal capacity of the photovoltaic generator set is also considered. Then the constraints of the photovoltaic generator set include:

[0138] The power generation of the photovoltaic generator set in the time period t∈T:

[0139]

[0140] Output probability constraint of the nominal capacity of photovoltaic generators:

[0141]

[0142] The power generation constraint of the photovoltaic generator set input into the main grid in each time period t∈T is:

[0143]

[0144] In the formula, represents the power generation of the photovoltaic generator set in the time period t∈T, represents the maximum power generation of the photovoltaic generator set, τ represents the power generation time of the photovoltaic generator set, represents the photovoltaic power input into the main grid, and t represents the photovoltaic power input into the main grid time.

[0145] 2) Electric energy can be stored in electric energy storage devices, such as batteries, to better manage the overall energy system of the prosumer, for example, storing solar energy or electricity purchased from retailers. Electric energy storage devices can be charged or discharged. For both cases, specific upper limits limit the charging and discharging power. Therefore, the electric energy storage device constraints include:

[0146] Charging power constraints:

[0147]

[0148] Discharge power constraint:

[0149]

[0150] Energy balance constraints for electrical energy storage devices:

[0151]

[0152] Capacity constraints of electrical energy storage equipment:

[0153]

[0154] For electric energy storage devices, initial and termination conditions need to be set, and the charge state constraints of the electric energy storage device at the beginning and end of the time range t are:

[0155]

[0156] In the formula, represents the charging energy of the energy storage device at time t, Indicates the maximum discharge capacity of the electrical energy storage device, E t represents the amount of power stored at the beginning of the peak power consumption period or the non-peak power consumption period t, α bat Represents the self-discharge rate of the electrical energy storage device, E t-1 represents the amount of power released at the beginning of the peak power consumption period or the non-peak power consumption period t, represents the discharged energy of the energy storage device at time t, η bat-d Represents the discharge efficiency of the electrical energy storage device, η bat-c Represents the charging efficiency of the electrical energy storage device, represents the charging energy of the energy storage device at time t-1, Indicates the maximum capacity of the electrical energy storage device, Indicates the energy of the electrical energy storage device at the beginning, represents the initial and minimum final energy in the electrical storage device, represents the energy of the electrical energy storage device at the end, Indicates the discharge capacity of the energy storage device. Indicates the maximum capacity of the electrical energy storage device. bat ∈[0,1) is the self-discharge rate of the energy storage device, η bat-c ∈(0,1],η bat-d ∈(0, 1] is the efficiency of the charging and discharging process. And, in any given time period, the electrical energy storage device can be charged or discharged, but not both at the same time.

[0157] 3) For heat production, the present invention assumes that the prosumer has a heat pump that uses electricity to provide heat at a certain ideal temperature level. And the heat pump is constrained:

[0158] Output power constraints of heat pumps:

[0159]

[0160] In the formula, represents the energy required to operate the heat pump, represents the maximum output power of the heat pump, and t represents the time of the maximum output power of the heat pump.

[0161] 4) For thermal energy storage, it is assumed that the prosumer can use a hot water tank as a thermal energy storage device to store thermal energy. According to the capacity of the hot water tank, the minimum and maximum water temperature, the thermal energy storage device is constrained. The thermal energy storage device constraints include:

[0162] Energy storage constraints of thermal energy storage equipment:

[0163]

[0164] Charging constraints for thermal energy storage devices:

[0165]

[0166] Energy balance constraints for thermal energy storage equipment:

[0167]

[0168] For thermal energy storage devices, initial and termination conditions need to be set, and the charge state constraints of the thermal energy storage device at the beginning and end of the time range t are:

[0169]

[0170] In the formula, Indicates the minimum energy level of the thermal energy storage device, H t represents the thermal energy stored in the thermal energy storage device at time t, represents the maximum energy level of the thermal energy storage device, αhsu represents the self-discharge rate of the thermal energy storage device, H t-1 represents the thermal energy stored in the thermal energy storage device at time t-1, represents the heat energy released by the thermal energy storage device at time t-1, η hsu-d represents the discharge efficiency of the thermal energy storage device, η hsu-c represents the charging efficiency of the thermal energy storage device, represents the thermal energy stored in the thermal energy storage device at time t-1, represents the amount of heat stored in the thermal energy storage device at the beginning of time period t, represents the initial and minimum final energy in the thermal storage device, Indicates the charge capacity of the thermal energy storage device, represents the amount of electricity charged by the thermal energy storage device in time period t, represents the heat load of the producer during time period t.

[0171] 5) The power balance constraints of the second model include:

[0172]

[0173] In the formula, represents the total electricity demand in time period t, Indicates the amount of electricity consumed by the heat pump. Indicates the amount of electricity charged by the thermal energy storage device, Indicates the amount of electricity discharged by the thermal energy storage device, represents the photovoltaic power generation input into the main grid in each time period t∈T, represents the photovoltaic power generation in each time period t∈T.

[0174] 6) Assume that the heat demand of the prosumer is completely controlled by the thermal energy storage device in each corresponding time period. At this time, the heat demand constraints include:

[0175]

[0176] In the formula, Represents the thermal energy charged by the thermal energy storage device, Indicates the heat energy released by thermal energy storage equipment.

[0177] Subsequently, in step S430, the basic parameters of the first model are substituted into the first model, the first model is solved with the goal of maximizing the profit of the electricity retailer, and the electricity price selection plan is output; and the basic parameters of the second model are substituted into the second model, the second model is solved with the goal of maximizing the profit of the prosumer, and the output plan of each unit in the prosumer is output.

[0178] The units among the prosumers may include distributed wind turbines, distributed photovoltaic units, gas boilers, cogeneration units, heat pumps, electric refrigerators, absorption refrigerators, electric energy storage devices, thermal energy storage devices and cold energy storage devices, etc.

[0179] It should be noted that in the objective function under the TOU electricity price scenario, due to the existence of the product The problem of the second model is a mixed integer quadratic problem. There is a coupling relationship between the constructed two-layer models, and there are nonlinear constraints, which are difficult to solve directly. In order to effectively solve the above model, in one embodiment of the present invention, the present invention first converts the two-layer model into a single-layer optimization model that is easier to solve, and then quickly solves the single-layer optimization model.

[0180] It should be understood that there are many methods for solving the model, and the present invention is not limited to a specific implementation method. All methods that can solve the above model are within the protection scope of the present invention.

[0181] For example, the strong duality theorem of linear optimization can be used to illustrate the optimality conditions of the lower-level model. After this linearization, the second model becomes a mixed integer linear problem. For fixed variables of the second model, the first model problem depends on the parameters of the prosumer optimization model. Using the strong duality theory of linear optimization, the optimality conditions for the linear programming of the second model can be given. By adding these optimal conditions and the original constraints and the dual lower-level constraints to the first model, we obtain a single-level mixed integer nonlinear problem, which is an equivalent re-simulation of the two-level model, thereby facilitating model solving. That is, the present invention uses the strong duality theorem of linear optimization to establish an electricity price optimization model that takes into account electricity retailers and prosumers.

[0182] Figure 4 1 shows a structural block diagram of a device 400 for constructing an electricity price optimization model taking into account electricity retailers and prosumers according to an embodiment of the present invention. The device 400 may reside in a computing device 200, such as Figure 4 As shown, the device 400 includes: a parameter acquisition unit 410, a model construction unit 420 and a model solving unit 430.

[0183] The parameter acquisition module 410 is adapted to acquire basic parameters, wherein the basic parameters include the type of electricity price, and the type of electricity price includes a fixed electricity price, a real-time electricity price, and a time-of-use electricity price;

[0184] A model building unit 420 is adapted to establish a first model and a second model for electricity price optimization taking into account electricity retailers and prosumers by using bilinear and mixed integer theories, wherein the first model includes a first objective function and a first constraint condition, and the second model includes a second objective function and a second constraint condition;

[0185] The model solving unit 430 is adapted to substitute the basic parameters into the first model, solve the first model with the goal of maximizing the profit of the power retailer, and output a scheme for selecting the electricity price, and is also adapted to substitute the basic parameters into the second model, solve the second model with the goal of maximizing the profit of the prosumer, and output a scheme for the output of each unit in the prosumer and the type of electricity price;

[0186] Among them, the first objective function includes:

[0187] Fixed electricity price scenario:

[0188] Real-time electricity price scenario: maxf RTP =ρ RTP

[0189] Time-of-use electricity price scenario:

[0190] In the formula, maxf Convrepresents the maximum profit of the electricity retailer under the fixed electricity price scenario, t represents the time of electricity imported from the main grid, γ base Indicates the unit price of purchasing electricity in a fixed electricity price scenario, represents the market price of electricity unit price, represents the amount of electricity purchased by the prosumer from the electricity retailer during time t, maxf RTP represents the maximum profit of the electricity retailer under the real-time electricity price, ρ RTP represents the one-time payment made by the prosumer to the electricity retailer for purchasing electricity. The one-time payment is determined in advance through negotiation between the prosumer and the electricity retailer. maxf TOU represents the maximum profit of the electricity retailer under the TOU electricity price scenario, γ ret represents the electricity price during off-peak hours under the TOU electricity price scenario, γ tou represents the surcharge of peak-hour electricity price in the time-of-use electricity price scenario, T i,peak Indicates peak hours of the day;

[0191] Among them, the second objective function includes:

[0192] In the formula, maxf c represents the maximum profit of the prosumer, f rev represents the revenue from the sale of solar power generated by the prosumer, f tax represents a fixed amount that prosumers must pay to the authority, Represents the cost of purchasing electricity from electricity retailers.

[0193] It should be noted that the working principle of the construction device 400 for the electricity price optimization model taking into account electricity retailers and prosumers is similar to the above-mentioned construction method 300 for the electricity price optimization model taking into account electricity retailers and prosumers. For relevant matters, please refer to the description of the above-mentioned method 300, which will not be repeated here.

[0194] A specific case will be used below to verify the numerical simulation of the electricity price optimization model taking into account electricity retailers and prosumers constructed by the present invention.

[0195] In the present invention, accurate data on different aspects (such as photovoltaic generators, electrical storage devices, thermal storage devices and electricity demand) are collected for typical farms in Germany. In the present invention, a prosumer with a photovoltaic generator and an electric heat pump is selected and the data is processed by Pythonda.

[0196] The invention uses actual hourly measurements from 2015 to represent the output generated by the photovoltaic generator set, the heat demand and the electricity demand of the prosumer. The capacity of the photovoltaic generator set is 0.01008 MWh, the maximum capacity of the thermal storage unit of the prosumer is 0.033276 MWh, and the hourly self-discharge rate of the water tank is 1%. The performance coefficient η of the heat pump of the hydrogen pump is assumed to be 3. In order to better reflect the current situation in Germany, in the model of the invention, the prosumer is equipped with a battery (electrical storage device) and the average market price of the German energy supply fixed electricity price is used.

[0197] Prosumers pay considerable fees and taxes for the electricity they purchase from electricity retailers. This has a significant impact on the operation of prosumer facilities when resolving the trade-off between immediate own consumption and storing electrical energy for later own consumption, feeding into the grid and later purchasing electrical energy from the grid. In order to evaluate prosumers more realistically, the present invention must explicitly take into account the taxes and fees paid for different electricity prices. For the levels of fees and taxes paid by prosumers, the present invention adopts the values ​​of final household consumers in Germany in 2017. The calculation of the considered two-level model results in an instance that does not allow for hourly discretization to be solved over the entire time range of one year. Therefore, from the available data for 2015, the present invention selects a representative winter, mid-season and summer season and calculates continuously in this order. Therefore, the present invention obtains calculation results for one year based on three representative weeks selected for each season.

[0198] The linear models are solved to a relative optimality gap without imposing any time limit. The runtime for solving each linear model does not exceed 1 second. The time limit for all nonlinear instances is 15 minutes and the optimality gap is set to 1%. The present invention uses a model of time-of-use electricity prices and solves the model based on a low-level reconstruction of strong duality.

[0199] Two-layer model input and output results:

[0200] For fixed electricity prices, we achieve this by artificially extending the fixed electricity price model to only contain the upper level of the electricity retailer's objective function under the fixed electricity price scenario. This model is solved using the same low-level reconstruction based on strong duality as for time-of-use electricity prices. The optimal objective function value for the prosumer is the same as the value of the objective function of the prosumer model (second model) under the fixed electricity price scenario.

[0201] Real-time electricity prices naturally exhibit optimistic behavior from prosumers, since both the prosumer and the retailer aim to maximize the prosumer’s profit. Under real-time electricity prices, the electricity retailer’s entire profit is simply the one-time payment collected from the prosumer. This one-time payment allows the electricity retailer to extract all of the prosumer’s profit, beyond what the prosumer would have earned under a fixed electricity price. Therefore, under real-time electricity prices, there is no need to reformulate the extended problem, and the solution to the objective function of the prosumer model under real-time electricity prices directly yields the prosumer’s optimistic behavior.

[0202] In the optimal solution of the real-time electricity price problem, the goal is to maximize the profit of the electricity retailer, and the corresponding competition constraint satisfies equality. Therefore, in the time-of-use electricity price, due to the competition constraint of the time-of-use electricity price, the income of the prosumer cannot be lower than the fixed electricity price. Therefore, the attractiveness of the time-of-use electricity price to the prosumer is at least as great as the other two electricity prices. In all the proposed calculation results, the competition constraint of the time-of-use electricity price also satisfies equality, making the profit of the prosumer equal in all three types of electricity prices.

[0203] Before discussing the actual calculation results, some details of the prosumer model for each of the three types of electricity prices are provided in Table 1. Note that these details are always given for the corresponding single-level reformulations containing optimistic assumptions.

[0204] Table 1 Model size including optimistic assumptions

[0205] Electricity Prices Conv RTP TOU Two-layer model no no yes Total number of variables 8574 3529 10160 Number of discrete variables 0 0 72 Number of nonlinear terms 0 0 4032 Constraint Quantity 5047 1517 7618

[0206] Results of an optimistic prosumer solution:

[0207] All results presented here in Table 2 have been extended to a time horizon of one year, while the figures include three example weeks without scaling. Table 2 presents the most important properties of the optimal solutions under all three optimistic assumptions for the considered electricity prices. The gain (in percentage) compared to the conventional electricity price is the difference between the considered and conventional electricity retailer profits divided by the electricity retailer profits for the considered alternative electricity price. The relative optimality gap is the gap obtained after exceeding the time limit. Therefore, electricity retailer profits (and corresponding gains) are likely to be higher for TOU electricity prices. The profits of electricity retailers for TOU electricity prices are higher than those for fixed electricity prices, with the real-time electricity price, which is the “flexibility benchmark”, showing the highest gains compared to fixed electricity prices. The gains for TOU electricity prices are at least 1.48%. The larger optimality gap of 3.24% in the TOU electricity price solution still leaves some room for larger gains. The prices faced by prosumers vary widely between electricity prices, with the lowest average price being obtained for the fixed electricity price objective function. In this case, the compensation payment is a lump sum to adjust the prosumer's profit to the level of the fixed electricity price, and the profit of the electricity retailer will not be much different despite the huge price difference compared to the benchmark electricity price. The price level generated by the time-of-use electricity price is roughly the same as the fixed price electricity price. In the objective function of the real-time electricity price, there is no significant difference in the price of different time periods. The highest price fluctuation is shown in the real-time price scenario.

[0208] Table 2 Expanding the three-week study results to one year (based on optimistic assumptions)

[0209] Electricity Prices FP RTP TOU Retailer profit 505.60 530.38 513.19 Gain - 4.67 1.48 Relative Optimality Gap 0 0 3.24 One-time payment 0 530.38 0 Average price 64.20 31.10 63.89 Lowest Price 64.20 -20.07 57.76 Highest Price 64.20 75.87 66.70

[0210] One purpose of designing a flexible energy supply tariff is to incentivize prosumers to apply load shifting. Therefore, the present invention considers the load management of prosumers in more detail. To this end, we first provide the relevant summary input data in Table 3, such as the total electrical and thermal loads and the total power generation of the photovoltaic power generation group magnified to one year. For all types of tariffs, the input data are the same at this time.

[0211] Table 3 summarizes producer parameters and results (scaled to one year MWh)

[0212]

[0213]

[0214] In addition, we specify summary results, such as the total amount of electric power purchased from or fed into the main grid per year, and the aggregated losses of batteries and thermal energy storage caused by load shifting. As expected, these results can vary for different electricity prices. Depending on the amount of imported electricity, the prosumer's profit is the smallest in the fixed electricity price scenario, followed by the time-of-use electricity price scenario and the real-time electricity price scenario. The order of the amount of electricity purchased from the main grid is exactly the same as the order in which the losses of batteries and thermal energy storage devices increase over the entire electricity price. In fact, due to the high volatility of the import price for a certain electricity price, the more power shifting occurs, the higher the losses of the prosumer's batteries and thermal energy storage devices. The real-time electricity price is the most flexible electricity price, and the losses caused by the time-of-use electricity price scenario are the highest. The real-time electricity price drives the prosumer to purchase the largest amount of electricity from the main grid. The losses caused by the time-of-use electricity price scenario are smaller and lead to a decrease in the amount of electricity purchased from the main grid. It is worth noting that the amount of PV electricity fed into the main grid is the same for the three types of electricity prices, because the energy of the PV generator is mainly used for self-consumption. Only when the prosumer's demand is met, the surplus electricity is sold, so the amount of energy from the PV generator set sold does not depend on the electricity price. The profit of the prosumer is obviously the same for the three types of electricity prices, which is due to the constraint that all electricity prices must bring profits to consumers from at least the fixed price electricity price.

[0215] In summary, it is observed that the higher the flexibility of the electricity price, the more load shifting occurs, which results in power losses. To study the respective seasonal dependencies more clearly, the optimal load management of each prosumer over three demonstration weeks is presented. First, a fixed electricity price is discussed under optimistic assumptions. The corresponding load management of the prosumers is shown in Figure 5 As shown in Figure 2. Energy sources such as electricity purchased from the main grid, electricity generated by photovoltaic generators, and electricity discharged from batteries are Figure 5 The upper half of the graph is shown below zero. The power used by prosumers to cover electrical loads and heat pumps, feed into the main grid and charge batteries is plotted above zero. Figure 5 The lower part of the graph shows the price of electricity purchased by the prosumer in each time period (red line in EUR / MWh, color not shown in the figure).

[0216] Larger amounts of electricity purchased from the main grid correspond to higher heat demand in winter and mid-season (and therefore higher electricity demand for heat pumps), while smaller amounts coincide with higher PV generation, especially in summer. Since the price of electricity in this tariff is constant over time, the prosumer's cost / profit curve is closely linked to imports (winter and mid-season weeks) and the fixed unit price curve (summer weeks).

[0217] In contrast to the curves in the conventional electricity price graph, the real-time electricity price scenario shows more jagged lines, such as Figure 6This is especially true for the cost / profit curves of heat pumps and prosumers for electricity purchased from the main grid. The use of batteries still depends mainly on the output of the PV plant, just like in conventional electricity tariffs: For both tariffs, batteries are used to store excess solar energy for later consumption.

[0218] Optimal prosumer load management in time-of-use electricity pricing Figure 7 As shown. The peak time starts at 6 am and lasts until midnight. Therefore, the off-peak time starts at midnight and lasts until 6 am. It can be seen that the price difference between peak and off-peak periods is much lower compared to the real-time electricity price, in which the spot market price is directly forwarded to the prosumer. It is also visible that the time-of-use electricity price induces a certain regular pattern of prosumer behavior. Before the end of the off-peak period, the prosumer purchases electricity and uses it directly for the heat pump to store heat in the thermal energy storage device, which is then used during the peak period. Therefore, for the time-of-use telephone scenario and the real-time electricity price scenario, the main storage in the colder period is the thermal energy storage device, just like the three types of electricity prices. At the same time, the specific moment of purchase is reasonable, because early purchases will lead to the loss of thermal energy storage devices, and batteries do not play an important role in cold weeks. This changes in the warmer period, where batteries are mainly used to store electricity generated by photovoltaic generators in the middle of the season and in the summer.

[0219] The results show that the design of flexible electricity prices is extremely complex and that the performance of electricity prices depends on many different details of the electricity price structure and also on the behavioral patterns of prosumers.

[0220] From the above, it can be seen that the present invention analyzes the potential of incentivizing prosumers in electricity production, storage and consumption and heat production, storage and consumption energy systems through flexible tariffs, thereby evaluating the possibility of electricity retailers to increase additional profits by using these tariffs. We focus on the design of tariffs for electricity retailers that consider the specific load shifting potential of prosumers with small generation and storage facilities. The prosumer model explicitly considers the interaction of electricity, heat production and storage within households, which is a special feature of the proposed approach.

[0221] If a prosumer buys electricity from an electricity retailer to cover the surplus load, he faces an electricity price that makes his internal system operate optimally. To properly model the specific decision-making environment, the interaction between electricity retailers and prosumers, we adopt a two-level model approach. In our analysis, three types of electricity prices are considered: fixed electricity prices, real-time pricing, and time-of-use electricity prices. To make a proper comparison, we ensure that if a prosumer changes from a traditional electricity price to any of the two more flexible electricity prices, he will maintain his profit level.

[0222] The study of the present invention provides many new insights into flexible pricing. In the framework of the present invention, the simple transmission of the overall sales price in the real-time electricity price generates the highest profit for the electricity retailer. This is because the price signal from the market encourages the optimal use of the flexibility of the prosumer. In the analysis of the present invention, the time-of-use electricity price only generates moderate additional benefits. If the detailed technical configuration is considered in combination with the fixed time window of the peak price, the possibility of load shifting is moderate. In the optimistic case, in addition to the potential for low efficiency, in the case of time-of-use electricity prices, flexible electricity prices mean that there are multiple solutions to the optimization problem of the prosumer, increasing the revenue uncertainty of the electricity retailer. In essence, the electricity retailer must bear the risk that the prosumer will go against it, which makes flexible electricity prices such as time-of-use electricity prices less attractive. The model provided by the present invention also lays the foundation for finding prosumers with alternative technical equipment. The efficiency potential of flexible electricity prices may be higher if the prosumer has equipment with controllable energy production.

Claims

1. A method for constructing an electricity price optimization model taking into account electricity retailers and prosumers, suitable for execution in a computing device, the method comprising: Obtaining basic parameters, wherein the basic parameters include electricity price types, and the electricity price types include fixed electricity price, real-time electricity price, and time-of-use electricity price; Using the strong duality theorem of linear optimization, a first model and a second model for electricity price optimization taking into account electricity retailers and prosumers are established, wherein the first model includes a first objective function and a first constraint condition, and the second model includes a second objective function and a second constraint condition; Substituting the basic parameters into the first model, solving the first model with the goal of maximizing the profit of the electricity retailer, and outputting a plan for electricity price selection; Substituting the basic parameters into the second model, solving the second model with the goal of maximizing the profit of the prosumer, and outputting the output of each unit and the electricity price type of the prosumer; Wherein, the first objective function includes: Fixed electricity price scenario: Real-time electricity price scenario: maxf RTP =ρ RTP Time-of-use electricity price scenario: In the formula, maxf Conv represents the maximum profit of the electricity retailer under the fixed electricity price scenario, t represents the time of importing electricity from the main grid, γ base Indicates the unit price of purchasing electricity in a fixed electricity price scenario, represents the spot market electricity price, represents the amount of electricity purchased by the prosumer from the electricity retailer during time t, maxf RTP represents the maximum profit of the electricity retailer under the real-time electricity price, ρ RTP represents the one-time payment made by the prosumer to the electricity retailer for purchasing electricity. The one-time payment is determined in advance through negotiation between the prosumer and the electricity retailer. maxf TOU represents the maximum profit of the electricity retailer under the TOU electricity price scenario, γ ret represents the electricity price during off-peak hours under the TOU electricity price scenario, γ tou represents the surcharge of peak-hour electricity price in the time-of-use electricity price scenario, T i,peak Indicates peak hours of the day; Wherein, the second objective function includes: In the formula, maxf c represents the maximum profit of the prosumer, f rev represents the revenue from the sale of solar power generated by the prosumer, f tax represents a fixed amount that prosumers must pay to the authority, Represents the cost of purchasing electricity from electricity retailers.

2. The method of claim 1, wherein: The second objective function includes: The second objective function under the fixed electricity price scenario: The second objective function in the real-time electricity price scenario: The second objective function under the time-of-use electricity price scenario: In the formula, They represent the maximum profits of producers and consumers under the fixed electricity price scenario, the real-time electricity price scenario, and the time-of-use electricity price scenario, respectively. represents the cost of purchasing electricity from electricity retailers at a fixed price, represents the cost of purchasing electricity from electricity retailers at real-time electricity prices, represents the cost of purchasing electricity from electricity retailers at time-of-use electricity prices, express, It represents the time of inputting electricity from the main grid in the TOU electricity price scenario, mod represents the modulo operator, and N represents an integer.

3. The method of claim 1, wherein: The first constraint condition includes: the period constraint of the time-of-use electricity price, the time interval of the time-of-use electricity price repetition, and each T i The peak and off-peak time constraints, electricity price constraints and each T i The second constraint condition comprises one or more of the price increase constraints during the peak period in the model, wherein the second constraint condition comprises one or more of the constraints of photovoltaic power generation group, electric energy storage equipment, heat pump, thermal energy storage equipment, electric energy balance constraint of the second model and thermal energy demand constraint.

4. The method of claim 3, wherein: The periodic constraints of the time-of-use electricity price include: T = 1 / year The time-of-use electricity price repetition time interval includes: T i =1 / day Each T i The peak and off-peak constraints in include: T i,peak ∪T i,off-peak =T i The electricity price constraints include: c ret ∈R≥0、γ ret ≤γ base Each T i The peak-hour price increase constraints in include: c tou ∈R≥0 Competitive constraints include: In the formula, T represents the period of time-of-use electricity price, in years, T i Indicates the time interval for the repetition of the time-of-use electricity price, in days, T i,peak , T i,off-peak Respectively represent each T i In the peak period and non-peak period, R represents a natural number. represents the profit of the prosumer at a fixed electricity price, It represents the profit of prosumers in time-of-use electricity price.

5. The method of claim 4, wherein: The photovoltaic generator set constraints include: The power generation of the photovoltaic generator set in the time period t∈T: Output probability constraint of the nominal capacity of photovoltaic generators: The power generation constraint of the photovoltaic generator set input into the main grid in each time period t∈T is: In the formula, represents the power generation of the photovoltaic generator set in the time period t∈T, represents the maximum power generation of the photovoltaic generator set, τ represents the maximum power generation time of the photovoltaic generator set, represents the photovoltaic power input into the main grid, and t represents the power input time from the main grid.

6. The method according to any one of claims 3 to 5, wherein: The electric energy storage device constraints include: Charging power constraints: Discharge power constraint: Energy balance constraints for electrical energy storage devices: Capacity constraints of electrical energy storage equipment: The charge state constraints of the electric energy storage device at the beginning and end of the time range t are: In the formula, represents the charging energy of the energy storage device at time t, Indicates the maximum discharge capacity of the electrical energy storage device, E t represents the amount of power stored at the beginning of the peak power consumption period or the non-peak power consumption period t, α bat Represents the self-discharge rate of the electrical energy storage device, E t-1 *The amount of power stored at the end of the peak or off-peak period t, represents the discharged energy of the energy storage device at time t, η bat-d Represents the discharge efficiency of the electrical energy storage device, η bat-c Represents the charging efficiency of the electrical energy storage device, represents the charging energy of the energy storage device at time t-1, Indicates the maximum capacity of the electrical energy storage device, Indicates the initial electrical energy of the energy storage device. represents the initial and minimum final energy in the electrical storage device, represents the energy stored in an electrical storage device, Represents the electrical energy discharged from an electrical storage device, Indicates the maximum capacity of an electrical storage device.

7. The method according to any one of claims 3 to 5, wherein: The heat pump constraints include: Output power constraints of heat pumps: In the formula, represents the energy required to operate the heat pump, represents the maximum output power of the heat pump, and t represents the power input time from the main grid.

8. The method according to any one of claims 3 to 5, wherein: The thermal energy storage device constraints include: Energy storage constraints of thermal energy storage equipment: Charging constraints for thermal energy storage devices: Energy balance constraints for thermal energy storage equipment: The charge state constraints of the thermal energy storage device at the beginning and end of the time range t are: In the formula, H Indicates the minimum energy level of the thermal energy storage device, H t represents the thermal energy stored in the thermal energy storage device at time t, represents the maximum energy level of the thermal energy storage device, α hsu Represents the self-discharge rate of thermal energy storage equipment, H t-1 represents the thermal energy stored in the thermal energy storage device at time t-1, represents the heat energy released by the thermal energy storage device at time t-1, η hsu-d represents the discharge efficiency of the thermal energy storage device, η hsu-c represents the charging efficiency of the thermal energy storage device, represents the thermal energy stored in the thermal energy storage device at time t-1, express, represents the initial and minimum final energy in the thermal storage device, represents the thermal energy stored in the thermal energy storage device, represents the thermal energy charged to the thermal energy storage device, represents the heat load of the electricity retailer during time period t.

9. The method according to any one of claims 3 to 5, wherein: The power balance constraints of the second model include: In the formula, represents the total electricity demand in time period t, Indicates the amount of electricity consumed by the heat pump, Indicates the amount of electricity charged by the energy storage device. Indicates the amount of electricity discharged by the energy storage device. represents the power generation of the photovoltaic generator set input into the main grid in each time period t∈T, Represents the power generation of the photovoltaic generator set in each time period t∈T.

10. The method according to any one of claims 3 to 5, wherein: The thermal energy demand constraints include: In the formula, Represents the thermal energy charged by the thermal energy storage device, Represents the heat energy released by thermal energy storage equipment.

11. The method according to any one of claims 1 to 5, wherein: The basic parameters include the basic parameters of the first model, and the basic parameters of the first model include: a one-time payment basic price, taxes, electricity demand of prosumers, unit cost of purchasing electricity from the upper-level power grid, electricity purchase volume from the upper-level power grid, effective period of time-of-use electricity price, repetition time interval of time-of-use electricity price, peak period and non-peak period in each repetition time interval, fixed electricity price, base electricity price during time-of-use electricity price, and surcharge during peak period during time-of-use electricity price.

12. The method according to any one of claims 1 to 5, wherein: The basic parameters also include basic parameters of the second model, which include: electricity purchased from electricity retailers, electric energy discharged by electric energy storage devices, thermal energy in thermal energy storage devices, thermal energy stored in thermal energy storage devices, thermal energy released by thermal energy storage devices, energy required to run heat pumps, nominal capacity of photovoltaic generator sets, electricity generated by photovoltaic generator sets, grid-connected electricity prices of photovoltaic generator sets, charging efficiency of electric energy storage devices, discharging efficiency of electric energy storage devices, maximum charging capacity of electric energy storage devices, maximum discharging capacity of electric energy storage devices, maximum capacity of electric energy storage devices, self-discharge rate of electric energy storage devices, and thermal energy storage devices. One or more of charging efficiency, discharging efficiency of thermal energy storage equipment, coefficient of performance of heat pump, self-discharge rate of thermal energy storage equipment, initial and minimum final heat in thermal energy storage equipment, initial and minimum final energy in electrical energy storage equipment, minimum energy level of thermal energy storage equipment, maximum energy level of thermal energy storage equipment, maximum output power of heat pump, taxes, basic price, electricity demand, fixed electricity price, one-time payment basic price, effective period of time-of-use electricity price, repetition time interval of time-of-use electricity price, peak hours and off-peak hours in each repetition time interval, fixed electricity price, base electricity price during time-of-use electricity price, and surcharge during peak hours during time-of-use electricity price.

13. A device for constructing an electricity price optimization model taking into account electricity retailers and prosumers, suitable for execution in a computing device, comprising: A parameter acquisition module, adapted to acquire basic parameters, wherein the basic parameters include electricity price types, and the electricity price types include fixed electricity price, real-time electricity price, and time-of-use electricity price; A model building unit, adapted to establish a first model and a second model for electricity price optimization taking into account electricity retailers and prosumers using bilinear and mixed integer theories, wherein the first model includes a first objective function and a first constraint condition, and the second model includes a second objective function and a second constraint condition; A model solving unit, adapted to substitute the basic parameters into the first model, solve the first model with the goal of maximizing the profit of the power retailer, and output a scheme for electricity price selection, and also adapted to substitute the basic parameters into the second model, solve the second model with the goal of maximizing the profit of the prosumer, and output a scheme for the output of each unit in the prosumer and the type of electricity price; Wherein, the first objective function includes: Fixed electricity price scenario: Real-time electricity price scenario: maxf RTP =ρ RTR Time-of-use electricity price scenario: In the formula, maxf Conv represents the maximum profit of the electricity retailer under the fixed electricity price scenario, t represents the time of electricity imported from the main grid, γ base Indicates the unit price of purchasing electricity in a fixed electricity price scenario, represents the market price of electricity unit price, represents the amount of electricity purchased by the prosumer from the electricity retailer during time t, maxf RTR represents the maximum profit of the electricity retailer under the real-time electricity price, ρ RTR represents the one-time payment made by the prosumer to the electricity retailer for purchasing electricity. The one-time payment is determined in advance through negotiation between the prosumer and the electricity retailer. maxf TOU represents the maximum profit of the electricity retailer under the TOU electricity price scenario, γ ret represents the electricity price during off-peak hours under the TOU electricity price scenario, γ tou represents the surcharge of peak-hour electricity price in the time-of-use electricity price scenario, T i,peak Indicates peak hours of the day; Wherein, the second objective function includes: In the formula, maxf c represents the maximum profit of the prosumer, f rev represents the revenue from the sale of solar power generated by the prosumer, f tax represents a fixed amount that prosumers must pay to the authority, Represents the cost of purchasing electricity from electricity retailers.

14. A computing device comprising: at least one processor; and A memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, and the program instructions include instructions for executing the method according to any one of claims 1 to 12.

15. A readable storage medium storing program instructions, when the program instructions are read and executed by a computing device, the computing device executes the method according to any one of claims 1 to 12.

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