CN115425668A - Energy storage capacity optimal configuration method based on power system time sequence production simulation - Google Patents

Energy storage capacity optimal configuration method based on power system time sequence production simulation Download PDF

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CN115425668A
CN115425668A CN202211163037.7A CN202211163037A CN115425668A CN 115425668 A CN115425668 A CN 115425668A CN 202211163037 A CN202211163037 A CN 202211163037A CN 115425668 A CN115425668 A CN 115425668A
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邓少平
赵璐
龚青
彭朝钊
范黎
张哲原
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PowerChina Hubei Electric Engineering Co Ltd
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Abstract

The invention discloses an energy storage capacity optimal configuration method based on power system time sequence production simulation, which comprises the following steps: determining a power system time sequence production simulation model based on unit aggregation, taking minimized system operation cost and maximized new energy output as optimization targets, and performing simulation calculation on the power system time sequence operation process in a target planning time period to obtain an energy storage planning capacity reference value; determining a power system time sequence production simulation model based on daily rolling, and solving a new energy consumption value under the designated energy storage planning capacity; performing iterative optimization of the energy storage planning capacity according to the set new energy consumption value boundary, so that the new energy consumption rate corresponding to the energy storage planning capacity approaches to the preset new energy consumption rate; and obtaining the optimal energy storage planning capacity of the target planning time interval. According to the method, the energy storage capacity optimal configuration meeting the new energy consumption requirement can be rapidly and accurately analyzed, and the new energy consumption capacity and the operation stability of the power system are improved.

Description

Energy storage capacity optimal configuration method based on power system time sequence production simulation
Technical Field
The invention relates to the technical field of power system planning, in particular to an energy storage capacity optimal configuration method based on power system time sequence production simulation.
Background
In recent years, the wind power and photovoltaic industries of China have been developed rapidly, however, the contradiction of unbalanced new energy development is increasingly highlighted, especially the problem of new energy consumption is prominent, and the healthy and sustainable development of the power industry is severely restricted. Under the environment, the development of the energy storage technology has important significance for ensuring the large-scale development of new energy and the safety of a power grid. The stored energy is used as a flexible, quick and adjustable resource, can be used for stabilizing work such as fluctuation, demand response, frequency modulation and emergency storage, participates in an energy system, has the functions of relieving peak load of a user, delaying line capacity expansion, solving new energy consumption of the system and the like, has important significance for promoting new energy consumption and ensuring safe and stable operation of a power system, and is one of technologies with development prospects in the future.
Energy storage capacity planning is a complex optimization problem involving the economics of energy storage devices, system operating flexibility, and new energy output uncertainty. And only by matching with the stored energy, the problem of consumption and stability can be better solved, so that the adjusting capability and the possibility of surfing the internet of the new energy system are greatly improved.
Compared with the traditional operation mode, the power system needs more standby resources by combining the current development situation of the power system, the development direction of low carbon and the actual demand of a power grid company on the scheduling system to deal with the uncertainty caused by the new energy access. In order to solve the problems, in the energy storage capacity planning process, the operation characteristics of all resources of a system are fully considered by taking account of the existing adjustable traditional generator set, and a more refined method is further provided for energy storage capacity planning.
Disclosure of Invention
The invention aims to provide an energy storage capacity optimal configuration method based on power system time sequence production simulation, which can quickly and accurately analyze and obtain energy storage capacity configuration meeting the consumption requirement of new energy, realize optimization of energy storage capacity configuration and improve the consumption capacity and operation stability of the new energy.
In order to achieve the purpose, the invention adopts the technical scheme that: an energy storage capacity optimal configuration method based on power system time sequence production simulation comprises the following steps:
determining a power system time sequence production simulation model based on unit aggregation;
performing simulation calculation on the power system time sequence operation process in the target planning time period by using the power system time sequence production simulation model based on the unit aggregation and taking the minimized system operation cost and the maximized new energy output as optimization targets, and solving to obtain an energy storage planning capacity reference value in the target planning time period;
determining a power system time sequence production simulation model based on daily rolling, and solving a new energy consumption value under the designated energy storage planning capacity;
performing iterative optimization of the energy storage planning capacity according to the set new energy consumption value boundary, so that the new energy consumption rate corresponding to the energy storage planning capacity approaches to a preset new energy consumption rate expected value: adjusting the energy storage planning capacity in each iteration based on the energy storage planning capacity reference value of the target planning time interval, and calculating a corresponding new energy consumption value by using the power system time sequence production simulation model based on daily rolling based on the adjusted energy storage planning capacity;
and taking the optimal energy storage planning capacity obtained by iterative optimization as the energy storage planning capacity of the target planning time interval.
Optionally, the method for constructing the power system time sequence production simulation model based on the unit aggregation includes:
classifying all thermal power generating units in a large-area power system according to the unit types, the capacity levels and the operation characteristics;
regarding thermal power generating units with the same or similar operation characteristics as a whole, and constructing a cluster thermal power generating unit;
and determining variables for representing the running states of the cluster thermal power generating units, and describing the aggregation effect of the time sequence running states of the multiple thermal power generating units.
Optionally, the power system time sequence production simulation model based on the unit aggregation includes:
a) The operation state of a single thermal power generating unit at the time t is represented as follows:
Figure BDA0003860951310000021
in the formula, x i (t) represents a grid-connected state variable u i (t) represents a startup state variable, d i (t) represents a shutdown state variable, x i (t) equal to 1 or 0 represents that the state of the unit i at the moment t is a grid-connected operation state or an off-line state; u. u i (t) when the value is equal to 1, the unit i is started at the moment t and is converted from an off-line state to a grid-connected operation state; d i (t) when the value is equal to 1, the unit i stops at the moment t, and the grid-connected operation state is changed into an off-line state; u. u i (t) and d i (t) being equal to 0 means that the operating state of the unit i has not changed at time t;
b) The starting state of the cluster thermal power generating unit is represented as follows:
Figure BDA0003860951310000031
Figure BDA0003860951310000032
Figure BDA0003860951310000033
in the formula (I), the compound is shown in the specification,
Figure BDA0003860951310000034
respectively representing the starting capacity, the starting capacity and the stopping capacity of the cluster thermal power generating unit j; j represents the number of equivalent units in the cluster thermal power generating units,
Figure BDA0003860951310000035
representing the rated capacity of the unit i;
the startup, startup and shutdown capacity variables meet:
Figure BDA0003860951310000036
c) The discrete variables of the starting capacity, the starting capacity and the stopping capacity of the clustered thermal power generating unit are approximately described as follows:
Figure BDA0003860951310000037
Figure BDA0003860951310000038
Figure BDA0003860951310000039
the equivalent unit capacity is:
Figure BDA00038609513100000310
in the formula, the integer variable x j (t)、u j (t) and d j (t) respectively indicating the number of equivalent units in a grid-connected operation state, a starting state and a stopping state at the moment t;
the value range of the continuous variable of the operation state of the cluster thermal power generating unit is represented as follows:
Figure BDA00038609513100000311
wherein S is j The total capacity of the clustered thermal power generating units is represented and is the sum of rated capacities of all the units;
the change of the starting capacity of the cluster thermal power generating unit j between adjacent moments is represented as follows:
Figure BDA00038609513100000312
in the technical scheme, the startup capacity and the shutdown capacity of the cluster unit are modeled by adopting continuous variables, the cluster unit comprising J units is taken as one unit, the running state of the unit is assumed to be not only two states of shutdown 0 and grid-connected 1, but also a plurality of running states between 0 and 1 exist, and the continuously changed running state is represented by the continuous startup capacity of the unit. The set aggregation-based power system time sequence production simulation model can effectively reduce the number of variables, and the cluster set running state, output power, climbing constraint and minimum start-stop time constraint established on the basis of the continuous variable of the starting capacity of the cluster set can accurately simulate the time sequence running process of the cluster set.
Optionally, the method includes performing simulation calculation on the power system time sequence operation process in the target planning period by taking the minimized system operation cost and the maximized new energy output as optimization targets, and expressing corresponding objective functions as:
Figure BDA0003860951310000041
in the formula (I), the compound is shown in the specification,
Figure BDA0003860951310000042
respectively representing the power generation cost, the starting cost and the shutdown cost of all the cluster units, wherein the sum of the three is the total operation cost of the system; theta is a penalty coefficient of the new energy electricity-limiting output,
Figure BDA0003860951310000043
the new energy limited power output of the regional power grid k in the system at the moment t is shown, and the new energy limited power output comprises the following components:
Figure BDA0003860951310000044
wherein the content of the first and second substances,
Figure BDA0003860951310000045
represents the maximum exergy of the new energy in the regional power grid k at the time t,
Figure BDA0003860951310000046
representing the generated power of the new energy source in the regional power grid k at time t.
According to the technical scheme, the established power system time sequence production simulation model based on unit aggregation takes the minimized system operation cost and the maximized new energy grid-connected power generation quantity as optimization targets, can simulate the time sequence operation process of a large-scale power system under the constraints of power balance, standby requirements, new energy output, unit operation characteristics and the like, and realizes the rapid calculation of the medium-term and long-term time sequence production simulation of the large-area power system.
Further, when the power system time sequence production simulation model based on the unit aggregation is used for power system time sequence production simulation, the input parameters of the model include: a time sequence load power curve, a time sequence new energy maximum power generation curve, a load standby coefficient meeting the reliability requirement, a standby coefficient of new energy output, a transmission power limit of a power transmission section and operation parameters of each generator set; the operation parameters of the generator set comprise rated capacity, a minimum technical output ratio, a maximum technical output ratio, an upward climbing rate, a downward climbing rate, minimum starting time, minimum stopping time, a linear operation coal consumption coefficient and coal consumption requirements of each starting and stopping; the model output result comprises time sequence operation information of a target planning time interval;
and calculating the energy storage planning capacity of the target planning time interval according to the time sequence operation information.
And the power transmission section information is used for splitting the power system so as to cluster the generator sets belonging to the same regional power grid and obtain the operating parameters of the cluster generator sets.
Optionally, the objective function of the power system time sequence production simulation model based on daily rolling is represented as:
Figure BDA0003860951310000051
in the formula, F represents the total operation cost of the power system, and K represents the number of regional power grids in the system; t represents the total running time;
Figure BDA0003860951310000052
representing the power generation cost of the thermal power generating unit;
Figure BDA0003860951310000053
representing the starting cost of the thermal power generating unit;
Figure BDA0003860951310000054
representing the shutdown cost of the thermal power generating unit; theta S Representing a light abandoning penalty; theta.theta. W Representing a wind curtailment penalty; p is a radical of S,k (t) represents the actual power generation of the photovoltaic power plant; p is a radical of formula W,k (t) represents the actual power generation of the wind farm;
Figure BDA0003860951310000055
respectively representing the maximum power generation capacity of photovoltaic and wind power obtained by converting meteorological data;
the solution constraints of the objective function include:
(1) And power balance constraint:
Figure BDA0003860951310000056
wherein p is G,j (t) the actual output of the thermal power generating unit; t is I,k (t)、T O,k (t) the power flowing in and out of the kth regional power grid tie line respectively; p is a radical of L,k (t) is the power load of the regional power grid;
(2) Standby constraint:
Figure BDA0003860951310000057
wherein u is i The state of the start-stop of the unit is as follows,
Figure BDA0003860951310000058
the theoretical maximum output of the thermal power generating unit is 1 when the thermal power generating unit is in a grid-connected state, otherwise, the theoretical maximum output of the thermal power generating unit is 0; epsilon W,k 、ε S,k Maximum prediction errors of wind power and photovoltaic power stations of a regional power grid k are respectively obtained; eta L,k The standby coefficient of the load is generally 5 percent;
(3) And (3) grid frame constraint:
for interconnected regional grids, there are:
Figure BDA0003860951310000061
wherein p is i,j (t) represents the switching power on the link,
Figure BDA0003860951310000062
represents a line transmission power limit;
(4) And (3) output restraint of the generator set:
Figure BDA0003860951310000063
Figure BDA0003860951310000064
Figure BDA0003860951310000065
wherein the content of the first and second substances,
Figure BDA0003860951310000066
andp G,i (t) respectively representing rated capacity and minimum technical output of the thermal power generating unit;
(5) And (3) climbing restraint:
Figure BDA0003860951310000067
Figure BDA0003860951310000068
wherein R is U,i 、R D,i The unit power climbing capacity per unit time and the unit power climbing capacity per unit time are respectively, and M is a large constant;
(6) And (3) limiting the upper limit of the power of the switch-on and switch-off:
Figure BDA0003860951310000069
Figure BDA00038609513100000610
wherein S is U,i 、S D,i Respectively a power upper limit per unit value at the startup time and a power upper limit per unit value at the shutdown time;
(7) Minimum start-stop time constraint:
Figure BDA0003860951310000071
Figure BDA0003860951310000072
wherein, T U,i 、T D,i Respectively the minimum running time and the minimum shutdown time of the unit;
(8) Output constraint of the cogeneration unit:
Figure BDA0003860951310000073
Figure BDA0003860951310000074
wherein h is i (t) is the thermal load per unit value; a is i 、b i Is a maximum main steam pressure limiting parameter; c. C i 、d i And the minimum steam pressure limiting parameter of the low-pressure cylinder is obtained.
Optionally, when the power system time sequence production simulation model based on day-to-day rolling is solved, a time domain decomposition method and a solution-free automatic rollback method are adopted for solving.
According to the technical scheme, the power system time sequence production simulation model based on daily rolling aims at the lowest system operation cost (including electricity abandonment cost), system operation constraints are considered in a refined mode, indexes such as new energy consumption rate are evaluated, and a new energy consumption value under a specific energy storage planning capacity can be solved accurately.
Optionally, the iterative optimization of the energy storage planning capacity is performed according to the set new energy absorption value boundary, and a binary iteration method is adopted, including:
s41, determining a preset new energy consumption rate target value k 0 ％；
S42, planning the capacity reference value M according to the energy storage of the target planning time interval (0) Calculating a corresponding new energy consumption value by using the power system time sequence production simulation model based on day-by-day rolling, and calculating a new energy consumption rate k% according to the new energy consumption value;
if k% ≠ k 0 % is based on k% and k 0 % size relation, determining an adjustment interval of the energy storage planning capacity: if k%<k 0 Percent, the energy storage planning capacity interval is [ D ] (0) ,U (0) ]Wherein U is (0) ＝M (0) ，D (0) ＝1/2M (0) (ii) a If k%>k 0 Percent, then energy is storedPlanning a capacity interval [ D (0) ,U (0) ]In (D) (0) ＝M (0) ，U (0) ＝2M (0) (ii) a Turning to the step S43 to carry out iterative optimization of the energy storage planning capacity;
if k% = k 0 Percent, stopping iteration and adding M (0) As an optimal energy storage planning capacity;
s43, in the ith iteration, according to M (i) ＝(D (i-1) +U (i-1) ) /2, updating the energy storage capacity planning value, and updating the current energy storage capacity planning value M (i) Substituting the new energy consumption value into the power system time sequence production simulation model based on the day-to-day rolling to obtain a corresponding new energy consumption value, and calculating a new energy consumption rate k according to the new energy consumption value i % of new energy consumption rate k i % and k 0 % comparison:
a) If k is i ％>k 0 And percent, updating the energy storage planning capacity interval to [ D ] (i) ,U (i) ]Wherein, U (i) ＝M (i) ，D (i) ＝D (i-1) Returning to the step S43 to perform iteration for the (i + 1) th time;
b) If k is i ％<k 0 And percent, updating the energy storage planning capacity interval to [ D ] (i) ,U (i) ]Wherein, U (i) ＝U (i-1) ，D (i) ＝M (i) Returning to the step S43 to perform iteration for the (i + 1) th time;
c) If k is i ％＝k 0 % stopping iteration, and planning the current energy storage capacity value M (i) As an optimal energy storage planning capacity;
and S44, determining the optimal energy storage planning capacity as the energy storage planning configuration of the target planning time interval.
In a second aspect, the present invention provides an energy storage capacity optimal configuration device based on power system time sequence production simulation, which is characterized by comprising:
a first power system time series production simulation model determination module configured to determine a power system time series production simulation model based on unit aggregation;
the energy storage planning capacity reference value calculation module is configured to utilize the power system time sequence production simulation model based on the unit aggregation to perform simulation calculation on the power system time sequence operation process in a target planning time period by taking the minimized system operation cost and the maximized new energy output as optimization targets, and solve to obtain an energy storage planning capacity reference value in the target planning time period;
the second power system time sequence production simulation model determining module is configured to determine a power system time sequence production simulation model based on daily rolling and to solve a new energy consumption value under the designated energy storage planning capacity;
the iterative optimization module is configured to perform iterative optimization of the energy storage planning capacity according to the set new energy absorption value boundary, so that the new energy absorption rate corresponding to the energy storage planning capacity approaches a preset new energy absorption rate expected value: adjusting the energy storage planning capacity in each iteration based on the energy storage planning capacity reference value of the target planning time interval, and calculating a corresponding new energy consumption value by using the power system time sequence production simulation model based on daily rolling based on the adjusted energy storage planning capacity;
and the energy storage planning capacity determining module is configured to use the optimal energy storage planning capacity obtained by the iterative optimization as the energy storage planning capacity of the target planning time interval.
In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the energy storage capacity optimization configuration method based on power system time series production simulation as described in the first aspect.
Advantageous effects
The energy storage capacity optimization method comprises the following steps: establishing a mathematical model for describing the time sequence operation characteristics of the cluster unit and establishing a power system time sequence production simulation model based on unit aggregation by establishing the cluster unit and continuously processing integer variables for describing the time sequence operation state of the cluster unit; further, the method takes the minimized system operation cost and the maximized new energy grid-connected power generation quantity as optimization targets, realizes the simulation and rapid calculation of long-term time sequence production in a large-area power system, and roughly solves the energy storage planning capacity of the target year in whole year; then, system operation constraints are considered in a refined mode, a power system time sequence production simulation model based on daily rolling is built, and new energy consumption is evaluated, so that a new energy consumption value under a specific energy storage planning capacity can be solved accurately; and finally, according to the roughly solved energy storage planning capacity, utilizing the two time sequence production simulation models for iterative optimization, taking the set new energy consumption boundary as a target, adjusting the energy storage capacity to enable the system to gradually approach the preset new energy consumption rate, and finally obtaining the optimal energy storage capacity configuration in the target planning period, so that the optimization of the energy storage capacity configuration can be realized, and the new energy consumption capacity and the operation stability are improved.
Meanwhile, iteration optimization is carried out by adopting a dichotomy, and the energy storage capacity configuration meeting the consumption requirement of new energy can be quickly and accurately searched.
Drawings
FIG. 1 is a schematic flow chart illustrating an implementation of the method of the present invention in one embodiment;
fig. 2 is a schematic view showing an operation state of a cluster block in consideration of an operation state of a single block;
FIG. 3 is a schematic view showing the operation state of the cluster blocks irrespective of the operation state of the individual block;
FIG. 4 is a schematic diagram of an algorithm flow for solving a time series production simulation model.
Detailed Description
The following further description is made in conjunction with the accompanying drawings and the specific embodiments.
The technical conception of the invention is as follows: the method comprises the steps that a power system time sequence production simulation model based on unit aggregation takes minimized system operation cost and maximized new energy grid-connected power generation quantity as optimization targets, system operation constraints are simplified and considered on the basis of traditional power planning and load prediction results, and rough energy storage capacity planning is carried out; according to the energy storage capacity planning result, system operation constraint is considered in a refined mode, a power system time sequence production simulation model based on daily rolling is established to evaluate new energy consumption, iterative optimization is carried out through a dichotomy based on the rough energy storage capacity planning result, and energy storage capacity configuration meeting the new energy consumption requirement is found quickly and accurately.
Example 1
The embodiment introduces an energy storage capacity optimal configuration method based on power system time sequence production simulation, which includes:
determining a power system time sequence production simulation model based on unit aggregation;
performing simulation calculation on the power system time sequence operation process in the target planning time period by using the power system time sequence production simulation model based on the unit aggregation and taking the minimized system operation cost and the maximized new energy output as optimization targets, and solving to obtain an energy storage planning capacity reference value in the target planning time period;
determining a power system time sequence production simulation model based on daily rolling, and solving a new energy consumption value under the designated energy storage planning capacity;
performing iterative optimization of the energy storage planning capacity according to the set new energy consumption value boundary, so that the new energy consumption rate corresponding to the energy storage planning capacity approaches a preset new energy consumption rate expected value: adjusting the energy storage planning capacity in each iteration based on the energy storage planning capacity reference value of the target planning time interval, and calculating a corresponding new energy consumption value by using the power system time sequence production simulation model based on daily rolling based on the adjusted energy storage planning capacity;
and taking the optimal energy storage planning capacity obtained by iterative optimization as the energy storage planning capacity of the target planning time interval.
Specific implementation of the present embodiment with reference to fig. 1, the following is specifically referred to.
1. Electric power system time sequence production simulation model based on unit aggregation is established
In this part, in this embodiment, by using a method for constructing a cluster thermal power generating unit, a continuous variable is used to represent an aggregation effect of a single-period operating state of a plurality of units, and a mathematical model describing sequential operating characteristics such as output power, climbing, start-stop time, and the like of the cluster unit is established, so that a power system sequential production simulation model based on unit aggregation is established.
The grid structure topology of a power grid needs to be ignored when the cluster thermal power generating unit is constructed, and the fact that the power generating unit and the load are connected to the same bus is assumed. Considering that the transmission capacity constraint faced by the operation of the power system is mainly embodied on a small number of power transmission sections, but not all power transmission lines, the large-area power system needs to be divided according to the power transmission sections, the grid structure of the divided area power system is neglected, thermal power generating units in the area power system are clustered respectively, and the constraint effect of the power transmission sections among the areas is reserved.
The cluster thermal power generating unit J consists of J thermal power generating units which belong to the same subarea subsystem and have the same or similar rated capacity and operation characteristics. For a single thermal power generating unit in the cluster thermal power generating unit j, a grid-connected state variable x can be adopted i (t), starting state variable u i (t) and a shutdown state variable d i (t) describing the operation state of a single thermal power generating unit at the moment t by the three binary integer variables:
Figure BDA0003860951310000111
wherein x is i (t) equal to 1 or 0 represents that the unit i is in a grid-connected operation or off-line state at the moment t; u. u i (t) when the value is equal to 1, the unit i is started at the moment t and is converted into a grid-connected operation state from an off-line state; d i (t) when the value is equal to 1, the unit i stops at the moment t and is converted into an offline state from a grid-connected operation state; u. of i (t) and d i (t) equal to 0 means that the operating state of the unit i has not changed at the time t.
When any unit in the cluster thermal power generating units j changes the operation state at the moment t, the overall externally-expressed starting capacity of the cluster thermal power generating units changes. Therefore, the starting-up state of the cluster thermal power generating unit can be represented by the starting-up capacity of the unit. Introducing starting capacity to the cluster thermal power generating unit j
Figure BDA0003860951310000112
Starting capacity
Figure BDA0003860951310000113
And shutdown capacity
Figure BDA0003860951310000114
And (4) variable quantity. The mathematics are described as follows:
Figure BDA0003860951310000115
Figure BDA0003860951310000116
Figure BDA0003860951310000117
wherein the content of the first and second substances,
Figure BDA0003860951310000118
representing the rated capacity of unit i. The startup, startup and shutdown capacity variables meet:
Figure BDA0003860951310000121
according to the above formula, the starting capacity
Figure BDA0003860951310000122
Starting capacity
Figure BDA0003860951310000123
And shutdown capacity
Figure BDA0003860951310000124
The value of the discrete variable is determined by the operation state of each unit in the cluster thermal power generating unit j at the moment t. Albeit at boot capacity
Figure BDA0003860951310000125
Starting capacity
Figure BDA0003860951310000126
And shutdown capacity
Figure BDA0003860951310000127
The effect of the J sets after the running states are aggregated is accurately described, but the three discrete variables can only be obtained by calculation according to the running states of the sets at the moment t and cannot be directly decided.
Therefore, the cluster thermal power generating unit J is assumed to be composed of J equivalent units, and an integer variable x is introduced j (t)、u j (t) and d j (t) to describe the number of units in the grid-connected operation state, the starting state and the shutdown state at the moment t respectively. By this method, the boot capacity
Figure BDA0003860951310000128
Starting capacity
Figure BDA0003860951310000129
And shutdown capacity
Figure BDA00038609513100001210
The discrete variables can be described approximately as:
Figure BDA00038609513100001211
Figure BDA00038609513100001212
Figure BDA00038609513100001213
wherein, equivalent unit capacity:
Figure BDA00038609513100001214
introduced integer variable x j (t)、u j (t) and d j (t) instead of a binary variable, canAnd reducing the number of integral variables describing the operation state of the cluster thermal power generating unit j at the time t from 3 to 3. The number of the units in the starting state at a certain operation time in the cluster thermal power generating units is described by using an integer variable, and the operation state of the discrete cluster thermal power generating units can be approximately represented, as shown in fig. 2 and 3. The modeling method considers the cluster thermal power generating units as the aggregation of J equivalent units, and further determines the starting capacity of the cluster thermal power generating units.
Because the change of the starting and stopping states of the units directly affects the change of the whole starting capacity of the cluster units, if the operation state of each unit is not considered, the starting capacity in the total capacity of the cluster thermal power unit j at the moment t can be directly regarded as a decision variable, as shown in fig. 2. In the method, a continuous variable is introduced to describe the starting-up capacity in the total capacity of the clustered thermal power generating units so as to approximately represent the operating state of the clustered thermal power generating units. Continuous variable of starting capacity
Figure BDA00038609513100001215
The total capacity of the cluster unit j is shown as t
Figure BDA00038609513100001216
The unit(s) is/are operated in a grid-connected mode, and can participate in system power balance and provide system standby. Starting capacity continuous variable
Figure BDA0003860951310000131
Indicates that the total shared capacity is from the time t-1 to the time t
Figure BDA0003860951310000132
The unit needs to be started, and the running state of the unit is converted into a grid-connected state from an off-line state. Continuous variation of shutdown capacity
Figure BDA0003860951310000133
Indicates that the total shared capacity is from the time t-1 to the time t
Figure BDA0003860951310000134
The unit needs to be stopped, and the operation state is from a grid-connected operation stateAnd the system is switched to an off-line state. The value range of the continuous variable describing the running state of the cluster thermal power generating unit is as follows:
Figure BDA0003860951310000135
wherein the total capacity S of the thermal power generating units is clustered j Is the sum of the rated capacities of all the units,
Figure BDA0003860951310000136
the change of the starting capacity of the clustered thermal power generating unit j between adjacent moments can be represented as follows:
Figure BDA0003860951310000137
the left side and the right side of the equation constraint both represent the unit capacity continuously operated from the time t-1 to the time t, and the unit corresponding to the part of the capacity does not change the operation state at the two times. Starting capacity
Figure BDA0003860951310000138
The capacity belongs to the unit offline capacity at the moment t-1 and the unit startup capacity at the moment t. Capacity of shutdown
Figure BDA0003860951310000139
On the contrary, the described part belongs to the unit startup capacity at the time t-1 and is the offline capacity at the time t. And if the starting capacity of the cluster thermal power generating unit j at the moment t is different from the starting capacity at the moment t-1, indicating that a unit starting and stopping event exists.
The method comprises the steps of modeling the starting capacity, the starting capacity and the stopping capacity of a cluster unit by adopting continuous variables, taking the cluster unit comprising J units as one unit, and assuming that the running state of the unit is not only two states of stopping 0 and grid-connected 1, but also can have a plurality of running states between 0 and 1, and representing the continuously changed running state by the continuous starting capacity of the unit.
In the above, when the power system time sequence production simulation model based on the unit aggregation is established, the number of variables can be effectively reduced by the cluster unit establishing method, and the time sequence operation process of the cluster unit can be accurately simulated by the cluster unit operation state, the output power, the climbing constraint and the minimum start-stop time constraint established based on the continuous variable of the starting capacity of the cluster unit.
2. Performing power system time sequence production simulation based on unit aggregation, and outputting energy storage planning capacity of a target planning time period such as a target year
The power system time sequence production simulation model based on unit aggregation only considers key power transmission sections (trans-provincial and trans-regional interconnection channels and limited lines in internal net racks) of the interconnected power grid and does not consider other detailed power grid topological structures.
The objective function considers as much as possible of the new energy contribution to accommodate for the minimum system operating cost, namely:
Figure BDA0003860951310000141
wherein the total operation cost is the power generation cost of all cluster units
Figure BDA0003860951310000142
Cost of start-up
Figure BDA0003860951310000143
And cost of down time
Figure BDA0003860951310000144
Adding; theta is a punishment coefficient of the new energy electricity-limiting output;
Figure BDA0003860951310000145
and the new energy electricity-limiting output representing the k moment t of the regional power grid is defined as the new energy output which cannot be consumed by grid connection:
Figure BDA0003860951310000146
wherein the content of the first and second substances,
Figure BDA0003860951310000147
represents the maximum exergy of the new energy in the regional power grid k at the time t,
Figure BDA0003860951310000148
representing the generated power of the new energy source in the regional power grid k at time t.
The established power system time sequence production simulation model based on the unit aggregation takes the minimization of the system operation cost and the maximization of the new energy grid-connected power generation quantity as optimization targets, simulates the time sequence operation process of a large-scale power system under the constraints of power balance, standby requirements, new energy output, unit operation characteristics and the like, and realizes the simulation and rapid calculation of long-term time sequence production in a large-area power system.
When the power system time sequence production simulation based on unit aggregation is carried out on a large-scale power system, the input parameters comprise: the system comprises a time sequence load power curve, a time sequence new energy maximum power-generating curve, a load reserve coefficient meeting the reliability requirement, a reserve coefficient of new energy output, a transmission power limit of a transmission section and operation parameters (including rated capacity, a minimum technology output ratio, a maximum technology output ratio, an upward climbing rate, a downward climbing rate, a minimum starting time, a minimum stopping time, a linear operation coal consumption coefficient and coal consumption requirements of starting and stopping each time) of each generator set. And the power transmission section information is used for splitting the power system so as to cluster the generator sets belonging to the same regional power grid and obtain the operating parameters of the cluster generator sets. The output result comprises time sequence operation information, and the energy storage planning capacity of the target year in the whole year can be roughly solved.
3. Electric power system time sequence production simulation model based on day-to-day rolling is established
The core of the time sequence production simulation is a unit combination model, which is generally modeled as a mixed integer linear programming model. And adopting a unit combination model with the time step length of one hour.
And the objective function is that the scheduling mechanism arranges the start-stop state and the output condition of all the units according to the minimum total operation cost of the system. In addition, in order to promote new energy consumption, a wind curtailment penalty and a light curtailment penalty are usually included in the objective function.
Figure BDA0003860951310000151
Wherein F represents the total operation cost of the power system, and K represents the number of regional power grids in the system; t represents the total run time, when the time step is 1 hour, T =8760 (8784 in case of leap years);
Figure BDA0003860951310000152
representing the power generation cost of the thermal power generating unit;
Figure BDA0003860951310000153
representing the starting cost of the thermal power generating unit;
Figure BDA0003860951310000154
representing the shutdown cost of the thermal power generating unit; theta S Representing a light abandonment penalty; theta W Representing a wind curtailment penalty; p is a radical of S,k (t) represents the actual power generation of the photovoltaic power plant; p is a radical of W,k (t) represents the actual power generation of the wind farm;
Figure BDA0003860951310000155
Figure BDA0003860951310000156
respectively representing the maximum photovoltaic and wind power generation quantities obtained by converting meteorological data;
the constraint conditions include:
(1) Power balance constraint
Figure BDA0003860951310000157
Wherein p is G,j (t) the actual output of the thermal power generating unit; t is I,k (t)、T O,k (t) are each independentlyThe inflow power and the outflow power of k regional power grid tie lines; p is a radical of L,k And (t) is the power load of the regional power grid.
(2) Standby restraint
Figure BDA0003860951310000158
Wherein u is i The state of the start-stop of the unit is as follows,
Figure BDA0003860951310000161
in order to obtain the theoretical maximum output of the thermal power generating unit, the power generating unit is 1 when in a grid-connected state, otherwise, the power generating unit is 0; epsilon W,k 、ε S,k Respectively the maximum prediction errors of wind power and photovoltaic power stations of a regional power grid k; eta L,k The redundancy factor for the load is generally 5%.
(3) Net rack constraint
For interconnected subsystems, the switching power on the link must not be above the line transmission power limit.
Figure BDA0003860951310000162
Wherein p is i,j (t) represents the switching power on the link,
Figure BDA0003860951310000163
representing the line transmission power limit.
(4) Generator set output restraint
Figure BDA0003860951310000164
Figure BDA0003860951310000165
Figure BDA0003860951310000166
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003860951310000167
p G，i and respectively the rated capacity and the minimum technical output of the thermal power generating unit.
(5) Climbing restraint
Figure BDA0003860951310000168
Figure BDA0003860951310000169
Wherein R is U,i 、R D,i The unit power climbing capacity and the unit power climbing capacity in unit time are respectively, and M is expressed as a large constant.
(6) Upper limit of power for switching on and off
Figure BDA00038609513100001610
Figure BDA0003860951310000171
Wherein S is U,i 、S D,i The power upper limit per unit value at the startup time and the power upper limit per unit value at the shutdown time of the unit are respectively.
(7) Minimum on-off time constraint
Figure BDA0003860951310000172
Figure BDA0003860951310000173
Wherein, T U,i 、T D,i Respectively the minimum running time and the minimum shutdown time of the unit.
(8) Output constraint of cogeneration unit
Figure BDA0003860951310000174
Figure BDA0003860951310000175
Wherein h is i (t) is the thermal load per unit value, a i 、b i Is a maximum main steam pressure limiting parameter; c. C i 、d i And the minimum steam pressure limiting parameter of the low-pressure cylinder is obtained.
Due to the limitation of the solving scale of the mixed linear planning problem, for a regional power system containing hundreds of generator sets, annual operation simulation cannot be solved through single calculation, so that a day-by-day rolling simulation mode is generally adopted for power system time sequence operation simulation at present. In order to improve the solving speed of the time sequence operation simulation and avoid the problem that the operation simulation result cannot be obtained due to no solution in rolling, a time domain decomposition technology and a solution-free automatic rollback technology are provided for solving a time sequence operation model of the power system.
The time sequence decomposition method is to decompose the annual operation simulation into a plurality of time period parallel operations, the automatic rollback without solution technology is to not directly quit the solution process when the rolling solution meets the non-solution, but to bring the previous simulation time period into the simulation, if the solution still does not exist, the rolling back is continued, when the rolling back simulation result is feasible, the original simulation result in the previous time period is covered by the simulation result obtained by the rolling back, and the specific solution flow is shown in fig. 4.
According to the energy storage capacity planning scheme, aiming at the lowest system operation cost (including electricity abandonment cost), the system operation constraint is considered in a refined mode, indexes such as new energy consumption rate and the like are evaluated, new energy consumption is evaluated, and a new energy consumption value under the specific energy storage planning capacity is solved accurately.
4. Iterative optimization solution of energy storage planning capacity is carried out on the basis of the two models constructed in the first part and the third part, and an optimal energy storage optimization configuration scheme of a target planning time period is obtained
The technical concept of the part is that based on the energy storage planning capacity reference value obtained by the rough solution of the second part, through the model constructed by the third part, the new energy consumption obtained by the time sequence production simulation of the power system under the specific energy storage planning capacity is subjected to iterative optimization by using a dichotomy, the model is solved by taking the method of combining the time sequence operation simulation and the iterative solution into consideration, the optimal energy storage capacity configuration scheme is obtained, and the solving steps are approximately: and adjusting the energy storage capacity by taking the set new energy consumption boundary as a target, so that the system gradually approaches the preset new energy consumption rate, and finally obtaining the optimal energy storage capacity configuration.
Specifically, the method comprises the following steps: firstly, determining upper and lower limits of energy storage planning capacity based on a roughly calculated energy storage capacity reference value; secondly, gradually searching in half in an effective interval of the energy storage planning capacity, and substituting the searched new value into the power system time sequence production simulation again for optimization calculation; and finally, continuously and iteratively optimizing the energy storage capacity to enable the actual new energy consumption rate to just meet the preset new energy consumption rate target, and ending the solving process.
In this embodiment, the iterative optimization of the energy storage planning capacity is performed according to the set new energy absorption value boundary, and a binary iteration method is adopted, including:
s41, determining a preset new energy consumption rate target value k 0 ％；
S42, according to the energy storage planning capacity reference value M of the target planning time interval obtained by the rough calculation of the second part (0) Calculating a corresponding new energy consumption value by using a power system time sequence production simulation model rolling day by day, and calculating a new energy consumption rate k% according to the new energy consumption value;
if k% ≠ k 0 % is based on k% and k 0 % size relation, determining an adjustment interval of the energy storage planning capacity: if k%<k 0 Percent, the energy storage planning capacity adjustment interval is [ D (0) ,U (0) ]Wherein U is (0) ＝M (0) ，D (0) ＝1/2M (0) (ii) a If k%>k 0 Percent, the energy storage planning capacity interval [ D (0) ,U (0) ]In (D) (0) ＝M (0) ，U (0) ＝2M (0) (ii) a Turning to the step S43 to carry out iterative optimization of the energy storage planning capacity;
if k% = k 0 Percent, stopping iteration and adding M (0) As an optimal energy storage planning capacity;
s43, in the ith iteration, according to M (i) ＝(D (i-1) +U (i-1) ) /2 updating the energy storage capacity planning value and setting the current energy storage capacity planning value M (i) Substituting the new energy consumption value into a power system time sequence production simulation model based on daily rolling to obtain a corresponding new energy consumption value, and calculating a new energy consumption rate k according to the new energy consumption value i Percent, new energy consumption rate k i % and k 0 % comparison:
a) If k is i ％>k 0 And percent, updating the energy storage planning capacity interval to be D (i) ,U (i) ]Wherein, U (i) ＝M (i) ，D (i) ＝D (i-1) Returning to the step S43 to carry out the iteration for the (i + 1) th time;
b) If k is i ％<k 0 And percent, updating the energy storage planning capacity interval to be D (i) ,U (i) ]Wherein, U (i) ＝U (i-1) ，D (i) ＝M (i) Returning to the step S43 to perform iteration for the (i + 1) th time;
c) If k is i ％＝k 0 % stopping iteration, and obtaining the current energy storage planning capacity value M (i) As an optimal energy storage planning capacity;
and S44, determining the optimal energy storage planning capacity as the energy storage planning configuration of the target planning time interval.
In the above way, the energy storage capacity configuration meeting the new energy consumption requirement can be quickly and accurately found through the dichotomy.
Example 2
Based on the same inventive concept as embodiment 1, this embodiment introduces an energy storage capacity optimal configuration device based on power system time sequence production simulation, which includes:
a first power system time series production simulation model determination module configured to determine a power system time series production simulation model based on unit aggregation;
the energy storage planning capacity reference value calculation module is configured for utilizing the power system time sequence production simulation model based on the unit aggregation to perform simulation calculation on the power system time sequence operation process in the target planning time period by taking the minimized system operation cost and the maximized new energy output as optimization targets, and solving to obtain an energy storage planning capacity reference value in the target planning time period;
the second power system time sequence production simulation model determining module is configured to determine a power system time sequence production simulation model based on daily rolling and solve a new energy consumption value under the specified energy storage planning capacity;
the iterative optimization module is configured to perform iterative optimization on the energy storage planning capacity according to the set new energy consumption value boundary, so that the new energy consumption rate corresponding to the energy storage planning capacity approaches a preset new energy consumption rate expected value: adjusting the energy storage planning capacity in each iteration based on the energy storage planning capacity reference value of the target planning time interval, and calculating a corresponding new energy consumption value by using the power system time sequence production simulation model based on daily rolling based on the adjusted energy storage planning capacity;
and the energy storage planning capacity determining module is configured to use the optimal energy storage planning capacity obtained through the iterative optimization as the energy storage planning capacity of the target planning time interval.
The specific function implementation of each functional module refers to the relevant content in the method in embodiment 1.
Example 3
This embodiment introduces a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the energy storage capacity optimal configuration method based on power system time series production simulation as introduced in the embodiment.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
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.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (10)

1. An energy storage capacity optimal configuration method based on power system time sequence production simulation is characterized by comprising the following steps:
determining a power system time sequence production simulation model based on unit aggregation;
performing simulation calculation on the power system time sequence operation process in the target planning time period by using the power system time sequence production simulation model based on the unit aggregation and taking the minimized system operation cost and the maximized new energy output as optimization targets, and solving to obtain an energy storage planning capacity reference value in the target planning time period;
determining a power system time sequence production simulation model based on daily rolling, and solving a new energy consumption value under the designated energy storage planning capacity;
performing iterative optimization of the energy storage planning capacity according to the set new energy consumption value boundary, so that the new energy consumption rate corresponding to the energy storage planning capacity approaches a preset new energy consumption rate expected value: adjusting the energy storage planning capacity in each iteration based on the energy storage planning capacity reference value of the target planning time interval, and calculating a corresponding new energy consumption value by using the power system time sequence production simulation model based on daily rolling based on the adjusted energy storage planning capacity;
and taking the optimal energy storage planning capacity obtained by iterative optimization as the energy storage planning capacity of the target planning time interval.
2. The method as claimed in claim 1, wherein the method for constructing the power system time series production simulation model based on the unit aggregation comprises:
classifying all thermal power generating units in a large-area power system according to the unit type, the capacity level and the operation characteristics;
regarding thermal power generating units with the same or similar operation characteristics as a whole to construct a cluster thermal power generating unit;
and determining variables for representing the running states of the cluster thermal power generating units, and describing the aggregation effect of the time sequence running states of the multiple thermal power generating units.
3. The method of claim 1 or 2, wherein the crew aggregation-based power system time series production simulation model comprises:
a) The operation state of a single thermal power generating unit at the time t is represented as follows:
Figure FDA0003860951300000011
in the formula, x i (t) represents a grid-connected state variable u i (t) represents a startup state variable, d i (t) represents a shutdown state variable, x i (t) equal to 1 or 0 represents that the state of the unit i at the moment t is a grid-connected operation state or an off-line state; u. of i (t) when the value is equal to 1, the unit i is started at the moment t and is converted from an off-line state to a grid-connected operation state; d is a radical of i (t) when the value is equal to 1, the unit i stops at the moment t and is converted into an offline state from a grid-connected operation state; u. of i (t) and d i (t) being equal to 0 means that the operating state of the unit i has not changed at time t;
b) The starting state of the cluster thermal power generating unit is represented as follows:
Figure FDA0003860951300000021
Figure FDA0003860951300000022
Figure FDA0003860951300000023
in the formula (I), the compound is shown in the specification,
Figure FDA0003860951300000024
respectively representing the starting-up capacity, the starting-up capacity and the stopping capacity variables of the cluster thermal power generating unit j; j represents the number of equivalent units in the clustered thermal power generating units,
Figure FDA00038609513000000211
representing the rated capacity of the unit i;
the startup, startup and shutdown capacity variables meet:
Figure FDA0003860951300000025
c) The discrete variables of the starting capacity, the starting capacity and the stopping capacity of the clustered thermal power generating unit are approximately described as follows:
Figure FDA0003860951300000026
Figure FDA0003860951300000027
Figure FDA0003860951300000028
the equivalent unit capacity is:
Figure FDA0003860951300000029
in the formula, the integer variable x j (t)、u j (t) and d j (t) respectively represents that the moment t is in a grid-connected operation state, a starting state and a stopping stateThe number of state equivalent units;
the value range of the continuous variable of the operation state of the cluster thermal power generating unit is represented as follows:
Figure FDA00038609513000000210
wherein S is j The total capacity of the clustered thermal power generating units is represented and is the sum of rated capacities of all the units;
the change of the starting-up capacity of the cluster thermal power generating unit j between adjacent moments is represented as follows:
Figure FDA0003860951300000031
4. the method of claim 3, wherein the simulation calculation is performed on the time-series operation process of the power system in the target planning period with the optimization objectives of minimizing the system operation cost and maximizing the new energy output, and the corresponding objective function is expressed as:
Figure FDA0003860951300000032
in the formula (I), the compound is shown in the specification,
Figure FDA0003860951300000033
respectively representing the power generation cost, the starting cost and the shutdown cost of all cluster units, wherein the sum of the three is the total operation cost of the system; theta is a penalty coefficient of the new energy electricity-limiting output,
Figure FDA0003860951300000034
the new energy limited power output of the regional power grid k in the system at the moment t is shown, and the new energy limited power output comprises the following components:
Figure FDA0003860951300000035
wherein the content of the first and second substances,
Figure FDA0003860951300000036
represents the maximum possible output of new energy in the regional grid k at time t,
Figure FDA0003860951300000037
representing the generated power of the new energy source in the regional power grid k at time t.
5. The method of claim 4, wherein when performing the power system time sequence production simulation based on the power system time sequence production simulation model of the unit aggregation, the input parameters of the model comprise: a time sequence load power curve, a time sequence new energy maximum power generation curve, a load standby coefficient meeting the reliability requirement, a standby coefficient of new energy output, a transmission power limit of a power transmission section and operation parameters of each generator set; the operation parameters of the generator set comprise rated capacity, a minimum technical output ratio, a maximum technical output ratio, an upward climbing rate, a downward climbing rate, minimum starting time, minimum stopping time, a linear operation coal consumption coefficient and coal consumption requirements of each starting and stopping; the model output result comprises time sequence operation information of a target planning time interval;
and calculating the energy storage planning capacity of the target planning time interval according to the time sequence operation information.
6. The method of claim 4, wherein the objective function of the power system time series production simulation model based on daily rolling is represented as:
Figure FDA0003860951300000041
in the formula, F represents the total operation cost of the power system, and K represents the number of regional power grids in the system; t represents the total running time;
Figure FDA0003860951300000042
representing the power generation cost of the thermal power generating unit;
Figure FDA0003860951300000043
representing the starting cost of the thermal power generating unit;
Figure FDA0003860951300000044
representing the shutdown cost of the thermal power generating unit; theta S Representing a light abandoning penalty; theta.theta. W Representing a wind curtailment penalty; p is a radical of S,k (t) represents the actual power generation of the photovoltaic power plant; p is a radical of W,k (t) represents the actual power generation of the wind farm;
Figure FDA0003860951300000045
respectively representing the maximum photovoltaic and wind power generation quantities obtained by converting meteorological data;
the solution constraints of the objective function include:
(1) And power balance constraint:
Figure FDA0003860951300000046
wherein p is G,j (t) is the actual output of the thermal power generating unit; t is a unit of I,k (t)、T O,k (t) the power flowing in and out of the kth regional power grid tie line respectively; p is a radical of L,k (t) is the power load of the regional power grid;
(2) Standby constraint:
Figure FDA0003860951300000047
wherein u is i Is the starting and stopping state of the unit,
Figure FDA0003860951300000048
the theoretical maximum output of the thermal power generating unit is obtained; epsilon W,k 、ε S,k Respectively the maximum prediction errors of wind power and photovoltaic power stations of a regional power grid k; eta L,k A stand-by coefficient for the load;
(3) And (3) grid restraint:
for interconnected regional power grids, there are:
Figure FDA0003860951300000049
wherein p is i,j (t) represents the switching power on the link,
Figure FDA0003860951300000051
represents a line transmission power limit;
(4) And (3) output restraint of the generator set:
Figure FDA0003860951300000052
Figure FDA0003860951300000053
Figure FDA0003860951300000054
wherein the content of the first and second substances,
Figure FDA0003860951300000055
andp G,i (t) respectively representing rated capacity and minimum technical output of the thermal power generating unit;
(5) And (3) climbing restraint:
Figure FDA0003860951300000056
Figure FDA0003860951300000057
wherein R is U,i 、R D,i The unit power climbing capacity and the unit power climbing capacity in unit time are respectively, and M is a larger constant;
(6) And (3) limiting the upper limit of the power of the switch-on and switch-off:
Figure FDA0003860951300000058
Figure FDA0003860951300000059
wherein S is U,i 、S D,i Respectively representing the power upper limit per unit value at the starting-up time and the power upper limit per unit value at the shutdown time of the unit;
(7) Minimum start-stop time constraint:
Figure FDA00038609513000000510
Figure FDA00038609513000000511
wherein T is U,i 、T D,i Respectively the minimum running time and the minimum shutdown time of the unit;
(8) Output constraint of the cogeneration unit:
Figure FDA0003860951300000061
Figure FDA0003860951300000062
wherein h is i (t) is the thermal load per unit value, a i 、b i Is a maximum main steam pressure limiting parameter; c. C i 、d i And the minimum steam pressure limiting parameter of the low-pressure cylinder is obtained.
7. The method as claimed in claim 6, wherein the solving of the power system time series production simulation model based on daily rolling is performed by using a time domain decomposition method and a solution-free automatic back-rolling method.
8. The method of claim 1, wherein the iterative optimization of the energy storage planning capacity according to the set new energy absorption value boundary adopts a binary iteration method, comprising:
s41, determining a preset new energy consumption rate target value k 0 ％；
S42, planning the capacity reference value M according to the energy storage of the target planning time interval (0) Calculating a corresponding new energy consumption value by using the power system time sequence production simulation model based on day-by-day rolling, and calculating a new energy consumption rate k% according to the new energy consumption value;
if k% ≠ k 0 % is based on k% and k 0 % size relation, determining an adjustment interval of the energy storage planning capacity: if k%<k 0 Percent, the energy storage planning capacity interval is [ D% (0) ,U (0) ]Wherein U is (0) ＝M (0) ，D (0) ＝1/2M (0) (ii) a If k%>k 0 Percent, the energy storage planning capacity interval [ D (0) ,U (0) ]In (D) (0) ＝M (0) ，U (0) ＝2M (0) (ii) a Turning to the step S43 to carry out iterative optimization of the energy storage planning capacity;
if k% = k 0 Percent, stopping iteration and adding M (0) As an optimal energy storage planning capacity;
s43, in the ith iteration, according to M (i) ＝(D (i-1) +U (i-1) ) /2 updating the energy storage capacity planning value and setting the current energy storage capacity planning value M (i) When substituting the electric power system based on rolling day by dayProducing the simulation model in sequence, obtaining corresponding new energy consumption value, and calculating new energy consumption rate k according to the new energy consumption value i Percent, new energy consumption rate k i % and k 0 % comparison:
a) If k is i ％>k 0 And percent, updating the energy storage planning capacity interval to [ D ] (i) ,U (i) ]Wherein, U (i) ＝M (i) ，D (i) ＝D (i-1) Returning to the step S43 to perform iteration for the (i + 1) th time;
b) If k is i ％<k 0 And percent, updating the energy storage planning capacity interval to be D (i) ,U (i) ]Wherein, U (i) ＝U (i-1) ，D (i) ＝M (i) Returning to the step S43 to perform iteration for the (i + 1) th time;
c) If k is i ％＝k 0 % stopping iteration, and obtaining the current energy storage planning capacity value M (i) As an optimal energy storage planning capacity;
and S44, determining the optimal energy storage planning capacity as the energy storage planning configuration of the target planning time interval.
9. An energy storage capacity optimal configuration device based on electric power system time sequence production simulation is characterized by comprising:
a first power system time series production simulation model determination module configured to determine a power system time series production simulation model based on unit aggregation;
the energy storage planning capacity reference value calculation module is configured for utilizing the power system time sequence production simulation model based on the unit aggregation to perform simulation calculation on the power system time sequence operation process in the target planning time period by taking the minimized system operation cost and the maximized new energy output as optimization targets, and solving to obtain an energy storage planning capacity reference value in the target planning time period;
the second power system time sequence production simulation model determining module is configured to determine a power system time sequence production simulation model based on daily rolling and solve a new energy consumption value under the specified energy storage planning capacity;
the iterative optimization module is configured to perform iterative optimization on the energy storage planning capacity according to the set new energy consumption value boundary, so that the new energy consumption rate corresponding to the energy storage planning capacity approaches a preset new energy consumption rate expected value: adjusting the energy storage planning capacity in each iteration based on the energy storage planning capacity reference value of the target planning time interval, and calculating a corresponding new energy consumption value by using the power system time sequence production simulation model based on daily rolling based on the adjusted energy storage planning capacity;
and the energy storage planning capacity determining module is configured to use the optimal energy storage planning capacity obtained by the iterative optimization as the energy storage planning capacity of the target planning time interval.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method for energy storage capacity optimal configuration based on a time series production simulation of an electric power system according to any one of claims 1 to 8.
CN202211163037.7A 2022-09-23 2022-09-23 Energy storage capacity optimal configuration method based on power system time sequence production simulation Pending CN115425668A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115566680A (en) * 2022-12-05 2023-01-03 中国电力科学研究院有限公司 New energy power system time sequence production simulation operation optimization method and device
CN115879330A (en) * 2023-02-28 2023-03-31 南方电网数字电网研究院有限公司 Multi-energy power supply multi-point layout determination method and device based on time sequence production simulation
CN116611711A (en) * 2023-07-05 2023-08-18 深圳海辰储能控制技术有限公司 Energy storage project analysis system, method, equipment and readable storage medium

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115566680A (en) * 2022-12-05 2023-01-03 中国电力科学研究院有限公司 New energy power system time sequence production simulation operation optimization method and device
CN115879330A (en) * 2023-02-28 2023-03-31 南方电网数字电网研究院有限公司 Multi-energy power supply multi-point layout determination method and device based on time sequence production simulation
CN115879330B (en) * 2023-02-28 2023-12-12 南方电网数字电网研究院有限公司 Multi-energy power supply multipoint layout determining method and device based on time sequence production simulation
CN116611711A (en) * 2023-07-05 2023-08-18 深圳海辰储能控制技术有限公司 Energy storage project analysis system, method, equipment and readable storage medium
CN116611711B (en) * 2023-07-05 2023-10-13 深圳海辰储能控制技术有限公司 Energy storage project analysis system, method, equipment and readable storage medium

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