CN117937559A - Wind-solar-fire storage system multi-objective decision-making method based on trusted capacity substitution effect - Google Patents
Wind-solar-fire storage system multi-objective decision-making method based on trusted capacity substitution effect Download PDFInfo
- Publication number
- CN117937559A CN117937559A CN202311784605.XA CN202311784605A CN117937559A CN 117937559 A CN117937559 A CN 117937559A CN 202311784605 A CN202311784605 A CN 202311784605A CN 117937559 A CN117937559 A CN 117937559A
- Authority
- CN
- China
- Prior art keywords
- unit
- power
- capacity
- scene
- adequacy
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 104
- 238000003860 storage Methods 0.000 title claims abstract description 32
- 230000000694 effects Effects 0.000 title claims abstract description 24
- 238000006467 substitution reaction Methods 0.000 title claims abstract description 24
- 230000008030 elimination Effects 0.000 claims abstract description 8
- 238000003379 elimination reaction Methods 0.000 claims abstract description 8
- 238000000342 Monte Carlo simulation Methods 0.000 claims abstract description 7
- 230000009467 reduction Effects 0.000 claims abstract description 5
- 238000004146 energy storage Methods 0.000 claims description 48
- 238000010248 power generation Methods 0.000 claims description 44
- 230000008569 process Effects 0.000 claims description 24
- 238000004364 calculation method Methods 0.000 claims description 18
- 238000011156 evaluation Methods 0.000 claims description 17
- 238000005457 optimization Methods 0.000 claims description 14
- 230000009194 climbing Effects 0.000 claims description 13
- 238000010977 unit operation Methods 0.000 claims description 10
- 230000001932 seasonal effect Effects 0.000 claims description 8
- 238000007599 discharging Methods 0.000 claims description 6
- 238000012423 maintenance Methods 0.000 claims description 6
- 238000011160 research Methods 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 6
- 238000005516 engineering process Methods 0.000 claims description 4
- 230000009471 action Effects 0.000 claims description 3
- 230000008878 coupling Effects 0.000 claims description 3
- 238000010168 coupling process Methods 0.000 claims description 3
- 238000005859 coupling reaction Methods 0.000 claims description 3
- 230000003828 downregulation Effects 0.000 claims description 3
- 230000014759 maintenance of location Effects 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 230000003827 upregulation Effects 0.000 claims description 3
- 230000033228 biological regulation Effects 0.000 description 3
- 230000001276 controlling effect Effects 0.000 description 3
- 230000005611 electricity Effects 0.000 description 3
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 229910052739 hydrogen Inorganic materials 0.000 description 2
- 239000001257 hydrogen Substances 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 238000012614 Monte-Carlo sampling Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a wind, light and fire storage system multi-objective decision method based on a trusted capacity substitution effect, which is characterized in that: comprising the following steps: step 1: aiming at the uncertainty problem of wind power, photovoltaics and loads, an error model is established through an autoregressive moving average model, a Monte Carlo method is utilized to generate a scene, a rapid previous generation elimination method based on probability distance is utilized to carry out scene reduction, and the uncertainty problem is converted into a deterministic problem through the scene method; step 2: taking the substitution effect of the new energy source on the traditional energy source into consideration to obtain the abundant capacity of the new energy source, and calculating the sufficient capacity of the system; step 3: establishing an objective function of a scheduling decision model based on two-stage random planning; step 4: constraint conditions of a scheduling decision model of system adequacy are considered; according to the method, the economical efficiency and the adequacy demand of the power system during operation are comprehensively considered, a multi-objective decision model is established, and the purpose of influencing the degree of influence of the economical efficiency and adequacy of the manual intervention system is achieved by adopting a weighted summation method.
Description
Technical Field
The invention relates to the technical field of power system operation scheduling, in particular to a wind, light and fire storage system multi-objective decision method based on a trusted capacity substitution effect.
Background
Under the domestic background of deepening the reform of the electric power system and constructing a novel electric power system taking new energy as a main body, the clean energy installation capacity of China is continuously increased, and the clean energy installation capacity is an important part of electric power supply. However, the increase of the new energy source ratio also brings problems and challenges, and the utilization hours of the thermal power is obviously reduced due to the increase of the new energy source, the output ratio of the adjustable unit in the system is obviously reduced, and the running rate of the power unit is overall low, so that the overall adequacy of the power system is reduced or even insufficient. Insufficient adequacy can lead to the capability of the system to cope with sudden events such as load surge or main power supply failure, for example, voltage breakdown phenomenon can occur to the system due to lack of power supply when a main power unit jumps, and large-scale power failure accidents can occur when new energy output is lower than prediction and load surge occurs. Ensuring power reliability supply is the first fundamental principle to deepen the reform of the power system, so as an important part of the reliability of the power system, it is important to ensure the adequacy of the system in power dispatching.
The structure of the traditional energy system is relatively simple and can be adjusted to a certain extent, so that the adequacy of the traditional energy system is often only required to consider the primary energy characteristic. However, for the wind, light and fire combined system, the complex influence of randomness, volatility and non-adjustability of the new energy unit on the adequacy of the power system needs to be considered. Firstly, considering uncertainty of wind power, photovoltaic and load, converting the uncertainty problem into a deterministic problem, and providing basic input data for a scheduling decision model; secondly, considering the generating capacity adequacy of the electric power system, fully considering the difference between the conventional unit and the new energy unit and the difference between the stored energy when the adequacy is evaluated, and considering that the new energy and the stored energy system can replace the capacity of the conventional unit to characterize the available generating capacity under the condition of operating adequacy; then, calculating the power generation capacity adequacy of the system on the basis of the available power generation capacity to quantify the adequacy of the power system; finally, the optimal scheduling scheme of the wind, light and fire storage system is obtained by taking the optimal economy and adequacy of the system during operation as targets and taking the adequacy of the power generation capacity, the output of a unit, climbing, minimum startup and shutdown and the like as constraint conditions.
Many researches on the operation decision of a complex power system containing new energy are conducted at present, but the researches considering the adequacy of the power generation capacity of the system are insufficient.
The existing patent documents for complex power system operation decision and adequacy assessment are as follows:
Patent document 1: the invention discloses a two-stage regulation and control method (application number: CN 202310894198.1) of an electric power system taking source-load flexibility modification into consideration, which comprises the steps of constructing an upper model by taking maximum wind power consumption as a target, constructing a lower model by taking optimal thermal power dispatching power distribution, optimal wind power dispatching power distribution and high energy consumption load dispatching power distribution as targets, regulating and controlling the electric power system, and improving wind power consumption.
Patent document 2: the Chinese patent (application number: CN 202310442884.5) discloses a method for evaluating the peak shaving adequacy of a power system, provides a method for evaluating the peak shaving adequacy of the system taking the wind-light space-time correlation into account, adopts a Monte Carlo method for sampling and calculating peak shaving adequacy indexes, and adds all levels of peak shaving adequacy indexes to obtain the total peak shaving adequacy index of the system.
Patent document 3: the China patent (CN 202310761097.7) discloses a short-term battery energy storage and seasonal hydrogen storage collaborative planning method and system, which are used for constructing seasonal electric power and electric quantity balance indexes for achieving seasonal matching of renewable energy and load demands and establishing a typical daily operation model based on generalized adequacy assessment indexes so as to determine a short-term battery energy storage and seasonal hydrogen storage joint planning model which takes the generalized adequacy demands into account.
The above patent document 1 builds a flexible regulation method with the aim of wind power consumption, the patent document 2 builds a peak regulation adequacy index based on monte carlo sampling, and the patent document 3 builds a seasonal electric power and electricity balance index and a generalized adequacy evaluation index. Most of the optimization patent documents (such as document 1) currently mainly consider economy, new energy consumption or carbon emission, while the optimization problems (such as documents 2-3) related to adequacy are mainly based on the overall adequacy condition of the actual operation state evaluation system. Based on the above analysis, the deficiencies of the prior art patents are specifically as follows:
(1) The factors influencing the actual power generation capacity of the power supply, such as primary energy supply, unit technical characteristics and the like, are not considered enough.
(2) Only the adequacy of the system as a whole is of interest, without quantifying the power generation capacity of the different types of units and the value they contribute to the power generation capacity adequacy.
(3) When the power generation capacity of the new energy unit is quantized, the concept of utilizing the trusted capacity is not considered to measure the substitution effect of 'new energy+stored energy' on the traditional energy.
Disclosure of Invention
The invention aims to overcome the defects and provide a wind-solar-fire storage system multi-objective decision method based on a trusted capacity substitution effect so as to solve the problems in the background technology.
In order to solve the technical problems, the invention adopts the following technical scheme: a wind-light-fire storage system multi-objective decision method based on a trusted capacity substitution effect comprises the following steps:
Step 1: aiming at the uncertainty problem of wind power, photovoltaics and loads, an error model is established through an autoregressive moving average model, a Monte Carlo method is utilized to generate a scene, a rapid previous generation elimination method based on probability distance is utilized to carry out scene reduction, and the uncertainty problem is converted into a deterministic problem through the scene method;
step 2: taking the substitution effect of the new energy source on the traditional energy source into consideration to obtain the abundant capacity of the new energy source, and calculating the sufficient capacity of the system;
Step 3: establishing an objective function of a scheduling decision model based on two-stage random planning;
step 4: constraints of the scheduling decision model that consider system adequacy.
Further, in the step 1, an autoregressive moving average model is adopted to generate an error scene of wind-light output prediction:
Wherein a and b are the orders of the autoregressive moving average model autoregressive and the moving average part respectively; phi i is an autoregressive parameter; θ j is a running average parameter; omega t is white noise subject to normal distribution with mean 0 and variance sigma 2;
The error scene is combined with the prediction data to obtain a series of random scenes, and the random scenes are set as a set S;
s∈S
Wherein X t,s is a vector of a random scene s at time t; The load, wind power and photovoltaic power under the random scene s at the time t are respectively.
Further, in the step1, the scene is cut based on the fast previous generation elimination technology of the probability distance, so as to obtain a cut scene set and a corresponding probability, and the specific steps are as follows:
1.1, calculating the geometric distance of each pair of scenes S and S' in the set S;
1.2, selecting a scene d with the smallest sum of probability distances to the rest scenes;
1.3, replacing the scene d with the scene r closest to the geometric distance of the scene d in the set S, adding the probability of d to the probability of the scene r, and eliminating d to form a new set S';
1.4, judging whether the number of the residual scenes meets the requirement; if not, repeating the steps 1.1-1.3; if so, ending.
Further, the step 2 specifically includes the following steps:
2.1, calculating the surplus capacity of the conventional unit:
the initial capacity of the conventional unit is determined according to the maximum power generation capacity of the unit under the conditions of supply limitation of energy sources, seasonal characteristics of supply and demand balance and the like, and the unit power consumption condition and annual scheduled maintenance condition of the unit are considered, and the initial capacity is adjusted as follows:
In the method, in the process of the invention, The surplus capacity of the unit i after the planned outage factors are considered; /(I)The historical maximum power of the thermal power generating unit in the period of three years is obtained; /(I)A penalty factor proportional to the power plant of the generator set; /(I)A penalty factor proportional to annual scheduled service maintenance time for the genset;
Considering the unplanned outage factors of the generator set, describing the unplanned outage situation of the generator set by adopting a multi-state generator set probability model according to the historical and empirical statistics of the forced outage rate of the generator set, and calculating the abundance capacity of the thermal power unit as follows:
In the method, in the process of the invention, Is the abundant capacity/>, of the conventional unit iEpsilon i is the forced outage rate of the unit, and p run、pbreak is the outage probability caused by normal operation and failure of the unit respectively;
2.2, starting from the power generation side, the method for calculating the trusted capacity of the new energy comprises the following steps:
on the premise of equal reliability, the new energy unit can replace the capacity of the conventional unit in calculation:
f(Cg,Cw,Cv,L)＝f(Cg,Cn,L)
wherein f is a reliability test function, C g is the installed capacity of a conventional unit contained in the system, C w、Cv is the installed capacity of a wind power unit and a photovoltaic unit contained in the system, L is the load power level of the system, and C n is the equivalent unit capacity of the new energy unit replaced by the conventional unit; in the calculation, the reliability test function may take the probability of generating insufficient power (LOLP), the expected time for system load loss (LOLE), and the expected value for power shortage (EENS) as evaluation criteria for system reliability, and specifically as follows:
fLOLP＝p{[Xdc(t-1)+Pg(t)+Pw(t)+Pv(t)<Pl(t)]}
fLOLE＝T·p{[min(Xdc(t-1),Pdc,max)+Pg(t)+Pw(t)+Pv(t)<Pl(t)]}
Wherein T is a research period, P is the occurrence probability of an event, X dc is the residual available electric quantity of the energy storage device, P dc,max is the maximum power of the energy storage device, and P g、Pw、Pv、Pl is the power of a conventional unit, a wind unit, a photovoltaic unit and a load respectively;
after the new energy power generation capacity is equivalent to the trusted capacity, the new energy power generation capacity can be adjusted by adopting a conventional unit abundant capacity calculation method;
2.3, calculating the power generation capacity adequacy of the system:
The system power generation capacity adequacy is defined as the ratio of the system capacity adequacy to the system peak load to describe the ability of the power system to supply power to all power consumers during normal operation:
wherein G is the sufficient power generation capacity of the power system, Is the abundant capacity of the conventional unit i/>For the equivalent of the wind-solar unit as the calculated abundant capacity after the conventional unit, D peak is the peak load of the system, and the average value of the load corresponding to the m highest load periods in the annual load curve is calculated in a certain system or subsystem.
Further, the step 3 specifically includes the following steps:
3.1, objective function one: the unit operation cost is minimized:
The first stage aims at minimizing the start-stop cost of the thermal power generating unit, and the second stage aims at minimizing the running cost of the generating unit (comprising wind, light and fire) and energy storage, and an optimization problem model is established:
minF1＝FI+E[FR]
Wherein F I is the first stage cost, E [ F R ] is the expected value of the second stage cost;
The thermal power generating unit cannot be controlled through real-time decision, and the start-stop cost is high, so that the start-stop of the thermal power generating unit needs to be decided in advance, and the first stage aims at minimizing the start-stop cost of the thermal power generating unit, and the expression is as follows:
Wherein T is the scheduling duration of the lower model, and N G is the number of thermal power units; The starting/stopping cost of the thermal power; x n,t represents a starting action state of the thermal power unit n at the time t; x n,t =1 indicates a start of the unit, and X n,t =0 indicates no unit operation; y n,t represents a shutdown operation state of the thermal power generating unit n at the time t, y n,t =1 represents unit shutdown, and y n,t =0 represents unit inactivity;
after determining a start-stop strategy of the thermal power generating unit, controlling the output of each unit in actual operation; the second stage takes the running cost as a scheduling target:
Wherein S represents the scene number; ρ s represents the probability of scene s; Representing the cost of the nth thermal power generating unit under a scene s at the moment t; /(I) Representing the cost of the mth wind turbine generator under the scene s at the moment t; /(I)Representing the cost of the first photovoltaic unit under a scene s at the moment t; /(I)Representing the cost of energy storage in a scene s at the moment t;
3.2, objective function two: system power generation capacity adequacy optimization:
Optimizing the power generation capacity adequacy of the system, namely establishing an objective function with the maximum power generation capacity adequacy of the system; as can be seen from the adequacy calculating method in step 1, the adequacy of the system depends on the output of each unit, so in the two-stage stochastic programming, the adequacy objective function has only the second stage, namely:
3.3, converting the multi-target into a single target by using a min-max standardization and weighted summation method:
the dimension of economy and adequacy are inconsistent, so that min-max normalization processing is needed for the two indexes; the positive index is better in evaluation as the index value is larger, the negative index is better in evaluation as the index value is smaller, so that the economy is the target of the negative index, and the adequacy index is the positive index;
forward index—adequacy index:
negative index-economic index:
wherein F 1、F2 is an original evaluation index value, and F 1′、F2' is a normalized evaluation index value;
Converting the weighted sum of the economic and the adequacy targets into a single target:
min F＝ω1F1'+ω2F2'
where ω 1、ω2 is the weight of target one and target two, respectively, and can be set according to the decision maker's preference.
Further, the step 4 specifically includes the following steps:
4.1, a first-stage constraint condition;
Thermal power generating unit start-stop constraint:
Wherein, beta n,t is a unit working state mark, beta n,t =1 indicates that the unit n is in a working state from time t to time t+1, and beta n,t =0 indicates that the unit n is in a stop state from time t to time t+1;
Thermal power generating unit operation time constraint:
the start-stop of the thermal power generating unit is required to observe the constraint of the running time, and the unit must not be started when the unit does not reach the minimum waiting time, and the unit must not be stopped when the unit does not reach the minimum running time;
In the method, in the process of the invention, Representing the minimum waiting time of the unit n/>Representing the minimum running time of the unit n;
4.2, constraint conditions of the second stage;
abundance constraints:
Gmin≤G≤Gmax
Wherein G min、Gmax is the lower limit and the upper limit allowed by the system adequacy respectively, which can be set by the decision maker;
Power supply and demand balance constraint:
The wind, light and fire storages meet the electric load requirements together, and the balance of power supply and demand is ensured;
Pt g+Pt w+Pt p+Pt s＝Pt load
In the method, in the process of the invention, The power of thermal power, wind power, photovoltaic power, energy storage and load at the moment t respectively;
upper and lower limit constraint for safe operation of thermal power generating unit:
In the method, in the process of the invention, The lower limit and the upper limit of the running power of the thermal power unit are respectively set;
Climbing constraint of thermal power generating unit:
The climbing constraint of the unit is a coupling relation of the output of the unit in adjacent time, and the active power of the unit cannot be instantly completed, so that the limitation of the up-regulation (climbing) and down-regulation (landslide) of the unit is required;
In the method, in the process of the invention, The upper limit of landslide and climbing power in a unit period is respectively set, and delta t is the step length;
Upper and lower limit constraint of charging and discharging power of the energy storage system:
In the method, in the process of the invention, The upper limit and the lower limit of the energy storage charge and discharge power are set;
Energy storage state of charge constraints:
The state of charge calculation formula is as follows:
The SOC (t) is the state of charge of energy storage at the moment t; p (t) is the charge and discharge power of energy storage at t moment, the discharge is +, and the charge is-; e is the rated capacity of energy storage; gamma c、γd is energy storage charging efficiency and energy storage discharging efficiency; the state of charge constraints of the energy storage system are as follows:
In the method, in the process of the invention, The lower and upper limits of the operating range are allowed for the stored state of charge.
The invention has the beneficial effects that:
1. The invention adopts the equal-reliability credible capacity substitution effect to quantify the generating capacity adequacy of the new energy, and the new energy is equivalent to a conventional unit, thereby being convenient for evaluating and comparing the contribution of the conventional unit and the new energy unit to the generating capacity of the electric power system when the source and the load are cooperatively operated;
2. The invention adopts a two-stage stochastic programming model based on a scene method to solve the problem of uncertainty in system scheduling, and obtains an optimal unit scheduling scheme;
3. According to the method, the economical efficiency and the adequacy demand of the power system during operation are comprehensively considered, a multi-objective decision model is established, and the purpose of influencing the degree of influence of the economical efficiency and adequacy of the manual intervention system is achieved by adopting a weighted summation method;
4. The invention constructs a wind-solar-fire storage system comprising a common traditional unit and a new energy unit, and provides a power generation capacity adequacy assessment method of the system; the new energy unit is converted into a conventional unit by utilizing the new energy credible capacity substitution effect under the principle of equal reliability, and the new energy unit is used for measuring the contribution of the new energy unit to the system adequacy; the economy and the adequacy of power system dispatching are comprehensively considered, so that a decision maker can balance the economy and the adequacy of the system according to the system requirements.
Drawings
FIG. 1 is a flow chart of a scene method of the invention;
FIG. 2 is a multi-objective two-stage planning structure diagram of the wind-solar-fire storage system of the invention;
FIG. 3 is a graph illustrating the output of a typical solar energy, wind and fire storage system according to an embodiment of the present invention;
FIG. 4 is a graph of wind power output scenario generation and curtailment according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an optimal start-stop plan of a conventional unit according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and the specific examples.
Example 1: a wind-light-fire storage system multi-objective decision method based on a trusted capacity substitution effect comprises the following steps:
Step 1: aiming at the uncertainty problem of wind power, photovoltaics and loads, an error model is established through an autoregressive moving average model, a Monte Carlo method is utilized to generate a scene, a rapid previous generation elimination method based on probability distance is utilized to carry out scene reduction, and the uncertainty problem is converted into a deterministic problem through the scene method; as shown in fig. 1.
Further, in the step 1, an autoregressive moving average model is adopted to generate an error scene of wind-light output prediction:
Wherein a and b are the orders of the autoregressive moving average model autoregressive and the moving average part respectively; phi i is an autoregressive parameter; θ j is a running average parameter; omega t is white noise subject to normal distribution with mean 0 and variance sigma 2;
The error scene is combined with the prediction data to obtain a series of random scenes, and the random scenes are set as a set S;
s∈S
Wherein X t,s is a vector of a random scene s at time t; The load, wind power and photovoltaic power under the random scene s at the time t are respectively.
Further, in the step1, the scene is cut based on the fast previous generation elimination technology of the probability distance, so as to obtain a cut scene set and a corresponding probability, and the specific steps are as follows:
1.1, calculating the geometric distance of each pair of scenes S and S' in the set S;
1.2, selecting a scene d with the smallest sum of probability distances to the rest scenes;
1.3, replacing the scene d with the scene r closest to the geometric distance of the scene d in the set S, adding the probability of d to the probability of the scene r, and eliminating d to form a new set S';
1.4, judging whether the number of the residual scenes meets the requirement; if not, repeating the steps 1.1-1.3; if so, ending.
Step 2: taking the substitution effect of the new energy source on the traditional energy source into consideration to obtain the abundant capacity of the new energy source, and calculating the sufficient capacity of the system;
Further, the step 2 specifically includes the following steps:
2.1, calculating the surplus capacity of the conventional unit:
the initial capacity of the conventional unit is determined according to the maximum power generation capacity of the unit under the conditions of supply limitation of energy sources, seasonal characteristics of supply and demand balance and the like, and the unit power consumption condition and annual scheduled maintenance condition of the unit are considered, and the initial capacity is adjusted as follows:
In the method, in the process of the invention, The historical maximum power of the thermal power generating unit in the period of three years is obtained; /(I)The surplus capacity of the unit i after the planned outage factors are considered; /(I)A penalty factor proportional to the power plant of the generator set; /(I)A penalty factor proportional to annual scheduled service maintenance time for the genset;
Considering the unplanned outage factors of the generator set, describing the unplanned outage situation of the generator set by adopting a multi-state generator set probability model according to the historical and empirical statistics of the forced outage rate of the generator set, and calculating the abundance capacity of the thermal power unit as follows:
In the method, in the process of the invention, Is the abundant capacity of the conventional unit i/>Epsilon i is the forced outage rate of the unit, and p run、pbreak is the outage probability caused by normal operation and failure of the unit respectively;
2.2, starting from the power generation side, the method for calculating the trusted capacity of the new energy comprises the following steps:
on the premise of equal reliability, the new energy unit can replace the capacity of the conventional unit in calculation:
f(Cg,Cw,Cv,L)＝f(Cg,Cn,L)
wherein f is a reliability test function, C g is the installed capacity of a conventional unit contained in the system, C w、Cv is the installed capacity of a wind power unit and a photovoltaic unit contained in the system, L is the load power level of the system, and C n is the equivalent unit capacity of the new energy unit replaced by the conventional unit; in the calculation, the reliability test function may take the probability of generating insufficient power (LOLP), the expected time for system load loss (LOLE), and the expected value for power shortage (EENS) as evaluation criteria for system reliability, and specifically as follows:
fLOLP＝p{[Xdc(t-1)+Pg(t)+Pw(t)+Pv(t)<Pl(t)]}
fLOLE＝T·p{[min(Xdc(t-1),Pdc,max)+Pg(t)+Pw(t)+Pv(t)<Pl(t)]}
Wherein T is a research period, P is the occurrence probability of an event, X dc is the residual available electric quantity of the energy storage device, P dc,max is the maximum power of the energy storage device, and P g、Pw、Pv、Pl is the power of a conventional unit, a wind unit, a photovoltaic unit and a load respectively;
after the new energy power generation capacity is equivalent to the trusted capacity, the new energy power generation capacity can be adjusted by adopting a conventional unit abundant capacity calculation method;
2.3, calculating the power generation capacity adequacy of the system:
The system power generation capacity adequacy is defined as the ratio of the system capacity adequacy to the system peak load to describe the ability of the power system to supply power to all power consumers during normal operation:
wherein G is the sufficient power generation capacity of the power system, Is the abundant capacity of the conventional unit i/>For the equivalent of the wind-solar unit as the calculated abundant capacity after the conventional unit, D peak is the peak load of the system, and the average value of the load corresponding to the m highest load periods in the annual load curve is calculated in a certain system or subsystem.
Step 3: establishing an objective function of a scheduling decision model based on two-stage random planning;
further, the step 3 specifically includes the following steps:
3.1, objective function one: the unit operation cost is minimized:
The first stage aims at minimizing the start-stop cost of the thermal power generating unit, and the second stage aims at minimizing the running cost of the generating unit (comprising wind, light and fire) and energy storage, and an optimization problem model is established:
minF1＝FI+E[FR]
Wherein F I is the first stage cost, E [ F R ] is the expected value of the second stage cost;
The thermal power generating unit cannot be controlled through real-time decision, and the start-stop cost is high, so that the start-stop of the thermal power generating unit needs to be decided in advance, and the first stage aims at minimizing the start-stop cost of the thermal power generating unit, and the expression is as follows:
Wherein T is the scheduling duration of the lower model, and N G is the number of thermal power units; The starting/stopping cost of the thermal power; x n,t represents a starting action state of the thermal power unit n at the time t; x n,t =1 indicates a start of the unit, and X n,t =0 indicates no unit operation; y n,t represents a shutdown operation state of the thermal power generating unit n at the time t, y n,t =1 represents unit shutdown, and y n,t =0 represents unit inactivity;
after determining a start-stop strategy of the thermal power generating unit, controlling the output of each unit in actual operation; the second stage takes the running cost as a scheduling target:
Wherein S represents the scene number; ρ s represents the probability of scene s; Representing the cost of the nth thermal power generating unit under a scene s at the moment t; /(I) Representing the cost of the mth wind turbine generator under the scene s at the moment t; /(I)Representing the cost of the first photovoltaic unit under a scene s at the moment t; /(I)Representing the cost of energy storage in a scene s at the moment t;
3.2, objective function two: system power generation capacity adequacy optimization:
Optimizing the power generation capacity adequacy of the system, namely establishing an objective function with the maximum power generation capacity adequacy of the system; as can be seen from the adequacy calculating method in step 1, the adequacy of the system depends on the output of each unit, so in the two-stage stochastic programming, the adequacy objective function has only the second stage, namely:
3.3, converting the multi-target into a single target by using a min-max standardization and weighted summation method:
the dimension of economy and adequacy are inconsistent, so that min-max normalization processing is needed for the two indexes; the positive index is better in evaluation as the index value is larger, the negative index is better in evaluation as the index value is smaller, so that the economy is the target of the negative index, and the adequacy index is the positive index;
forward index—adequacy index:
negative index-economic index:
wherein F 1、F2 is an original evaluation index value, and F 1′、F2' is a normalized evaluation index value;
Converting the weighted sum of the economic and the adequacy targets into a single target:
min F＝ω1F1'+ω2F2'
where ω 1、ω2 is the weight of target one and target two, respectively, and can be set according to the decision maker's preference.
Step 4: constraints of the scheduling decision model that consider system adequacy.
Further, the step 4 specifically includes the following steps:
4.1, a first-stage constraint condition;
Thermal power generating unit start-stop constraint:
Wherein, beta n,t is a unit working state mark, beta n,t =1 indicates that the unit n is in a working state from time t to time t+1, and beta n,t =0 indicates that the unit n is in a stop state from time t to time t+1;
The correlation between the unit operation state parameter beta n,t and the unit start-stop state parameter x n,t、yn,t is shown in table 1:
table 1 table for comparing working state parameters of machine set and start-stop state of machine set
Thermal power generating unit operation time constraint:
the start-stop of the thermal power generating unit is required to observe the constraint of the running time, and the unit must not be started when the unit does not reach the minimum waiting time, and the unit must not be stopped when the unit does not reach the minimum running time;
In the method, in the process of the invention, Representing the minimum waiting time of the unit n/>Representing the minimum running time of the unit n;
4.2, constraint conditions of the second stage;
abundance constraints:
Gmin≤G≤Gmax
Wherein G min、Gmax is the lower limit and the upper limit allowed by the system adequacy respectively, which can be set by the decision maker;
Power supply and demand balance constraint:
The wind, light and fire storages meet the electric load requirements together, and the balance of power supply and demand is ensured;
Pt g+Pt w+Pt p+Pt s＝Pt load
In the method, in the process of the invention, The power of thermal power, wind power, photovoltaic power, energy storage and load at the moment t respectively;
upper and lower limit constraint for safe operation of thermal power generating unit:
In the method, in the process of the invention, The lower limit and the upper limit of the running power of the thermal power unit are respectively set;
Climbing constraint of thermal power generating unit:
The climbing constraint of the unit is a coupling relation of the output of the unit in adjacent time, and the active power of the unit cannot be instantly completed, so that the limitation of the up-regulation (climbing) and down-regulation (landslide) of the unit is required;
In the method, in the process of the invention, The upper limit of landslide and climbing power in a unit period is respectively set, and delta t is the step length;
Upper and lower limit constraint of charging and discharging power of the energy storage system:
In the method, in the process of the invention, The upper limit and the lower limit of the energy storage charge and discharge power are set;
Energy storage state of charge constraints:
The state of charge calculation formula is as follows:
The SOC (t) is the state of charge of energy storage at the moment t; p (t) is the charge and discharge power of energy storage at t moment, the discharge is +, and the charge is-; e is the rated capacity of energy storage; gamma c、γd is energy storage charging efficiency and energy storage discharging efficiency; the state of charge constraints of the energy storage system are as follows:
In the method, in the process of the invention, The lower and upper limits of the operating range are allowed for the stored state of charge.
In this embodiment, the system research object is a hybrid system including thermal power, wind power, photovoltaic and energy storage, and the model is a two-stage stochastic programming model considering economy and adequacy of the system, as shown in fig. 2:
From an optimization objective, the multi-objective decision model includes economy and adequacy: ① The running cost of the wind-solar-fire storage system is considered in economical efficiency, wherein the start-stop cost of the thermal power unit is extremely high in the duty ratio of the power generation cost, and the start-stop of the thermal power unit is determined to be one of main targets of scheduling, so that the model separately considers the start-stop cost of the thermal power unit and the wind-solar-fire storage and transportation cost; ② The abundant computing objects are divided into a conventional unit and a new energy unit, and due to the fluctuation and the non-adjustability of the new energy, the abundant quantification adopts the reliability substitution effect, such as the like, and the new energy and the energy storage are equivalent to the conventional unit.
From the double-layer model, the model is a two-stage stochastic programming model: ① The upper layer makes a decision on the start-stop of the thermal power generating unit, the thermal power generating unit needs longer time from the start-stop command issuing to the actual running or the stop, and the start-stop cost is higher, and the thermal power generating unit cannot be controlled through real-time decision, so that the start-stop decision of the thermal power generating unit needs to be carried out under the condition that the wind power output and the electric load demand of the system are uncertain; ② The lower layer controls the power of the unit, the lower layer comprises an economical index and an adequacy index, a min-max standardization and weighted summation method is adopted to convert multiple targets into single targets, and besides, the uncertainty problem is converted into a deterministic scene by a scene method because the power of wind power, photovoltaic power and load has uncertainty.
Example 2: the set power system structure comprises 4 thermal power plants, 3 wind power stations, 3 photovoltaic power stations and 1 energy storage power station, and the total capacity of the assembly machine and corresponding parameters of each power station are shown in table 2.
Table 2 wind, light and fire storage system parameter table
And adopting a scene method to perform scene generation and scene reduction. And generating 500 wind power, photovoltaic and load output scenes by adopting a Monte Carlo method, reducing the scenes to 8 by adopting a rapid previous generation elimination method, and obtaining the probability of each scene. For example, as shown in fig. 3, the probability of eight scenes is 10%, 0.2%, 2.6%, 9.6%, 42.2%, 14.6%, 20.6%, and 0.2% respectively. Three scenes with the scene probability lower than 5% in eight scenes represent small probability events with the possible difference between the predicted value and the actual value being very large, and two scenes with the scene probability reaching more than 20% represent small probability events with the difference between the predicted value and the actual value being smaller. The simulation method has the advantages that not only is a small probability event simulated, but also a large probability event is simulated, the randomness of the Monte Carlo method is reflected, and the simulation data can be used for checking the capability of a dispatching plan to cope with random events such as power system prediction errors or sudden faults.
Substituting the unit parameters and the simulated output data into an optimization model, constructing an economical and abundant objective function, reasonably defining constraint conditions, and solving the model by using CPLEX of software MATLAB to obtain an optimal start-stop plan of 10 conventional units as shown in figure 4.
According to the richness of the power supply side power supply types and the difference of optimal scheduling targets of decision makers, three calculation examples are set for comparison:
Calculation example 1: the system is only powered by a conventional unit, and only economic efficiency is considered.
Calculation example 2: the system is powered only by conventional units, taking economy and adequacy into account.
Calculation example 3: the system is powered by wind power and photovoltaic of a conventional thermal power unit and a new energy unit in a cooperative manner, and only economic efficiency is considered.
Calculation example 4: the system is powered by wind power and photovoltaic of a conventional thermal power unit and a new energy unit in a coordinated manner, and economical efficiency and adequacy are considered.
And the related results are obtained through an optimization algorithm, and the maximum output power of the thermal power, the power generation cost, the adequacy, the electricity shortage and the electricity abandonment of the system in the four calculation examples are shown in a table 3. As can be seen from comparison of the examples 1,3, 2 and 4, the thermal power unit only bears the great power supply pressure in a single system containing the thermal power unit, and the maximum output power of the thermal power is far greater than that of the wind, light and fire storage system, so that a planner needs to set more thermal power units to meet the power demand of a user side. In addition, the power generation cost of the single thermal power generating unit system is far higher than that of the wind, light and fire storage system, and the risks of power shortage and power abandonment are more likely to be faced. As is clear from comparison of example 1 and example 2, example 3 and example 4, the system power generation cost is low but the adequacy is low when only the economical objective is considered, and the system power generation cost is slightly raised but the adequacy is greatly reduced when the economical and adequacy objective is considered in combination. In addition, as compared with single-objective optimization, the method and the device have the advantages that when multi-objective optimization is considered, the cost rise amplitude of the wind, light and fire storage system is smaller than that of a single thermal power system, but the adequacy rise amplitude of the wind, light and fire storage system is far larger than that of the single thermal power system. Compared with a single thermal power system, the wind-light storage system multi-target decision method based on the trusted capacity substitution effect is more suitable for wind-light-fire storage systems.
Table 3 comparison table of four scenario optimization results
Through the analysis of the examples, the rationality and feasibility of the wind-solar energy storage system multi-objective decision method based on the trusted capacity substitution effect are verified. The new energy unit is equivalent to a conventional unit by considering the credible capacity substitution effect of the new energy, so that the contribution of the wind power and the photovoltaic of the unregulated resources in the adequacy of the power system can be measured. And simulating the uncertainty of new energy output by a scene method, constructing a multi-target two-stage stochastic programming model considering economy and adequacy, obtaining an optimal unit start-stop plan of the wind-light fire storage system, and providing reference for a power system scheduling decision maker.
The above embodiments are only preferred embodiments of the present invention, and should not be construed as limiting the present invention, and the scope of the present invention should be defined by the claims, including the equivalents of the technical features in the claims. I.e., equivalent replacement modifications within the scope of this invention are also within the scope of the invention.
Claims (6)
1. A wind-solar-fire storage system multi-objective decision method based on a trusted capacity substitution effect is characterized by comprising the following steps of: it comprises the following steps:
Step 1: aiming at the uncertainty problem of wind power, photovoltaics and loads, an error model is established through an autoregressive moving average model, a Monte Carlo method is utilized to generate a scene, a rapid previous generation elimination method based on probability distance is utilized to carry out scene reduction, and the uncertainty problem is converted into a deterministic problem through the scene method;
step 2: taking the substitution effect of the new energy source on the traditional energy source into consideration to obtain the abundant capacity of the new energy source, and calculating the sufficient capacity of the system;
Step 3: establishing an objective function of a scheduling decision model based on two-stage random planning;
step 4: constraints of the scheduling decision model that consider system adequacy.
2. The wind-solar-fire storage system multi-objective decision-making method based on the trusted capacity substitution effect according to claim 1, wherein the method is characterized by comprising the following steps of: in the step 1, an autoregressive moving average model is adopted to generate an error scene of wind-light output prediction:
Wherein a and b are the orders of the autoregressive moving average model autoregressive and the moving average part respectively; phi i is an autoregressive parameter; θ j is a running average parameter; omega t is white noise subject to normal distribution with mean 0 and variance sigma 2;
The error scene is combined with the prediction data to obtain a series of random scenes, and the random scenes are set as a set S;
s∈S
Wherein X t,s is a vector of a random scene s at time t; The load, wind power and photovoltaic power under the random scene s at the time t are respectively.
3. The wind-solar-fire storage system multi-objective decision-making method based on the trusted capacity substitution effect according to claim 1, wherein the method is characterized by comprising the following steps of: in the step 1, the scene is cut based on the probability distance quick previous generation elimination technology, and a cut scene set and a corresponding probability are obtained, and the specific steps are as follows:
1.1, calculating the geometric distance of each pair of scenes S and S' in the set S;
1.2, selecting a scene d with the smallest sum of probability distances to the rest scenes;
1.3, replacing the scene d with the scene r closest to the geometric distance of the scene d in the set S, adding the probability of d to the probability of the scene r, and eliminating d to form a new set S';
1.4, judging whether the number of the residual scenes meets the requirement; if not, repeating the steps 1.1-1.3; if so, ending.
4. The wind-solar-fire storage system multi-objective decision-making method based on the trusted capacity substitution effect according to claim 1, wherein the method is characterized by comprising the following steps of: the step 2 specifically comprises the following steps:
2.1, calculating the surplus capacity of the conventional unit:
the initial capacity of the conventional unit is determined according to the maximum power generation capacity of the unit under the conditions of supply limitation of energy sources, seasonal characteristics of supply and demand balance and the like, and the unit power consumption condition and annual scheduled maintenance condition of the unit are considered, and the initial capacity is adjusted as follows:
In the method, in the process of the invention, The surplus capacity of the unit i after the planned outage factors are considered; /(I)The historical maximum power of the thermal power generating unit in the period of three years is obtained; /(I)A penalty factor proportional to the power plant of the generator set; /(I)A penalty factor proportional to annual scheduled service maintenance time for the genset;
Considering the unplanned outage factors of the generator set, describing the unplanned outage situation of the generator set by adopting a multi-state generator set probability model according to the historical and empirical statistics of the forced outage rate of the generator set, and calculating the abundance capacity of the thermal power unit as follows:
In the method, in the process of the invention, Is the abundant capacity of the conventional unit i/>Epsilon i is the forced outage rate of the unit, and p run、pbreak is the outage probability caused by normal operation and failure of the unit respectively;
2.2, starting from the power generation side, the method for calculating the trusted capacity of the new energy comprises the following steps:
on the premise of equal reliability, the new energy unit can replace the capacity of the conventional unit in calculation:
f(Cg,Cw,Cv,L)＝f(Cg,Cn,L)
wherein f is a reliability test function, C g is the installed capacity of a conventional unit contained in the system, C w、Cv is the installed capacity of a wind power unit and a photovoltaic unit contained in the system, L is the load power level of the system, and C n is the equivalent unit capacity of the new energy unit replaced by the conventional unit; in the calculation, the reliability test function may take the probability of generating insufficient power (LOLP), the expected time for system load loss (LOLE), and the expected value for power shortage (EENS) as evaluation criteria for system reliability, and specifically as follows:
fLOLP＝p{[Xdc(t-1)+Pg(t)+Pw(t)+Pv(t)<Pl(t)]}
fLOLE＝T·p{[min(Xdc(t-1),Pdc,max)+Pg(t)+Pw(t)+Pv(t)<Pl(t)]}
Wherein T is a research period, P is the occurrence probability of an event, X dc is the residual available electric quantity of the energy storage device, P dc,max is the maximum power of the energy storage device, and P g、Pw、Pv、Pl is the power of a conventional unit, a wind unit, a photovoltaic unit and a load respectively;
after the new energy power generation capacity is equivalent to the trusted capacity, the new energy power generation capacity can be adjusted by adopting a conventional unit abundant capacity calculation method;
2.3, calculating the power generation capacity adequacy of the system:
The system power generation capacity adequacy is defined as the ratio of the system capacity adequacy to the system peak load to describe the ability of the power system to supply power to all power consumers during normal operation:
wherein G is the sufficient power generation capacity of the power system, Is the abundant capacity of the conventional unit i/>For the equivalent of the wind-solar unit as the calculated abundant capacity after the conventional unit, D peak is the peak load of the system, and the average value of the load corresponding to the m highest load periods in the annual load curve is calculated in a certain system or subsystem.
5. The wind-solar-fire storage system multi-objective decision-making method based on the trusted capacity substitution effect according to claim 1, wherein the method is characterized by comprising the following steps of: the step 3 specifically comprises the following steps:
3.1, objective function one: the unit operation cost is minimized:
The first stage aims at minimizing the start-stop cost of the thermal power generating unit, and the second stage aims at minimizing the running cost of the generating unit (comprising wind, light and fire) and energy storage, and an optimization problem model is established:
minF1＝FI+E[FR]
Wherein F I is the first stage cost, E [ F R ] is the expected value of the second stage cost;
The thermal power generating unit cannot be controlled through real-time decision, and the start-stop cost is high, so that the start-stop of the thermal power generating unit needs to be decided in advance, and the first stage aims at minimizing the start-stop cost of the thermal power generating unit, and the expression is as follows:
Wherein T is the scheduling duration of the lower model, and N G is the number of thermal power units; The starting/stopping cost of the thermal power; x n,t represents a starting action state of the thermal power unit n at the time t; x n,t =1 indicates a start of the unit, and X n,t =0 indicates no unit operation; y n,t represents a shutdown operation state of the thermal power generating unit n at the time t, y n,t =1 represents unit shutdown, and y n,t =0 represents unit inactivity;
after determining a start-stop strategy of the thermal power generating unit, controlling the output of each unit in actual operation; the second stage takes the running cost as a scheduling target:
Wherein S represents the scene number; ρ s represents the probability of scene s; Representing the cost of the nth thermal power generating unit under a scene s at the moment t; /(I) Representing the cost of the mth wind turbine generator under the scene s at the moment t; /(I)Representing the cost of the first photovoltaic unit under a scene s at the moment t; /(I)Representing the cost of energy storage in a scene s at the moment t;
3.2, objective function two: system power generation capacity adequacy optimization:
Optimizing the power generation capacity adequacy of the system, namely establishing an objective function with the maximum power generation capacity adequacy of the system; as can be seen from the adequacy calculating method in step 1, the adequacy of the system depends on the output of each unit, so in the two-stage stochastic programming, the adequacy objective function has only the second stage, namely:
3.3, converting the multi-target into a single target by using a min-max standardization and weighted summation method:
the dimension of economy and adequacy are inconsistent, so that min-max normalization processing is needed for the two indexes; the positive index is better in evaluation as the index value is larger, the negative index is better in evaluation as the index value is smaller, so that the economy is the target of the negative index, and the adequacy index is the positive index;
forward index—adequacy index:
negative index-economic index:
wherein F 1、F2 is an original evaluation index value, and F 1′、F2' is a normalized evaluation index value;
Converting the weighted sum of the economic and the adequacy targets into a single target:
minF＝ω1F1'+ω2F2'
where ω 1、ω2 is the weight of target one and target two, respectively, and can be set according to the decision maker's preference.
6. The wind-solar-fire storage system multi-objective decision-making method based on the trusted capacity substitution effect according to claim 1, wherein the method is characterized by comprising the following steps of: the step 4 specifically comprises the following steps:
4.1, a first-stage constraint condition;
Thermal power generating unit start-stop constraint:
Wherein, beta n,t is a unit working state mark, beta n,t =1 indicates that the unit n is in a working state from time t to time t+1, and beta n,t =0 indicates that the unit n is in a stop state from time t to time t+1;
Thermal power generating unit operation time constraint:
the start-stop of the thermal power generating unit is required to observe the constraint of the running time, and the unit must not be started when the unit does not reach the minimum waiting time, and the unit must not be stopped when the unit does not reach the minimum running time;
In the method, in the process of the invention, Representing the minimum waiting time of the unit n/>Representing the minimum running time of the unit n;
4.2, constraint conditions of the second stage;
abundance constraints:
Gmin≤G≤Gmax
Wherein G min、Gmax is the lower limit and the upper limit allowed by the system adequacy respectively, which can be set by the decision maker;
Power supply and demand balance constraint:
The wind, light and fire storages meet the electric load requirements together, and the balance of power supply and demand is ensured;
In the method, in the process of the invention, The power of thermal power, wind power, photovoltaic power, energy storage and load at the moment t respectively;
upper and lower limit constraint for safe operation of thermal power generating unit:
In the method, in the process of the invention, The lower limit and the upper limit of the running power of the thermal power unit are respectively set;
Climbing constraint of thermal power generating unit:
The climbing constraint of the unit is a coupling relation of the output of the unit in adjacent time, and the active power of the unit cannot be instantly completed, so that the limitation of the up-regulation (climbing) and down-regulation (landslide) of the unit is required;
In the method, in the process of the invention, The upper limit of landslide and climbing power in a unit period is respectively set, and delta t is the step length;
Upper and lower limit constraint of charging and discharging power of the energy storage system:
In the method, in the process of the invention, The upper limit and the lower limit of the energy storage charge and discharge power are set;
Energy storage state of charge constraints:
The state of charge calculation formula is as follows:
The SOC (t) is the state of charge of energy storage at the moment t; p (t) is the charge and discharge power of energy storage at t moment, the discharge is +, and the charge is-; e is the rated capacity of energy storage; gamma c、γd is energy storage charging efficiency and energy storage discharging efficiency; the state of charge constraints of the energy storage system are as follows:
In the method, in the process of the invention, The lower and upper limits of the operating range are allowed for the stored state of charge.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311784605.XA CN117937559A (en) | 2023-12-21 | 2023-12-21 | Wind-solar-fire storage system multi-objective decision-making method based on trusted capacity substitution effect |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311784605.XA CN117937559A (en) | 2023-12-21 | 2023-12-21 | Wind-solar-fire storage system multi-objective decision-making method based on trusted capacity substitution effect |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117937559A true CN117937559A (en) | 2024-04-26 |
Family
ID=90769147
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311784605.XA Pending CN117937559A (en) | 2023-12-21 | 2023-12-21 | Wind-solar-fire storage system multi-objective decision-making method based on trusted capacity substitution effect |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117937559A (en) |
-
2023
- 2023-12-21 CN CN202311784605.XA patent/CN117937559A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR20170095580A (en) | Method for power management of Energy Storage System connected renewable energy | |
| CN109473972A (en) | Whole source lotus is assisted to store up optimal control method based on more power curve | |
| CN112907129A (en) | Energy storage comprehensive benefit evaluation index system | |
| CN112886645B (en) | New energy power system operation simulation method based on ultrahigh hydrogen energy proportion | |
| CN110768302A (en) | Evaluation and regulation method for renewable energy consumption capability of regional power grid | |
| CN115133548A (en) | Storage-contained power system optimal scheduling method considering operation flexibility | |
| CN107359611B (en) | Power distribution network equivalence method considering various random factors | |
| Huangfu et al. | An optimal energy management strategy with subsection bi-objective optimization dynamic programming for photovoltaic/battery/hydrogen hybrid energy system | |
| CN117077974A (en) | Virtual power plant resource optimal scheduling method, device, equipment and storage medium | |
| CN111641233A (en) | Electric power system day-based flexible peak regulation method considering new energy and load uncertainty | |
| Liu et al. | Two-stage optimal operation management of a microgrid considering electricity-hydrogen coupling dynamic characteristics | |
| CN110336308B (en) | Opportunity constraint-based active power distribution network economic dispatching method | |
| Gu et al. | Research on Day-ahead Optimal Scheduling of Wind-photovoltaic-thermal-energy Storage Combined Power Generation System Based on Opportunity-constrained Programming | |
| CN116014797A (en) | Evaluation method for improving new energy acceptance capacity of distribution network | |
| Yang et al. | Collaborative optimization of wind-solar-storage configuration in county distribution network considering near-zero carbon emission | |
| CN117937559A (en) | Wind-solar-fire storage system multi-objective decision-making method based on trusted capacity substitution effect | |
| CN113919676A (en) | Virtual power plant operation effect evaluation method considering demand response and electric hydrogen production system | |
| CN112436510B (en) | Wind-light-fire ultrahigh voltage direct current outgoing dispatching method and system | |
| Zhang et al. | Model Predictive Control Method of Multi-Energy Flow System Considering Wind Power Consumption | |
| Huang et al. | Cascade hydropower stations optimal dispatch considering flexible margin in renewable energy power system | |
| Wang et al. | Intraday net load reserve demand assessment based on catboost and kernel density estimation | |
| Lu et al. | Optimal Operation Scheduling of Multi-energy Complementary Systems Including Offshore Wind Power | |
| Zhao et al. | Planning methods for high penetration renewable energy systems considering flexibility requirements | |
| CN115392631A (en) | Multi-dimensional value evaluation method for electricity-to-hydrogen device based on fuzzy hierarchical analysis | |
| Ge et al. | Unit commitment considering the characteristics of nuclear and wind power |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |