CN117057463A - Optimization method, device and storage medium for intelligent building shared energy storage and power supply - Google Patents

Optimization method, device and storage medium for intelligent building shared energy storage and power supply Download PDF

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CN117057463A
CN117057463A CN202311023285.6A CN202311023285A CN117057463A CN 117057463 A CN117057463 A CN 117057463A CN 202311023285 A CN202311023285 A CN 202311023285A CN 117057463 A CN117057463 A CN 117057463A
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王静
赵宇明
彭毅
王振尚
陈婧文
李治
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Shenzhen Power Supply Co ltd
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Abstract

The invention provides an optimization method for sharing energy storage and power supply of an intelligent building, which is used in a system comprising a shared energy storage power station and a plurality of intelligent buildings connected with the shared energy storage power station, and comprises the following steps: establishing an intelligent building energy management model matched with a plurality of intelligent buildings, and establishing a shared energy storage management model matched with the shared energy storage power station; based on Lagrangian functions and KKT conditions of the intelligent building energy management model, converting the intelligent building energy management model into constraint conditions of the shared energy storage management model, and adjusting the shared energy storage management model; and carrying out power supply management on the intelligent buildings based on the adjusted shared energy storage management model. By implementing the method, the double-layer model consisting of the intelligent building energy management model and the shared energy storage management model can be converted into the single-layer model only comprising the shared energy storage management model, so that the complexity of the model is reduced, the solving speed is improved, the power supply strategy is quickly adjusted according to the change of the power consumption requirements of a plurality of intelligent buildings, and the power supply efficiency is improved.

Description

Optimization method, device and storage medium for intelligent building shared energy storage and power supply
Technical Field
The invention relates to the technical field of intelligent energy, in particular to an optimization method, device and storage medium for intelligent building shared energy storage and power supply.
Background
With the increase of the urban level, the number of large buildings is increasing, and the electric energy consumption is increasing in the duty ratio of all electric loads. Along with the shortage of energy sources and gradual improvement of people's environmental protection concepts, the intellectualization of building systems is more and more favored by people, and the intelligent building scale is rapidly increased, and the problems of high energy consumption and high electricity consumption are accompanied. How to reduce the increasing electricity cost of intelligent buildings is a major problem and challenge for sustainable development.
Disclosure of Invention
The invention aims to solve the technical problem of providing an optimization method, an optimization device and a storage medium for intelligent building shared energy storage and power supply, which can reduce model complexity, improve solving speed, quickly adjust power supply strategies according to the change of power demand of a plurality of intelligent buildings and improve power supply efficiency.
To solve the above-mentioned technical problem, as a first aspect of the present invention, there is provided an optimization method for sharing energy storage and power supply of an intelligent building, for a system including a shared energy storage power station and a plurality of intelligent buildings connected with the shared energy storage power station, which includes:
establishing an intelligent building energy management model matched with a plurality of intelligent buildings and establishing a shared energy storage management model matched with the shared energy storage power station, wherein the intelligent building energy management model is used for determining the cost and the income of the plurality of intelligent buildings, and the plurality of intelligent buildings are provided with new energy power generation devices; the shared energy storage power station provides charge and discharge service for the intelligent buildings;
based on Lagrangian functions and KKT conditions of the intelligent building energy management model, converting the intelligent building energy management model into constraint conditions of the shared energy storage management model, and adjusting the shared energy storage management model;
and carrying out power supply management on the intelligent buildings based on the adjusted shared energy storage management model.
Optionally, the converting the intelligent building energy management model into the constraint condition of the shared energy storage management model based on the lagrangian function and the KKT condition of the intelligent building energy management model, and adjusting the shared energy storage management model includes:
constructing a Lagrange function of an intelligent building energy management model;
respectively calculating partial differentiation of the Lagrange function of the intelligent building energy management model aiming at the decision variables, and converting the intelligent building energy management model into a nonlinear constraint condition of a shared power station model by combining the Lagrange function and a KKT condition of the intelligent building energy management model;
based on a large M method, converting the nonlinear constraint condition of the shared energy storage management model into a linear constraint condition so as to adjust the shared energy storage management model.
Optionally, the method further comprises:
the shared energy storage management model is provided with an objective function with the lowest cost of the shared energy storage power station, the cost of the shared energy storage power station comprises investment maintenance cost of the shared energy storage power station and cost of electricity purchasing of the shared energy storage power station from the plurality of intelligent buildings; the benefits of the shared energy storage power station include benefits of selling electricity to the plurality of intelligent buildings and shared energy storage service fees charged from the plurality of intelligent buildings;
the intelligent building energy management model is provided with an objective function with the lowest cost of the plurality of intelligent buildings, wherein the cost of the plurality of intelligent buildings comprises the cost of purchasing electricity from a commercial power network of the plurality of intelligent buildings, the cost of purchasing electricity from the shared energy storage power station of the plurality of intelligent buildings and service fees paid to the shared energy storage power station; the benefits of the plurality of intelligent buildings include benefits of selling electricity to the shared energy storage power station;
the shared energy storage power station is provided with a state of charge constraint and a charge and discharge power constraint;
the plurality of intelligent buildings are provided with the following constraints: thermal balance constraint, electric power balance constraint, energy storage power station charge and discharge power balance constraint, air conditioner operation constraint, climbing constraint, temperature interval constraint, power purchase from a power grid constraint and power purchase from an intelligent building and an energy storage power station.
Optionally, the constraint condition of converting into the shared energy storage management model is specifically:
wherein u is j Lagrangian multiplier constrained by inequality; h is a j For inequality constraints, γ is a boolean variable and M is a sufficiently large integer.
As a second aspect of the present invention, there is also provided an optimizing apparatus for intelligent building shared energy storage power supply, comprising: the device comprises a building module, a conversion module and a control module; wherein,
the building module is used for building an intelligent building energy management model matched with a plurality of intelligent buildings and building a shared energy storage management model matched with the shared energy storage power station, wherein the intelligent building energy management model is used for determining the cost and the income of the plurality of intelligent buildings, and the plurality of intelligent buildings are all provided with new energy power generation devices; the shared energy storage power station provides charge and discharge service for the intelligent buildings;
the conversion module is used for converting the intelligent building energy management model into constraint conditions of the shared energy storage management model based on Lagrangian functions and KKT conditions of the intelligent building energy management model, and adjusting the shared energy storage management model;
and the control module is used for carrying out power supply management on the intelligent buildings based on the adjusted shared energy storage management model.
Optionally, the conversion module further comprises:
the function construction unit is used for constructing a Lagrange function of the intelligent building energy management model;
the nonlinear constraint conversion unit is used for respectively calculating partial differentiation of the Lagrange function of the intelligent building energy management model aiming at the decision variables, and converting the intelligent building energy management model into nonlinear constraint conditions of the shared power station model by combining the Lagrange function and KKT conditions of the intelligent building energy management model;
and the linear processing unit is used for converting the nonlinear constraint condition of the shared energy storage management model into a linear constraint condition based on a large M method so as to adjust the shared energy storage management model.
Optionally, wherein:
the shared energy storage management model is provided with an objective function with the lowest cost of the shared energy storage power station, the cost of the shared energy storage power station comprises investment maintenance cost of the shared energy storage power station and cost of electricity purchasing of the shared energy storage power station from the plurality of intelligent buildings; the benefits of the shared energy storage power station include benefits of selling electricity to the plurality of intelligent buildings and shared energy storage service fees charged from the plurality of intelligent buildings;
the intelligent building energy management model is provided with an objective function with the lowest cost of the plurality of intelligent buildings, wherein the cost of the plurality of intelligent buildings comprises the cost of purchasing electricity from a commercial power network of the plurality of intelligent buildings, the cost of purchasing electricity from the shared energy storage power station of the plurality of intelligent buildings and service fees paid to the shared energy storage power station; the benefits of the plurality of intelligent buildings include benefits of selling electricity to the shared energy storage power station;
the shared energy storage power station is provided with a state of charge constraint and a charge and discharge power constraint;
the plurality of intelligent buildings are provided with the following constraints: thermal balance constraint, electric power balance constraint, energy storage power station charge and discharge power balance constraint, air conditioner operation constraint, climbing constraint, temperature interval constraint, power purchase from a power grid constraint and power purchase from an intelligent building and an energy storage power station.
Optionally, the linear constraint condition obtained by the linear processing unit is as follows:
wherein u is j Lagrangian multiplier constrained by inequality; h is a j For inequality constraints, γ is a boolean variable and M is a sufficiently large integer.
As a third aspect of the present invention, there is also provided an optimizing apparatus for intelligent building shared energy storage power supply, comprising: a processor coupled to the memory;
the processor is used for reading and executing the program or the instruction stored in the memory, so that the device executes the optimization method of the intelligent building shared energy storage power supply.
As a fourth aspect of the present invention, there is also provided a computer-readable storage medium storing a program or instructions that, when read and executed by a computer, cause the computer to perform the above-described optimization method of intelligent building shared energy storage power supply.
The embodiment of the invention has the following beneficial effects:
the optimization method, the device and the storage medium for the shared energy storage power supply of the intelligent building can convert the intelligent building energy management model into the additional constraint condition of the shared energy storage management model based on the Lagrangian function and the KKT condition of the intelligent building energy management model, so that a double-layer model comprising the intelligent building energy management model and the shared energy storage management model is converted into a single-layer model only comprising the shared energy storage management model, the complexity of the model is reduced, the solving speed is improved, the power supply strategy is quickly adjusted according to the change of the power consumption requirements of a plurality of intelligent buildings, and the power supply efficiency is improved.
Meanwhile, the optimization method for intelligent building shared energy storage power supply can convert the nonlinear constraint condition into a linear constraint condition based on a large M method, and can reduce model solving difficulty again to improve solving efficiency, so that the speed of adjusting a power supply strategy is further improved, and the power supply efficiency is improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are required in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that it is within the scope of the invention to one skilled in the art to obtain other drawings from these drawings without inventive faculty.
FIG. 1 is a schematic diagram of the architecture principle of an intelligent building shared energy storage power supply optimization system according to the present invention;
FIG. 2 is a schematic flow chart of an optimization method for intelligent building shared energy storage and power supply according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an optimizing device for intelligent building shared energy storage and power supply according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the converting module in FIG. 3;
fig. 5 is a schematic structural diagram of another energy-storage and power-supply device capable of being shared by buildings according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings, for the purpose of making the objects, technical solutions and advantages of the present invention more apparent.
Fig. 1 is a schematic architecture diagram of an optimization system for intelligent building shared energy storage power supply according to an embodiment of the present invention. First, referring to fig. 1, a shared energy storage power supply system according to an embodiment of the present invention is described.
As shown in fig. 1, the power supply system includes a shared energy storage power station, N intelligent buildings, a commercial power network, and the like, and each intelligent building can be powered by a self-built new energy source (such as photovoltaic, wind power, and the like), the shared energy storage power station, and the commercial power network, wherein the energy consumption mainly comes from an air conditioner, N is a positive integer, and N >2.
An energy storage power station regulation and control center is arranged in the shared energy storage power station, and operation scheduling can be performed according to the charging and discharging requirements of the intelligent building in each period. The operation scheduling can be performed according to the charging and discharging requirements of the intelligent building in each period. For example, if the total demand of all intelligent buildings that access the shared energy storage power station is charging during a certain period of time, the dispatch center uses the energy storage device to charge to store excess electrical energy of the intelligent buildings. For another example, if the total demand of all intelligent buildings connected to the shared energy storage power station is discharge in another period, the dispatching center uses the energy storage device to discharge to meet the electricity demand of the intelligent buildings, and the electricity consumption behavior of the intelligent buildings is settled in the form of electricity purchase and selling. The dispatching center configures corresponding capacity and maximum charge and discharge power according to the energy utilization behaviors of all intelligent buildings in one period. The shared energy storage power station sets the time-sharing electricity purchase price, and mainly realizes profit by using the price difference of electricity purchase and selling of the intelligent building in different time periods and charging service fees to the intelligent building, wherein the service fees are charged in unit power.
It should be understood that, because wind power and photovoltaic power output have intermittence and volatility, the electricity demand of each intelligent building also has a difference in time, so that the supply and demand balance condition inside each intelligent building changes with time, and the demand of energy storage also changes. On the one hand, in the scene that the new energy source is low in output and the intelligent building energy demand is large, in order to reduce the commercial power purchase quantity of electricity price peak sections, the intelligent building needs energy storage with larger capacity to realize the transfer of electric energy in time. On the other hand, under the high-power scene of new energy, the demand of intelligent building on energy storage is greatly reduced. If the intelligent building is configured to store energy alone, the problem of energy storage resource shortage in some scenes and energy storage resource idling in some scenes can be caused. The intelligent energy storage system also provides sufficient opportunities for a plurality of intelligent buildings to participate in the shared energy storage service, the plurality of intelligent buildings can participate in the shared energy storage service together, capacity allocation is carried out again on the difference and complementarity of energy storage demands of the intelligent buildings under different operation scenes, the utilization rate of energy storage equipment is improved, and the operation economy of the intelligent buildings can be improved.
The following describes in detail the specific implementation of the method for optimizing the shared energy storage power supply of the intelligent building provided by the embodiment of the invention with reference to fig. 2, and the method can be applied to the shared energy storage power supply of the intelligent building shown in fig. 1 to supply power to N intelligent buildings.
Fig. 2 is a schematic flow chart of an optimization method for intelligent building shared energy storage power supply according to an embodiment of the present invention. As shown in fig. 2, the method comprises the steps of:
step S201, an intelligent building energy management model matched with a plurality of intelligent buildings is built, and a shared energy storage management model matched with the shared energy storage power station is built.
The intelligent building energy management model is used for determining the cost and the income of a plurality of intelligent buildings, and the intelligent buildings have new energy power generation capacity, such as wind power, photovoltaic and the like.
The intelligent building energy management model is designed with the aim of lowest cost of a plurality of intelligent buildings, and is provided with an objective function with the aim of lowest cost of the plurality of intelligent buildings; the cost of the plurality of intelligent buildings comprises the cost of the plurality of intelligent buildings purchasing electricity from a commercial power network, the cost of the plurality of intelligent buildings purchasing electricity from the shared energy storage power station and the service fee paid to the shared energy storage power station; the benefits of the plurality of intelligent buildings include benefits of selling electricity to the shared energy storage power station;
the intelligent building energy management model meets the following constraint conditions: thermal balance constraint, electric power balance constraint, energy storage power station charge and discharge power balance constraint, air conditioner operation constraint, climbing constraint, temperature interval constraint, power purchase from a power grid constraint and power purchase from an intelligent building and an energy storage power station.
The method for constructing the intelligent building energy management model is described in detail below by taking the example that the total requirement of a plurality of intelligent buildings is electricity selling in a certain period, namely the charge and discharge states of the shared energy storage power station in the period are charging.
Specifically, the intelligent building double-layer optimization configuration based on the shared energy storage service takes the lowest annual operation cost of the intelligent building as an objective function as follows:
wherein C is 1 For a number of intelligent building year operating costs,the cost of electricity purchase from the utility network for multiple intelligent buildings on the w-th typical day,/->For the electricity selling benefits of the w-th typical day for a plurality of intelligent buildings selling electricity to the shared energy storage power station,service fees paid to the shared energy storage power station for the w-th typical day for a plurality of intelligent buildings.
Wherein,
wherein alpha is t Electricity prices are purchased from the power grid at the w-th typical day t;the power purchased from the grid at time t is the nth intelligent building on the w-th typical day.
And then determining constraint conditions of the intelligent building energy management model. Constraint conditions which the intelligent buildings need to meet include intelligent building heat balance constraint, electric power balance constraint, energy storage power station charge and discharge power balance constraint, air conditioner operation constraint, climbing constraint, temperature interval constraint, power purchase from a power grid and power purchase and selling constraint between the intelligent building and an energy storage power station, and the constraint conditions are as follows:
(1) Intelligent building heat balance constraint condition
Wherein,the heat capacity of the wall body; />The temperature of the wall body; />The temperature in the intelligent building room is; />Is the temperature in the adjacent room; />Is wall thermal resistance; if the wall body is exposed to external solar radiation +.>Taking 1 or 0; />The heat absorption rate of the wall body; />The wall area is; />The illumination intensity of the wall body in the corresponding direction; />Is room heat capacity; />Is outdoor temperature; n (N) room Refers to adjacent area nodes; />Is the thermal resistance of the window body; />Is the window transmissivity; />Is the window area;the illumination intensity of the window body in the corresponding direction; />Is an internal heat source; e (E) EER The energy efficiency ratio of the air conditioner is; />The air conditioning power of the kth room of the nth intelligent building at the time t is calculated; />Is a dual variable corresponding to the constraint.
(2) Electric power balance constraint
Wherein,the wind power generation power of the nth intelligent building at the time t is the w typical day; />Photovoltaic power generation power of the nth intelligent building at the time t is the w typical day; />The conventional load power at time t for the nth intelligent building on the w-th typical day.
(3) Energy storage power station charge-discharge power balance constraint
The sum of the electricity purchasing power of each intelligent building and the shared energy storage power station is the charge and discharge power of the shared energy storage power station.
(4) Air conditioner operation constraint
Wherein,an upper limit for the operating power of the air conditioning system; />And->The corresponding lagrangian multiplier is constrained for inequality.
(5) Climbing constraint
Wherein,and->The upper limit and the lower limit of the running power change of the air conditioner are respectively.
(6) Temperature interval constraint
Wherein,and->The lower limit and the upper limit of the comfort temperature in the intelligent building room are respectively ensured.
(7) Purchasing power constraints from a power grid
Wherein,maximum power for intelligent building to purchase electricity from the grid.
(8) Intelligent building and energy storage power station purchase and sale electric power constraint
Wherein,and (5) trading the maximum power of the electric quantity for the intelligent building and the energy storage power station.
The shared energy storage management model is used for determining the cost and the income of the shared energy storage power station, and the shared energy storage power station provides charge and discharge services for a plurality of intelligent buildings.
Optionally, the shared energy storage management model is designed with the aim of lowest cost of the shared energy storage power station, and is provided with an objective function with the lowest cost of the shared energy storage power station; costs of the shared energy storage power station include investment maintenance costs of the shared energy storage power station, and costs of the shared energy storage power station purchasing electricity from a plurality of intelligent buildings; the benefits of the shared energy storage power station include benefits of selling electricity to the plurality of intelligent buildings, and shared energy storage service fees charged from the plurality of intelligent buildings, wherein the shared energy storage power station satisfies state of charge constraints and charge-discharge power constraints.
The method for constructing the shared energy storage management model is described in detail below by taking the example that the total requirement of a plurality of intelligent buildings is electricity selling in a certain period, namely the charge and discharge states of the shared energy storage power station in the period are charging.
Specifically, the double-layer optimization configuration of the intelligent building based on the shared energy storage service optimizes the annual operation cost of the shared energy storage power station as an upper-layer objective function, wherein the annual operation cost comprises investment cost, maintenance cost and electricity purchasing cost from the intelligent building; economic benefits include selling electricity to intelligent buildings and sharing energy storage service fees.
Wherein C is 2 For sharing the annual operation cost of the energy storage power station, W is the typical daily number; tw is the number of days corresponding to the w-th typical day;daily average investment maintenance costs for shared energy storage power stations; />The electricity selling benefits of selling electricity to the shared energy storage power station for a plurality of intelligent buildings on the w-th typical day; />Service fees charged from multiple intelligent buildings for sharing energy storage power stations for the w-th typical day.
Wherein,
wherein the method comprises the steps of，α p Investment cost for unit power of the energy storage power station; alpha s Investment cost for unit capacity of the energy storage power station;maximum charge and discharge power of the energy storage power station; />The maximum capacity of the energy storage power station; t (T) s The service life of the energy storage power station is prolonged; m is M ess Daily maintenance costs for energy storage power stations; n is the number of intelligent buildings; t is the number of scheduling period time periods; delta t The electricity price of the electricity is traded for the energy storage power station at the time t and the intelligent building; />The power of the electric quantity is traded between the energy storage power station and the intelligent building at the time t for the nth intelligent building on the w typical day; Δt is the scheduling period; θ t And charging the service fee unit price for the energy storage power station.
Constraints of the shared energy storage management model are then determined. Specifically, constraints that the shared energy storage power station needs to meet include state of charge constraints and charge-discharge power constraints.
Wherein,the residual electric quantity of the energy storage power station at the moment t; η (eta) abs The charging efficiency of the energy storage power station is improved; η (eta) relea The discharge efficiency of the energy storage power station; />Charging power of energy storage power station at t moment +.>Discharging of energy storage power station at t momentAn electric power; />The total power of the energy storage power station and the intelligent building transaction electric quantity is the total power of the energy storage power station and the intelligent building transaction electric quantity at the t moment on the w-th typical day; />And (5) the initial residual electric quantity of the energy storage power station.
It should be noted that, the above formulas (1) - (13) are all described by taking a certain period of time, where the total requirement of a plurality of intelligent buildings is electricity selling, that is, the charging and discharging states of the shared energy storage power station in the period of time are charging as an example, to describe the construction methods of the intelligent building energy management model and the shared energy storage management model. It can be understood that when the total demand of the plurality of intelligent buildings in another period is electricity purchasing, that is, the charge and discharge state of the shared energy storage power station in the period is discharging, the following formulas (14) and (15) are respectively adjusted by the formula (1):
that is, in the formula (14),the electricity purchase cost of multiple intelligent buildings from the shared energy storage power station for the w typical day corresponds to the electricity purchase cost, in formula (15)>And sharing electricity selling benefits of the energy storage power station to a plurality of intelligent buildings for the w-th typical day.
Step S202, converting the intelligent building energy management model into constraint conditions of the shared energy storage management model based on Lagrange functions and KKT conditions of the intelligent building energy management model.
Optionally, in some specific examples, the step S202 further includes:
and 1, constructing a Lagrange function of the intelligent building energy management model.
Typically, the Lagrangian function is as follows:
wherein lambda is i And u j Lagrangian multipliers for equality constraints and inequality constraints; g i And h j Respectively representing equality constraint and inequality constraint, P n And T n Representing variables in the constraint; x and y represent the number of equality and inequality constraints. The Lagrangian multiplier is a decision variable, and the dimension of the Lagrangian multiplier is matched with the dimension of the variable in the constraint condition in order to ensure that the equation is established.
Aiming at the intelligent building energy management model provided by the embodiment of the invention, the Lagrangian function is as follows:
step 2, the Lagrangian function of the intelligent building energy management model comprises a plurality of decision variables. Respectively calculating partial differentiation of Lagrangian functions of the intelligent building energy management model aiming at a plurality of decision variables to obtain dual constraint and further obtain KKT (Karush-Kuhn-Tucker, ka Lu Shen-Coulomb-Take) conditions; thereby converting the intelligent building energy management model into a nonlinear constraint condition of the shared power station model.
It will be appreciated that the KKT condition is a set of sufficient requirements in the optimization problem to determine the existence and nature of the optimal solution. The KKT condition is applicable to constraint optimization problems including linear programming, nonlinear programming, convex optimization, and the like.
For a typical constraint optimization problem, taking Lagrangian function and equality constraints as an example, the KKT condition includes the following aspects:
stability condition (Stationarity Condition): the gradient of the lagrangian function to the decision variable is equal to zero, which means that at the optimal solution, the trend of the function to the decision variable is zero.
Original feasibility condition (Primal Feasibility Condition): for inequality constraints, the value of each constraint is equal to or greater than zero, indicating that the optimal solution satisfies the constraints of the original problem.
Double complementation condition (Dual Complementarity Condition): multiplying the Lagrangian multiplier by the corresponding constraint relationship is equal to zero, indicating that the optimal solution satisfies the constraint condition and the non-negative characteristic of the Lagrangian multiplier.
Linear constraint (Linear Equality Constraint): for the equality constraint, the value of the constraint is equal to zero, indicating that the optimal solution satisfies the equality constraint condition.
Together, these conditions are referred to as the KKT conditions, and by satisfying these conditions, the existence and nature of the optimal solution can be determined, and can also be used to solve the optimization problem.
When the intelligent building energy management model is converted into the additional constraint condition of the shared energy storage management model, a single-layer model can be built by combining with the KKT condition, so that the complexity of the model is reduced, and the solving speed is improved. Therefore, the power supply strategy can be quickly adjusted, and the power supply efficiency can be improved, so that the power consumption requirement change of a plurality of intelligent buildings can be met.
Specifically, in step 2, partial differentiation is performed on the constructed lagrangian function for each decision variable, and the lagrangian function and the KKT condition of the intelligent building energy management model are combined to convert the intelligent building energy management model into additional constraint conditions of the shared power station model, as shown in the following formula:
where k represents the full set of decision variables in the equality and inequality constraints. It should be noted that the lagrangian function in equation (18) can yield the same additional constraint as the number of decision variables after partial differentiation of all decision variables separately.
And step 3, linearizing the shared energy storage management model.
The converted shared energy storage management model is a mixed integer nonlinear optimization model, and as shown in a formula (19), the additional constraint condition converted by the intelligent building energy management model is nonlinear constraint, and linearization is needed to improve the solving efficiency of the shared energy storage management model.
u j h j (P j ,T j )＝0 (19)
In the present invention, the large M method can be used to perform the above-described linearized transformation.
It can be appreciated that the principle of the large M method is as follows: which is one of the commonly used methods for converting nonlinear constraints in linear programming into linear constraints. It approximates the nonlinear constraint by introducing a larger positive number M and converts it into a combination of equality and inequality constraints.
In the large M method, for an optimization problem with nonlinear constraints, the nonlinear constraints are expressed as follows:
g(x)≤0
where g (x) is a function representing a constraint, and x is a decision variable.
Then, by introducing a large positive number M, the constraint is rewritten as follows:
g(x)+s≤M
where s is the auxiliary variable introduced and M is a larger positive number.
After the original nonlinear constraint condition is converted into the linear constraint condition, the problem can be solved by a linear programming solving method, and the solving efficiency is greatly improved. It should be noted that when applying the large M method, an appropriate value of M needs to be selected. Too large a value of M may cause problems to become difficult, while too small a value of M may cause approximation inaccuracy.
Specifically, for equation (19) above, the above nonlinear constraint can be converted into the following form using the large M method:
wherein γ is a boolean variable and M is a sufficiently large integer.
It can be appreciated that the additional constraint condition of the shared energy storage management model converted by the intelligent building energy management model is usually a nonlinear constraint condition, and the solving difficulty is still high. Thus, in step 3, the method is performed by, based on the transformed shared energy storage management model, prior to powering a plurality of intelligent buildings: based on the large M method, the nonlinear constraint condition of the shared energy storage management model is converted into the linear constraint condition, so that the model solving difficulty can be reduced again, the solving efficiency is improved, the speed of adjusting the power supply strategy is further improved, and the power supply efficiency is improved.
Step S203, power is supplied to a plurality of intelligent buildings based on the converted shared energy storage management model.
Specifically, a power supply strategy can be formulated for a plurality of intelligent buildings based on the converted shared energy storage management model, and power can be supplied to the plurality of intelligent buildings based on the power supply strategy. The power supply policy may include one or more of the following policies one to three:
the method comprises the steps that firstly, if the new energy generating capacity of a plurality of intelligent buildings is larger than the electricity consumption requirement of the plurality of intelligent buildings, the plurality of intelligent buildings are indicated to sell electricity to a shared energy storage power station preferentially;
and if the new energy generating capacity of the plurality of intelligent buildings is smaller than the electricity consumption requirement of the plurality of intelligent buildings, indicating the plurality of intelligent buildings to purchase electricity from the shared energy storage power station preferentially.
In other words, for a plurality of intelligent buildings, the new energy electric energy generated by the intelligent buildings is preferentially used for self-demand, namely when the self-generated energy is higher than the self-used electricity demand, electricity can be sold to the shared energy storage power station to store the electric energy, and when the self-generated energy is lower than the self-used electricity demand, electricity is preferentially purchased from the shared energy storage power station with lower electricity price instead of the commercial power network with higher electricity price, the self-generated energy can be used as much as possible to meet the electricity demand, and the electricity cost is reduced.
When the electricity price of the commercial power is in the valley section, the second strategy indicates the plurality of intelligent buildings to purchase electricity from the commercial power network preferentially and sell electricity to the shared energy storage power station;
and when the commercial power price is in a peak section, indicating the plurality of intelligent buildings to purchase electricity from the shared energy storage power station preferentially.
In other words, for a plurality of intelligent buildings, the time-sharing electricity price of the commercial network can be utilized to be different, and electricity can be purchased and stored in the electricity price valley section of the commercial network as much as possible, so that the electricity can be used in the electricity price peak section of the commercial network, the electricity purchasing quantity of electricity purchased from the commercial network in the electricity price peak section can be reduced as much as possible, and the electricity consumption cost can be reduced.
A third strategy, when the utility power price is in the valley section, indicating the plurality of intelligent buildings to purchase electricity from the utility network preferentially, and improving the power of the air conditioning system in the plurality of intelligent buildings;
and when the commercial power price is in a peak section, indicating the plurality of intelligent buildings to purchase electricity from the shared energy storage power station preferentially, and reducing the power of an air conditioning system in the plurality of intelligent buildings.
In other words, for a plurality of intelligent buildings, the thermal inertia of the intelligent buildings can be utilized, the air conditioner is controlled to heat or refrigerate the intelligent buildings in advance at the electricity price valley section of the commercial power network, and the indoor temperature of the intelligent buildings is adjusted to be a proper temperature, so that the electricity purchasing quantity of electricity from the commercial power network at the electricity price peak section is reduced, and the electricity consumption cost is reduced.
It should be noted that, the above-mentioned policies one to three may be implemented separately or may be implemented in combination, and the embodiments of the present invention are not limited.
The optimization method for the shared energy storage power supply of the intelligent building can convert the intelligent building energy management model into the additional constraint condition of the shared energy storage management model based on the Lagrangian function and the KKT condition of the intelligent building energy management model, so that a double-layer model comprising the intelligent building energy management model and the shared energy storage management model is converted into a single-layer model only comprising the shared energy storage management model, the complexity of the model is reduced, the solving speed is improved, the power supply strategy is quickly adjusted according to the change of the power consumption requirements of a plurality of intelligent buildings, and the power supply efficiency is improved.
The method for optimizing the shared energy storage and power supply of the intelligent building provided by the embodiment of the invention is described in detail above with reference to fig. 2, and the device for optimizing the shared energy storage and power supply of the intelligent building provided by the embodiment of the invention is described below with reference to fig. 3 and 4.
Fig. 3 is a schematic structural diagram of an intelligent building sharing energy storage and power supply optimizing device 300 according to an embodiment of the present invention. The device 300 may perform the method for optimizing the shared energy storage power supply of the intelligent building according to the method embodiment.
As shown in fig. 3, the apparatus 300 includes: a building module 301, a conversion module 302 and a control module 303; wherein,
the building module 301 is configured to build an intelligent building energy management model and a shared energy storage management model, where the intelligent building energy management model is configured to determine costs and benefits of a plurality of intelligent buildings, the plurality of intelligent buildings have new energy power generation capability, the shared energy storage management model is configured to determine costs and benefits of a shared energy storage power station, and the shared energy storage power station provides charge and discharge services for the plurality of intelligent buildings;
the conversion module 302 is configured to convert the intelligent building energy management model into constraint conditions of the shared energy storage management model based on a lagrangian function and KKT conditions of the intelligent building energy management model;
the control module 303 is configured to supply power to a plurality of intelligent buildings based on the converted shared energy storage management model.
Optionally, as shown in fig. 4, in a specific example, the conversion module 302 further includes:
a function construction unit 304, configured to construct a lagrangian function of the intelligent building energy management model;
the nonlinear constraint conversion unit 305 is configured to calculate partial derivatives of the lagrangian function of the intelligent building energy management model with respect to the plurality of decision variables, and convert the intelligent building energy management model into nonlinear constraint conditions of the shared power station model by combining the lagrangian function and KKT conditions of the intelligent building energy management model;
and the linear processing unit 306 is configured to convert the nonlinear constraint condition of the shared energy storage management model into a linear constraint condition based on a large M method, so as to adjust the shared energy storage management model.
For more details, reference is made to the foregoing descriptions of fig. 1 to 2, and no further description is given here.
Fig. 5 is a schematic structural diagram of another intelligent building shared energy storage and power supply optimizing device according to an embodiment of the present invention. The device 400 can execute the optimization method of the intelligent building shared energy storage power supply according to the embodiment of the method.
As shown in fig. 5, the apparatus includes: a processor 401, the processor 401 being coupled to the memory 402; the processor 401 is configured to read and execute a program or instructions stored in the memory 402, so that the apparatus 400 performs the method for optimizing the energy storage and power supply of the intelligent building according to the above method embodiment.
Optionally, the apparatus 400 may further comprise a transceiver 403 for the apparatus 400 to communicate with other apparatuses.
It should be noted that, for convenience of explanation, fig. 3 and 5 only show the main components of the intelligent building shared energy storage and power supply optimizing device. In practical applications, the intelligent building shared energy storage and power supply optimizing device may also comprise components or assemblies not shown in the drawings. For more details, reference is made to the foregoing descriptions of fig. 1 to 2, and no further description is given here.
The embodiment of the invention also provides a computer readable storage medium which stores a program or instructions, and when the program or instructions are read and executed by a computer, the computer is caused to execute the optimization method for the intelligent building shared energy storage power supply. For more details, reference is made to the foregoing descriptions of fig. 1 to 2, and no further description is given here.
The embodiment of the invention has the following beneficial effects:
the optimization method, the device and the storage medium for the shared energy storage power supply of the intelligent building can convert the intelligent building energy management model into the additional constraint condition of the shared energy storage management model based on the Lagrangian function and the KKT condition of the intelligent building energy management model, so that a double-layer model comprising the intelligent building energy management model and the shared energy storage management model is converted into a single-layer model only comprising the shared energy storage management model, the complexity of the model is reduced, the solving speed is improved, the power supply strategy is quickly adjusted according to the change of the power consumption requirements of a plurality of intelligent buildings, and the power supply efficiency is improved.
Meanwhile, the optimization method for intelligent building shared energy storage power supply can convert the nonlinear constraint condition into a linear constraint condition based on a large M method, and can reduce model solving difficulty again to improve solving efficiency, so that the speed of adjusting a power supply strategy is further improved, and the power supply efficiency is improved.
It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above disclosure is only a preferred embodiment of the present invention, and it is needless to say that the scope of the invention is not limited thereto, and therefore, the equivalent changes according to the claims of the present invention still fall within the scope of the present invention.

Claims (10)

1. An optimization method for sharing energy storage and power supply of an intelligent building, which is used in a system comprising a shared energy storage power station and a plurality of intelligent buildings connected with the shared energy storage power station, is characterized by comprising the following steps:
establishing an intelligent building energy management model matched with a plurality of intelligent buildings and establishing a shared energy storage management model matched with the shared energy storage power station, wherein the intelligent building energy management model is used for determining the cost and the income of the plurality of intelligent buildings, and the plurality of intelligent buildings are provided with new energy power generation devices; the shared energy storage power station provides charge and discharge service for the intelligent buildings;
based on Lagrangian functions and KKT conditions of the intelligent building energy management model, converting the intelligent building energy management model into constraint conditions of the shared energy storage management model, and adjusting the shared energy storage management model;
and carrying out power supply management on the intelligent buildings based on the adjusted shared energy storage management model.
2. The method of claim 1, wherein the converting the intelligent building energy management model to the constraint of the shared energy storage management model based on the lagrangian function and KKT conditions of the intelligent building energy management model, adjusting the shared energy storage management model comprises:
constructing a Lagrange function of an intelligent building energy management model;
respectively calculating partial differentiation of the Lagrange function of the intelligent building energy management model aiming at the decision variables, and converting the intelligent building energy management model into a nonlinear constraint condition of a shared power station model by combining the Lagrange function and a KKT condition of the intelligent building energy management model;
based on a large M method, converting the nonlinear constraint condition of the shared energy storage management model into a linear constraint condition so as to adjust the shared energy storage management model.
3. The method as recited in claim 2, further comprising:
the shared energy storage management model is provided with an objective function with the lowest cost of the shared energy storage power station, the cost of the shared energy storage power station comprises investment maintenance cost of the shared energy storage power station and cost of electricity purchasing of the shared energy storage power station from the plurality of intelligent buildings; the benefits of the shared energy storage power station include benefits of selling electricity to the plurality of intelligent buildings and shared energy storage service fees charged from the plurality of intelligent buildings;
the intelligent building energy management model is provided with an objective function with the lowest cost of the plurality of intelligent buildings, wherein the cost of the plurality of intelligent buildings comprises the cost of purchasing electricity from a commercial power network of the plurality of intelligent buildings, the cost of purchasing electricity from the shared energy storage power station of the plurality of intelligent buildings and service fees paid to the shared energy storage power station; the benefits of the plurality of intelligent buildings include benefits of selling electricity to the shared energy storage power station;
the shared energy storage power station is provided with a state of charge constraint and a charge and discharge power constraint;
the plurality of intelligent buildings are provided with the following constraints: thermal balance constraint, electric power balance constraint, energy storage power station charge and discharge power balance constraint, air conditioner operation constraint, climbing constraint, temperature interval constraint, power purchase from a power grid constraint and power purchase from an intelligent building and an energy storage power station.
4. The method of claim 3, wherein the constraints translated into the shared energy storage management model are specifically:
wherein u is j Lagrangian multiplier constrained by inequality; h is a j For inequality constraints, γ is a boolean variable and M is a sufficiently large integer.
5. An intelligent building shared energy storage power supply optimizing device, which is characterized by comprising: the device comprises a building module, a conversion module and a control module; wherein,
the building module is used for building an intelligent building energy management model matched with a plurality of intelligent buildings and building a shared energy storage management model matched with the shared energy storage power station, wherein the intelligent building energy management model is used for determining the cost and the income of the plurality of intelligent buildings, and the plurality of intelligent buildings are all provided with new energy power generation devices; the shared energy storage power station provides charge and discharge service for the intelligent buildings;
the conversion module is used for converting the intelligent building energy management model into constraint conditions of the shared energy storage management model based on Lagrangian functions and KKT conditions of the intelligent building energy management model, and adjusting the shared energy storage management model;
and the control module is used for carrying out power supply management on the intelligent buildings based on the adjusted shared energy storage management model.
6. The apparatus of claim 5, wherein the conversion module further comprises:
the function construction unit is used for constructing a Lagrange function of the intelligent building energy management model;
the nonlinear constraint conversion unit is used for respectively calculating partial differentiation of the Lagrange function of the intelligent building energy management model aiming at the decision variables, and converting the intelligent building energy management model into nonlinear constraint conditions of the shared power station model by combining the Lagrange function and KKT conditions of the intelligent building energy management model;
and the linear processing unit is used for converting the nonlinear constraint condition of the shared energy storage management model into a linear constraint condition based on a large M method so as to adjust the shared energy storage management model.
7. The apparatus of claim 6, wherein:
the shared energy storage management model is provided with an objective function with the lowest cost of the shared energy storage power station, the cost of the shared energy storage power station comprises investment maintenance cost of the shared energy storage power station and cost of electricity purchasing of the shared energy storage power station from the plurality of intelligent buildings; the benefits of the shared energy storage power station include benefits of selling electricity to the plurality of intelligent buildings and shared energy storage service fees charged from the plurality of intelligent buildings;
the intelligent building energy management model is provided with an objective function with the lowest cost of the plurality of intelligent buildings, wherein the cost of the plurality of intelligent buildings comprises the cost of purchasing electricity from a commercial power network of the plurality of intelligent buildings, the cost of purchasing electricity from the shared energy storage power station of the plurality of intelligent buildings and service fees paid to the shared energy storage power station; the benefits of the plurality of intelligent buildings include benefits of selling electricity to the shared energy storage power station;
the shared energy storage power station is provided with a state of charge constraint and a charge and discharge power constraint;
the plurality of intelligent buildings are provided with the following constraints: thermal balance constraint, electric power balance constraint, energy storage power station charge and discharge power balance constraint, air conditioner operation constraint, climbing constraint, temperature interval constraint, power purchase from a power grid constraint and power purchase from an intelligent building and an energy storage power station.
8. The apparatus of claim 7, wherein the linear constraint obtained by the linear processing unit is as follows:
wherein u is j Lagrangian multiplier constrained by inequality; h is a j For inequality constraints, γ is a boolean variable and M is a sufficiently large integer.
9. An intelligent building shared energy storage power supply optimizing device, which is characterized by comprising: a processor coupled to the memory;
wherein the processor is configured to read and execute the program or instructions stored in the memory, to cause the apparatus to perform the method of optimizing the intelligent building shared energy storage power supply of any one of claims 1-4.
10. A computer-readable storage medium, characterized in that a program or instructions is stored, which, when read and executed by a computer, causes the computer to perform the optimization method of the intelligent building shared energy storage power supply according to any one of claims 1-4.
CN202311023285.6A 2023-08-14 2023-08-14 Optimization method, device and storage medium for intelligent building shared energy storage and power supply Pending CN117057463A (en)

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