CN115062831A - Construction method of electricity price optimization model considering electricity retailers and producers and consumers - Google Patents
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Abstract
The invention discloses a construction method of an electricity price optimization model considering an electricity retailer and a producer and a consumer, which comprises the following steps: acquiring basic parameters, wherein the basic parameters comprise electricity price types, and the electricity price types comprise fixed electricity prices, real-time electricity prices and time-of-use electricity prices; establishing a first model and a second model taking into account the electricity price optimization of the electricity retailer and the producer and consumer by utilizing a linear optimization strong duality theorem; substituting the basic parameters into the first model, solving the first model by taking the highest profit of the electric power retailer as a target, and outputting a scheme for selecting the electricity price; and substituting the basic parameters into the second model, solving the second model by taking the highest profit of the producers and the consumers as a target, and outputting a scheme of the output and electricity price types of each unit in the producers and the consumers. Therefore, the invention introduces a flexible electricity price policy into the model, enables the electricity retailer to have an opportunity to increase income by providing different electricity price policies, and enables the producer and the consumer to transfer electricity consumption to the period of low electricity price for overall sale, thereby improving the profit of the producer and the consumer.
Description
Technical Field
The invention relates to the field of power systems, in particular to a method, a device and computing equipment for constructing a power price optimization model considering power retailers and producers and consumers.
Background
The producer and the consumer can be understood as the consumers participating in the production activity, and the producer and the consumer can be understood as an energy consumption system, which is shown in fig. 1 and comprises a distributed wind turbine, a gas boiler, a heat pump, a heat storage device, a cold storage device and an electricity storage device. The producer and the consumer are energy consumers of the electric power retailer and energy suppliers of the next-level producer and the consumer shown in fig. 1 can obtain energy from the upper-level power grid and can also serve as an energy supplier of the energy consumers. The retailer, which may be understood as the primary power grid of the producer or consumer, provides the producer or consumer with energy, may include a power grid company, which may also purchase energy from the primary power grid.
In recent years, renewable energy sources and distributed power generation systems have rapidly increased worldwide. The intelligent electric meter is combined with the production, storage and intelligent power grid technology, so that the possibility of optimizing power consumption, production and storage of producers and consumers is greatly widened. In the building sector, the heat generated by electricity (e.g. heat pumps) and the storage of heat becomes more important. Most of the current energy consumption modes are mainly as follows: the producer and consumer purchase energy from the electricity retailer, who pays an energy fee, and the purchased energy may be self-consumed or resell, and the electricity retailer makes a profit by selling the energy to the producer and consumer. It is therefore desirable to reduce costs to the producer and the consumer and to increase profits for the electricity retailer.
Disclosure of Invention
To this end, the present invention provides a method, apparatus and computing device for building a price of electricity optimization model that accounts for electricity retailers and producers and consumers in an effort to solve, or at least alleviate, the problems identified above.
According to one aspect of the present invention, there is provided a method of building a power price optimization model that accounts for power retailers and producers and consumers, adapted to be executed in a computing device, the method comprising: acquiring basic parameters, wherein the basic parameters comprise electricity price types, and the electricity price types comprise fixed electricity prices, real-time electricity prices and time-of-use electricity prices; establishing a first model and a second model which take power price optimization of the power retailer and the producer and consumer into account by utilizing a strong duality theorem of linear optimization, wherein the first model comprises a first objective function and a first constraint condition, and the second model comprises a second objective function and a second constraint condition; substituting the basic parameters into the first model, solving the first model by taking the highest profit of the electric power retailer as a target, and outputting a scheme for selecting the electricity price; substituting the basic parameters into the second model, solving the second model by taking the highest profit of the producers and the consumers as a target, and outputting a scheme of the output and electricity price types of each unit in the producers and the consumers;
wherein the first objective function comprises:
fixed electricity price scenario:
real-time electricity price scenario: m isaxf RTP ＝ρ RTP
Time-of-use electricity price scenario:
in the formula, maxf Conv Representing the maximum profit of the electricity retailer in a fixed electricity price scenario, t representing the time of input of electricity from the main grid, γ base Represents a unit price of purchasing electric energy in a fixed electricity price scenario,
the spot-market electricity prices are indicated,
representing the amount of electric energy, maxf, purchased by the producer or consumer from the electric power retailer over time t RTP Representing the maximum profit, ρ, of the electricity retailer at real-time electricity prices RTP One-time payment representing the purchase of electricity by the producer and the consumer to the electricity retailer, maxf determined by the producer and the electricity retailer in advance TOU Representing the maximum profit, γ, of the electricity retailer in a time of use price scenario ret Representing electricity prices, gamma, during off-peak hours in a time-of-use electricity price scenario tou Representing the premium, T, of peak hour electricity prices in a time-of-use electricity price scenario i,peak Representing a period of high peak in the day;
wherein the second objective function comprises:
in the formula, maxf c Representing maximum profit of the parity person, f rev Showing income of the producer and the consumer selling the solar power generation, f tax A fixed amount indicating that the destroyer must pay the authorities,
representing the cost of purchasing electricity from an electricity retailer.
Optionally, the second objective function comprises:
a second objective function in a fixed electricity price scenario:
a second objective function in the real-time electricity price scene:
a second objective function in the time-of-use electricity price scenario:
in the formula (I), the compound is shown in the specification,
respectively represents the maximum profits of the producers and the consumers under the fixed electricity price scene, the real-time electricity price scene and the time-of-use electricity price scene,
represents the cost of purchasing electricity from an electricity retailer at a fixed electricity price,
represents the cost of purchasing electricity from an electricity retailer at a real-time electricity rate,
represents the cost of purchasing electricity from an electricity retailer at a time of use price,
it is shown that,
represents a scoreThe time of inputting power from the main grid in the electricity price-per-hour scenario, mod represents the modulus operator, and N represents an integer.
Optionally, the first constraint comprises: periodic constraints of time of use prices, time of use price repetition time intervals, each T i Peak and off-peak time constraints, electricity price constraints and per T i In the peak hours, the second constraints comprising: one or more of photovoltaic generator set constraints, electrical energy storage device constraints, heat pump constraints, thermal energy storage device constraints, electrical energy balance constraints and thermal energy demand constraints of the second model.
Optionally, the period constraint of the time of use electricity price includes: and T is 1/year, and the time-of-use price repetition time interval comprises the following steps: t is i 1/day, each T i The peak and off-peak time constraints in (1) include: t is i,peak ∪T i,off-peak ＝T i The electricity price constraints include: gamma ray ret ∈R≥0、γ ret ≤γ base Each T of i The pricing constraints for peak hours in (1) include: gamma ray tou E R is larger than or equal to 0, and the competition constraint comprises:
wherein T represents the period of time-of-use electricity price in the unit of year, T i Represents the repeated time interval of the time-of-use electricity price, the unit is day, T i,peak 、T i,off-peak Each represents T respectively i In the middle of the peak and off-peak periods, R represents a natural number,
indicating the profit of the producer and consumer in the fixed electricity rates,
indicating the profit of the producer and consumer in the time of use electricity rates.
Optionally, the photovoltaic power generation unit constraint comprises:
the power generation amount of the photovoltaic generator set in the time period T epsilon T is as follows:
output probability constraint of nominal capacity of photovoltaic generator set:
and inputting the generated energy constraint of the photovoltaic generator set of the main power grid in each time period T epsilon T:
in the formula (I), the compound is shown in the specification,
representing the power generation of the photovoltaic generator set in the time period T epsilon T,
represents the maximum power generation amount of the photovoltaic generator set, tau represents the maximum power generation amount time of the photovoltaic generator set,
represents the photovoltaic power input to the main grid, and t represents the time of the power input from the main grid.
Optionally, the electrical energy storage device constraint comprises:
and (3) constraint of charging power:
and (3) discharge power constraint:
energy balance constraints of the electrical energy storage device:
capacity constraints of the electrical energy storage device:
state of charge constraints of the electrical energy storage device at the beginning and at the end of the time range t:
in the formula (I), the compound is shown in the specification,
representing the charging power of the electrical energy storage device at time t,
denotes the maximum discharge capacity, E, of the electrical energy storage device t Representing the amount of power stored at the beginning of the peak electricity time period or off-peak electricity time period t, alpha bat Indicating the self-discharge rate of the electrical energy storage device, E t-1* The amount of power stored at the end of the peak electricity time period or off-peak electricity time period t,
electric energy, η, representing the discharge of the electric energy storage device at time t bat-d Representing the discharge efficiency, η, of the electrical energy storage device bat-c Indicating the charging efficiency of the electrical energy storage device,
representing the charging power of the electrical energy storage device at time t-1,
represents the maximum capacity of the electrical energy storage device,
indicating that the electrical energy storage device is initially powered,
representing the initial and minimum final energies in the electrical storage device,
representing the energy stored in the electrical storage device,
represents the electrical energy discharged by the electrical storage device,
representing the maximum capacity of the electrical storage device.
Optionally, the heat pump constraints comprise:
output power constraint of heat pump:
in the formula (I), the compound is shown in the specification,
representing the energy required to operate the heat pump,
represents the maximum output power of the heat pump and t represents the time of the electric power input from the main grid.
Optionally, the thermal energy storage device constraints comprise:
energy storage constraint of thermal energy storage device:
charging constraints of the thermal energy storage device:
energy balance constraint of thermal energy storage device:
state of charge constraints of the thermal energy storage device at the beginning and at the end of the time range t:
in the formula (I), the compound is shown in the specification,Hrepresents the minimum energy level, H, of the thermal energy storage device t Representing the thermal energy stored in the thermal energy storage device at time t,
representing the maximum energy level, alpha, of the thermal energy storage device hsu Indicating the self-discharge rate of the thermal energy storage device, H t-1 Representing the thermal energy stored in the thermal energy storage device at time t-1,
representing the heat energy, eta, discharged from the thermal energy storage unit at time t-1 hsu-d Representing the discharge efficiency, η, of the thermal energy storage device hsu-c Indicating the charging efficiency of the thermal energy storage device,
representing the thermal energy stored to the thermal energy storage device at time t-1,
it is shown that,
representing the initial and minimum final energies in the thermal storage device,
representing the thermal energy stored in the thermal energy storage device,
representing the thermal energy charged to the thermal energy storage device,
representing the thermal load of the electricity retailer over time period t.
Optionally, the power balance constraint of the second model comprises:
in the formula (I), the compound is shown in the specification,
representing the total power demand over the time period t,
which represents the amount of electricity consumed by the heat pump,
representing the amount of charge that the electrical energy storage device is charging,
represents the amount of electrical energy discharged by the electrical energy storage device,
representing the amount of generated electricity of the photovoltaic generator set input into the main power grid in each time period T epsilon T,
and representing the power generation amount of the photovoltaic generator set in each time period T epsilon T.
Optionally, the thermal energy demand constraint comprises:
in the formula (I), the compound is shown in the specification,
representing the thermal energy charged by the thermal energy storage device,
representing the thermal energy given off by the thermal energy storage device.
Optionally, the basic parameters include basic parameters of the first model, and the basic parameters of the first model include: one-time payment basic price, tax, producer and consumer electric power demand, unit cost of purchasing electricity from a higher-level power grid, amount of electricity purchased from a higher-level power grid, time-of-use electricity price effective period, repeating time interval of time-of-use electricity price, peak time and off-peak time in each repeating time interval, fixed electricity price, reference electricity price at time-of-use electricity price, and additional price at peak time at time-of-use electricity price.
Optionally, the basic parameters further include basic parameters of a second model, and the basic parameters of the second model include: electrical power purchased from an electrical power retailer, electrical energy discharged from an electrical energy storage device, thermal energy in a thermal energy storage device, thermal energy stored to a thermal energy storage device, thermal energy discharged from a thermal energy storage device, energy required to operate a heat pump, nominal capacity of a photovoltaic generator set, electrical power generated by a photovoltaic generator set, grid-up electricity price of a photovoltaic generator set, charging efficiency of an electrical energy storage device, discharging efficiency of an electrical energy storage device, maximum charging capacity of an electrical energy storage device, maximum discharging capacity of an electrical energy storage device, self-discharging rate of an electrical energy storage device, charging efficiency of a thermal energy storage device, discharging efficiency of a thermal energy storage device, coefficient of performance of a heat pump, self-discharging 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, energy stored in a thermal energy storage device, and/or a thermal energy storage device, One or more of a maximum energy level of the thermal energy storage device, a maximum output power of the heat pump, a tax, a base price, an electricity demand, a fixed electricity price, a pay-once base price, a time of use electricity price active period, repeating time intervals of the time of use electricity price, peak and off-peak periods in each repeating time interval, a fixed electricity price, a reference electricity price at the time of use electricity price, a premium price at the peak time at the time of use electricity price.
According to one aspect of the present invention, there is provided an apparatus for constructing an electricity price optimization model taking into account electricity retailers and producers and consumers, adapted to be executed in a computing device, comprising: the parameter acquisition module is suitable for acquiring basic parameters, wherein the basic parameters comprise electricity price types, and the electricity price types comprise fixed electricity prices, real-time electricity prices and time-of-use electricity prices; a model building unit adapted to build a first model and a second model taking into account electricity price optimization of the electricity retailer and the producer and consumer using bilinear and mixed integer theory, the first model comprising a first objective function and a first constraint, the second model comprising a second objective function and a second constraint; the model solving unit is suitable for substituting the basic parameters into the first model, solving the first model with the aim of the highest profit of the electricity retailer and outputting a scheme for selecting the electricity price, and is also suitable for substituting the basic parameters into the second model, solving the second model with the aim of the highest profit of the producer and the consumer and outputting a scheme for the output of each unit and the type of the electricity price in the producer and the consumer; wherein the first objective function comprises:
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 in the fixed electricity price scenario, t represents the time of electricity input from the main grid, γ base Represents a unit price of purchasing electric energy in a fixed electricity price scenario,
the price of the electricity consumption unit is shown,
representing the amount of electric energy, maxf, purchased by the producer or consumer from the electric power retailer over time t RTP Representing the maximum profit, ρ, of the electricity retailer at real-time electricity prices RTP One-time payment representing the purchase of electricity by the producer and the consumer to the electricity retailer, maxf determined by the producer and the electricity retailer in advance TOU Representing the maximum profit, γ, of the electricity retailer in a time of use price scenario ret Representing the electricity prices, gamma, during off-peak hours in a time-of-use electricity price scenario tou Pricing, T, representing peak hour electricity prices in a time of use pricing scenario i,peak Representing a period of high peak in the day;
wherein the second objective function comprises:
in the formula, maxf c Representing maximum profit of the parity person, f rev Showing revenue of sales of solar power generation by the producer and the consumer, f tax A fixed amount indicating that the destroyer must pay the authorities,
representing the cost of purchasing electricity from an electricity retailer.
According to an aspect of the 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 executed by the at least one processor, the program instructions comprising instructions for performing the method as described above.
According to an aspect of the present invention, there is provided a readable storage medium storing program instructions which, when read and executed by a computing device, cause the computing device to perform the method as described above.
According to the technical scheme of the invention, the construction method of the electricity price optimization model considering the electricity retailers and the producers and consumers is provided, and the model comprises a first model and a second model. In the first model, aiming at the highest profit of the electric power retailer, a plurality of electric power rate schemes are introduced into an objective function of the electric power retailer model, a scheme of the electric power rate scheme is determined, and the determined electric power rate scheme is transmitted to the second model. In the second model, the highest profit of the producers and the consumers is taken as the target, the electricity price scheme determined by the first model and the actual conditions of the units of the producers and the consumers are considered when using energy, and the operation of the units of the producers and the consumers and the electricity consumption of the producers and the consumers are output.
The system has the advantages that various electricity price mechanisms, namely flexible electricity price policies are introduced into an electricity retailer and an producer and consumer system, so that the electricity retailer is given an opportunity to increase income by providing different electricity price policies, the producer and consumer can transfer electricity consumption to the period of low overall selling electricity price, electricity consumption cost of the producer and consumer is reduced, and profits of the producer and consumer are improved.
Drawings
FIG. 1 shows a schematic view of a stillboard according to one embodiment of the present invention;
FIG. 2 illustrates a block diagram of a computing device 200, according to one embodiment of the invention;
FIG. 3 illustrates a flow chart of a method 300 of building a price of electricity optimization model that accounts for power retailers and producers and consumers in accordance with one embodiment of the invention;
FIG. 4 shows a block diagram illustrating a construction apparatus 400 that accounts for electricity price optimization models for electricity retailers and producers and consumers, according to one embodiment of the invention;
FIG. 5 is a diagram illustrating energy balance, costs and profits of a producer and a consumer in a fixed electricity price scenario, according to one embodiment of the invention;
FIG. 6 illustrates a schematic diagram of energy balance, cost and profit for a producer and a consumer in a real-time electricity price scenario, according to one embodiment of the present invention;
fig. 7 illustrates a diagram of energy balance, costs and profits of a producer and a consumer in a time of use pricing scenario, according to an embodiment of the invention.
Detailed Description
The invention provides a construction method of an electricity price optimization model considering an electricity retailer and a producer and consumer, and a flexible electricity price mechanism is introduced. In some real-time approaches, the flexible electricity price mechanism includes three types, fixed electricity price, real-time electricity price, and time-of-use electricity price. These three types of electricity prices are well established in the agreement of the electricity retailer with the producer and consumer, and do not change during the period of validity of the contract. In practical applications, one of the three electricity prices can be selected so that the electricity retailer has the greatest profit.
The fixed electricity price is a basic electricity price specified in a contract made by the electricity retailer and the producer/consumer in a set time range, and is in units of electricity per degree.
Unlike the fixed electricity prices, in the real-time electricity prices, the real-time electricity prices are one-time payments stipulated in a contract made by the electric power retailer with the producer and consumer within a set time range. For example, a one-time payment is a fee that the producer agrees to pay directly to the electricity retailer. At this time, the profit of the electric power retailer is only one-time payment, and since the electric power retailer resells electric energy to the producer and the consumer only at the spot market price without any additional fee except for one-time payment, the entire profit of the electric power retailer is made up of one-time payment paid by the producer and the consumer.
The time-of-use electricity price is that a set time range is divided into time repetition intervals, and the time repetition intervals are divided into electricity utilization peak periods and non-electricity utilization peak periods. The set time range and the repeated interval of each time can be set according to the actual application scenario, which is not limited in the present invention. For example, the time range may be one year, the time divided in one year is repeated at intervals of one day, for example 365 days in one year, and then the time divided in one year is repeated at intervals of 365.
In some embodiments, each time repetition interval includes a peak power utilization period and a peak non-power utilization period, and the invention is not limited to specific time periods of the peak power utilization period and the peak non-power utilization period. For example, points 9 to 18 are peak periods of electricity consumption, and points 19 to the next day 8 are peak periods of non-electricity consumption.
In some embodiments, during the non-electricity peak period, the electricity price of the producer and the consumer is a fixed electricity price, and during the electricity peak period, the electricity price of the producer and the consumer is the sum of the fixed electricity price and the added price. It is to be noted that the prices added in the fixed electricity prices, the real-time electricity prices, and the time-of-use electricity prices are fixed and unchangeable during the validity period of the contract made by the producer and consumer with the electricity retailer.
The invention provides a construction method of a comprehensive energy system scheduling model considering a carbon transaction mechanism and demand response, which is suitable for being executed in computing equipment. FIG. 2 shows a block diagram of a computing device 200, according to one embodiment of the invention. A block diagram of a computing device 200 as shown in fig. 2, in a basic configuration 202, the 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 the processor 204 and the system memory 206.
Depending on the desired configuration, the processor 204 may be any type of processing, including but not limited to: a microprocessor (μ P), a microcontroller (μ C), a Digital Signal Processor (DSP), or any combination thereof. The processor 204 may include one or more levels of cache, such as a level one cache 210 and a level two cache 212, a processor core 214, and registers 216. The example processor core 314 may include an Arithmetic Logic Unit (ALU), a Floating Point Unit (FPU), a digital signal processing core (DSP core), or any combination thereof. The example memory controller 218 may be used with the processor 204, or in some implementations the memory controller 218 may be an internal part of the processor 204.
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, the application 222 can be arranged to operate with program data 224 on an operating system.
Computing device 200 also includes storage device 232, storage device 232 including removable storage 236 and non-removable storage 238, each of removable storage 236 and non-removable storage 238 being connected to storage interface bus 234. In the present invention, the data related to each event occurring during the execution of the program and the time information indicating the occurrence of each event may be stored in the storage device 232, and the operating system 220 is adapted to manage the storage device 232. The storage device 232 may be a magnetic disk.
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. The exemplary output device 242 includes an image processing unit 248 and an audio processing unit 250. They may 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 can include a serial interface controller 254 and a parallel interface controller 256, which can be configured to facilitate communications with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device) or other peripherals (e.g., printer, scanner, etc.) via one or more I/O ports 258. An example communication device 246 may include a network controller 260, which may be arranged to facilitate communications with one or more other computing devices 262 over a network communication link via one or more communication ports 264.
A network communication link may be one example of a communication medium. Communication media may typically be embodied by computer readable instructions, data structures, program modules, and may include any information delivery media, such as carrier waves or other transport mechanisms, in a modulated data signal. A "modulated data signal" may be a signal that has one or more of its data set or its changes made in such a manner as to encode information in the signal. By way of non-limiting example, communication media may include wired media such as a wired network or private-wired network, and various wireless media such as acoustic, Radio Frequency (RF), microwave, Infrared (IR), or other wireless media. The term computer readable media as used herein may include both storage media and communication media.
Computing device 200 may be implemented as a server, such as a file server, a database server, an application server, a WEB server, etc., or as part of a small-form factor portable (or mobile) electronic device, such as a cellular telephone, a Personal Digital Assistant (PDA), a personal media player device, a wireless WEB-browsing device, a personal headset device, an application-specific device, or a hybrid device that include any of the above functions. Computing device 200 may also be implemented as a personal computer including both desktop and notebook computer configurations. In some embodiments, the computing device 200 is configured to perform a method 300 in accordance with the present invention.
FIG. 3 shows a schematic diagram of a method 300 of building a power price optimization model that accounts for power retailers and producers and consumers, suitable for execution in resident computing device 200 shown in FIG. 2, according to one embodiment of the invention. As shown in FIG. 3, the method 300 includes steps S310 to S330
In step S410, basic parameters are acquired. The basic data is input data as a model, the basic data includes basic parameters of a first model and basic parameters of a second model, the basic parameters of the first model include: one-time payment basic price, tax, producer and consumer electric power demand, unit cost of purchasing electricity from a higher-level power grid, amount of electricity purchased from a higher-level power grid, time-of-use electricity price effective period, repeating time interval of time-of-use electricity price, peak time and off-peak time in each repeating time interval, fixed electricity price, reference electricity price at time-of-use electricity price, and additional price at peak time at time-of-use electricity price.
The basic parameters of the second model include: electrical power purchased from an electrical power retailer, electrical energy discharged from an electrical energy storage device, thermal energy in a thermal energy storage device, thermal energy stored to a thermal energy storage device, thermal energy discharged from a thermal energy storage device, energy required to operate a heat pump, nominal capacity of a photovoltaic generator set, electrical power generated by a photovoltaic generator set, grid-up electricity price of a photovoltaic generator set, charging efficiency of an electrical energy storage device, discharging efficiency of an electrical energy storage device, maximum charging capacity of an electrical energy storage device, maximum discharging capacity of an electrical energy storage device, self-discharging rate of an electrical energy storage device, charging efficiency of a thermal energy storage device, discharging efficiency of a thermal energy storage device, coefficient of performance of a heat pump, self-discharging 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, energy stored in a thermal energy storage device, and/or a thermal energy storage device, One or more of a maximum energy level of the thermal energy storage device, a maximum output power of the heat pump, a tax, a base price, an electrical demand, a fixed electricity price, a pay-once-only base price, an active period of time of use electricity prices, repeating time intervals of time of use electricity prices, peak and off-peak periods in each repeating time interval, a fixed electricity price, a reference electricity price at time of use electricity prices, a premium price at peak time of use electricity prices.
Subsequently, in step S420, a power rate optimization model that accounts for the power retailer and the producer and consumer is established using the strong duality theorem of linear optimization, the model including an objective function and constraints.
The model in the present invention includes a first model (i.e., an electricity retailer model) applied to an electricity retailer and a second model (i.e., a producer model) corresponding to a first objective function and a first constraint. A second model is applied to the stills, the second model corresponding to a second objective function and a second constraint. The first objective function and the second objective function are collectively called an objective function, and the first constraint and the second constraint are collectively called a constraint.
The first model targets the power retailer as having the highest profit. The first model is a model based on a flexible electricity price policy. The objective function of the first model includes:
fixed electricity price scenario:
real-time electricity price scene: maxf RTP ＝ρ RTP
Time-of-use electricity price scenario:
in the formula, maxf Conv Representing the maximum profit of the electricity retailer in a fixed electricity price scenario, t representing the time of input of electricity from the main grid, γ base Represents a unit price for purchasing electric energy in a fixed electricity price scenario,
the price of electricity in the electricity unit price market is shown,
representing the amount of electric energy, maxf, purchased by the producer or consumer from the electric power retailer over time t RTP Representing the maximum profit, ρ, of the electricity retailer at real-time electricity prices RTP One-time payment representing the purchase of electricity by the producer and the consumer to the electricity retailer, maxf determined by the producer and the electricity retailer in advance TOU Representing the maximum profit, γ, of the electricity retailer in a time of use price scenario ret Representing the electricity prices, gamma, during off-peak hours in a time-of-use electricity price scenario tou Representing the premium, T, of peak hour electricity prices in a time-of-use electricity price scenario i,peak Representing peak hours of the day.
The first constraint corresponding to the objective function of the first model includes: periodic constraints of time of use prices, time of use price repetition time intervals, each T i Peak and off-peak time constraints, electricity price constraints and per T i One or more of the peak hour pricing constraints. In particular, the amount of the solvent to be used,
1) the period constraint of the time-of-use electricity price includes:
T＝1/year
2) the time-of-use electricity price repetition time interval includes:
T i ＝1/day
3) each T i The peak and off-peak time constraints in (1) include:
T i,peak ∪T i,off-peak ＝T i
4) the electricity price constraints include:
γ ret ∈R≥0、γ re t≤γ base
5) each T i The pricing constraints for peak hours in (1) include:
γ tou ∈R≥0
6) and (3) competitive constraint:
wherein T represents the period of time-of-use electricity price in the unit of year, T i Represents the repeated time interval of the time-of-use electricity price, the unit is day, T i,peak 、T i,off-peak Each represents T respectively i In the middle of peak hours and off-peak hours, R represents a natural number,
represents an optimal solution to the fixed electricity price model,
representing a real-time electricity price optimal solution.
The second model aims at the highest profit of the producer and the consumer, and the objective function is as follows:
in the formula, maxf c Representing maximum profit of the parity person, f rev Showing income of the producer and the consumer selling the solar power generation, f tax A fixed amount indicating that the destroyer must pay the authorities,
representing the cost of purchasing electricity from an electricity retailer.
In some embodiments, the flexible electricity prices include three types, i.e., fixed electricity prices, real-time electricity prices, and time-of-use electricity prices, and then the objective function of the second model specifically includes:
a second objective function in a fixed electricity price scenario:
a second objective function in the real-time electricity price scene:
a second objective function in the time-of-use electricity price scenario:
in the formula (I), the compound is shown in the specification,
respectively represents the maximum profits of the producers and the consumers under the fixed electricity price scene, the real-time electricity price scene and the time-of-use electricity price scene,
represents the cost of purchasing electricity from an electricity retailer at a fixed electricity price,
represents the cost of purchasing electricity from an electricity retailer at a real-time electricity rate,
represents the cost of purchasing electricity from an electricity retailer at a time of use price,
it is shown that,
time representing the time of day, mod represents the modulo operator, and N represents an integer.
In the real-time electricity price scenario, the only decision variable for the retailer is one-time payment ρ RTP It constitutes a profit for the electricity retailer and balances the profit of the producer and the consumer in real time electricity prices compared to fixed electricity prices. One-time payments occur in the lower electricity price interval as if a constant term were added to the objective function of the producer and the consumer. Thus, due to the simple price shifting approach and the lack of other electricity pricing mechanisms, the electricity retailer cannot influence the behavior of the producer or consumer, which means that lower level solutions (i.e., fixed electricity pricing schemes) are independent of higher level decisions (i.e., real-time electricity pricing schemes). Therefore, the solution can be solved in the following order. Firstly, the lower layer problem is optimized to obtain the optimal solution
Based on
And optimal solution of fixed electricity price model
Calculating an optimal one-time payment that satisfies a competitive constraint
The following were used:
then the objective function of the second model in the context of real-time electricity prices, with the retailer's optimal one-time payment maximized, includes:
in the formula (I), the compound is shown in the specification,
representing the income of the producers and the consumers for selling the solar power generation,
indicating a tax paid by the producer to the authorities,
representing the power input from the main grid,
representing an optimal one-time payment to the electricity retailer.
And the second constraint condition corresponding to the second objective function comprises one or more of photovoltaic generator set constraint, electric energy storage equipment constraint, heat pump constraint, heat storage equipment constraint, electric energy balance constraint and heat energy demand constraint of the second model.
1) Assume that the producer's power production is by a rooftop Photovoltaic (PV) power generation unit. The generating capacity of the photovoltaic generator set in a time period T epsilon T
For these generated energy, the producer and consumer can consume it directly, can store it in the electric energy storage device, and can also enter the main power network to supply power. And simultaneously, the upper power output limit of the nominal capacity of the photovoltaic generator set is also considered. Photovoltaic generator set constraints then include:
the power generation amount of the photovoltaic generator set in the time period T epsilon T is as follows:
output probability constraint of nominal capacity of photovoltaic generator set:
and inputting the generated energy constraint of the photovoltaic generator set of the main power grid in each time period T epsilon T:
in the formula (I), the compound is shown in the specification,
representing the power generation of the photovoltaic generator set in the time period T epsilon T,
represents the maximum power generation amount of the photovoltaic generator set, tau represents the power generation time of the photovoltaic generator set,
represents the photovoltaic power input to the main grid, and t represents the photovoltaic power time input to the main grid.
2) The electrical energy may be stored in an electrical energy storage device, such as a battery, to better manage the producer's overall energy system, such as storing solar energy or electrical energy purchased from a retailer. The electrical energy storage device may be charged or discharged. For both cases, a certain upper limit limits the charge and discharge power. Thus, electrical energy storage device constraints include:
and (3) constraint of charging power:
and (3) discharge power constraint:
energy balance constraints of the electrical energy storage device:
capacity constraints of the electrical energy storage device:
for an electrical energy storage device, initial and end conditions need to be set, the state of charge constraints of the electrical energy storage device at the beginning and at the end of the time range t:
in the formula (I), the compound is shown in the specification,
representing the charging power of the electrical energy storage device at time t,
denotes the maximum discharge capacity, E, of the electrical energy storage device t Representing the amount of power stored at the beginning of the peak electricity time period or off-peak electricity time period t, alpha bat Indicating the self-discharge rate of the electrical energy storage device, E t-1 Representing the amount of power released at the beginning of the peak electricity usage period or off-peak electricity period t,
electric energy, η, representing the discharge of the electric energy storage device at time t bat-d Representing the discharge efficiency, η, of the electrical energy storage device bat-c Indicating the charging efficiency of the electrical energy storage device,
representing the charging power of the electrical energy storage device at time t-1,
represents the maximum capacity of the electrical energy storage device,
representing the energy of the electrical energy storage device at the beginning,
representing the initial and minimum final energies in the electrical storage device,
representing the energy of the electrical energy storage device at the end,
indicating the amount of electrical energy discharged by the electrical energy storage device,
representing the maximum capacity of the electrical energy storage device. Alpha (alpha) ("alpha") bat E [0, 1) is the self-discharge rate, η, of the electrical energy storage device bat-c ∈(0，1]、η bat-d ∈(0，1]Is the efficiency of the charging and discharging process. Also, the electrical energy storage devices may be charged or discharged, but not simultaneously, during any given period of time.
3) For heat production, it is assumed in the present invention that the producer and the consumer possess a heat pump that uses electricity to provide heat at some desired temperature level. And to constrain the heat pump:
output power constraint of heat pump:
in the formula (I), the compound is shown in the specification,
representing 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.
4) For the storage of thermal energy, it is assumed that the deputy can use a hot water tank as a thermal energy storage device to store the thermal energy. The thermal energy storage device is constrained according to the capacity of the hot water tank, the minimum and maximum water temperatures. Thermal energy storage device constraints include:
energy storage constraint of thermal energy storage device:
charging constraints of the thermal energy storage device:
energy balance constraint of thermal energy storage device:
for a thermal energy storage device, initial and end conditions need to be set, the state of charge constraints of the thermal energy storage device at the beginning and at the end of the time range t:
in the formula (I), the compound is shown in the specification,
represents the minimum energy level, H, of the thermal energy storage device t Representing the thermal energy stored in the thermal energy storage device at time t,
representing the maximum energy level of the thermal energy storage device, alpha hsu representing the self-discharge rate of the thermal energy storage device, H t-1 Representing the thermal energy stored in the thermal energy storage device at time t-1,
representing the thermal energy given off by the thermal energy storage device at time t-1,η hsu-d indicating the discharge efficiency, eta, of the thermal energy storage device hsu-c Indicating the charging efficiency of the thermal energy storage device,
representing the thermal energy stored to the thermal energy storage device at time t-1,
representing the amount of heat stored in the thermal energy storage device at the beginning of the time period t,
representing the initial and minimum final energies in the thermal storage device,
represents the charge capacity of the thermal energy storage device,
representing the amount of charge of the thermal energy storage device over time period t,
representing the thermal load of the manufacturer over time period t.
5) The power balance constraints of the second model include:
in the formula (I), the compound is shown in the specification,
representing the total power demand over the time period t,
the amount of electricity consumed by the heat pump is indicated,
representing the amount of charge to which the thermal energy storage device is charged,
representing the amount of electricity discharged by the thermal energy storage device,
representing the photovoltaic power production of the input main grid per time period te,
representing the photovoltaic power generation per time period T e T.
6) Assuming that the heat demand of the producer is fully controlled by the thermal energy storage facility at each respective time period, the thermal energy demand constraints then include:
in the formula (I), the compound is shown in the specification,
representing the thermal energy charged by the thermal energy storage device,
representing the thermal energy released by the thermal storage energy storage device.
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 the highest profit of the electric power retailer, the scheme of electricity price selection 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 the highest profit of the producer and the consumer, and the contribution scheme of each unit in the producer and the consumer is output.
The units in the producer and the consumer may include a distributed wind turbine, a distributed photovoltaic unit, a gas boiler, a cogeneration unit, a heat pump, an electric refrigerator, an absorption refrigerator, an electric energy storage device, a heat energy storage device, a cold energy storage device, and the like.
In the objective function in the time-of-use electricity price scenario, the product exists
The problem of the second model is a mixed integer quadratic form problem, a coupling relation exists between the constructed double-layer models, nonlinear constraint exists, and direct solving is difficult to carry out. In order to effectively solve the model, in one embodiment of the invention, the two-layer model is equivalently converted into a single-layer optimization model which is easier to solve, and then the single-layer optimization model is quickly solved.
It should be understood that there are many ways to solve the model, and the present invention is not limited to the specific implementation, and all ways to solve the model are within the scope of the present invention.
For example, the strong duality theorem of linear optimization can be used to illustrate the optimality condition of the lower layer model. After this linearization, the second model becomes a mixed integer linearity problem. For fixed second model variables, the first model problem is to optimize the parameters of the model in dependence on the stills. By using the strong dual theory of linear optimization, the optimality condition of the linear programming of the second model can be given. By adding these optimal conditions, as well as the original constraints and the dual-floor constraints to the first model, we obtain a single-layer mixed integer nonlinear problem, which is an equivalent re-simulation of the two-layer model, thereby facilitating the model solution. That is, the present invention builds a power price optimization model that accounts for power retailers and producers and consumers using the strong duality theorem of linear optimization.
Fig. 4 shows a block diagram of an apparatus 400 for building an electricity price optimization model that accounts for electricity retailers and producers and consumers according to one embodiment of the invention, the apparatus 400 may reside in a computing device 200, as shown in fig. 4, the apparatus 400 comprising: an acquisition parameter unit 410, a model construction unit 420, and a model solution unit 430.
The parameter obtaining module 410 is adapted to obtain 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 construction unit 420 adapted to build a first model and a second model taking into account electricity price optimization of the electricity retailer and the producer and consumer using bilinear and mixed integer theory, the first model comprising a first objective function and a first constraint, the second model comprising a second objective function and a second constraint;
the model solving unit 430 is suitable for substituting the basic parameters into the first model, solving the first model with the aim of the highest profit of the electricity retailer, and outputting a scheme for selecting the electricity price, and is also suitable for substituting the basic parameters into the second model, solving the second model with the aim of the highest profit of the producer and the consumer, and outputting a scheme for each unit of the producer and the consumer to output the type of the output and the electricity price;
wherein the first objective function comprises:
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 in a fixed electricity price scenario, t represents the time of input of electricity from the main grid, γ base Represents a unit price of purchasing electric energy in a fixed electricity price scenario,
the price of the electricity consumption unit is shown,
representing the amount of electric energy, maxf, purchased by the producer or consumer from the electric power retailer over time t RTP Representing the maximum profit, ρ, of the electricity retailer at real-time electricity prices RTP One-time payment representing the purchase of electricity by the producer and the consumer to the electricity retailer, maxf determined by the producer and the electricity retailer in advance TOU Representing electric retailers in a time-of-use electricity price scenarioMaximum profit of, gamma ret Representing the electricity prices, gamma, during off-peak hours in a time-of-use electricity price scenario tou Pricing, T, representing peak hour electricity prices in a time of use pricing scenario i,peak Representing a period of high peak in the day;
wherein the second objective function comprises:
in the formula, maxf c Representing maximum profit of the parity person, f rev Showing income of the producer and the consumer selling the solar power generation, f tax A fixed amount indicating that the destroyer must pay the authorities,
representing the cost of purchasing electricity from an electricity retailer.
It should be noted that the operation principle of the device 400 for constructing an electricity price optimization model considering an electricity retailer and a producer is similar to that of the method 300 for constructing an electricity price optimization model considering an electricity retailer and a producer, and reference may be made to the description of the method 300 for relevant points, which is not described herein again.
Specific cases will be used below to verify that the electricity price optimization model constructed by the present invention, which accounts with electricity retailers and producers and consumers, performs numerical example simulations.
In the present invention, accurate data is collected for various aspects (such as photovoltaic generator sets, electrical storage devices, thermal storage devices, and power demand) of a typical farm in germany. In the invention, a producer and a consumer with a photovoltaic generator set and an electric heat pump are selected to process data through Pythonda.
The present invention uses actual hourly measurements of 2015 to represent the output produced by the photovoltaic generator set, the heat demand, and the electricity demand of the producer and consumer. The capacity of the photovoltaic generator set is 0.01008MWh, the maximum capacity of the heat storage unit of the producer and the consumer is 0.033276MWh, and the self-discharge rate per hour is 1% of the water tank. The coefficient of performance η hydrogen pump heat pump is assumed to be 3. To better reflect the current state of germany, in the model of the invention, producers and consumers are equipped with batteries (electrical storage devices) and the average market price of the fixed electricity price of the german energy supply is adopted.
The producer and consumer pays a considerable fee and tax on the electricity purchased from the electricity retailer. This severely impacts the operation of the producer and consumer facility in addressing the tradeoff between immediate consumption of electricity by itself and storage of electricity for later consumption by itself, feeding into the grid, and later purchasing of electricity from the grid. In order to more practically evaluate the deceased, the present invention must explicitly consider taxes and fees paid for different electricity prices. For the fees and tax levels paid by the producers and consumers, the value view of the final home consumer in 2017 in germany is used herein. The calculation of the bi-level model under consideration results in an example that is not allowed to be solved discretely hourly over the entire time of the year. Thus, from the available data of 2015, the present invention selects a representative winter season, mid-season and summer season and calculates continuously in this order. Thus, the present invention obtains a one year calculation based on three representative weeks selected for each season.
The linear model is solved to a relative optimality gap without imposing any time constraints. The running time for solving each linear model does not exceed 1 s. The time limit for all non-linear examples was 15 minutes, with the optimality gap set to 1%. The invention uses a model of time-of-use electricity prices and solves the model based on a low-level reconstruction of strong duality.
Inputting and outputting results by the double-layer model:
for fixed electricity prices, we achieve this goal by artificially extending the fixed electricity price model to an upper layer that contains only the objective function of the electricity retailer in the fixed electricity price scenario. This model is solved using a strong binary-based low-level reconstruction that is identical to time-of-use power prices. The optimal objective function value of the birth and consumption person is the same as the value of the objective function of the birth and consumption person model (second model) in the fixed electricity price scenario.
Real-time electricity prices naturally represent an optimistic behaviour for the producer and the consumer, since the goal of both the producer and the retailer is to maximize the profit of the producer and the consumer. In a real-time electricity price scenario, the full profit of the electricity retailer is simply a one-time payment from the producer or consumer. This one-time payment allows the electricity retailer to extract all of the producer's profit beyond what the producer obtained at the fixed electricity price. Therefore, in the real-time electricity price scenario, there is no need to reformulate the expansion problem, and the solution of the objective function of the producer-consumer model in the real-time electricity price scenario directly produces the optimistic behavior of the producer.
In the optimal solution of the real-time electricity price problem, with the aim of maximizing the profit of the electricity retailer, the corresponding competitive constraint is satisfied equally. Therefore, in the time of use electricity prices, the income of the producer and the consumer cannot be lower than the fixed electricity prices due to the time of use electricity price competition constraint. Therefore, the attraction of the time-of-use electricity prices to the prosumers is at least as great as the other two electricity prices. In all the proposed calculations, the time of use electricity price competition constraint is also satisfied on par, so that the profit of the producer and the consumer is equal among the three types of electricity prices.
Before discussing the actual calculation results, some details of the respective models of the birth and consumption for the three types of electricity prices are provided in table 1. Note that these details are always given for a corresponding single level reformulation that includes optimistic assumptions.
TABLE 1 model sizes including optimistic assumptions
| Electricity price | Conv | RTP | TOU |
| Double-layer model | Whether or not | Whether or not | Is that |
| Total number of variables | 8574 | 3529 | 10160 |
| Discrete variable number | 0 | 0 | 72 |
| Number of non-linear terms | 0 | 0 | 4032 |
| Number of constraints | 5047 | 1517 | 7618 |
The result of an optimistic prenatal solution:
all results provided in table 2 herein have been extended to a time frame of one year, while the numbers include three example weeks without scaling. Table 2 lists the most important attributes of the optimal solution under all three optimistic assumptions regarding electricity prices. The revenue (in percent) compared to the traditional electricity price is the difference between the consideration and the traditional electricity retailer profit divided by the electricity retailer profit for the alternative electricity price under consideration. The relative optimality gap is the gap obtained after the time limit is exceeded. Thus, the electricity retailer's profit (and corresponding revenue) may be higher at the time of use electricity price. The profit of the electricity retailer at the time-of-use electricity price is higher than that of the fixed electricity price, and the real-time electricity price as a "flexibility reference" shows the highest profit compared to the fixed electricity price. The gain of the time-of-use electricity price is at least 1.48%. The larger optimality gap in time-of-use pricing solutions is 3.24%, still leaving some room for greater gain. The prices faced by the producers and consumers vary greatly between electricity prices, with the lowest average price being obtained from a fixed electricity price objective function. In this case, the compensation payment of the one-time payment adjusts the profit of the producer and the consumer to the level of the fixed electricity price, and although there is a great price difference from the reference phase electricity price ratio, the profit of the electricity retailer does not have a great difference. The price level generated by the time-of-use electricity price is approximately the same as the fixed price electricity price. The prices in different time periods do not have significant difference in the objective function of the real-time electricity prices. And displaying the highest price fluctuation in a real-time price scene.
Table 2 expands the results of the three week study to one year (according to optimistic assumptions)
| Price of electricity | FP | RTP | TOU |
| Retailer profit | 505.60 | 530.38 | 513.19 |
| Gain of | - | 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 |
| Maximum price | 64.20 | 75.87 | 66.70 |
One goal of designing flexible energy supply rates is to incentivize the producers and consumers to apply load shifting. Thus, the present invention contemplates load management of the victim and the victim in more detail. To this end, we first provide relevant summary input data in table 3, such as total electrical and thermal load and the total generation of a photovoltaic power generation unit scaled up to one year. The input data is the same for all types of electricity prices at this time.
Table 3 summarizes the producer parameters and results (extended to one year MWh)
Furthermore, we also specify aggregated results, such as the total amount of electric power purchased or fed from or to the main grid each year, and the aggregate losses of batteries and thermal energy storage devices caused by load transfer. As expected, these results may vary for different electricity prices. The imported electric power is different, and the profit of the producer and the consumer is the minimum under the fixed electricity price scene, and then under the time-of-use electricity price scene and the real-time electricity price scene. The sequence of the amount of electricity purchased from the main grid coincides exactly with the sequence of the batteries and the thermal energy storage devices increasing the losses throughout the electricity price. In fact, the more power transfer that occurs, the higher the losses of the batteries and the thermal energy storage of the producers, due to the higher fluctuations in the import price for a certain electricity price. The real-time electricity price is the most flexible electricity price, and the loss caused in the time-of-use electricity price scene is the highest. Real-time electricity prices drive producers to purchase the maximum amount of electricity from the main grid. The loss caused in the time-of-use electricity rate scenario is small and results in a reduction in the power purchased from the main grid. It is worth noting that the energy of the photovoltaic generator set is mainly used for self consumption, so that the photovoltaic electric quantity input into the main power grid by the three types of electricity prices is the same. The surplus electric energy is sold only when the needs of the producer and the consumer are satisfied, and therefore, the energy amount of the sold photovoltaic generator set does not depend on the electricity price. The profit of the producer and consumer is apparently the same for the three types of electricity prices, because all electricity prices must bring the consumer profit constraints at least from the fixed price electricity prices.
In summary, the higher the flexibility of electricity prices is observed, the more load transfer, resulting in power loss. To study the seasonal dependence of each more clearly, the best load management of the respective victims over the three demonstration weeks is given. Fixed electricity prices are first discussed under optimistic assumptions. The corresponding load management of the producer and seller is shown in figure 5. Energy sources such as power purchased from the main grid, power generated by the photovoltaic generator set, and power discharged from the battery are shown below zero in the upper half of fig. 5. The producer and consumer are used to cover the electrical load and the heat pump, and the power fed into the main grid and charging the battery is plotted above zero. The lower half of fig. 5 shows the price of electricity purchased by the producer during each time period (red line, in euro/megawatt hours, colors not shown in the figure).
The amount of power purchased from the main grid is greater, corresponding to a higher heat demand in winter and in the peak seasons (and therefore a higher power demand for the heat pump), while a small amount of power purchased coincides with a larger amount of power generation by the photovoltaic generator set, especially in summer. Since the electricity prices in this electricity price are constant over time, the cost/profit curve of the producer and the consumer is closely related to the import (winter and mid-season weeks) and the fixed unit price curve (summer week).
In contrast to the curves in the conventional electricity rate graph, more jagged lines are displayed in the real-time electricity rate scene, as shown in fig. 6. This is especially true for electricity purchased from the main grid, the cost/profit curves for the heat pump and the producer and consumer. The use of the battery is still mainly dependent on the photovoltaic generator set output, just like the traditional electricity price: for both prices, the battery is used to store excess solar energy for later consumption.
The best producer load management in time of use electricity prices is shown in fig. 7. The peak hours began at 6 a.m. and continued until midnight. Thus, off-peak hours start at midnight and continue until 6 am. It can be seen that the price difference between peak and off-peak hours is much lower than real-time electricity prices, and spot market prices in real-time electricity prices are directly forwarded to the prosumers. It is also seen that the time of use price induces some regular pattern of the behavior of the person who gives birth or who gives up the electricity. Before the end of the off-peak period, the producer purchases electricity and uses it directly in the heat pump to store heat in the thermal storage facility and then uses it during the peak period. Thus, for time of use telephone scenarios and real time electricity prices scenarios, the main storage for 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, since early purchase would lead to the loss of thermal energy storage, batteries do not play an important role in cold weeks, which changes during warmer periods, where batteries are mainly used for storing the electrical energy produced by photovoltaic generator sets in the seasons and summer.
The results show that the design of flexible electricity prices is extremely complex, and the performance of electricity prices depends on many different details of the electricity price structure and also on the behavior patterns of the producers and consumers.
From the foregoing, the present invention analyzes the potential of motivating producers and consumers through flexible electricity prices in power generation, storage, and consumption and heat generation, storage, and consumption energy systems, thereby evaluating the possibility of electric retailers to increase additional profits using these electricity prices. We are concerned with the electricity price design of electricity retailers, who consider the specific load transfer potential of producers and consumers who own small power generation and storage facilities. The destroyer model explicitly takes into account the interactions of electricity, heat production and storage within the home, which is a special feature of the proposed method.
If the producer purchases electricity from the electricity retailer to make up for the remaining load, the price of electricity he is confronted with will optimize the operation of his internal systems. To properly model the interaction between a particular decision-making environment, the electricity retailer and the producer or consumer, we employ a two-layer model approach. In our analysis, three types of electricity prices are considered: fixed electricity prices, real-time pricing and time-of-use electricity prices. For proper comparison, we ensure that the producer/consumer will maintain his profit level if he changes from traditional electricity prices to either of the two more flexible electricity prices.
The present invention provides many new insights into flexible pricing. Within the framework of the invention, the simple transfer of the overall selling price in real time electricity prices yields the highest profits for the electricity retailer. This is because price signals from the market promote optimal utilization of the flexibility of the destroyer. In the analysis of the present invention, the time of use price yields only modest additional gains. The possibility of load shifting is moderate if the detailed technical configuration is considered in combination with a fixed time window of peak prices. In optimistic situations, in addition to the potential for inefficiency, flexible electricity prices in the case of time-of-use electricity prices means that there are multiple solutions to the producer and consumer optimization problem, increasing revenue uncertainty for electricity retailers. In essence, the electricity retailer must take the risk of the producer or consumer being disadvantaged, which makes flexible electricity prices such as time of use electricity prices less attractive. The model provided by the invention also lays a foundation for searching the producers and the consumers with the alternative technical equipment. The efficiency potential of flexible electricity prices may be higher if the producer or consumer has equipment with controlled energy production.
Claims (10)
1. A method of building a power price optimization model that accounts for power retailers and producers and consumers, adapted to be executed in a computing device, the method comprising:
acquiring basic parameters, wherein the basic parameters comprise electricity price types, and the electricity price types comprise fixed electricity prices, real-time electricity prices and time-of-use electricity prices;
establishing a first model and a second model taking into account electricity price optimization of the electricity retailer and the producer and consumer by utilizing a strong duality theorem of linear optimization, wherein the first model comprises a first objective function and a first constraint condition, and the second model comprises a second objective function and a second constraint condition;
substituting the basic parameters into the first model, solving the first model by taking the highest profit of the electric retailer as a target, and outputting a scheme for selecting the electricity price;
substituting the basic parameters into the second model, solving the second model by taking the highest profit of the producer and the consumer as a target, and outputting a scheme of the output and electricity price types of each unit in the producer and the consumer;
wherein the first objective function comprises:
fixed electricity price scenario:
real-time electricity price scenario: maxf RTP ＝ρ RTP
Time-of-use electricity price scenario:
in the formula, maxF Conv Representing the maximum profit of the electricity retailer in a fixed electricity price scenario, t representing the time of input of electricity from the main grid, γ base Represents a unit price for purchasing electric energy in a fixed electricity price scenario,
the spot market electricity price is represented,
representing the amount of electric energy, maxf, purchased by the producer or consumer from the electric power retailer over time t RTP Representing the maximum profit, ρ, of the electricity retailer at real-time electricity prices RTP One-time payment representing the purchase of electricity by the producer and the consumer to the electricity retailer, maxf determined by the producer and the electricity retailer in advance TOU Representing the maximum profit, γ, of the electricity retailer in a time of use price scenario ret Representing electricity prices, gamma, during off-peak hours in a time-of-use electricity price scenario tou Representing the premium, T, of peak hour electricity prices in a time-of-use electricity price scenario i,peak Representing a period of high peak in the day;
wherein the second objective function comprises:
in the formula, maxf c Representing maximum profit of the parity person, f rev Showing revenue of sales of solar power generation by the producer and the consumer, f tax A fixed amount indicating that the destroyer must pay the authorities,
representing the cost of purchasing electricity from an electricity retailer.
2. The method of claim 1, wherein the second objective function comprises:
a second objective function in a fixed electricity price scenario:
a second objective function in the real-time electricity price scene:
a second objective function in the time-of-use electricity price scenario:
in the formula (I), the compound is shown in the specification,
respectively represents the maximum profits of the producers and the consumers under the fixed electricity price scene, the real-time electricity price scene and the time-of-use electricity price scene,
represents the cost of purchasing electricity from an electricity retailer at a fixed electricity price,
represents the cost of purchasing electricity from an electricity retailer at a real-time electricity rate,
represents the cost of purchasing electricity from an electricity retailer at a time of use price,
it is shown that,
representing the time of power input from the mains grid in a time-of-use tariff scenario, mod represents the modulus operator, and N represents an integer.
3. The method of claim 1 or 2, wherein the first constraint comprises: periodic constraints of time of use prices, time of use price repetition time intervals, each T i Peak and off-peak time constraints, electricity price constraints and per T in (1) i In the peak hours, the second constraints comprising: one or more of photovoltaic generator set constraints, electrical energy storage device constraints, heat pump constraints, thermal energy storage device constraints, electrical energy balance constraints and thermal energy demand constraints of the second model.
4. The method of claim 4, wherein,
the period constraint of the time-of-use electricity price comprises:
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 time constraints in (1) include:
T 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 pricing constraints for peak hours in (1) include:
γ tou ∈R≥0
the competition constraint includes:
wherein T represents the period of time-of-use electricity price in the unit of year, T i Representing time-of-use electricity price repeat timeEvery, in units of days, T i,peak 、T i,off-peak Each represents T respectively i In the middle of peak hours and off-peak hours, R represents a natural number,
indicating the profit of the producer and consumer in the fixed electricity rates,
indicating the profit of the producer and consumer in the time of use electricity rates.
5. The method of claim 3 or 4, wherein the photovoltaic generator set constraints comprise:
the power generation amount of the photovoltaic generator set in the time period T epsilon T is as follows:
output probability constraint of nominal capacity of photovoltaic generator set:
and inputting the generated energy constraint of the photovoltaic generator set of the main power grid in each time period T epsilon T:
in the formula (I), the compound is shown in the specification,
representing the power generation of the photovoltaic generator set in a time period T epsilon T,
representing the maximum power generation, τ, of the photovoltaic generator setThe maximum power generation amount time of the photovoltaic generator set is represented,
represents the photovoltaic power input to the main grid, and t represents the time of the power input from the main grid.
6. The method of any one of claims 3 to 5, wherein the electrical energy storage device constraints comprise:
constraint of charging power:
and (3) discharge power constraint:
energy balance constraints of the electrical energy storage device:
capacity constraints of the electrical energy storage device:
state of charge constraints of the electrical energy storage device at the beginning and at the end of the time range t:
in the formula (I), the compound is shown in the specification,
representing the charging power of the electrical energy storage device at time t,
denotes the maximum discharge capacity, E, of the electrical energy storage device t Representing the amount of power stored at the beginning of the peak electricity time period or off-peak electricity time period t, alpha bat Indicating the self-discharge rate of the electrical energy storage device, E t-1 Power amount stored at the end of the peak electricity utilization time period or peak electricity non-utilization time period t,
electric energy, η, representing the discharge of the electric energy storage device at time t bat-d Indicating the discharge efficiency, eta, of the electrical energy storage device bat-c Indicating the efficiency of charging of the electrical energy storage device,
representing the charging power of the electrical energy storage device at time t-1,
represents the maximum capacity of the electrical energy storage device,
indicating that the electrical energy storage device is initially powered,
representing the initial and minimum final energies in the electrical storage device,
representing the energy stored in the electrical storage device,
represents the electrical energy discharged by the electrical storage device,
representing the maximum capacity of the electrical storage device.
7. The method of any of claims 3 to 6, wherein the heat pump constraints comprise:
output power constraint of heat pump:
in the formula (I), the compound is shown in the specification,
representing the energy required to operate the heat pump,
represents the maximum output power of the heat pump and t represents the time of the electric power input from the main grid.
8. An apparatus for constructing a power price optimization model that accounts for power retailers and producers and consumers, adapted to be executed in a computing device, comprising:
the parameter obtaining module is suitable for obtaining basic parameters, wherein the basic parameters comprise electricity price types, and the electricity price types comprise fixed electricity prices, real-time electricity prices and time-of-use electricity prices;
a model building unit adapted to build a first model and a second model taking into account electricity price optimization of the electricity retailer and the producer and consumer using bilinear and mixed integer theory, the first model comprising a first objective function and a first constraint, the second model comprising a second objective function and a second constraint;
the model solving unit is suitable for substituting the basic parameters into the first model, solving the first model with the aim of the highest profit of the electric retailer as an objective and outputting a scheme for selecting the electricity price, and is also suitable for substituting the basic parameters into the second model, solving the second model with the aim of the highest profit of the producer and the consumer and outputting a scheme for outputting the type of the output and the electricity price of each unit in the producer and the consumer;
wherein the first objective function comprises:
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 in the fixed electricity price scenario, t represents the time of the electricity input from the main grid, γ base Represents a unit price of purchasing electric energy in a fixed electricity price scenario,
the price of the electricity consumption unit is shown,
representing the amount of electric energy, maxf, purchased by the producer or consumer from the electric power retailer over time t RTP Representing the maximum profit, ρ, of the electricity retailer at real-time electricity prices RTP A one-time payment representing the purchase of electricity by the producer and the consumer to the electricity retailer, the one-time payment being determined by the producer and the electricity retailer in advance by negotiation, maxf TOU Representing maximum profit, γ, of an electricity retailer in a time of use price scenario ret Representing the electricity prices, gamma, during off-peak hours in a time-of-use electricity price scenario tou Representing the premium, T, of peak hour electricity prices in a time-of-use electricity price scenario i,peak Representing a period of high peak in the day;
wherein the second objective function comprises:
in the formula, maxf c Representing maximum profit of the parity person, f rev Showing revenue of sales of solar power generation by the producer and the consumer, f tax A fixed amount indicating that the destroyer must pay the authorities,
representing the cost of purchasing electricity from an electricity retailer.
9. A computing device, comprising:
at least one processor; and
a memory storing program instructions configured for execution by the at least one processor, the program instructions comprising instructions for performing the method of any of claims 1-7.
10. A readable storage medium storing program instructions that, when read and executed by a computing device, cause the computing device to perform the method of any of claims 1-7.
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| CN117791598A (en) * | 2024-02-27 | 2024-03-29 | 国网山西省电力公司经济技术研究院 | Aggregation method for participating in power spot market by communication base station cluster |
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| CN117455210A (en) * | 2023-12-26 | 2024-01-26 | 山东建筑大学 | Comprehensive energy system scheduling method, system, medium and equipment |
| CN117455210B (en) * | 2023-12-26 | 2024-04-05 | 山东建筑大学 | Comprehensive energy system scheduling method, system, medium and equipment |
| CN117791598A (en) * | 2024-02-27 | 2024-03-29 | 国网山西省电力公司经济技术研究院 | Aggregation method for participating in power spot market by communication base station cluster |
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