CN116742664A - Short-term battery energy storage and seasonal hydrogen storage collaborative planning method and system - Google Patents

Abstract

The invention discloses a short-term battery energy storage and seasonal hydrogen storage collaborative planning method and system, and belongs to the technical field of power system planning. Firstly, in order to achieve seasonal matching of renewable energy and load demands, seasonal electric power and electric quantity balance indexes are constructed, secondly, fluctuation and uncertainty of short-time scale of renewable energy output are considered, an uncertain set of renewable energy output is constructed, a configuration decision process model of various power generation equipment including battery energy storage equipment and seasonal hydrogen storage devices is built, a typical daily operation model is built based on generalized adequacy assessment indexes, a short-term battery energy storage and seasonal hydrogen storage joint planning model considering generalized adequacy demands is determined, the model is solved by adopting an improved column constraint generation algorithm, and configuration capacity of various power generation equipment is planned. The invention performs planning of multiple types of resources in terms of season and daily regulation requirements, and can ensure reliable operation of a high-proportion new energy power system.

Description

Short-term battery energy storage and seasonal hydrogen storage collaborative planning method and system

Technical Field

The invention relates to the technical field of power system planning, in particular to a short-term battery energy storage and seasonal hydrogen storage collaborative planning method and system.

Background

Efficient use of renewable energy sources represented by wind energy and solar energy is the mainstream of future energy development. Because of the obvious seasonal difference between the output of wind power generation and photovoltaic power generation, seasonal mismatch between renewable energy supply and load demand is increasingly prominent, resulting in reduction of renewable energy output and electric quantity shortage phenomena of the system. The renewable energy curtailment in 2021 china was statistically over 27TWh, including 20.61TWh wind power generation and 6.78TWh photovoltaic power generation. From the analysis data, approximately 70% of renewable energy source reduction occurs in spring and autumn, because the load requirements in spring and autumn are relatively low, and higher requirements are set for the electric quantity adequacy of the system.

In addition, the fluctuations and uncertainties of renewable energy output also present challenges to the real-time power balance of the system. Conventional adequacy indicators are difficult to adapt to the requirements of a novel power system planning, and the system planning is urgent to pay attention to adequacy and flexibility requirements under different time scales, namely generalized adequacy. As the installed share of conventional units is continuously replaced by wind power and photovoltaic power generation, the proportion of traditional flexible resources (such as thermal power units and hydroelectric power generation) is gradually reduced. The energy storage is used as a rapid energy balance technology, so that new energy output can be effectively smoothed when the system fluctuates, and the short-term flexibility requirement of the system is met. Seasonal energy storage may balance the long-term power fluctuations of the system and manage the mismatch of seasonal supplies and demands of the system, thereby providing monthly/quarterly flexibility to the system. Therefore, coordinated planning of short-term and seasonal energy storage is of great importance for stable operation of the power system. However, the adequacy index in the existing planning method is insufficient to characterize adequacy and flexibility requirements of the novel power system; in addition, the ability and mechanism by which different energy storage resources provide adequacy is a challenge for short-term and seasonal energy storage system planning.

Disclosure of Invention

The invention aims to provide a short-term battery energy storage and seasonal hydrogen storage collaborative planning method and a short-term battery energy storage and seasonal hydrogen storage collaborative planning system, which are used for planning multiple types of resources in terms of adjustment requirements in seasons and days and can ensure the reliable operation of a high-proportion new energy power system.

In order to achieve the above object, the present invention provides the following solutions:

a short-term battery energy storage and seasonal hydrogen storage collaborative planning method, comprising:

constructing seasonal electric power and electric quantity balance indexes to achieve seasonal matching of renewable energy and load demands;

taking the fluctuation and uncertainty of the short-time scale of renewable energy output into consideration, and constructing an uncertain set of renewable energy output;

the method comprises the steps of considering electric energy adequacy, power adequacy and flexibility, and constructing a generalized adequacy assessment index;

establishing a configuration decision process model of various power generation equipment including battery energy storage equipment and seasonal hydrogen storage devices by combining seasonal electric power and electric quantity balance indexes;

establishing a typical daily operation model based on an uncertain set of battery energy storage operation constraints, seasonal hydrogen storage operation constraints and renewable energy output and generalized adequacy assessment indexes;

determining a short-term battery energy storage and seasonal hydrogen storage joint planning model considering generalized adequacy requirements according to the configuration decision process model and the typical daily operation model;

And solving a short-term battery energy storage and seasonal hydrogen storage combined planning model which takes into account generalized adequacy requirements by adopting a column constraint generation algorithm, and introducing a 0-1 type auxiliary variable vector into a sub-problem of the column constraint generation algorithm to perform dual conversion so as to obtain the planning configuration capacity of various power generation equipment.

Optionally, the seasonal power balance indicator includes a power balance indicator and a seasonal power demand indicator;

the expression of the power balance index is as follows:wherein lambda is ^G 、λ ^K 、λ ^N The available supply coefficients of the thermal power unit, the hydroelectric generating set and the new energy generating set are respectively; />The installed capacities of the g thermal power generating unit, the k hydroelectric generating unit and the n new energy generating unit are respectively; l (L) ^peak Is the maximum load demand; ρ is the load reserve capacity coefficient of the system;g ', K ' and N ' are the total number of the thermal power unit, the hydroelectric unit and the new energy unit respectively;

the seasonal electric quantity demand index comprises seasonal electric quantity abundance constraint considering hydrogen energy transfer, annual utilization hour constraint of a thermal power generating unit, hydrogen storage and release amount constraint of each period, hydrogen storage climbing constraint and underground hydrogen storage capacity constraint;

the seasonal electricity abundance constraint considering the hydrogen energy transfer is expressed as follows:

In (1) the->The annual utilization hours of the g thermal power generating unit, the k hydroelectric generating unit and the n new energy generating unit in the season s are respectively +.>Seasonal energy storage electric quantity, generating capacity and predicted electric quantity requirements;

the annual utilization hour constraint expression of the thermal power generating unit is as follows:in the formula, h ^max The maximum annual utilization time of the thermal power generating unit is used; omega _s For the proportion of each ordered cluster period during season s;

the expression of the constraint of the hydrogen storage and the hydrogen release amount in each period is as follows:wherein eta is ^ED Is the electro-hydrogen conversion coefficient; epsilon is the mass of hydrogen per cubic meter; />Configuration capacity for seasonal hydrogen storage; η (eta) ^GT The power generation efficiency of the hydrogen gas turbine;

the expression of the hydrogen storage amount climbing constraint is as follows:in (1) the->And->Hydrogen storage capacity for seasons s and s-1; gamma ray _loss Self-release coefficient for seasonal hydrogen storage; omega _s-1 The proportion of each ordered cluster period during season s-1; zeta type ^ch 、ζ ^re The hydrogen storage and release efficiency of the seasonal hydrogen storage device are respectively;

the expression of the underground hydrogen storage capacity constraint is as follows:in xi ^SHS Is the minimum hydrogen storage rate of seasonal hydrogen storage.

Optionally, the uncertain set of renewable energy output is:

wherein U is an uncertain set of renewable energy source output, p _n,t,r For the actual output of the renewable energy source,auxiliary variables of type 0-1, respectively, describing whether the renewable energy source output reaches an uncertainty set boundary,/v>The positive error and the negative error of the output prediction of the renewable energy source are respectively, w is the type of the renewable energy source, t is the time, and the subscript r represents the r-th typical day.

Optionally, the generalized adequacy assessment indicator includes: seasonal electric energy loss rate evaluation index, load loss rate evaluation index and renewable energy reduction rate evaluation index;

the seasonal electric energy loss rate evaluation index has the expression:in delta _E For seasonal electric energy loss rate, < >>The power loss for season s;

the expression of the load loss rate evaluation index is as follows:in delta _load For load loss rate, ΔP _d,t,r To lose load power, P _d,t,r D represents the d-th load;

the expression of the renewable energy reduction rate evaluation index is as follows:in delta _cur Is a renewable energy source reduction rate.

Optionally, the configuration decision process model is:

wherein C is ^Inv For annual decision coefficient, c ^N 、c ^B 、c ^E 、c ^ED 、c ^GT 、c ^SHS The configuration coefficients of the renewable energy source, the energy and power stored by the battery, the electrolytic tank, the hydrogen gas turbine and the seasonal hydrogen storage device are respectively; The configuration capacities of the nth renewable energy source, the energy and power stored by the b battery, the ith electrolytic cell, the jth hydrogen gas turbine and the h seasonal hydrogen storage device are respectively set; alpha is the discount rate; t (T) _life For life of useA mission;

constraints include system decision coefficient expected constraints and maximum installable capacity constraints for each device; each device comprises: the system comprises an electrolytic tank, a hydrogen gas turbine, a seasonal hydrogen storage device, battery energy storage equipment and a new energy unit;

the system decision coefficient expectation constraint is: c (C) ^Inv ≤Π ^max The method comprises the steps of carrying out a first treatment on the surface of the In II ^max The upper limit of the programming coefficient is set;

the maximum installable capacity constraint of the electrolyzer is:in (1) the->The maximum configuration capacity of the electrolytic cell;

the maximum installable capacity constraint of a hydrogen gas turbine is:in (1) the->Maximum capacity for a hydrogen turbine;

the maximum installable capacity constraint of the seasonal hydrogen storage device is:in (1) the->Maximum deployment capacity for seasonal hydrogen storage devices;

the maximum mountable capacity constraint of the battery energy storage device is:in (1) the->The maximum configuration capacity of the battery energy storage energy;

the maximum installable capacity constraint of the new energy unit is as follows:in (1) the->Planning capacity for candidates of new energy units, +.>The capacity is configured for the maximum of the new energy unit.

Optionally, the typical day operation model is:

C ^Ope ＝C _g +C _n +C _Δload ；

wherein C is ^Ope As typical day operation coefficient, C _g For generating coefficient, C of thermal power generating unit _n Generating coefficient and C for new energy unit _Δload Is a load loss coefficient; c ^g 、c ^e 、c ^cur 、c ^load Respectively the power generation coefficient of the thermal power generating unit, the carbon emission coefficient of the thermal power generating unit, the renewable resource reduction and the load loss penalty coefficient; p (P) _i,t,r 、E _i,t,t 、ΔP _d,t,r Respectively outputting the thermal power unit, carbon emission and load loss power of the thermal power unit; pi _r Is a typical daily confidence;P _n,t,r the uncertainty output and the actual output of renewable energy sources are respectively, v is a 0-1 type binary variable representing start-stop state in the operation phase, and the xi is related to the variables v and p ^u Is a linear function of p ^u To represent a continuous variable of uncertainty output in the run phase, ψ is the set of all kinds of p.

Optionally, the system operation constraint condition of the typical day operation model in the typical day scene includes: basic constraint, uncertain set of renewable energy output, seasonal electric energy loss rate evaluation index, load loss rate evaluation index and renewable energy reduction rate evaluation index;

the basic constraints include: node power balance constraint, line power constraint, thermal power unit operation output upper and lower limit constraint, start-stop time constraint, unit climbing constraint, annual start-up hour constraint, hydro-generator unit output constraint, battery energy storage operation charge-discharge constraint, same-period charge-discharge state constraint, battery charge state constraint, last-period energy storage power threshold constraint, seasonal hydrogen storage operation electrolyzer operation constraint, hydrogen turbine operation constraint, storage and release amount upper limit constraint, same-period storage and release state constraint, typical day storage and release state single constraint, hydrogen storage amount climbing constraint, equipment hydrogen storage capacity constraint, first typical day initial hydrogen storage capacity constraint beyond the first typical day, last-period hydrogen storage amount threshold constraint, and renewable energy output constraint under uncertain conditions.

Optionally, the short-term battery energy storage and seasonal hydrogen storage joint planning model taking into account the generalized adequacy demand is:

minC ^total ＝C ^Inv +C ^Ope ；

wherein C is ^total Is the overall planning factor.

Optionally, a column constraint generation algorithm is adopted to solve a short-term battery energy storage and seasonal hydrogen storage combined planning model which accounts for generalized adequacy requirements, and a 0-1 type auxiliary variable vector is introduced into a sub-problem of the column constraint generation algorithm to perform dual transformation, so that planning configuration capacity of various power generation equipment is obtained, and the method specifically comprises the following steps:

modeling a short-term battery energy storage and seasonal hydrogen storage joint planning model as a two-stage robust planning model: the first stage considers configuration decisions of the system; the second stage considers the operation problem;

the goal of setting a two-stage robust planning model master problem is to minimize the annual decision coefficients and determine the master problem and constraints as:wherein x is a first stage 0-1 type planning decision variable, c is a continuous variable vector of first stage planning capacity, v is a second stage 0-1 type state decision variable, p _z Continuous variable vector, p, of actual output of renewable energy source for second stage planning _z ^worst For the worst scene output in the z-th iteration, κ is the maximum iteration number, η is the sub-problem- >Ξ(c,p ^u ) As to the continuous variable p ^u Is a linear function of (2): xi (c, p) ^u )＝{p ^u :M ^T p ^u ≥d-Dc-Fp ^u -a }; A. b, D, F, M, Q is a first, second, third, fourth, fifth and sixth constant coefficient parameter matrix corresponding to the variable contained in each constraint in the model, a, b, d, f, g, delta and lambda are first, second, third, fourth, fifth, sixth and seventh constant coefficient parameter vectors corresponding to the variable contained in each constraint in the model.

The determination of the sub-problems and constraints is:wherein p is ^u Uncertainty output for renewable energy, c ^* An optimal solution for the continuous variable c in the main problem;

introducing a 0-1 type auxiliary variable vector into the sub-problemThe original 0-1 type variable vector v is defined as a continuous variable such that v equals +.>Thereby rearranging the sub-problems and constraints into an optimization model:

performing dual transformation on the optimization model to obtain

Where μ is the dual variable vector of the second stage linear programming. Determining the minimum and maximum inequality

In (1) the->

Introducing the minimum and maximum inequality into an optimized model after dual transformation to obtain an equivalent objective function as

Linearizing an equivalent objective function by adopting a large M method to obtain a mixed integer linear programming model;

and solving the mixed integer linear programming model to obtain the programming configuration capacity of various power generation equipment.

A short-term battery energy storage and seasonal hydrogen storage collaborative planning system comprising:

the balance index construction module is used for constructing seasonal electric power and electric quantity balance indexes so as to achieve seasonal matching of renewable energy and load demands;

the uncertainty set construction module is used for constructing an uncertainty set of the renewable energy output by taking the fluctuation and uncertainty of the short-time scale of the renewable energy output into consideration;

the evaluation index construction module is used for considering the electric energy adequacy, the power adequacy and the flexibility to construct a generalized adequacy evaluation index;

the configuration decision process model building module is used for building configuration decision process models of various power generation equipment including battery energy storage equipment and seasonal hydrogen storage devices by combining seasonal electric power and electric quantity balance indexes;

the system comprises a typical day operation model building module, a power supply module and a power supply module, wherein the typical day operation model building module is used for building a typical day operation model based on an uncertain set of battery energy storage operation constraint, seasonal hydrogen storage operation constraint and renewable energy output and generalized adequacy assessment indexes;

the joint planning model determining module is used for determining a short-term battery energy storage and seasonal hydrogen storage joint planning model considering generalized adequacy requirements according to the configuration decision process model and the typical day operation model;

And the solving module is used for solving a short-term battery energy storage and seasonal hydrogen storage combined planning model which accounts for generalized adequacy demands by adopting a column constraint generating algorithm, introducing a 0-1 type auxiliary variable vector into a sub-problem of the column constraint generating algorithm to perform dual conversion, and obtaining the planning configuration capacity of various power generation equipment.

According to the specific embodiment provided by the invention, the invention discloses the following technical effects:

the invention discloses a short-term battery energy storage and seasonal hydrogen storage collaborative planning method and system, which are characterized in that firstly, seasonal electric power and electric quantity balance indexes are constructed for achieving seasonal matching of renewable energy sources and load demands, secondly, uncertainty collection of renewable energy source output is constructed by considering fluctuation and uncertainty of short-time scale of renewable energy source output, configuration decision process models of various power generation equipment including battery energy storage equipment and seasonal hydrogen storage devices are built, a typical daily operation model is built based on generalized adequacy assessment indexes, a short-term battery energy storage and seasonal hydrogen storage collaborative planning model which accounts for generalized adequacy demands is determined, and finally, an improved column constraint generation algorithm is adopted to solve the model, so that configuration capacity of various power generation equipment is planned. The invention performs planning of multiple types of resources in terms of season and daily regulation requirements, and can ensure reliable operation of a high-proportion new energy power system.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flowchart of a short-term battery energy storage and seasonal hydrogen storage collaborative planning method according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of an ordered clustering result of a system net energy curve according to an embodiment of the present invention;

FIG. 3 is a topology of an improved Garver-6 node system provided by an embodiment of the present invention;

fig. 4 is a comparison chart of planning coefficients of each case according to an embodiment of the present invention;

fig. 5 is a generalized adequacy evaluation index comparison chart of each case provided in the embodiment of the present invention;

fig. 6 is a graph of a result of a planning factor for each iteration in a solution algorithm according to an embodiment of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.

As shown in fig. 1, an embodiment of the present invention provides a short-term battery energy storage and seasonal hydrogen storage collaborative planning method, including:

step 1: and constructing seasonal electric power and electric quantity balance indexes to achieve seasonal matching of renewable energy and load demands.

The adequacy of an electrical power system refers to the ability of the system to meet the consumer's demands for electricity and energy under static conditions. Because the conventional power supply has good controllability and adjustability, the adequacy of the traditional power system generally adopts deterministic indexes such as generating capacity adequacy, power adequacy and the like, and the deterministic indexes are used for guiding long-term power supply planning; in addition, the traditional power balance problem is mainly caused by load prediction fluctuation with high accuracy and strong regularity.

The capacity reliability of renewable energy sources is very different compared to conventional power sources, while their output is very affected by the seasonality of natural resources. With the continuous improvement of the permeability of renewable energy sources, the power balance problem needs to additionally consider the time change characteristics of renewable energy source output and load in a short time so as to characterize the short-term uncertainty of the renewable energy sources and ensure the real-time power balance of a power system.

The broad adequacy proposed by the present invention refers to: the adequacy of the total capacity of various types of resources with respect to load demand is of concern in new power systems, while ensuring that various types of resources have sufficient capacity to cope with the net load fluctuations of the system on different time scales from hour to season.

Firstly, the seasonal mismatch between renewable energy sources and loads is solved, so that seasonal electric power and electric quantity adequacy is met. The seasonal power and power balance index mainly comprises a power balance index and a seasonal power demand index. The expression of the power balance index is:

wherein lambda is ^G 、λ ^K 、λ ^N The available supply coefficients of the thermal power unit, the hydroelectric generating set and the new energy generating set are respectively;the installed capacities of the g thermal power generating unit, the k hydroelectric generating unit and the n new energy generating unit are respectively; ρ is the load reserve capacity coefficient of the system; l (L) ^peak Is the maximum load demand; g ', K ' and N ' are respectively a thermal power generating unit, a hydroelectric generating unit and a new energy machineTotal number of groups.

The renewable energy output and the load demand have obvious seasonal differences, and the invention adopts an ordered clustering method to divide the net electric quantity curve. The ordered clustering method selects specific points in time sequence samples, divides the samples into 8760 time periods, and the classification basis is the least square sum of sample deviations after classification. As can be seen from fig. 2, 8760 divided curve clustering results reflect the surplus and shortage of power in each period of the system. The seasonal electric quantity demand index established by the invention comprises a seasonal electric quantity abundance constraint (2) considering hydrogen energy transfer, a annual utilization hour constraint (3) of a thermal power generating unit, a hydrogen storage and release amount constraint (4) of each period, a hydrogen storage amount climbing constraint (5) and an underground hydrogen storage capacity constraint (6), and specifically comprises the following steps:

In the method, in the process of the invention,the annual utilization hours of the thermal power generating unit, the hydroelectric generating unit and the new energy generating unit in the season s are respectively, h ^max The maximum annual utilization time of the thermal power generating unit is used; />Seasonal energy storage electric quantity, generating capacity and predicted electric quantity requirements; />Configuration capacity for seasonal hydrogen storage; omega _s A ratio for each ordered cluster period; epsilon is the mass of hydrogen per cubic meter and takes a value of 0.089kg/Nm ³ ；η ^GT The unit of the power generation efficiency is MWh/Nm of the hydrogen gas turbine ³ ；η ^ED Is the electro-hydrogen conversion coefficient, in Nm ³ /MWh；γ _loss Is the self-release coefficient of seasonal hydrogen storage, zeta ^SHS Minimum hydrogen storage rate for seasonal hydrogen storage,/-for hydrogen storage>Is hydrogen storage capacity; zeta type ^ch 、ζ ^re The hydrogen storage and release efficiencies of the seasonal hydrogen storage device are respectively.

Step 2: and (3) taking the fluctuation and uncertainty of the short-time scale of the renewable energy output into consideration, and constructing an uncertainty set of the renewable energy output.

The fluctuation and uncertainty of the short-time scale of the renewable energy source output are considered, and the flexibility requirement caused by unbalanced supply and demand in the day is solved. For uncertainty in the short term of renewable energy output, the invention adopts a robust optimization method to describe variables in a collective form. The uncertain set of renewable energy output is:

in the method, in the process of the invention, Uncertain output for new energy; />Beta is the new energy output prediction error; gamma-shaped article _T Is the uncertainty of timeBudget(s)>Auxiliary variables of type 0-1, respectively, describing whether the renewable energy source output reaches an uncertainty set boundary,/v>The positive error and the negative error of the output prediction of the renewable energy source are respectively, w is the type of the renewable energy source, t is the time, and the subscript r represents the r-th typical day.

Step 3: the generalized adequacy assessment index is constructed in consideration of electric energy adequacy, power adequacy and flexibility.

Indexes for evaluating the planning result are provided in terms of electric energy adequacy, power adequacy, flexibility and the like.

The electric energy adequacy is represented by a seasonal electric energy loss rate evaluation index. Seasonal power loss rates are used to evaluate power loss due to mismatch between renewable energy and system load supply and demand in each season of the year. The seasonal power loss rate throughout the year is expressed as:

in the method, in the process of the invention,for season s, loss of electric energy, < >>The predicted power demand for season s.

The power adequacy is represented by a load loss rate evaluation index. When a power system is disturbed, the power system is often out of load due to insufficient regulation capacity or unbalanced power of the system. The load loss rate constituting the generalized adequacy index is used for evaluating the load loss rate of the system caused by the insufficient power generation capacity or regulation capacity of the power system in a typical day, and the expression is as follows:

Wherein DeltaP _d,t,r To lose load power, P _d,t,r The power required for the load.

Among them, the typical day in the power system, also called the typical load calculation day. The determination of a typical day is based on the fact that the power production in winter and summer is divided into thermal power and hydroelectric power, and the power data related to each node of the whole system from a user gateway is collected once or several times per hour in the month and the month of the two seasons for 24 hours.

Flexibility is reflected by the renewable energy reduction rate evaluation index. The renewable energy output has randomness and uncertainty, and the power system needs to have good peak regulation capability to improve the capacity of the renewable energy. Renewable energy source reduction rate reflects system flexibility by evaluating the wind and light rejection rate caused by insufficient flexibility adjustment capability in a typical day of an electric power system, and the expression is as follows:

in the method, in the process of the invention,P _n,t,r the uncertainty output and the actual output of the renewable energy are respectively; />Capacity is configured for renewable energy sources.

Step 4: and (3) establishing a configuration decision process model of various power generation equipment including battery energy storage equipment and seasonal hydrogen storage devices by combining seasonal electric power and electric quantity balance indexes.

In the process of jointly planning various power generation equipment, annual decision coefficients of the power generation equipment are mainly considered, wherein the annual decision coefficients comprise a renewable energy unit, battery energy storage equipment, an electrolytic tank, a hydrogen gas turbine and seasonal hydrogen storage. The annual decision coefficient is expressed as:

Wherein, c ^N 、c ^B 、c ^E 、c ^ED 、c ^GT 、c ^SHS The configuration coefficients of the renewable energy source, the energy and power stored by the battery, the electrolytic tank, the hydrogen gas turbine and the seasonal hydrogen storage device are respectively;the configuration capacities of renewable energy sources, energy and power stored by batteries, an electrolytic tank, a hydrogen gas turbine and a seasonal hydrogen storage device are respectively; alpha is the discount rate; t (T) _life Is the service life.

Constraints of the planning process include system decision coefficient expectation constraints (11), maximum installable capacity constraints (12) - (17) for each device.

C ^Inv ≤Π ^max (12)

In the method, in the process of the invention,the capacity is respectively configured for planning of an electrolytic tank, a hydrogen turbine, seasonal hydrogen storage, battery energy storage equipment and a new energy unit; pi (II) ^max Is the upper limit of the programming coefficient.

Meanwhile, the seasonal electric power and electric quantity balance index proposed in the step A is required to be considered when various power generation equipment is planned, and mainly comprises an electric power balance index and a seasonal electric quantity demand index.

Step 5: and establishing a typical daily operation model based on the battery energy storage operation constraint, the seasonal hydrogen storage operation constraint, the uncertain set of renewable energy output and the generalized adequacy assessment index.

Typical day operating coefficient C ^Ope Comprising the following steps: generating coefficient C of thermal power generating unit _g Generating coefficient C of new energy unit _n Load loss factor C _Δload As shown in formulas (18) - (19).

C ^Ope ＝C _g +C _n +C _Δload (18)

Wherein C is ^Ope As typical day operation coefficient, C _g For generating coefficient, C of thermal power generating unit _n Generating coefficient and C for new energy unit _Δload Is a load loss coefficient; c ^g 、c ^e 、c ^cur 、c ^load Respectively the power generation coefficient of the thermal power generating unit, the carbon emission coefficient of the thermal power generating unit, the renewable resource reduction and the load loss penalty coefficient; p (P) _i,t,r 、E _i,t,t 、ΔP _d,t,r Respectively outputting the thermal power unit, carbon emission and load loss power of the thermal power unit; pi _r Is a typical daily confidence;P _n,t,r uncertainty output and actual output of renewable energy respectively, v is the start and stop in the operation phaseBinary variable of 0-1 type of state, the variable v, p ^u Is a linear function of p ^u To represent a continuous variable of uncertainty output in the run phase, ψ is the set of all kinds of p.

System operation constraint conditions under typical day scene

For a power system to operate normally on a typical day, it should include the following basic constraints: node power balancing constraints (20); line power constraints (21) - (23); the upper and lower limit constraint (24) of the output of the running of the thermal power generating unit, the start-up time constraint (25) - (26), the climbing constraint (27) of the unit and the annual start-up hour constraint (28); a hydroelectric generating set discharge force constraint (29); charging and discharging constraints (30) - (31) of battery energy storage operation, simultaneous period charging and discharging state constraint (32), battery charging state constraints (33) - (34) and last period energy storage electric quantity threshold constraint (35); cell operating constraints (36) - (37) for seasonal hydrogen storage operation, hydrogen turbine operating constraints (38) - (39), upper storage and release amount constraints (40) - (41), simultaneous storage and release state constraints (42), typical daily storage and release state single constraints (43), hydrogen storage amount climbing constraints (44) and equipment hydrogen storage capacity constraints (45); to simulate seasonal energy transfer, constraint (46) specifies a first typical day hydrogen storage capacity that is half of rated capacity, constraint (47) specifies initial hydrogen storage capacity for other typical days, constraint (48) is an end-period hydrogen storage capacity threshold constraint; the constraint (49) is an uncertainty-based output constraint of the renewable energy source.

/>

/>

Wherein P is _g,t,r 、P _k,t,r 、P _n,t,r 、P _l,t,r 、P _d,t,r 、ΔP _d,t,r The power supply unit is respectively a thermal power unit output, a hydroelectric unit output, a new energy unit output, an electrolysis bath input power, a hydrogen turbine output, a battery energy storage charging/discharging power, a line tide power, a load power and a load losing power; θ _m,t.r 、θ _o,t,r The voltage phase angles of the line start node and the line end node are respectively; x is x _l Reactance is the transmission line; u (u) _g,t,r 、/>Binary decision variables of a thermal power unit, an electrolytic tank, hydrogen storage and release states and hydrogen storage and release states respectively; zeta type toy ^G 、ξ ^K 、ξ ^B 、ξ ^ED 、ξ ^SHS The lowest load rates of the thermal power generating unit, the hydroelectric generating unit, the battery energy storage, the electrolytic tank and the seasonal hydrogen storage are respectively; />Continuous start/stop time of the thermal power generating unit; t (T) _on,g 、T _off,g The minimum continuous starting/stopping time of the thermal power generating unit; />The climbing rate is adjusted up/down for the thermal power generating unit; h is a ^max The annual maximum utilization time of the thermal power generating unit; />The unit output power of the hydroelectric generating set is; />A charge and discharge state binary variable for storing energy for the battery; v (v) ^SHS Power-to-capacity ratio for seasonal hydrogen energy plants; e, e _b,t,r A state of charge variable for storing energy for the battery; />η ^ED 、η ^GT Energy conversion efficiency, hydrogen energy-electric energy conversion coefficient Nm, of the energy stored by the battery respectively ³ /(MWh), hydrogen gas turbine power generation efficiency; / >Configuration capability for battery energy storage;the hydrogen output of the electrolytic tank, the hydrogen consumption of the hydrogen gas turbine and the storage and release variables of the hydrogen are respectively; />Is hydrogen storage capacity; gamma ray _loss Is the self-release rate of seasonal hydrogen storage; zeta type ^ch 、ζ ^re Hydrogen storage and release efficiency for seasonal hydrogen storage; r is the initial value of typical daily hydrogen storage amount, which is the charge/discharge power in the last season minus the self-discharge energy lossAccumulating; epsilon is the mass of hydrogen per cubic meter and takes a value of 0.089kg/Nm ³ 。

Meanwhile, the method also comprises the renewable energy output uncertainty set (7) in the step A, and seasonal electric energy loss rate index (8), load loss rate index (9) and renewable energy reduction rate index (10) for evaluating the planning result.

Step 6: and determining a short-term battery energy storage and seasonal hydrogen storage combined planning model considering generalized adequacy requirements according to the configuration decision process model and the typical daily operation model.

Based on the configuration decision process model of various power generation equipment, a short-term battery energy storage and seasonal hydrogen storage combined planning model which takes into account generalized adequacy requirements is established. The model includes an objective function and a planning constraint, wherein the objective function is a minimized annual decision coefficient and a typical daily operational coefficient:

minC ^total ＝C ^Inv +C ^Ope (50)

Planning constraint has system decision coefficient expected constraint, power balance constraint and seasonal electric quantity balance constraint; system operation constraints under typical days: node power balance constraint, line power transmission constraint, thermal power generating unit operation constraint, hydroelectric generating unit constraint, seasonal hydrogen storage operation constraint and new energy output uncertainty constraint.

Wherein C is ^total For the total planning factor C ^Inv For annual decision coefficient, C ^Ope Is a typical day operating coefficient; c ^g 、c ^e 、c ^cur 、c ^load Respectively the power generation coefficient of the thermal power generating unit, the carbon emission coefficient of the thermal power generating unit, the renewable resource reduction and the load loss penalty coefficient; p (P) _i,t,r 、E _i,t,t 、ΔP _d,t,r The output of the thermal power unit, the carbon emission of the thermal power unit, the output of renewable energy sources and the load loss power are respectively; pi _r Is a typical daily confidence.

Step 7: and solving a short-term battery energy storage and seasonal hydrogen storage combined planning model which takes into account generalized adequacy requirements by adopting a column constraint generation algorithm, and introducing a 0-1 type auxiliary variable vector into a sub-problem of the column constraint generation algorithm to perform dual conversion so as to obtain the planning configuration capacity of various power generation equipment.

The method is characterized in that the model is solved by adopting an improved C & CG method. The proposed planning model is modeled as a two-stage robust planning model: the first stage considers configuration decisions of the system; in the second stage, the operation problem is considered, and the worst influence caused by the uncertainty output of the new energy source on the system is avoided by maximizing the operation coefficient.

Step C1: solving a main problem in the two-stage robust planning model. The goal of the model major problem is to minimize the annual decision coefficients. Constraint conditions comprise planning constraint, thermal power generating unit start-stop constraint and C & CG constraint returned in the sub-problem. The main problem and its constraints can be expressed as:

wherein x is a 0-1 type planning decision variable of each power generation device in the first stage and corresponds to a thermal power unit start-stop variable unit u _g,t,r C is the first stageContinuous variable vectors of planning capacity of the electrolytic tank, the hydrogen gas turbine, seasonal hydrogen storage, battery energy storage equipment and the new energy unit; v is the second stage0-1 type state decision variable of thermal power generating unit, electrolytic tank, hydrogen storage and release state and battery energy storage charge and discharge state, p _z Corresponding P for second phase planning _g,t,r 、P _k,t,r 、P _n,t,r 、/>P _l,t,r 、P _d,t,r 、ΔP _d,t,r 、e _b,t,r 、/>Constant thermal power unit output, hydroelectric unit output, new energy unit output, electrolyzer input power, hydrogen turbine output, battery energy storage charge/discharge power, line tide power, load shedding power, battery energy storage charging state and continuous variable vector of hydrogen storage and release, p _z ^worst For the corresponding p in the z-th iteration _z The worst scene output of (a), k is the maximum iteration number, and eta is the sub-problem Total planning factor C in corresponding model ^total ，Ξ(c,p ^u ) As to the continuous variable p ^u Is a linear function of (2): xi (c, p) ^u )＝{p ^u :M ^T p ^u ≥d-Dc-Fp ^u }，p ^u Uncertainty output corresponding to each new energy source>A. B, D, F, M, Q is a first, second, third, fourth, fifth and sixth constant coefficient parameter matrix corresponding to the variable contained in each constraint in the model, a, b, d, f, g, delta and lambda are first, second, third, fourth, fifth, sixth and seventh constant coefficient parameter vectors corresponding to the variable contained in each constraint in the model.

Step C2: and solving the sub-problems in the two-stage robust planning model. C in the main question is introduced into the sub-question of the second stage. A sub-problem of the second stage is a system operation problem under a given planning decision. The sub-problem and its constraints can be expressed as:

/>

wherein p is ^u Uncertainty output for renewable energy, c ^* Is the optimal solution for the continuous variable c in the main problem.

The above model is a max-min problem, and its internal problem needs to be solved by converting the dual transform into a max problem. The inner layer of the sub-problem contains 0-1 type variable v, and binary conversion cannot be performed. Therefore, a 0-1 type auxiliary variable vector is introduced into the sub-problemThe original 0-1 type variable v is defined as a continuous variable, then the variable v is equal to +. >The sub-problem and its constraints are rearranged into the following optimization problem:

after the auxiliary variable is introduced, performing dual transformation on the model to obtain:

from the minimum maximum inequality, it is possible to obtain:

in objective functionBy listing +.>Possible combinations of 0 and 1 of (2) to determine:

wherein, omega= { delta is less than or equal to lambda, delta is less than or equal to 0}. By introducing (55) into (54), (54) the objective function can be equivalently:

wherein x is a first stage 0-1 type planning decision variable, c is a continuous variable vector of first stage planning capacity, v is a second stage 0-1 type state decision variable, p _z Continuous variable vector, p, of actual output of renewable energy source for second stage planning _z ^worst The worst scene output in the z-th iteration is z is iteration algebra, k is iteration times, and eta is a sub-problemXi (c, p) is a linear function with respect to uncertainty p: xi (c, p) = { p: M ^T P.gtoreq.d. -Dc-Fp }, U being the uncertainty set (7) mentioned in step A. A. B, D, F, M, Q is a determined constant coefficient matrix, a, b, d, f, g, δ, λ is a determined vector.

Step C3: bilinear terms in the final objective function of the linearization sub-problem. The final objective function (57) of the sub-problem contains a binary variable p ^u And continuous variable F, f ^T The bilinear terms formed by multiplication are linearized by a large M method.

Defining a new real variable X, which is defined as a binary variable p ^u And the value of the product of the continuous variable R. The intermediate quantity M is defined as very large, and the value of M is 100000. Adding a constraint to X:

then all X satisfying the above constraint are binary variables p ^u And (3) the product of the continuous variable R and the product of the continuous variable R is used for completing linearization.

Step C4: based on the steps, the proposed short-term battery energy storage and seasonal hydrogen storage combined planning model considering the generalized adequacy demand is converted into a mixed integer linear planning model, and a commercial solver such as Gurobi is adopted for solving.

The solution results are the planned configuration capacities of the electrolytic tank, the hydrogen turbine, the seasonal hydrogen storage, the battery energy storage equipment and the new energy unit, and the corresponding letters are respectively

The joint planning method meeting the generalized adequacy requirement, which is presented below, is applied to the improved Garver-6 node system to prove the effectiveness of the method of the invention.

In the improved Garver-6 node system, 3 thermal power units, 1 hydroelectric unit with 200MW, 1 300MW wind power station and 250MW photovoltaic power station, 3 load nodes and 2 outgoing transmission channels are shared; the peak load of the system at the planned level year is 700MW. The topology and candidate planning resources of the improved Garver-6 node system are shown in fig. 3.

The reference decision coefficients of various power supplies, electrolytic tanks and energy storage devices in 2030 year are shown in table 1 in combination with the range of coefficient change, the planned capacity of the battery energy storage device needs to ensure continuous discharge for 4 hours, and the relevant parameters of the operation of each device are shown in table 2. Renewable energy and load curves of 12 typical days of four seasons in a horizontal year are obtained through a clustering method, and the uncertainty coefficient of the new energy is set to be 0.2. To verify the advantages of the proposed method, the present invention sets 5 cases shown in table 3, which consider different factors, for comparative analysis. In case 1, only solar energy and wind energy are planned to meet the load demand of the system; in case 2, adding battery energy storage to the system plan; in case 3, seasonal hydrogen storage is added to the system plan; in case 4 and case 5, the battery storage and seasonal hydrogen storage are co-planned. To verify the importance of the proposed seasonal power balance constraint, cases 4 and 5 were performed without and with seasonal power capacity adequacy constraints, respectively. The above embodiment is implemented on MATLAB 2020b using Gurobi-10.0.0.

Table 1 parameters of various candidate devices

Table 2 parameters related to plant operation

TABLE 3 different factors considered for each case

Table 4 optimal planning results for each case

FIG. 4 shows the annual total planning factor for all cases, from which it can be seen that the total factor for case 1 is 2.59X10 ⁸ Dollars. The annual total coefficient of cases 2-5 was reduced by 19.38%, 33.36%, 40.66% and 37.19%, respectively, compared to case 1, indicating that the configuration of the battery energy storage device and seasonal hydrogen storage may improve the economics of the system to some extent. It can also be seen that cases 4 and 5 are more competitive in reducing the overall coefficient.

In the overall planning factor structure. Annual operating coefficients account for a significant proportion of which case 1 is the lowest and case 5 is the highest. The annual operation coefficient comprises four parts of a power generation coefficient, a carbon emission coefficient, renewable energy source reduction and load loss penalty coefficient. Because the installed capacity proportion of the thermal power generating unit is limited, the annual energy production and carbon emission coefficients of the thermal power generating unit are very close in all cases. Compared with cases 1 and 2, the renewable energy source reduction and the load loss penalty coefficient in cases 3 to 5 are obviously reduced, and it can be seen that seasonal hydrogen storage can realize seasonal electric energy transfer, so that the renewable energy source reduction and the load loss rate in the system are reduced. From the results, the common planning of the battery energy storage and the seasonal hydrogen storage can reduce the total planning coefficient of the power system, improve the running economic benefit of the power system and facilitate the development of the high-new-energy permeability power system.

Table 4 lists all the detailed planning results for the above 5 cases. Since the renewable energy sources have different output in typical days in different seasons, the installed capacities of wind power and photovoltaic power generation are the largest in case 1, but a large amount of renewable energy sources are also seen; in case 2, the battery energy storage can effectively balance the output of renewable energy sources in one day, and the configuration of the battery energy storage further reduces the configuration capacity of the renewable energy sources; the seasonal hydrogen storage is put into use in case 3, and the planning capacity of wind power and photovoltaic is reduced compared with that of the case, so that the seasonal hydrogen storage can effectively realize the cross-seasonal complementation of hydrogen energy and effectively promote the consumption of renewable energy sources in an electric power system. Case 5 further considers the combined effects of battery energy storage and seasonal hydrogen storage compared to case 3, and it can be seen from the planning results that the short-term battery energy storage and seasonal hydrogen storage operate in a complementary rather than competing manner, and can address the power imbalance problem of the power system at different time scales.

Fig. 5 shows generalized adequacy assessment index results in different cases. In case 1, annual power abundance is used to guide the planning of renewable energy sources. It can be seen that, due to the seasonal characteristic of the renewable energy output, the electric power loss occurs in case 1, so that the renewable energy reduction rate and the load loss rate are the highest; in case 2, the configuration of the battery energy storage effectively increases the rate of consumption of renewable energy sources, but seasonal power imbalances cause higher power losses and load loss rates; in case 4, the coordinated planning of battery energy storage and seasonal hydrogen storage reduces the load loss rate and the reduction rate of renewable energy sources, but also has a certain seasonal electric energy loss phenomenon. The seasonal power abundance is considered in the planning of the case 3 and the case 5, so that the situation of seasonal power imbalance is greatly improved; case 5 adopts a proper collaborative planning of battery energy storage and seasonal hydrogen storage to meet generalized adequacy index, and effectively reduces the power loss rate and renewable energy source reduction rate.

FIG. 6 illustrates an improved column constraint generation (C)&CG) algorithm solves the convergence of the min-max-min model of the binary variable. It can be seen that as the number of iterations increases, the upper and lower bounds of the target planning factor gradually converge. After 4 iterations, the running coefficient converged to 1.63×10 at a convergence gap of 0.09% ⁸ And (4) the cost is reduced. It can thus be seen that improved column constraint generation (C&CG) algorithm can effectively solve the robust planning model mentioned in the present invention.

The embodiment results show that the proposed generalized adequacy index can guide the planning of multiple types of resources in terms of season and daily regulation requirements, and can ensure the reliable operation of a high-proportion new energy power system.

In order to perform the above method to achieve the corresponding functions and technical effects, a short-term battery energy storage and seasonal hydrogen storage collaborative planning system is provided below, which includes:

the balance index construction module is used for constructing seasonal electric power and electric quantity balance indexes so as to achieve seasonal matching of renewable energy and load demands;

the uncertainty set construction module is used for constructing an uncertainty set of the renewable energy output by taking the fluctuation and uncertainty of the short-time scale of the renewable energy output into consideration;

The evaluation index construction module is used for considering the electric energy adequacy, the power adequacy and the flexibility to construct a generalized adequacy evaluation index;

the configuration decision process model building module is used for building configuration decision process models of various power generation equipment including battery energy storage equipment and seasonal hydrogen storage devices by combining seasonal electric power and electric quantity balance indexes;

the system comprises a typical day operation model building module, a power supply module and a power supply module, wherein the typical day operation model building module is used for building a typical day operation model based on an uncertain set of battery energy storage operation constraint, seasonal hydrogen storage operation constraint and renewable energy output and generalized adequacy assessment indexes;

the joint planning model determining module is used for determining a short-term battery energy storage and seasonal hydrogen storage joint planning model considering generalized adequacy requirements according to the configuration decision process model and the typical day operation model;

and the solving module is used for solving a short-term battery energy storage and seasonal hydrogen storage combined planning model which accounts for generalized adequacy demands by adopting a column constraint generating algorithm, introducing a 0-1 type auxiliary variable vector into a sub-problem of the column constraint generating algorithm to perform dual conversion, and obtaining the planning configuration capacity of various power generation equipment.

The short-term battery energy storage and seasonal hydrogen storage collaborative planning system provided by the embodiment of the invention is similar to the short-term battery energy storage and seasonal hydrogen storage collaborative planning method described in the embodiment, and therefore, the working principle and the beneficial effects are similar, and the detailed description is omitted herein, and the specific content can be seen in the description of the embodiment of the method.

In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.

The principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.

Claims (10)

1. A short-term battery energy storage and seasonal hydrogen storage collaborative planning method, comprising:

constructing seasonal electric power and electric quantity balance indexes to achieve seasonal matching of renewable energy and load demands;

taking the fluctuation and uncertainty of the short-time scale of renewable energy output into consideration, and constructing an uncertain set of renewable energy output;

the method comprises the steps of considering electric energy adequacy, power adequacy and flexibility, and constructing a generalized adequacy assessment index;

establishing a configuration decision process model of various power generation equipment including battery energy storage equipment and seasonal hydrogen storage devices by combining seasonal electric power and electric quantity balance indexes;

Establishing a typical daily operation model based on an uncertain set of battery energy storage operation constraints, seasonal hydrogen storage operation constraints and renewable energy output and generalized adequacy assessment indexes;

determining a short-term battery energy storage and seasonal hydrogen storage joint planning model considering generalized adequacy requirements according to the configuration decision process model and the typical daily operation model;

and solving a short-term battery energy storage and seasonal hydrogen storage combined planning model which takes into account generalized adequacy requirements by adopting a column constraint generation algorithm, and introducing a 0-1 type auxiliary variable vector into a sub-problem of the column constraint generation algorithm to perform dual conversion so as to obtain the planning configuration capacity of various power generation equipment.

2. The short term battery energy storage and seasonal hydrogen storage co-planning method of claim 1, wherein the seasonal power balance indicator comprises a power balance indicator and a seasonal power demand indicator;

the expression of the power balance index is as follows:wherein lambda is ^G 、λ ^K 、λ ^N The available supply coefficients of the thermal power unit, the hydroelectric generating set and the new energy generating set are respectively; />The installed capacities of the g thermal power generating unit, the k hydroelectric generating unit and the n new energy generating unit are respectively; l (L) ^peak Is the maximum load demand; ρ is the load reserve capacity coefficient of the system; g'K 'and N' are the total number of the thermal power unit, the hydroelectric unit and the new energy unit respectively;

the seasonal electric quantity demand index comprises seasonal electric quantity abundance constraint considering hydrogen energy transfer, annual utilization hour constraint of a thermal power generating unit, hydrogen storage and release amount constraint of each period, hydrogen storage climbing constraint and underground hydrogen storage capacity constraint;

the seasonal electricity abundance constraint considering the hydrogen energy transfer is expressed as follows:

in (1) the->The annual utilization hours of the g thermal power generating unit, the k hydroelectric generating unit and the n new energy generating unit in the season s are respectively +.>Seasonal energy storage electric quantity, generating capacity and predicted electric quantity requirements;

the annual utilization hour constraint expression of the thermal power generating unit is as follows:in the formula, h ^max The maximum annual utilization time of the thermal power generating unit is used; omega _s For the proportion of each ordered cluster period during season s;

the expression of the constraint of the hydrogen storage and the hydrogen release amount in each period is as follows:wherein eta is ^ED Is the electro-hydrogen conversion coefficient; epsilon is the mass of hydrogen per cubic meter; c (C) _h ^SHS Configuration capacity for seasonal hydrogen storage; η (eta) ^GT The power generation efficiency of the hydrogen gas turbine;

the expression of the hydrogen storage amount climbing constraint is as follows: In (1) the->And->Hydrogen storage capacity for seasons s and s-1; gamma ray _loss Self-release coefficient for seasonal hydrogen storage; omega _s-1 The proportion of each ordered cluster period during season s-1; zeta type ^ch 、ζ ^re The hydrogen storage and release efficiency of the seasonal hydrogen storage device are respectively;

the expression of the underground hydrogen storage capacity constraint is as follows:in xi ^SHS Is the minimum hydrogen storage rate of seasonal hydrogen storage.

3. The short term battery energy storage and seasonal hydrogen storage co-planning method of claim 2, wherein the uncertain set of renewable energy output is:

wherein U is an uncertain set of renewable energy source output,uncertain output for new energy source _T For time uncertainty budget, p _n,t,r Actual output for renewable energy, +.>Auxiliary variables of type 0-1, respectively, describing whether the renewable energy source output reaches an uncertainty set boundary,/v>The positive error and the negative error of the output prediction of the renewable energy source are respectively, w is the type of the renewable energy source, t is the time, and the subscript r represents the r-th typical day.

4. The short term battery energy storage and seasonal hydrogen storage co-planning method according to claim 3, wherein the generalized adequacy assessment indicator comprises: seasonal electric energy loss rate evaluation index, load loss rate evaluation index and renewable energy reduction rate evaluation index;

The seasonal electric energy loss rate evaluation index has the expression:in delta _E For seasonal electric energy loss rate, < >>The power loss for season s;

the expression of the load loss rate evaluation index is as follows:in delta _load For load loss rate, ΔP _d,t,r To lose load power, P _d,t,r D represents the d-th load;

the expression of the renewable energy reduction rate evaluation index is as follows:in delta _cur Is a renewable energy source reduction rate.

5. The short term battery energy storage and seasonal hydrogen storage co-planning method of claim 4, wherein the configuration decision process model is:

wherein C is ^Inv For annual decision coefficient, c ^N 、c ^B 、c ^E 、c ^ED 、c ^GT 、c ^SHS The configuration coefficients of the renewable energy source, the energy and power stored by the battery, the electrolytic tank, the hydrogen gas turbine and the seasonal hydrogen storage device are respectively;the configuration capacities of the nth renewable energy source, the energy and power stored by the b battery, the ith electrolytic cell, the jth hydrogen gas turbine and the h seasonal hydrogen storage device are respectively set; alpha is the discount rate; t (T) _life The service life is prolonged;

constraints include system decision coefficient expected constraints and maximum installable capacity constraints for each device; each device comprises: the system comprises an electrolytic tank, a hydrogen gas turbine, a seasonal hydrogen storage device, battery energy storage equipment and a new energy unit;

The system decision coefficient expectation constraint is: c (C) ^Inv ≤Π ^max The method comprises the steps of carrying out a first treatment on the surface of the In II ^max The upper limit of the programming coefficient is set;

the maximum installable capacity constraint of the electrolyzer is:in (1) the->The maximum configuration capacity of the electrolytic cell;

the maximum installable capacity constraint of a hydrogen gas turbine is:in (1) the->Maximum capacity for a hydrogen turbine;

Seasonthe maximum installable capacity constraint of the hydrogen storage device is:in (1) the->Maximum deployment capacity for seasonal hydrogen storage devices;

the maximum mountable capacity constraint of the battery energy storage device is:in (1) the->The maximum configuration capacity of the battery energy storage energy;

the maximum installable capacity constraint of the new energy unit is as follows:in (1) the->Planning capacity for candidates of new energy units, +.>The capacity is configured for the maximum of the new energy unit.

6. The short term battery energy storage and seasonal hydrogen storage co-planning method of claim 5, wherein the typical day operation model is:

C ^Ope ＝C _g +C _n +C _Δload ；

wherein C is ^Ope As typical day operation coefficient, C _g For generating coefficient, C of thermal power generating unit _n Generating coefficient and C for new energy unit _Δload Is a load loss coefficient; c ^g 、c ^e 、c ^cur 、c ^load Respectively the power generation coefficient of the thermal power generating unit, the carbon emission coefficient of the thermal power generating unit, the renewable resource reduction and the load loss penalty coefficient; p (P) _i,t,r 、E _i,t,t 、ΔP _d,t,r Respectively outputting the thermal power unit, carbon emission and load loss power of the thermal power unit; pi _r Is a typical daily confidence; v is a binary variable of type 0-1 representing start-stop state in the run phase, xi is a linear function with respect to uncertainty v, p is a continuous variable representing output in the run phase, and ψ is a set of all kinds of p.

7. The short term battery energy storage and seasonal hydrogen storage co-planning method according to claim 6, wherein the system operation constraint condition of the typical day operation model in a typical day scenario comprises: basic constraint, uncertain set of renewable energy output, seasonal electric energy loss rate evaluation index, load loss rate evaluation index and renewable energy reduction rate evaluation index;

the basic constraints include: node power balance constraint, line power constraint, thermal power unit operation output upper and lower limit constraint, start-stop time constraint, unit climbing constraint, annual start-up hour constraint, hydro-generator unit output constraint, battery energy storage operation charge-discharge constraint, same-period charge-discharge state constraint, battery charge state constraint, last-period energy storage power threshold constraint, seasonal hydrogen storage operation electrolyzer operation constraint, hydrogen turbine operation constraint, storage and release amount upper limit constraint, same-period storage and release state constraint, typical day storage and release state single constraint, hydrogen storage amount climbing constraint, equipment hydrogen storage capacity constraint, first typical day initial hydrogen storage capacity constraint beyond the first typical day, last-period hydrogen storage amount threshold constraint, and renewable energy output constraint under uncertain conditions.

8. The short-term battery energy storage and seasonal hydrogen storage collaborative planning method according to claim 7, wherein the short-term battery energy storage and seasonal hydrogen storage joint planning model accounting for generalized adequacy demand is:

minC ^total ＝C ^Inv +C ^Ope ；

wherein C is ^total Is the overall planning factor.

9. The short-term battery energy storage and seasonal hydrogen storage collaborative planning method according to claim 8, wherein a column constraint generation algorithm is adopted to solve a short-term battery energy storage and seasonal hydrogen storage joint planning model considering generalized adequacy requirements, and a 0-1 type auxiliary variable vector is introduced into a sub-problem of the column constraint generation algorithm to perform dual transformation, so as to obtain planning configuration capacity of various power generation equipment, and the method specifically comprises the following steps:

modeling a short-term battery energy storage and seasonal hydrogen storage joint planning model as a two-stage robust planning model: the first stage considers configuration decisions of the system; the second stage considers the operation problem;

the goal of setting a two-stage robust planning model master problem is to minimize the annual decision coefficients and determine the master problem and constraints as:wherein x is a first stage 0-1 type planning decision variable, c is a continuous variable vector of first stage planning capacity, v is a second stage 0-1 type state decision variable, p _z Continuous variable vector, p, of actual output of renewable energy source for second stage planning _z ^worst For the worst scene output in the z-th iteration, κ is the maximum iteration number, η is the sub-problem->Ξ(c,p ^u ) As to the continuous variable p ^u Is a linear function of (2): xi (c, p) ^u )＝{p ^u :M ^T p ^u ≥d-Dc-Fp ^u -a }; A. b, D, F, M, Q the changes contained in each constraint in the modelThe first, second, third, fourth, fifth and sixth constant coefficient parameter matrixes corresponding to the quantities, a, b, d, f, g, delta and lambda, are first, second, third, fourth, fifth, sixth and seventh constant coefficient parameter vectors corresponding to variables contained in each constraint in the model.

The determination of the sub-problems and constraints is:wherein p is ^u Uncertainty output for renewable energy, c ^* An optimal solution for the continuous variable c in the main problem;

introducing a 0-1 type auxiliary variable vector into the sub-problemThe original 0-1 type variable vector v is defined as a continuous variable such that v equals +.>Thereby rearranging the sub-problems and constraints into an optimization model:

performing dual transformation on the optimization model to obtain

Where μ is the dual variable vector of the second stage linear programming. Determining the minimum and maximum inequality

In (1) the->

Introducing the minimum and maximum inequality into an optimized model after dual transformation to obtain an equivalent objective function as

Linearizing an equivalent objective function by adopting a large M method to obtain a mixed integer linear programming model;

and solving the mixed integer linear programming model to obtain the programming configuration capacity of various power generation equipment.

10. A short-term battery energy storage and seasonal hydrogen storage collaborative planning system, comprising:

the balance index construction module is used for constructing seasonal electric power and electric quantity balance indexes so as to achieve seasonal matching of renewable energy and load demands;

the uncertainty set construction module is used for constructing an uncertainty set of the renewable energy output by taking the fluctuation and uncertainty of the short-time scale of the renewable energy output into consideration;

the evaluation index construction module is used for considering the electric energy adequacy, the power adequacy and the flexibility to construct a generalized adequacy evaluation index;

the configuration decision process model building module is used for building configuration decision process models of various power generation equipment including battery energy storage equipment and seasonal hydrogen storage devices by combining seasonal electric power and electric quantity balance indexes;

the system comprises a typical day operation model building module, a power supply module and a power supply module, wherein the typical day operation model building module is used for building a typical day operation model based on an uncertain set of battery energy storage operation constraint, seasonal hydrogen storage operation constraint and renewable energy output and generalized adequacy assessment indexes;

The joint planning model determining module is used for determining a short-term battery energy storage and seasonal hydrogen storage joint planning model considering generalized adequacy requirements according to the configuration decision process model and the typical day operation model;

and the solving module is used for solving a short-term battery energy storage and seasonal hydrogen storage combined planning model which accounts for generalized adequacy demands by adopting a column constraint generating algorithm, introducing a 0-1 type auxiliary variable vector into a sub-problem of the column constraint generating algorithm to perform dual conversion, and obtaining the planning configuration capacity of various power generation equipment.