CN111353820A - Method for making power grid peak-valley time-of-use electricity price considering flexible load unit - Google Patents
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Abstract
The invention discloses a method for making a power grid peak-valley time-of-use electricity price considering a flexible load unit, which comprises the steps of constructing an integer programming model aiming at the problem of the electricity price of a power grid; solving an optimal solution of the integer programming model; determining the peak-to-valley average electricity price; determining a load participation scheduling strategy; determining a load modeling mode and establishing a load electricity price response model according to the analysis of the influence of peak-valley time-of-use electricity price on the net charge income; and (3) taking the unit combination model of the flexible load into consideration to participate in scheduling, and establishing a power grid electricity price model taking the load unit combination into consideration. The invention has the beneficial effects that: the power grid company profit model considering the peak-valley time-of-use electricity price and the various types of loads can improve the profit of the power grid and better obtain user support on the basis of fully considering the response difference of various types of users to the electricity price, and obtains the optimal peak-valley average electricity price of various types of users.
Description
Technical Field
The invention relates to the technical field of time-of-use electricity prices, in particular to a method for making a power grid peak-valley time-of-use electricity price considering a flexible load unit.
Background
In recent years, time-of-use electricity price is one of effective demand response modes based on price, and a user is guided to make a reasonable electricity utilization plan by properly increasing the electricity price in a peak load period and properly reducing the electricity price in a valley load period, so that partial load in the peak load period is transferred to an underestimation period, and the purposes of peak clipping, valley filling and load balancing are achieved.
The time-of-use electricity price is an electricity price mechanism which can effectively reflect the power supply cost difference of the power system at different time periods, and common forms of the time-of-use electricity price are peak-valley electricity price, seasonal electricity price, withered electricity price and the like. The core of the time-of-use electricity price mechanism is mainly two aspects of peak-valley time period division and peak-valley time-of-use electricity price formulation, the peak-valley time period is scientifically divided, the time-of-use electricity price is reasonably formulated, the execution of the time-of-use electricity price is ensured to obtain an effect, and the purpose of optimizing and configuring social resources is achieved.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the technical problem solved by the invention is as follows: a method for making the peak-valley time-of-use electricity price of the power grid considering the flexible load unit is provided, and the optimal peak-valley average electricity price of each type of users is obtained.
In order to solve the technical problems, the invention provides the following technical scheme: a method for making a power grid peak-valley time-of-use electricity price considering a flexible load unit comprises the steps of constructing an integer programming model aiming at a power grid electricity price problem; solving an optimal solution of the integer programming model; determining the peak-to-valley average electricity price; determining a load participation scheduling strategy; determining a load modeling mode and establishing a load electricity price response model according to the analysis of the influence of peak-valley time-of-use electricity price on the net charge income; and (3) taking the unit combination model of the flexible load into consideration to participate in scheduling, and establishing a power grid electricity price model taking the load unit combination into consideration.
As a preferable scheme of the method for making the power grid peak-valley time-of-use electricity price considering the flexible load unit, the method comprises the following steps: the unit combination model considering the flexible load comprises determining a target function and establishing a constraint equation; the determined objective function comprises power generation cost and starting cost; the established constraint equation comprises thermal power generating unit output characteristic constraint, hydroelectric generating unit output characteristic constraint and power grid company profit constraint. .
As a preferable scheme of the method for making the power grid peak-valley time-of-use electricity price considering the flexible load unit, the method comprises the following steps: the unit combination model considering the flexible load takes the minimum system operation cost as an objective function, comprises the power generation cost and the starting cost, and comprises the following definitions:
Min:
C(pn(t))＝anpn(t)2+bnpn(t)+cn
wherein T is the time interval scheduled in the day ahead, and T is 24, Fn(t) is the power generation cost of the fire-electric generator set n within the time period t; the power generation cost of the thermal power generating unit consists of coal consumption cost and starting cost, wherein
Respectively, the starting cost of the thermal power generating unit n, C (p)n(t)) is the coal consumption cost of the thermal power generating unit n, and is the actual output pn(t) is a quadratic function of the decision variables, with characteristic coefficients an、bn、cn。
As a preferable scheme of the method for making the power grid peak-valley time-of-use electricity price considering the flexible load unit, the method comprises the following steps: the output characteristics of the thermal power generating unit comprise a unit start-stop time constraint equation, a unit output constraint equation and a unit climbing constraint equation; the unit start-stop time constraint equation comprises that for the ith unit and the tth time period, the minimum start-up time constraint is as follows:
the minimum off-time constraint is:
in the formula
And
minimum shutdown of nth unitStartup time, min (-) represents taking the minimum value; the unit output constraint equation comprises that for the ith unit, the following output constraints are required to be met:
in the formula (I), the compound is shown in the specification,
and
respectively the minimum output and the maximum output of the ith unit; the unit climbing constraint equation comprises that for the ith unit, the requirements of the up-down climbing speed are required to be met from the t-1 th time period to the t-th time period:
in the formula (I), the compound is shown in the specification,
and
and the upward climbing speed and the downward climbing speed of the nth thermal power generating unit are respectively set.
As a preferable scheme of the method for making the power grid peak-valley time-of-use electricity price considering the flexible load unit, the method comprises the following steps: the output characteristics of the hydroelectric generating set do not consider the operation cost of the hydroelectric generating set, then the constraint equation comprises an upper limit constraint, a lower limit constraint and a daily electric quantity constraint which are respectively defined as follows,
in the formula (I), the compound is shown in the specification,
respectively the minimum and maximum technical output of the hydroelectric plant h; the daily electric quantity of the hydropower station h is restricted, and the allowable electric energy production of the hydropower station h in the dispatching cycle is respectively W due to water quantity limitationh。
As a preferable scheme of the method for making the power grid peak-valley time-of-use electricity price considering the flexible load unit, the method comprises the following steps: the power grid company profitability constraint comprises the following definitions,
Rw＝Fsd-Fsw＞＝Rw0
in the formula, FsdRepresenting the electricity selling income of the power grid after the peak-valley time-of-use electricity price; fswRepresents the electricity purchase cost from the power supply side on the power grid side after the peak-valley time-of-use price. Rw0Representing the grid side revenue before peak-valley time-of-use electricity prices. Sales revenue is defined as follows:
Rm(t)＝λm*Pm(t)
in the formula, λmThe selling price of electricity for the mth type user; pm,iAnd (t) is the load of the mth class user at the time t.
The internet cost is defined as follows:
Fn(t)＝γ*Pn,i(t)
wherein gamma is the power price on the grid of the thermal power generating unit Pn,iAnd (t) the output of the ith thermal power generating unit in the t period.
As a preferable scheme of the method for making the power grid peak-valley time-of-use electricity price considering the flexible load unit, the method comprises the following steps: the user power utilization mode satisfaction degree constraint comprises that the power price can influence the user power utilization mode satisfaction degree when peak and valley time is implemented; the satisfaction degree of the electricity utilization mode means that the change of the electricity fee enables a user to selectively change the electricity utilization habit, and the change has influence on the comfort level of the user, the production plan of products and the like, a function is defined as follows,
wherein η is user satisfaction, ηj,minFor a given value, the determination of the objective function constitutes the grid price model taking into account the load price response characteristics.
As a preferable scheme of the method for making the power grid peak-valley time-of-use electricity price considering the flexible load unit, the method comprises the following steps: the integer programming is divided into pure integer programming, mixed integer programming and 0-1 programming; the model defining the integer program is:
max z＝CX
in the formula, all element numbers in the A matrix, the b vector and the c vector are integers or rational numbers, and the integer programming problem is considered as a linear programming problem:
max z＝CX
the above equation is referred to as the relaxation problem of the integer programming problem.
As a preferable scheme of the method for making the power grid peak-valley time-of-use electricity price considering the flexible load unit, the method comprises the following steps: the determination of the peak-to-valley level includes determining the peak-to-valley level based on the power rate pi,tInfluenced by the type of load and the peak-to-valley period; for the same type of load, assuming that the load comprises three sections of peak, valley and flat electricity price in one day; classifying the time periods of one day by adopting a semi-trapezoidal fuzzy membership function; for the valley period, a partial small semi-trapezoidal distribution function can be adopted for determination; for peak time period, determining by adopting a large-scale semi-trapezoidal distribution function; the other periods are the flat periods except for the valley period and the peak period.
As a preferable scheme of the method for making the power grid peak-valley time-of-use electricity price considering the flexible load unit, the method comprises the following steps: the formula of the partial small semi-trapezoidal membership function is as follows,
the formula of the partial large semi-trapezoidal membership function is as follows,
wherein x is the current load value, b represents the maximum load value in a day, a represents the minimum load value in a day, and the valley period x is defined as the load satisfying A1(x) A load of > α, the peak period x being defined such that the load satisfies A2(x)≥α。
The invention has the beneficial effects that: the power grid company profit model considering the peak-valley time-of-use electricity price and the various types of loads can improve the profit of the power grid and better obtain user support on the basis of fully considering the response difference of various types of users to the electricity price, and obtains the optimal peak-valley average electricity price of various types of users.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
FIG. 1 is a diagram illustrating a partial small semi-trapezoidal membership function according to a first embodiment of the present invention;
FIG. 2 is a diagram illustrating a partial large scale semi-trapezoidal membership function according to a first embodiment of the present invention;
FIG. 3 is a schematic diagram of a wind power predicted output photovoltaic predicted force diagram according to a third embodiment of the present invention;
FIG. 4 is a schematic view of a load distribution curve according to a third embodiment of the present invention;
fig. 5 is a schematic view of the load curve under 3 schemes according to the third embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
At present, the power system adopts a traditional power generation dispatching mode, namely, the load of the system is balanced by controlling the starting and stopping of a generator set and outputting power, and the problem of economic operation is solved. When large-scale uncontrollable wind power, photoelectricity and the like are connected to the grid, the power balance is not necessarily economical by adopting a power generation dispatching mode. Tracking the predicted output curve of the uncontrollable power source in a load scheduling manner may be an effective supplementary manner for scheduling of a future power system. The load scheduling refers to a scheduling mode with controllable load and uncontrollable power supply. The power utilization time of a part of load is flexible, such as heating, refrigeration, electric vehicles and the like, and energy storage equipment. The controllable loads are utilized to track the output change of the power generation of renewable energy sources such as wind power and photovoltaic, and loads are scheduled to balance the predicted output curves of the wind power and photovoltaic. The load scheduling is adopted to control the power transmission flow, so that the network loss can be reduced, the reliability is improved, and the utilization rate of power transmission and transformation is improved.
Specifically, the method for formulating the power grid peak-valley time-of-use electricity price considering the flexible load unit provided in this embodiment includes the following steps:
an integer programming model is constructed for the power grid electricity price problem;
solving an optimal solution of the integer programming model;
determining the peak-to-valley average electricity price;
determining a load participation scheduling strategy;
determining a load modeling mode and establishing a load electricity price response model according to the analysis of the influence of peak-valley time-of-use electricity price on the net charge income;
and (3) taking the unit combination model of the flexible load into consideration to participate in scheduling, and establishing a power grid electricity price model taking the load unit combination into consideration.
It should be noted that the problem of integer linear programming can be divided into pure integer programming (all variables are limited to integers), mixed integer programming (some variables are limited to integers), and 0-1 programming (all variables are limited to 0 or 1). The integer programming model is defined as follows:
max z＝CX
wherein all the element numbers in the A matrix, the b vector and the c vector are integers or rational numbers. If the condition of "X is an integer" in the (IP) problem is not considered, the integer programming problem can still be considered as a general Linear Programming (LP) problem:
max z＝CX
the above equation is called the relaxation problem (slavprobem) of the integer programming problem. For integer linear programming and relaxation problems thereof, from the characteristic of the solution, the two are closely related and essentially different. Any integer feasible solution of Linear Programming (LP) is one feasible solution of Integer Programming (IP), and it is clear that all solutions of (IP), including feasible solutions, correspond to integer feasible solutions of (LP). Further, if the optimal solution for (LP) is an integer solution, then this solution must also be the optimal solution for the (IP) problem.
In general, the optimal solution of (LP) will not be exactly an integer solution, and naturally not the optimal solution of (IP), and the optimal value of (IP) will not be better than the optimal value of (LP). When the optimal solution for (LP) is not an integer solution, it is generally not possible to "round off" or "round up" the non-integer solution to obtain the optimal solution for the (IP) problem.
Optionally, solving is performed by using a secant plane method.
The cutting plane method was first proposed in 1958 by the high morri (r.e. godory), and is therefore also called the godory cutting plane method, which was the earliest proposed method for solving integer programming. In the case of the secant plane method, the linear constraint for "cutting" for each increment is called the secant plane constraint or the Gocarry constraint.
There are many ways to construct the cut plane constraint, the most common one is introduced, which can be generated directly from the final simplex table of the corresponding linear program. In practical solving the problem, experience shows that if the row where the non-integer component with the largest (fractional) component part is selected from the optimal simple form table to construct the cutting plane constraint, the cutting effect can be improved and the cutting times can be reduced.
The basic flow is as follows: firstly, solving a relaxation linear programming of integer programming by utilizing a simplex method (or other methods); through judgment, if the integer condition of the variable cannot be achieved, a specific cutting plane is added for a certain non-integer variable, the non-integer part of the variable in the LP problem is removed, all parts with integer solutions are reserved, and meanwhile, the convexity of the feasible region after cutting is not changed. The above process is repeated one by one, and as long as the integer programming problem has the optimal integer solution, the optimal solution can be found in the vertexes of the convex feasible region after being cut for several times.
Optionally, the solution is performed by using a branch and bound method.
The method for directly solving the integer optimal solution through an enumeration method has no practical application value. The branch and bound method is a hidden enumeration method or a partial enumeration method, is not an effective algorithm, and is an improvement on the basis of the enumeration method. The method is designed by the idea of skillfully enumerating feasible solutions of integer programming problems, and the key steps are branching and delimitation. The branch-and-bound method can be used to solve pure integer or mixed integer programming problems. This method is now the main method of solving integer programming, since it is flexible and easy to solve with computers.
The basic flow is as follows: integer programming problem with maximization, IP, associated therewithThe linear programming problem of (2) is LP. Starting from the solution problem LP, if the optimal solution does not meet the integer condition of IP, the optimal objective function of LP must be the upper bound of the optimal objective function z of IP, and is marked as
Any feasible solution of IP must be the lower bound of z, denoted asz. The feasible region of the LP is divided into sub-regions (called branches) that gradually decrease, increase, and finally reach solution z. The embodiment specifically uses the GAMS software to solve, which is extremely efficient in solving the mixed integer program, and at the same time, it may select various solvers (such as CPLEX, SBB, etc.) and various excellent algorithms. Since the solved model is multi-objective, it is necessary to obtain a non-dominated solution of the model by changing the weight coefficients.
Further, determination of peak-to-valley level of electricity:
price of electricity pi,tMainly affected by the type of load and the peak-to-valley period. For the same type of load, it is assumed that it contains three sections of peak, valley, flat price per day. The time periods of the day are classified by using a semi-trapezoidal fuzzy membership function.
Referring to the slightly small semi-trapezoidal membership function illustrated in fig. 1, for the valley period, the slightly small semi-trapezoidal distribution function may be used for determination; referring to the larger half-trapezoid membership function illustrated in fig. 2, for the peak time interval, the larger half-trapezoid distribution function may be used for determination. The other periods are the flat periods except for the valley period and the peak period.
Wherein the formula of the partial small semi-trapezoidal membership function is as follows:
the formula of the partial large semi-trapezoidal membership function is as follows:
in the above two formulas, x is the current load value, and b represents the dayA represents the minimum load value in a day. Therefore, a valley period x may be defined as the load satisfying a1(x) A load of > α, the peak period x being defined such that the load satisfies A2(x)≥α。
Optionally, the proposed load participation scheduling method in this embodiment is as follows:
power rate based mode: the price signal guides a user to reasonably adjust and improve the electricity utilization structure and the electricity utilization mode, and the price signal comprises an electricity price decision model, and the influence of electricity price on the shape of a load curve, the reliability of a power grid and the like. Time of use (TOU) is generally established and released on a time scale of day ahead or earlier, and a power consumer has sufficient time to reasonably arrange a power utilization plan;
real-time electricity price (RTP) is a dynamic electricity price mechanism based on marginal cost theory, reflects the change relation between supply and demand at each moment, is beneficial to reasonably sharing market risk between power suppliers and users, and only plays a guiding role in quick response capability or electricity price sensitive flexible load.
Peak electricity price (CPP) is a dynamic electricity price mechanism formed by superimposing peak rates on the basis of TOU, and can effectively reduce the load of the system in a peak period. In general, users responding to the electricity price do not need to report own individual electricity utilization information to a power grid regulation and control department, so that the method is suitable for any large, medium and small users, but the uncertainty of the autonomous response behaviors of the users is high; in addition, there is a certain mutual influence relationship between the user response and the establishment of dynamic electricity prices, and when a large number of users simultaneously respond to the change of electricity prices, the user needs may be simultaneously shifted to a low electricity price period, so as to cause a new peak of electricity utilization, which is a problem that further attention is required by the electricity price establishment department.
Contract-based schema: the method is characterized in that an electric power company signs an agreement with users, appoints a calculation method for the consumption of a basic load and the reduction of the load of the users in advance, a determination method for an incentive rate, a default punishment measure and the like, and is an effective means for the electric power company to guide a flexible load to participate in the dispatching operation of a power grid.
If the user adjusts the amount of power consumption, it is called an Interruptible Load (IL), and the load reduction by a load control device of a utility company or a Load Aggregator (LA) is called Direct Load Control (DLC).
Currently, IL is adopted by almost all electric power companies in the United states as an important means for peak regulation, and IL management schemes are also formulated in Jiangsu, Hebei and other provinces in China. DLC is usually targeted to residential or small business users, and is simple, practical and highly reliable. Generally, the unit is combined with a DLC coordinated control strategy to reduce peak loads and operating costs. DLC solutions can also be studied in terms of controllable load numbers, control durations, and continuous progressive optimization to increase profits for power enterprises and benefits to customers.
Demand-side bidding model: the power load and the power generation side resource participate in market competition in a bidding mode and obtain economic benefits, and market operators obtain market clearance through global optimization. And the market bidding mode gives the user the right of reducing the load price and uniformly participating in the power market competition at the power generation side by declaring, so that the uniform allocation of the dispatching center to the unit and the load resource is realized.
In recent years, relevant research is mainly focused on bidding rules, multi-period characteristics of load response, joint optimization of demand-side resources and renewable energy sources, and the like. After load scheduling, under normal conditions, after receiving an adjusting instruction obtained by optimized calculation of a power company, a user responds to a scheduling requirement according to an appointed control period and an appointed control time sequence. It is noted that some users may experience a power consumption rebound phenomenon after reducing the power consumption in response to the power grid scheduling, and the power consumption rebound phenomenon is modeled by a deterministic method for specifying a transfer period and a transfer amount. There is also a possibility that the response cannot be made or is partially made due to the influence of factors such as the production and operation status of the user, the quality of the operator, and the content of the specific contract.
In this embodiment, it should be noted that the influence of the peak-valley time-of-use electricity price on the net charge gain is:
wherein the grid side revenue impacts:
before the peak-valley time-of-use electricity price is implemented, the power grid side supplies power to the load side at the same electricity selling price 24 hours a day; and after the peak-valley time of electricity price, the power grid side carries out different electricity prices on the load side at different time intervals for the purpose of realizing peak clipping and valley filling.
(1) The power grid side yield before the peak-valley time-of-use price is Rw0The specific expression represents that the electricity selling income of the power grid is subtracted by the electricity purchasing cost, and is as follows:
Rw0＝Fsd0-Fsw0
Rw0representing the power grid side income before the peak-valley time-of-use electricity price;
Fsd0representing the electricity selling income of the power grid before the peak-valley time-of-use electricity price;
Fsw0the electricity purchasing cost of the power grid from the power supply side before the peak-valley time-of-use electricity price is shown, and the electricity purchasing cost of the power grid is the income of the power supply side on-line electricity quantity.
(2) Power grid side income R after peak-valley time-of-use pricewThe electricity purchase cost from the power supply side before the electricity sale income after the peak-valley time electricity price is expressed by the following specific expression: rwRepresenting the power grid side income after the peak-valley time-of-use electricity price;
Rw＝Fsd-Fsw
Fsdrepresenting the electricity selling income of the power grid after the peak-valley time-of-use electricity price;
Fswrepresents the electricity purchase cost from the power supply side on the power grid side after the peak-valley time-of-use price.
Load side gain change:
(1) before the peak-valley time-of-use electricity price is implemented, the electricity fee expenditure on the load side is the electricity selling income of the power grid side before the peak-valley time-of-use electricity price, namely:
RD0＝Fsd0
(2) after the peak-valley time-of-use electricity price is executed, the electricity fee expenditure on the load side is the electricity selling profit of the power grid side after the peak-valley time-of-use electricity price, that is:
RD＝Fsd
the modeling approach for the load is illustrated as follows:
the flexible load modeling method comprises the steps that from the perspective of user autonomous response characteristics, flexible loads can be classified into 3 types, ① loads can be transferred, namely total power consumption is unchanged in a scheduling period (such as 1d), but power consumption characteristics are flexible, power consumption in each period can be flexibly adjusted, such as electric automobile power changing stations, ice storage, energy storage, partial loads of industrial and commercial users and the like, ② loads can be translated, the loads are restricted by production processes, power consumption curves can only be translated in different periods, such as industrial large users, ③ loads can be reduced, power consumption can be reduced to a certain extent according to needs, such as air conditioners, lighting and the like.
The three types of models can be calculated in the following ways:
the transferable load: taking the response price of electricity as an example, the transferable load can be summarized as follows:
ΔPshift(t)＝f`(P0(t),Δpshift(t),kshift(t),vshift(t)) (1)
in the formula: delta Pshift(t) is the response of the transferable load for a period of t; p0(t) is the base charge for a period t; Δ pshift(t) is a vector of price difference between the t period and other periods; k is a radical ofshift(t) is the mutual elastic vector of the t period relative to other periods; v. ofshape(t) is the transfer rate; t is a scheduling period.
Translatable load: the translatable load may be expressed as:
ΔPshape(t)＝f2(t+Δt(Δp))-f2(t) (3)
in the formula: delta Pshape(t) response of the translatable load during t, f2(t) is the initial power usage curve; Δ t (Δ p) is a load shift period due to the change in electricity price Δ p.
The load can be reduced: the reducible load can be expressed as:
ΔPre(t)＝f3(P0(t),Δpre,kre(t),vre(t)) (4)
in the formula: delta Pre(t) the response amount of the load can be reduced in the period t; Δ preThe variation of the electricity price in the t period; k is a radical ofre(t) is the coefficient of self-elasticity of the load for a period of t; v. ofre(t) is the clipping rate.
The user response electricity price model is mostly based on the electricity demand price elastic matrix, including self-elasticity and mutual elasticity, and is widely applied due to simple and intuitive algorithm, but the price elastic coefficient is mostly obtained by adopting industry statistical data, which reflects the macroscopic expression of the user response to the electricity price change, and the accuracy of the model is limited to a great extent. Considering the uncertainty of the user response electricity price, the uncertainty of the response behavior is described by the random error of a certain point on the elastic curve and is processed in a robust mode. In addition, due to the fact that the number and the types of the power consumers are large, the power grid dispatching is more concerned about the overall response characteristic after the aggregation of the power consumers.
Load electricity price response model in this embodiment:
if the original price of electricity p0For flat electricity prices, the peak-to-valley electricity price floating ratio is:
that is, the fluctuation of the electricity prices at the peak time and the valley time after the peak-valley time of the electricity prices is adopted, and the floating ratio of the peak-valley electricity prices is set to be the same in this embodiment. And the peak-to-valley electricity price ratio is:
after considering the multi-period response mode, the load shedding and transfer coefficients at any time are as follows:
in the formula, Tp、Tf、TvIs the peak, flat, valley time period; lambda [ alpha ]pf、λpv、λfvAll are load transfer coefficients, and the peak-to-valley electricity price floating ratio at the peak, flat and valley time is divided into kp＞0、kf＝0、kv< 0, therefore, λpf、λpv、λfv> 0, furthermore, due to εii＜0，λpp＜0，λvv＞0。
In the formula, LiThe load of the peak-valley time-of-use electricity rate period i is not implemented. L isp、Lf、LvThe average values of the total loads in the peak time period, the flat time period and the valley time period in the corresponding time periods are respectively.
Vector L'iAnd LiThe electricity consumption before and after the time-of-use electricity price is implemented by the user is respectively represented, the numerical value of each sub-block in the matrix lambda is determined by the peak-to-average electricity price floating ratio, the self elasticity and the cross elasticity of the load, when the number of the time segments is n, the lambda is n × n order matrix, for example, when the daily load curve takes 24 time segments, the lambda is 24 × 24 order matrix, and each element lambda of the matrix isijGiven the divided peak-to-valley flat periods for i and j, the value of each element in the 24 period can be determined.
Example 2
In this embodiment, after the electricity trading center determines the monthly clearing plan, it can be assumed that the electricity price of 24h per day is already determined. On the basis, the dispatching department can make a day-ahead plan so as to minimize the power generation cost. The objective function of the unit combination model considering the flexible load comprises three parts, namely power generation cost and starting cost. The considered constraint conditions comprise the constraint of the start-stop time of the unit, the constraint of the output of the unit and the constraint of the climbing rate of the unit. Meanwhile, a model of the flexible load needs to be considered in the model, and the model is specifically described as follows.
Therefore, the objective function proposed in the present embodiment is specifically described as follows.
The unit combination model considering the flexible load takes the minimum system operation cost as an objective function, and comprises the power generation cost and the starting cost. The details are as follows:
C(pn(t))＝anpn(t)2+bnpn(t)+cn(9)
in the formula, T is the number of time periods scheduled in the day ahead, and is generally taken as T24; fnAnd (t) is the power generation cost of the fire-electric generator set n in the time period t. In the formula (8), the power generation cost of the thermal power generating unit is composed of the coal consumption cost and the starting cost. Wherein
Respectively the starting cost of the thermal power generating unit n; c (p)n(t)) is the coal consumption cost of the thermal power generating unit n, and is the actual output pn(t) is a quadratic function of the decision variables, with characteristic coefficients an、bn、cnAs shown in formula (9).
Optionally, the constraint equation is illustrated as follows:
(1) and (4) the output characteristic of the thermal power generating unit.
(a) Constraint of start-stop time of the unit:
for the ith unit and the tth time interval, the minimum boot time constraint is as follows:
the minimum off-time constraint is:
in the formula (I), the compound is shown in the specification,
and
the minimum shutdown time and the minimum startup time of the nth unit are respectively, and min (-) represents the minimum value.
(b) Unit output restraint:
for the ith unit, the following output constraints need to be satisfied:
in the formula (I), the compound is shown in the specification,
and
respectively the minimum and maximum output of the ith unit.
(c) Unit climbing restraint:
for the ith unit, the up-down climbing speed requirement needs to be met from the t-1 th time interval to the t-th time interval:
in the formula (I), the compound is shown in the specification,
and
and the upward climbing speed and the downward climbing speed of the nth thermal power generating unit are respectively set.
(2) Output restraint of the hydroelectric generating set:
the constraint equation comprises an upper limit constraint, a lower limit constraint and a daily electric quantity constraint, regardless of the running cost of the hydroelectric generating set. In the formula (14), the compound represented by the formula (I),
respectively the minimum and maximum technical output of the hydroelectric plant h; equation (15) is the daily electric quantity constraint of the hydroelectric power plant h,the allowable power generation amount of the hydropower station h in the dispatching cycle is W respectively due to water quantity limitationh。
(3) User power utilization satisfaction constraint:
user satisfaction constraint (market sector considerations): the peak-valley time-of-use electricity price can have an influence on the satisfaction degree of the electricity utilization mode of the user. The satisfaction degree of the electricity utilization mode means that the change of the electricity fee enables a user to selectively change electricity utilization habits, and influences on the comfort level of the user, the production plan of products and the like can be generated.
Wherein η is user satisfaction, ηj,minAre given numerical values.
(4) The profitability constraint of the power grid company:
Rw＝Fsd-Fsw＞＝Rw0(17)
in the formula FsdRepresenting the electricity selling income of the power grid after the peak-valley time-of-use electricity price; fswRepresents the electricity purchase cost from the power supply side on the power grid side after the peak-valley time-of-use price. Rw0Representing the grid side revenue before peak-valley time-of-use electricity prices.
a) Sales revenue:
Rm(t)＝λm*Pm(t) (19)
in the formula, λmThe selling price of electricity for the mth type user; pm,iAnd (t) is the load of the mth class user at the time t.
b) The cost of surfing the Internet:
Fn(t)＝γ*Pn,i(t) (20)
in the formula, gamma is the power price of the thermal power generating unit on the internet; pn,iAnd (t) the output of the ith thermal power generating unit in the t period. Other types of generator sets are similar.
In summary, equations (7) to (20) constitute a grid company profit model, i.e., a grid electricity price model, in which the load electricity price response characteristics are considered.
Example 3
In order to verify the practical effect of the method for making the power price of the flexible load unit in consideration of the peak-valley time of the power grid, the following description is provided in this embodiment.
(1) And (4) load parameters. From the analysis, the user group participating in the demand side response is mainly large industrial power consumption, commercial power consumption and resident domestic power consumption. The response characteristics to price vary from industry to industry. To simplify the analysis, industrial users include building materials, steel, chemical, metallurgy, and the like. The commercial power utilization is diverse in kinds including administrative offices, commercial finance, department stores, hotel businesses, dining and entertainment, medical health, educational research, sports, and others. The load of residents is only integrated into one type of user. Sensitivity to electricity price: industrial load > commercial load > residential load. The survey results of the relevant parameters of the user type and the response characteristics are shown in table 1. The original electricity prices in the tables refer to sales electricity prices that different types of users have previously adopted, and are herein defined as electricity prices of different types of users.
Table 1: raw electricity price data and elastic coefficient.
(2) In the aspect of electricity price: the grid-connection electricity price of the thermal power generating unit in the power generation side is 0.35 yuan/kWh, the grid-connection electricity price of the hydroelectric power generating unit is 0.25 yuan/kWh, the grid-connection electricity price of the wind power generating unit is 0.61 yuan/kWh, and the grid-connection electricity price of the photovoltaic power generating unit is 1 yuan/kWh. In the user side, the average power rate of the industrial users is 0.61 yuan/kWh, the average power rate of the commercial users is 0.81 yuan/kWh, and the average power rate of the residential users is 0.48 yuan/kWh.
(3) And (3) controllable power supply aspect: the test system information is shown in table 2. The thermal power unit parameters are shown in table 3, the hydraulic power plant parameters are shown in table 4, and the load data are shown in table 5.
Table 2: and testing system information.
The wind power prediction curve and the photovoltaic power prediction curve of a typical day are shown in fig. 3 (schematically shown as a wind power prediction output photovoltaic prediction force diagram), and the load condition is shown in fig. 4 (a load distribution curve diagram).
Table 3: and (4) a thermal power generating unit characteristic table.
Table 4: and (5) a hydroelectric generating set characteristic table.
Table 5: different types of load data tables of the system.
In this embodiment, to better verify the effectiveness of the method of the present invention, the following 3 pricing models are defined:
to better verify the effectiveness of the method herein, the following 3 pricing models are defined:
1: scheme 1 is based on a traditional scheduling method, namely a scheduling model only considering power generation side resources, and each type of user does not implement peak-valley time-of-use electricity price and adopts flat electricity price;
2: scheme 2 is a scheduling model considering peak-valley time-of-use electricity price and multi-type loads, dividing peak-valley flat time periods according to a total load curve before implementing the peak-valley time-of-use electricity price, and adopting the same peak-valley electricity price ratio;
3: scheme 3 is a scheduling model considering peak-valley time-of-use electricity price and various types of loads, dividing peak-valley flat time according to various load characteristics, and adopting different peak-valley electricity price ratios.
The difference between the above three pricing schemes is shown in table 6, and the result of dividing the peak-to-valley period is shown in table 7.
Table 6: comparison of three pricing models.
Table 7: and dividing the peak-valley level period.
And (3) analyzing the results: the unit combination model considering the flexible load is a mixed integer programming model, and a linear interpolation method can be adopted to carry out linearization processing on the quadratic cost function in the objective function. The method takes the system operation cost as a target function, considers the output constraints of the thermal power generating unit and the hydroelectric generating unit, and the power grid profit constraint (the power grid profit after the peak-valley time-of-use price is implemented needs to be higher than the profit before the peak-valley time-of-use price is not implemented), and simultaneously considers the user power utilization mode satisfaction constraint, wherein the value is 0.9. The model is easily restricted by the profit of the power grid and the satisfaction degree of the power utilization mode of the user, and the obtained results are shown as follows.
(1) User satisfaction contrast with electricity:
tables 8 and 9 show the optimal peak-to-valley time-of-use electricity rates of the case 2 and the case 3, respectively. The same peak-to-valley electricity price ratio is adopted by all types of users, and because the A-type industrial load is sensitive to the electricity price response, the user satisfaction is lower than that of other user types. The peak to valley electricity rate ratio is mainly affected by the user satisfaction constraint of the industrial user, i.e., ═ 0.9. According to the scheme 3, the satisfaction degree of the electricity utilization mode of the user is considered while the peak-valley time-of-use electricity price is considered, the peak-valley average electricity price is divided according to the load curves of various types, the difference of the response capacity of various types of loads to the electricity price can be effectively reflected, and finally, the business data and the residential data are influenced by the constraint of the satisfaction degree of the respective user due to the fact that the sensitivity degree of the business user and the residential user to the electricity price is low.
Table 8: the optimal peak-to-valley time-of-use electricity price scheme of scheme 2.
Table 9: the optimal peak-to-valley time-of-use electricity price scheme of scheme 3.
(2) And comparing peak clipping and valley filling effects.
Fig. 5 (load curve under 3 scenarios) is a load graph under 3 modes. Table 10 shows comparative load analysis for different protocols. The analysis of the peak clipping and valley filling effects of the schemes is as follows:
compared with the scheme 2, the scheme 1 has the advantages that the scheme 2 considers the peak-valley time-of-use electricity price, the peak-valley electricity price ratio is increased, the user can be guided to reasonably use electricity to a certain extent, the peak-valley difference of the system is reduced from 8528.2 to 5862.6, and the load rate is improved.
Compared with the scheme 2, the scheme 3 fully considers the load characteristics of various types of users, divides the peak-valley level time period according to respective load curves, reduces the peak-valley difference of the system from 5862.6 to 5214.8, and effectively improves the load rate.
Compared with the scheme 2, the scheme 3 fully considers the load characteristics of various types of users, divides the peak-valley level time period according to respective load curves, reduces the peak-valley difference of the system from 5342.6 to 5109.5, and effectively improves the load rate. Due to the full consideration of the electricity price response characteristics of different types of users, the satisfaction degree of electricity utilization modes of commercial users and residential users is reduced, but the satisfaction degree is still more than 0.9, and the electricity utilization modes are easily accepted by the users.
Table 10: comparative load analysis under different protocols.
And (3) comparing the economical efficiency:
the economic comparison of the embodiment comprises system operation cost comparison, power grid profit comparison and user electricity utilization cost comparison. As can be seen from the table 11, compared with the first scheme, the second scheme has the advantages that the system power generation cost and the starting cost are both reduced, the system operation cost is reduced by 2%, and the power grid profit is improved by 1.2%. Compared with the scheme I, the scheme III obtained from the table 12 has the advantages that the system power generation cost and the starting cost are both reduced, the system operation cost is reduced by 1.3%, and the power grid profit is improved by 2.64%. The electricity cost of the user of the second scheme is lower than that of the third scheme from the table 13.
Table 11: and comparing the running cost and the expense of the system of each scheme.
Table 12: and comparing the profit cost of each scheme.
Table 13: and comparing the profit cost of each scheme.
In summary, on the basis of considering the satisfaction degree of the user electricity utilization manner, from the perspective of the system operation cost and the user electricity utilization cost, the second scheme is lower than the third scheme, and the second scheme is a recommended optimal model, but from the perspective of the profit of the power grid, the profit of the power grid of the third scheme is higher than 1.24% of that of the second scheme, and the third scheme is a recommended optimal model.
The embodiment makes the Guizhou peak-valley time-of-use electricity price based on the actual unit characteristics, the actual load, the unit internet electricity price and the user-side sales electricity price. Three targets are respectively designed, namely a power grid company profit model considering flexible loads, a unit combination model considering flexible loads and a minimum model considering system peak-valley difference, each target function is provided with three pricing modes, and actual data simulation of Guizhou is carried out. And finally, analyzing the satisfaction degree of the user power utilization mode, the peak clipping and valley filling effects and the economy (system operation cost, power grid profit and user power utilization cost) respectively to obtain a suggested selection strategy of each target, and providing diversified theoretical reference for the power grid to formulate peak-valley time-of-use power price.
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, the operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein. A computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
As used in this application, the terms "component," "module," "system," and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of example, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the internet with other systems by way of the signal).
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (10)
1. A method for making a power grid peak-valley time-of-use electricity price considering a flexible load unit is characterized by comprising the following steps of: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
an integer programming model is constructed for the power grid electricity price problem;
solving an optimal solution of the integer programming model;
determining the peak-to-valley average electricity price;
determining a load participation scheduling strategy;
determining a load modeling mode and establishing a load electricity price response model according to the analysis of the influence of peak-valley time-of-use electricity price on the net charge income;
and (3) taking the unit combination model of the flexible load into consideration to participate in scheduling, and establishing a power grid electricity price model taking the load unit combination into consideration.
2. The method for making a power rate of a flexible load unit in consideration of peak-to-valley time of a power grid according to claim 1, wherein: the unit combination model considering the flexible load comprises determining a target function and establishing a constraint equation;
the determined objective function comprises power generation cost and starting cost;
the established constraint equation comprises thermal power generating unit output characteristic constraint, hydroelectric generating unit output characteristic constraint and power grid company profit constraint.
3. The method for making a power rate of a flexible load unit in consideration of peak-to-valley time of a power grid according to claim 2, wherein: the unit combination model considering the flexible load takes the minimum system operation cost as an objective function, comprises the power generation cost and the starting cost, and comprises the following definitions:
C(pn(t))＝anpn(t)2+bnpn(t)+cn
wherein T is the time interval scheduled in the day ahead, and T is 24, Fn(t) is the power generation cost of the fire-electric generator set n within the time period t; the power generation cost of the thermal power generating unit consists of coal consumption cost and starting cost, wherein
Respectively, the starting cost of the thermal power generating unit n, C (p)n(t)) is the coal consumption cost of the thermal power generating unit n, and is the actual output pn(t) is a quadratic function of the decision variables, with characteristic coefficients an、bn、cn。
4. A method for making a power rate of a flexible load unit according to claim 2 or 3, wherein the method comprises the following steps: the output characteristics of the thermal power generating unit comprise a unit start-stop time constraint equation, a unit output constraint equation and a unit climbing constraint equation;
the unit start-stop time constraint equation comprises,
for the ith unit and the tth time interval, the minimum boot time constraint is as follows:
the minimum off-time constraint is:
in the formula
And
respectively the minimum shutdown time and the minimum startup time of the nth unit, wherein min (·) represents the minimum value;
the unit output constraint equation comprises,
for the ith unit, the following output constraints need to be satisfied:
Pn min≤Pn(t)≤Pn max
in the formula, Pn minAnd Pn maxRespectively the minimum output and the maximum output of the ith unit;
the unit hill climbing constraint equation comprises,
for the ith unit, the up-down climbing speed requirement needs to be met from the t-1 th time interval to the t-th time interval:
in the formula (I), the compound is shown in the specification,
and
and the upward climbing speed and the downward climbing speed of the nth thermal power generating unit are respectively set.
5. The method for making the power rate of the flexible load unit in consideration of the peak-to-valley time of the power grid as claimed in claim 4, wherein: the output characteristics of the hydroelectric generating set do not consider the operation cost of the hydroelectric generating set, then the constraint equation comprises an upper limit constraint, a lower limit constraint and a daily electric quantity constraint which are respectively defined as follows,
in the formula (I), the compound is shown in the specification,
respectively the minimum and maximum technical output of the hydroelectric plant h;
the daily electric quantity of the hydropower station h is restricted, and the allowable electric energy production of the hydropower station h in the dispatching cycle is respectively W due to water quantity limitationh。
6. The method for making a power rate of a flexible load unit in consideration of peak-to-valley time of a power grid according to claim 5, wherein: the power grid company profitability constraint comprises the following definitions,
Rw＝Fsd-Fsw＞＝Rw0
in the formula, FsdRepresenting the electricity selling income of the power grid after the peak-valley time-of-use electricity price; fswRepresents the electricity purchase cost from the power supply side on the power grid side after the peak-valley time-of-use price. Rw0Representing the grid side revenue before peak-valley time-of-use electricity prices.
Sales revenue is defined as follows:
Rm(t)＝λm*Pm(t)
in the formula, λmThe selling price of electricity for the mth type user; pm,i(t) isLoad of m types of users at time t.
The internet cost is defined as follows:
Fn(t)＝γ*Pn,i(t)
wherein gamma is the power price on the grid of the thermal power generating unit Pn,iAnd (t) the output of the ith thermal power generating unit in the t period.
7. The method for making the power rate of the flexible load unit in consideration of the peak-to-valley time of the power grid according to claim 5 or 6, wherein: the user power pattern satisfaction constraints include,
the peak-valley time-of-use electricity price can influence the satisfaction degree of the electricity utilization mode of the user;
the satisfaction degree of the electricity utilization mode means that the change of the electricity fee enables a user to selectively change the electricity utilization habit, and the change has influence on the comfort level of the user, the production plan of products and the like, a function is defined as follows,
wherein η is user satisfaction, ηj,minFor a given value, the determination of the objective function constitutes the grid price model taking into account the load price response characteristics.
8. The method for making a power rate of a flexible load unit in consideration of peak-to-valley time of a power grid according to claim 7, wherein: the integer programming is divided into pure integer programming, mixed integer programming and 0-1 programming;
the model defining the integer program is:
max z＝CX
in the formula, all element numbers in the A matrix, the b vector and the c vector are integers or rational numbers, and the integer programming problem is considered as a linear programming problem:
max z＝CX
the above equation is referred to as the relaxation problem of the integer programming problem.
9. The method for formulating the power rates of the flexible load units in consideration of the peak-to-valley time of the power grid as claimed in claim 8, wherein: the determination of the peak-to-valley level of electricity includes,
according to the price of electricity pi,tInfluenced by the type of load and the peak-to-valley period;
for the same type of load, assuming that the load comprises three sections of peak, valley and flat electricity price in one day;
classifying the time periods of one day by adopting a semi-trapezoidal fuzzy membership function;
for the valley period, a partial small semi-trapezoidal distribution function can be adopted for determination;
for peak time period, determining by adopting a large-scale semi-trapezoidal distribution function;
the other periods are the flat periods except for the valley period and the peak period.
10. The method for formulating the power rates of the flexible load units in consideration of the peak-to-valley time of the power grid as claimed in claim 9, wherein: the formula of the partial small semi-trapezoidal membership function is as follows,
the formula of the partial large semi-trapezoidal membership function is as follows,
wherein x is the current load value, b represents the maximum load value in a day, a represents the minimum load value in a day, and the valley period x is defined as the load satisfying A1(x) Load of > α, the peak period x being defined as the load satisfactionA2(x)≥α。
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112803454A (en) * | 2021-01-04 | 2021-05-14 | 上海电气分布式能源科技有限公司 | Power resource management method and device, electronic equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104953594A (en) * | 2015-07-02 | 2015-09-30 | 东南大学 | Optimal scheduling method for power selling company containing distributed power sources and transferable loads |
| CN107482618A (en) * | 2017-07-20 | 2017-12-15 | 国家电网公司 | Electricity pricing method with the generating of wind fire and the power network of flexible load containing polymorphic type |
| CN109617133A (en) * | 2018-12-18 | 2019-04-12 | 深圳供电局有限公司 | Power system scheduling method considering flexible load |
| CN110852519A (en) * | 2019-11-18 | 2020-02-28 | 贵州电网有限责任公司 | Optimal profit method considering various types of loads for electricity selling companies |
-
2020
- 2020-02-29 CN CN202010132435.7A patent/CN111353820A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104953594A (en) * | 2015-07-02 | 2015-09-30 | 东南大学 | Optimal scheduling method for power selling company containing distributed power sources and transferable loads |
| CN107482618A (en) * | 2017-07-20 | 2017-12-15 | 国家电网公司 | Electricity pricing method with the generating of wind fire and the power network of flexible load containing polymorphic type |
| CN109617133A (en) * | 2018-12-18 | 2019-04-12 | 深圳供电局有限公司 | Power system scheduling method considering flexible load |
| CN110852519A (en) * | 2019-11-18 | 2020-02-28 | 贵州电网有限责任公司 | Optimal profit method considering various types of loads for electricity selling companies |
Non-Patent Citations (2)
| Title |
|---|
| 邓俊: "机组组合混合整数线性规划模型的研究与改进" * |
| 马伟哲 等: "考虑柔性负荷的机组组合模型及求解" * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112803454A (en) * | 2021-01-04 | 2021-05-14 | 上海电气分布式能源科技有限公司 | Power resource management method and device, electronic equipment and storage medium |
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