CN113420991A - Power supply energy storage joint planning method and system considering expected power shortage linearization - Google Patents
Power supply energy storage joint planning method and system considering expected power shortage linearization Download PDFInfo
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Abstract
The invention provides a power supply energy storage joint planning method and system considering expected power shortage linearization. Acquiring investment cost data, operation cost data, standby cost data and reliability cost data of the generator set and the energy storage system; based on the acquired related data, under the set constraint condition, with the minimum annual comprehensive cost as an optimization target, solving a power supply energy storage joint planning model, and determining a power supply energy storage joint planning scheme which simultaneously meets the economical efficiency and the reliability, so as to guide the layout and the operation scheduling control of the generator set and the energy storage system; the reliability cost is the product of the unit price of lost load, the expected power shortage and the planning time; the expected power shortage is reduced to a linear representation by removing a 0-1 variable indicating whether or not a load loss occurs from an absolute value and partially linearizing the absolute value based on a power outage capacity probability table and an upper bound conversion.
Description
Technical Field
The invention belongs to the field of power transmission network planning of electric power systems, and particularly relates to a linearized power supply energy storage joint planning method and system considering expected power shortage.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Power supply planning is an important component of power system planning. To meet the requirements of future load growth and power grid development, power expansion planning is used to determine the time required to schedule the generator sets, the location of installation, and the size of the installation capacity. The result of power supply expansion planning will affect the power quality and grid structure of future power systems, further affecting the economy and reliability of the grid. The possibility of equipment failure is a must be considered in the planning problem, and power system equipment outages are one of the important factors affecting power system reliability and economy. The N-1 safety check criteria are typically used to assess the reliability of the power system, however, the criteria are overly conservative, and while reliability is met, may result in excessive wasted power generation and transmission resources. Therefore, the inventor finds that the current power grid planning method cannot give consideration to the economy and the reliability of the power system at the same time, and reduces the accuracy of the dispatching control of the generator set and the energy storage system.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a power supply energy storage joint planning method and system considering expected power shortage linearization, which can simultaneously give consideration to the economy and reliability of a power system, and improve the engineering application value of power transmission network planning and the accuracy of scheduling control of a generator set and an energy storage system.
In order to achieve the purpose, the invention adopts the following technical scheme:
the first aspect of the invention provides a power supply energy storage joint planning method for linearization by considering expected power shortage, which comprises the following steps:
acquiring investment cost data, operation cost data, standby cost data and reliability cost data of the generator set and the energy storage system;
based on the acquired related data, under the set constraint condition, with the minimum annual comprehensive cost as an optimization target, solving a power supply energy storage joint planning model, and determining a power supply energy storage joint planning scheme which simultaneously meets the economical efficiency and the reliability, so as to guide the layout and the operation scheduling control of the generator set and the energy storage system;
the power supply energy storage joint planning model is the sum of power supply energy storage investment cost, operation cost, standby cost and reliability cost; the reliability cost is the product of the unit price of lost load, the expected power shortage and the planning time; the expected power shortage is reduced to a linear representation by removing a 0-1 variable indicating whether or not a load loss occurs from an absolute value and partially linearizing the absolute value based on a power outage capacity probability table and an upper bound conversion.
A second aspect of the present invention provides a power supply energy storage joint planning system linearized in view of a desired amount of power shortage, comprising:
the data acquisition module is used for acquiring investment cost data, operation cost data, standby cost data and reliability cost data of the generator set and the energy storage system;
the planning scheme determining module is used for solving a power supply energy storage combined planning model based on the acquired related data and taking the minimum annual comprehensive cost as an optimization target under the set constraint condition, and determining a power supply energy storage combined planning scheme which simultaneously meets the economical efficiency and the reliability so as to guide the arrangement and the operation scheduling control of the generator set and the energy storage system;
the power supply energy storage joint planning model is the sum of power supply energy storage investment cost, operation cost, standby cost and reliability cost; the reliability cost is the product of the unit price of lost load, the expected power shortage and the planning time; the expected power shortage is reduced to a linear representation by removing a 0-1 variable indicating whether or not a load loss occurs from an absolute value and partially linearizing the absolute value based on a power outage capacity probability table and an upper bound conversion.
A third aspect of the invention provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the power supply energy storage joint planning method as described above, which takes into account a desired amount of starved power for linearization.
A fourth aspect of the present invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the steps of the power supply energy storage joint planning method as described above taking into account linearization of expected shortage power supply.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention constructs a power supply energy storage joint planning model, simplifies the expected power shortage amount into linear representation in the power supply energy storage joint planning model, wherein, the reliability cost item in the power supply energy storage joint planning model is calculated by the product of the unit price of lost load, the expected power shortage and the planning time, the cost calculation process comprises a large number of 0-1 variables and bounded continuous variables, the linear solution of the model is the main obstacle of the optimization process, the invention adopts a piecewise linearization method to analyze the expected power shortage expression and express the expected power shortage expression as a piecewise linear function for rotation standby, the model is more comprehensively established, the planning result is more comprehensive and reliable, and the solving speed is high, the engineering application value of power transmission network planning is improved, and the obtained power supply energy storage combined planning scheme which meets the economical efficiency and reliability simultaneously is used for guiding the arrangement and operation scheduling control of the generator set and the energy storage system.
(2) The invention removes the 0-1 variable representing whether the load loss occurs in the EENS expression based on the outage capacity probability table (COPT table) and the upper bound conversion method. Compared with the traditional direct linearization method, the method can greatly improve the calculation efficiency while ensuring the reliability and the rationality of the result.
(3) The invention simultaneously considers the problems of power supply expansion planning and energy storage system planning in the power transmission network, can analyze the whole power system, realizes the optimal solution of the whole power system by planning, and can effectively adapt to the new trend of the current power grid development.
(4) The invention also considers the flexibility resources provided by the set, the energy storage system and the adjustable load, and greatly relieves the power fluctuation caused by wind power injection and load injection, thereby improving the reliability and stability of the system.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of a power supply energy storage joint planning method considering linearization of expected power shortage amount according to an embodiment of the invention;
fig. 2 is a flowchart of an iterative solution of a power supply energy storage joint planning model according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
As shown in fig. 1, the present embodiment provides a power storage joint planning method for linearization in consideration of expected power shortage, which specifically includes the following steps:
step S101: and acquiring investment cost data, operation cost data, standby cost data and reliability cost data of the generator set and the energy storage system.
In a particular implementation, the investment cost data includes investment costs for a single generator set and a single energy storage system. The operation cost data comprises active output, time-of-use electricity price unit price and operation cost coefficient of the generator set in a set time period of the generator set and the energy storage system. The standby cost data comprises the standby number provided by the generator set and the energy storage system in a set time period and the load capacity of the adjustable load in the set time period. The reliability cost data comprises the unit price of lost load, the data related to the expected power shortage amount and the planning time.
Here, the expected power shortage amount is usually expressed by the EENS, and the reliability cost is calculated by multiplying the VOLL (unit price of lost load) by the EENS (expected power shortage amount). There are many expression methods for the expression of the EENS (expected power shortage), and the conventional EENS is generally applied to the field of power system scheduling, but for more refined power grid planning problems, it is also important to consider the EENS. However, in the whole optimization model, the linearization of the EENS is a major computational obstacle, because the expression contains a large number of variables 0-1 and bounded continuous variables, and if the linear linearization is direct, the computational complexity is greatly increased, and the computational efficiency is reduced. The researchers put forward the method of upper bound conversion, and the part which must be a positive value in the EENS expression is expressed by an absolute value, thereby greatly reducing the number of binary variables and obviously improving the calculation efficiency. However, this method does not achieve the effect of reducing fault scenarios, and when the system has too many scenarios, the computational efficiency is still low.
Power source extension planning (GEP) is an important component of power system planning. In order to meet the requirements of future load growth and power grid development, power expansion planning is used to determine the scheduling time, installation site and capacity of the required units. The results of the GEP will affect the reliability, economy, power quality and grid structure of future power systems. In addition, uncertainty in load and large wind turbines poses a significant challenge to power system planning, and equipment outage is a non-negligible problem in power system planning. In order to quantify the influence of equipment failure uncertainty, reliability indexes such as load loss probability (LOLP), expected power shortage (EENS), conditional risk value (CVaR) and the like are adopted in a probability method. Among them, the EENS is widely used in the plant assembly, the rotating standby optimization, and sometimes also in the GEP and the transmission network extended planning (TEP) problem.
However, in the EENS calculation process, a large number of 0-1 variables and continuous variables for representing fault scenes exist in the expression, and huge calculation burden is brought to the solution of the model. The traditional EENS linearization method cannot change the number of fault scenes, and the memory is often exhausted when high-order faults are calculated, so that the situation of no solution occurs. And completely neglecting the reliability cost to process the planning problem means that there is no equipment fault in the objective function, the calculation efficiency is high, but it is completely unreasonable to consider the reliability in the planning problem, and neglecting the reliability makes the objective function cost too optimistic, so it is of great significance to consider the equipment fault in the power transmission network planning problem.
Generally, in EENS computation, the reliability cost in the objective function is expressed as shown in equation (1), where DyearDays represented by typical years; VOLL is the unit cost of lost load, EENStThe expected power shortage in the time t is calculated as shown in formula (2).
Crel＝Dyear·[VOLL·EENSt] (1)
Wherein S istSet of all fault scenarios within time period t, ps,tAs the probability of occurrence of a fault scenario s in a time period t, bs,tTo characterize the 0-1 variable, Δ C, of whether a fault scenario s occurs over a time period ts,tFor the power failure capacity generated after the fault scene s occurs within the time period t, SSRtIs the total rotation spare quantity of the system. In this embodiment, dTIs set to 1 h. As shown in equation (2), the non-linear part of the 0-1 variable multiplied by the bounded continuous variable exists in the calculation equation of the EENS, so that the model is difficult to solve, and the direct linearization is complicated, so that the appropriate simplification of the calculation process of the EENS is very important in the planning problem.
Step S102: based on the acquired related data, under the set constraint condition, with the minimum annual comprehensive cost as an optimization target, solving a power supply energy storage joint planning model, and determining a power supply energy storage joint planning scheme which simultaneously meets the economical efficiency and the reliability, so as to guide the layout and the operation scheduling control of the generator set and the energy storage system;
the power supply energy storage joint planning model is the sum of power supply energy storage investment cost, operation cost, standby cost and reliability cost; the reliability cost is the product of the unit price of lost load, the expected power shortage and the planning time; the expected power shortage is reduced to a linear representation by removing a 0-1 variable indicating whether or not a load loss occurs from an absolute value and partially linearizing the absolute value based on a power outage capacity probability table and an upper bound conversion.
The objective function is: MinCinv+Cope+Cres+Crel (3)
As shown in equation (3), in the power supply planning problem, the objective function is usually the objective function with the minimum total cost, including the investment cost CinvOperating cost CopeSpare cost CresAnd a reliability cost Crel. These costs can be expressed explicitly as follows:
Crel＝Dyear·[VOLL·EENSt] (7)
in the formula, NG,
NTRespectively representing an existing generator set, a generator set to be built, a total generator set, an energy storage system set to be built and an optimization time period set;
the variable is 0-1 which respectively represents whether the unit to be built and the energy storage system are built or not;
and
the investment costs of a single unit and a single energy storage system respectively; dyearRepresents the number of days represented by a planned typical year; a isg,bgAnd cgIs the operating cost coefficient of the generator set; pg,tAnd Pe,tRespectively representing the active power output of the generator set g and the energy storage system e in a time period t; c. Ce,tThe price is the price of the time-of-use electricity; u shapeg,tRepresenting the 0-1 variable of the running state of the unit;
and
respectively representing cost coefficients of the generator set g, the energy storage system e and the adjustable load in the optimization time period t; rg,t，Re,tAnd
respectively representing the number of the spares provided by the generator set g in the time period t, the number of the spares provided by the energy storage system e in the time period t and the load capacity of the adjustable load in the time period t; VOLL is the unit cost of lost load, EENStThe desired amount of starved power for time period t.
In this embodiment, the set constraint conditions include a power balance constraint, a unit output constraint, an energy storage system charge and discharge constraint, a typical daily unit combination constraint, a rotation standby constraint, and an adjustable load constraint.
(1) Power balance constraint
In the formula (I), the compound is shown in the specification,
the predicted value of the wind power in the time period t is; dtLoad prediction value in time interval t; the constraint is the power balance of the whole system, namely the active output of the generator set and the stored energy and the net load of the system are balanced in each time period t.
(2) Unit output constraint
In the present formula, the first and second groups are,
and
the upper and lower output limits of the generator set g are respectively represented by the formula (9), the upper and lower output limits of the existing generator set are constrained, and the non-operating generator set has no active output; the formula (10) is the upper and lower output limits of the unit to be built, and the unit can output power only when the unit is built and in the running state and the two are met simultaneously.
(3) Energy storage system restraint
The formula (11) is the energy change constraint of the energy storage system in the front and back two periods, Se,tRepresenting the energy stored by the energy storage system e during the time period t,
and
respectively the charging and discharging efficiency of the energy storage system,
and
respectively the charging and discharging power of the energy storage system e in the time period t; equation (12) indicates that the stored energy of the energy storage system is equal in the first and last optimization periods, Se,startAnd Se,endRespectively representing the energy storage and storage energy in the initial optimization period and the energy storage and storage energy in the final optimization period; equation (13) represents the upper and lower energy storage limit constraints of the energy storage system,
and
respectively storing the lower limit and the upper limit of energy for the energy storage system e; equations (14) and (15) are respectively the charge-discharge power upper and lower limits constraints of the energy storage system,
and
respectively, a 0-1 variable for marking the energy storage system in a charging state or a discharging state, if the energy storage system is in the charging state or the discharging state in the t period
A value of 1 indicates that the energy storage system is in a charging state at this time, if
If the voltage is 1, the energy storage system is in a discharging state; equation (16) is a logical constraint, and the sum of the two 0-1 variables is less than or equal to 1 at any time, which indicates that the energy storage system can only be charged or discharged at any time.
(4) Typical day unit combination constraint
Wherein, the expressions (17) and (18) are respectively the minimum start-stop time constraint of the unit, tt and t are both NTThe index in (1) is set to (1),
and
respectively the minimum startup and the minimum shutdown time parameters of the unit,
for representing 0-1 variable, IC, of initial period state of the unitgThe number of sections of the unit in continuous operation before the optimization time period; equations (19) and (20) are the logical constraints of the Start-stop 0-1 variable and the run 0-1 variable, SUg,tAnd SDg,tRespectively representing the variables of 0-1 of the starting process and the stopping process of the unit; equations (21) and (22) are the initial minimum run time constraint and the initial minimum down time constraint of the unit g, respectively; equations (23) and (24) are respectively the climbing constraint of the unit g at the time t,
and
respectively limiting the maximum climbing rate and the maximum climbing rate of the unit; equations (25) and (26) are initial climbing constraints for the unit g when t is equal to 1.
(5) Rotational back-up restraint
In the formulae (27) and (28), R is only for the up-regulationg,t、Re,tThe standby quantities of the generator set g and the energy storage system e in the time period t are respectively provided, and tau is standby delivery time; equation (29) is the total rotational margin that the system can provide during time t.
(6) Adjustable load restraint
The adjustable load is one of flexible resources of the power system, and plays an important role in stabilizing wind power and fluctuation of the load. Equation (30) gives the upper and lower limits constraint for the adjustable load, where
And characterizing the upper limit of the adjustable load for the parameter.
In the present embodiment, the specific process of simplifying the power shortage amount to the linear representation is as follows:
first, the maximum output load of the system in a certain period is defined, as shown in equation (31):
the conventional EENS expression linearization method is shown in formula (32):
although the method carries out linearization processing on the variables 0-1, the great number of the variables 0-1 still becomes a main obstacle in the linearization calculation process. Therefore, the present embodiment does not directly linearize the 0-1 variable, but rather performs an upper bound translation of the EENS expression, eliminating the 0-1 variable in the EENS expression, based on the results of the COPT table. The processing of the 0-1 variable and the continuous variable in the EENS expression using the upper bound transformation is shown in equation (33).
Compared with the original EENS linearization expression, the simplified EENS expression based on the upper bound conversion improves the calculation efficiency.
The COPT table is a power failure capacity probability table which is established by a result optimized by a last model, the power failure capacity is divided into a plurality of sections, and the probability of the fault scene falling into each section is overlapped to approximately represent the probability of the power failure of the section. Therefore, the COPT table essentially compresses a huge fault scene and expresses EENS as a more simplified piecewise linear function related to SSR.
S in equation (2) after the COPT table is establishedt，ps,tAnd Δ Cs,tAll can be obtained from a COPT table, wherein StThe number of segments is consistent with the number of segments established in the COPT table, i.e. the number of failure scenarios is significantly compressed.
In (2), after the EENS is linearized, the whole planning problem belongs to an MILP (mixed integer linear programming) model, and the model can be solved by the most advanced MILP commercial solver. As shown in fig. 2, the concrete solving steps are as follows:
step 1, solving a source network joint planning model considering UC, wherein EENS is not considered in the step, and a corresponding COPT table is established according to an optimization result. Step 2, the EENS for reliability cost is calculated based on COPT, which is linearized using the upper bound translation method set forth in this disclosure, and the updated SSR plan makes the system more reliable. The value of the EENS in step 3 is updated according to step 2, and according to the result of step 2, the value of the EENS is updated in step 3, more units are called, and more available capacity is called to the scheduled unit. The EENS value of step 3 is slightly increased over step 2 because its invocation generates more SSRs, further improving the reliability of the system. With the iteration, the difference between the EENS value calculated in the optimization process (2) and the EENS value calculated by the COPT after the next optimization is smaller and smaller. When this difference is less than a given convergence threshold, the iterative process will stop.
Example two
The embodiment provides a power supply energy storage joint planning system considering expected power shortage linearization, which specifically includes the following modules:
the data acquisition module is used for acquiring investment cost data, operation cost data, standby cost data and reliability cost data of the generator set and the energy storage system;
the planning scheme determining module is used for solving a power supply energy storage combined planning model based on the acquired related data and taking the minimum annual comprehensive cost as an optimization target under the set constraint condition, and determining a power supply energy storage combined planning scheme which simultaneously meets the economical efficiency and the reliability so as to guide the arrangement and the operation scheduling control of the generator set and the energy storage system;
the power supply energy storage joint planning model is the sum of power supply energy storage investment cost, operation cost, standby cost and reliability cost; the reliability cost is the product of the unit price of lost load, the expected power shortage and the planning time; the expected power shortage is reduced to a linear representation by removing a 0-1 variable indicating whether or not a load loss occurs from an absolute value and partially linearizing the absolute value based on a power outage capacity probability table and an upper bound conversion.
It should be noted that, each module of the present embodiment corresponds to each step of the first embodiment one to one, and the specific implementation process is the same, which will not be described herein again.
EXAMPLE III
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the power supply energy storage joint planning method considering the expected shortage of power supply linearization.
Example four
The present embodiment provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the program, the processor implements the steps in the power storage joint planning method considering linearization of expected insufficient power supply as described in the first embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a 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, 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A power supply energy storage joint planning method considering expected power shortage linearization is characterized by comprising the following steps:
acquiring investment cost data, operation cost data, standby cost data and reliability cost data of the generator set and the energy storage system;
based on the acquired related data, under the set constraint condition, with the minimum annual comprehensive cost as an optimization target, solving a power supply energy storage joint planning model, and determining a power supply energy storage joint planning scheme which simultaneously meets the economical efficiency and the reliability, so as to guide the layout and the operation scheduling control of the generator set and the energy storage system;
the power supply energy storage joint planning model is the sum of power supply energy storage investment cost, operation cost, standby cost and reliability cost; the reliability cost is the product of the unit price of lost load, the expected power shortage and the planning time; the expected power shortage is reduced to a linear representation by removing a 0-1 variable indicating whether or not a load loss occurs from an absolute value and partially linearizing the absolute value based on a power outage capacity probability table and an upper bound conversion.
2. The method according to claim 1, wherein the set constraints include a power balance constraint, a unit output constraint, an energy storage system charge/discharge constraint, a typical daily unit combination constraint, a rotation standby constraint, and an adjustable load constraint.
3. The power supply energy storage joint planning method taking into account the linearization of the expected starved power amount according to claim 1, wherein the linearity of the expected starved power amount is expressed as:
wherein, EENStFor a desired amount of power shortage in a time period t, StSet of all fault scenarios within time period t, ps,tIs the probability of occurrence of a fault scenario s over a time period t, Δ Cs,tFor the power failure capacity generated after the fault scene s occurs within the time period t, SSRtIs the total rotation spare quantity of the system.
4. The linear power supply energy storage joint planning method considering the expected power shortage amount according to claim 1, wherein the outage capacity probability table is established by the result of the start-stop state and the output magnitude of the generator set and the energy storage system in a typical day optimized by the power supply energy storage joint planning model without considering the reliability cost, the outage capacity is divided into preset sections, and the probability of the outage in each section is overlapped to approximately represent the probability of the outage in the section.
5. The method of claim 1, wherein the investment cost data comprises investment costs for a single generator set and a single energy storage system.
6. The power supply energy storage joint planning method considering expected starved power linearization of claim 1, wherein the operation cost data comprises an active output of the generator set and the energy storage system in a set period of time, a time-of-use price per unit price and an operation cost coefficient of the generator set.
7. The power supply energy storage joint planning method considering expected shortage of power supply linearization as claimed in claim 1, wherein the backup cost data comprises the backup quantity provided by the generator set and the energy storage system in a set period and the load quantity of the adjustable load in the set period.
8. A power supply energy storage joint planning system linearized in view of a desired amount of starved power, comprising:
the data acquisition module is used for acquiring investment cost data, operation cost data, standby cost data and reliability cost data of the generator set and the energy storage system;
the planning scheme determining module is used for solving a power supply energy storage combined planning model based on the acquired related data and taking the minimum annual comprehensive cost as an optimization target under the set constraint condition, and determining a power supply energy storage combined planning scheme which simultaneously meets the economical efficiency and the reliability so as to guide the arrangement and the operation scheduling control of the generator set and the energy storage system;
the power supply energy storage joint planning model is the sum of power supply energy storage investment cost, operation cost, standby cost and reliability cost; the reliability cost is the product of the unit price of lost load, the expected power shortage and the planning time; the expected power shortage is reduced to a linear representation by removing a 0-1 variable indicating whether or not a load loss occurs from an absolute value and partially linearizing the absolute value based on a power outage capacity probability table and an upper bound conversion.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for joint planning of energy storage for power supplies taking into account the linearization of the expected amount of starved power supply as claimed in any one of claims 1 to 7.
10. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor when executing the program implements the steps in the method for joint planning of energy storage for a power supply taking into account linearization of expected starved power supply according to any of claims 1-7.
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