CN116540545A - Photovoltaic power generation hydrogen production cluster random optimization scheduling method based on ember process - Google Patents
Photovoltaic power generation hydrogen production cluster random optimization scheduling method based on ember process Download PDFInfo
- Publication number
- CN116540545A CN116540545A CN202310589172.6A CN202310589172A CN116540545A CN 116540545 A CN116540545 A CN 116540545A CN 202310589172 A CN202310589172 A CN 202310589172A CN 116540545 A CN116540545 A CN 116540545A
- Authority
- CN
- China
- Prior art keywords
- model
- power generation
- hydrogen production
- scheduling
- photovoltaic power
- 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
- 239000001257 hydrogen Substances 0.000 title claims abstract description 102
- 229910052739 hydrogen Inorganic materials 0.000 title claims abstract description 102
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 title claims abstract description 96
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 68
- 238000005457 optimization Methods 0.000 title claims abstract description 62
- 238000000034 method Methods 0.000 title claims abstract description 54
- 238000010248 power generation Methods 0.000 title claims abstract description 41
- 230000008569 process Effects 0.000 title claims abstract description 23
- 230000035945 sensitivity Effects 0.000 claims abstract description 27
- 238000003860 storage Methods 0.000 claims abstract description 17
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 12
- PXFBZOLANLWPMH-UHFFFAOYSA-N 16-Epiaffinine Natural products C1C(C2=CC=CC=C2N2)=C2C(=O)CC2C(=CC)CN(C)C1C2CO PXFBZOLANLWPMH-UHFFFAOYSA-N 0.000 claims abstract description 11
- 230000008901 benefit Effects 0.000 claims abstract description 7
- 229910052900 illite Inorganic materials 0.000 claims abstract description 5
- VGIBGUSAECPPNB-UHFFFAOYSA-L nonaaluminum;magnesium;tripotassium;1,3-dioxido-2,4,5-trioxa-1,3-disilabicyclo[1.1.1]pentane;iron(2+);oxygen(2-);fluoride;hydroxide Chemical compound [OH-].[O-2].[O-2].[O-2].[O-2].[O-2].[F-].[Mg+2].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[Al+3].[K+].[K+].[K+].[Fe+2].O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2.O1[Si]2([O-])O[Si]1([O-])O2 VGIBGUSAECPPNB-UHFFFAOYSA-L 0.000 claims abstract description 5
- 238000009826 distribution Methods 0.000 claims abstract description 4
- 230000001131 transforming effect Effects 0.000 claims abstract description 4
- 238000006243 chemical reaction Methods 0.000 claims description 8
- 230000003190 augmentative effect Effects 0.000 claims description 6
- 230000009194 climbing Effects 0.000 claims description 6
- 150000002431 hydrogen Chemical class 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 6
- 238000005096 rolling process Methods 0.000 claims description 5
- 230000005611 electricity Effects 0.000 claims description 4
- 241000256111 Aedes <genus> Species 0.000 claims description 3
- 230000005653 Brownian motion process Effects 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 3
- 238000009792 diffusion process Methods 0.000 claims description 3
- 238000011084 recovery Methods 0.000 claims description 3
- 230000007704 transition Effects 0.000 claims description 3
- 244000119298 Emblica officinalis Species 0.000 claims 1
- 235000015489 Emblica officinalis Nutrition 0.000 claims 1
- 238000011217 control strategy Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000004146 energy storage Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 241000196324 Embryophyta Species 0.000 description 1
- 235000014787 Vitis vinifera Nutrition 0.000 description 1
- 240000006365 Vitis vinifera Species 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005485 electric heating Methods 0.000 description 1
- 239000012535 impurity Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- WFKWXMTUELFFGS-UHFFFAOYSA-N tungsten Chemical compound [W] WFKWXMTUELFFGS-UHFFFAOYSA-N 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Abstract
The invention discloses a random optimization scheduling method of a photovoltaic power generation hydrogen production cluster based on an illite process, which comprises the steps of firstly establishing a photovoltaic power generation hydrogen production system model, wherein the photovoltaic power generation hydrogen production system model comprises an illite process model of photovoltaic output, an electrolytic cell cluster scheduling model and a hydrogen storage tank model; then, taking the highest expected economic benefit and the highest energy utilization rate under the influence of uncertainty of photovoltaic output prediction errors as objective functions, and establishing a random optimization scheduling model of the electrolytic tank cluster based on a photovoltaic power generation hydrogen production system model and by utilizing a control law of an affine strategy design system; and finally, based on the track sensitivity decomposition, transforming the random optimization control model into a deterministic optimization problem, and adopting a model predictive control mode to roll and optimize the solution. The invention can dynamically adjust the running state and power distribution of each electrolytic cell, effectively realize high-efficiency electro-hydrogen production under the uncertain condition of photovoltaic output and fully absorb fluctuating photovoltaic output.
Description
Technical Field
The invention relates to the technical field of electrolytic cell cluster scheduling, in particular to a photovoltaic power generation hydrogen production cluster random optimization scheduling method based on an illite process.
Background
The renewable energy power generation hydrogen production such as photovoltaic and the like is one of important technical routes for constructing a clean low-carbon society in China. The hydrogen production system is limited by the capacity of a single machine, and a cluster is formed by a plurality of to tens of large hydrogen production machines to meet the hydrogen production requirement. In addition, uncertainty in renewable energy output will further impact electrical hydrogen production benefits and reliability. Therefore, the method for randomly optimizing and scheduling the electrolytic tank clusters by researching and considering uncertainty of photovoltaic output has great practical significance for high-benefit economic operation and participation in photovoltaic power generation.
In the existing electrolytic cell cluster scheduling research, shen Xiaojun and other proposed off-grid wind power hydrogen production alkaline electrolytic cell array optimization control strategies considering electric heating characteristics provide an electrolytic cell array wheel value coordination control strategy in a wind power hydrogen production system based on constraint conditions such as thermal characteristics and regulation characteristics of electrolytic cells. Qiu Y et al extend the load flexibility of industrial P2H plants: in the process constraint sensing scheduling method, the hydrogen impurity accumulation effect in oxygen and the temperature of the hydrogen production machine are simultaneously considered, and a variable load control method for the hydrogen production cluster to absorb wind-solar power generation is provided. Li Y and the like propose an electrolytic cell circulation and rotation strategy of the wind power hydrogen production system in the discussion of the configuration and operation rules of a multi-electrolytic cell mixed system of a large-scale alkaline water hydrogen production system so as to balance the working time of each electrolytic cell. Niu Meng and the like, and a modularized hydrogen production control strategy for relieving the influence of renewable energy on the hydrogen energy storage system is provided based on a reaction mechanism of electrolytic hydrogen production equipment in the hydrogen energy storage system. Yuan Tiejiang and the like propose a day-ahead output optimization model of the hydrogen production system considering the start-stop characteristics of the electrolytic tank based on the operational state conversion relation of the electrolytic tank in the day-ahead output plan of the hydrogen production system considering the start-stop characteristics of the electrolytic tank.
However, the researches are all based on deterministic optimization of electricity price or renewable energy output prediction, and random optimization scheduling of the electrolytic cell clusters under the condition of no photovoltaic output uncertainty is studied.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a random optimal scheduling method for an electrolytic cell cluster of a photovoltaic power generation hydrogen production system, which is based on uncertainty of modeling photovoltaic output in the process of the Embecto, can realize random optimal scheduling for the electrolytic cell cluster under the condition of accounting for the uncertainty of the photovoltaic output, realizes high-efficiency electric hydrogen production and improves the capability of absorbing fluctuating photovoltaic.
The technical proposal is as follows:
a photovoltaic power generation hydrogen production cluster random optimization scheduling method based on an illite process comprises the following steps:
step 1: establishing a photovoltaic power generation hydrogen production system model, wherein the photovoltaic power generation hydrogen production system model comprises an Italian procedure model of photovoltaic output, an electrolytic cell cluster scheduling model and a hydrogen storage tank model;
step 2: taking the highest expected economic benefit and the highest energy utilization rate under the influence of uncertainty of photovoltaic output prediction errors as objective functions, and establishing a random optimization scheduling model of the electrolytic tank cluster based on a photovoltaic power generation hydrogen production system model and by utilizing a control law of an affine strategy design system;
step 3: based on the track sensitivity decomposition, a random optimization control model is transformed into a deterministic optimization problem, and model predictive control mode is adopted to roll optimization solution.
Further, the method comprises the following steps of:
wherein: p (P) t PV The actual output of the photovoltaic power is provided; p (P) t PV,pred The photovoltaic output predicted value;is a prediction error;
the model of the process of the aedes with prediction error is modeled as:
wherein: w (W) t Representing a standard wiener process;is a drift term; />Is a diffusion term; a is the time constant of the cloud shadow change; b is the average value recovery target of the drift term; t is t r 、t s Sunrise and sunset times respectively; the sine function represents the solar altitude; c represents the uncertainty intensity; d and e represent the variation intervals of irradiation intensity.
Still further, the electrolytic cell cluster scheduling model includes:
logical constraint of single cell operating state conversion:
wherein: n represents the nth electrolytic cell in the electrolytic cell cluster;and->Three operating states of the electrolyzer are respectively represented: production, standby and shutdown; />And->The start-up and shut-down operation of the electrolyzer, respectively, < >>Indicating the transition of the electrolyzer from the standby state to the operating state; the variables are represented by binary variables; subscripts t-1 and t-2 represent a last scheduling period and a further last scheduling period, respectively, when the scheduling period is t;
physical constraints of the cell clusters themselves:
wherein: n is the total number of electrolytic cells; p (P) n,t The power of the nth electrolytic cell at the t moment; p (P) max 、P min Respectively restricting the upper and lower limits of power of a single electrolytic tank in a production state; p (P) SB The constant power of the auxiliary machine is used for a single electrolytic tank in a standby state; f (F) n,t Hydrogen production for the nth cell; a is that 1 、A 2 、A 3 The constant is determined by the characteristics of the electrolytic cell.
Still further, the hydrogen tank model includes:
the hydrogen buffer tank storage capacity and capacity constraint are:
wherein:the storage capacity of the hydrogen buffer tank is t; f (F) t out Supplying hydrogen for downstream application for the hydrogen buffer tank outlet flow; />And->The upper limit and the lower limit of the available storage interval of the hydrogen buffer tank are respectively; />The climbing rate for hydrogen gas discharge; Δt is the scheduling step size; />The upper limit and the lower limit of the climbing rate of the hydrogen discharge are respectively set.
Further, the step 2 specifically includes:
step 2.1: the objective function is:
wherein:is shown in the initial state x of the system 0 And prediction error initial value +.>The following conditions are expected; />And C PV The electricity-measuring cost is respectively hydrogen selling price and electricity purchasing cost for the photovoltaic power station; c (C) SU And C SD Operating costs for starting and stopping the electrolyzer respectively; alpha and beta are cost conversion coefficients, the quadratic term corresponding to alpha represents maximum utilization energy, and the quadratic term corresponding to beta represents that the reserve of the hydrogen buffer tank is as close to an initial value as possible after the whole scheduling period; />And->The hydrogen buffer tank storage capacity at the end T moment and the initial moment are respectively stored; />Is an augmented state variable, wherein: />Representing state variables +.>Representing a control variable; q is a constant coefficient matrix; t is the scheduling period, x T A state variable at the end T moment;
step 2.2: inequality constraint in a photovoltaic power generation hydrogen production system is expressed as a probability form, opportunity constraint is constructed, and the inequality constraint is expressed as a vector form:
wherein: gamma represents the tolerance size; pr []Representing the probability;coefficient vector representing inequality constraint, +.>An upper limit value that is an inequality constraint; omega shape C A set of all constraints;
step 2.3: and (3) parameterizing a control command into an affine function of a photovoltaic output prediction error by adopting a control law of an affine strategy design system:
wherein:is a constant term, K t Is a gain coefficient matrix; therefore will->Reduced to->
Step 2.4: the established objective function, constraint condition and control function are combined, and a random optimization scheduling model of the electrolytic cell cluster is as follows:
s.t.
wherein:is an integer variable; phi (phi) i Coefficients that are integer variables; />Is a constraint upper limit; A. b, C and D are coefficient matrices of state variables, control variables, random variables, and integer variables, respectively.
Further, the step 3 specifically includes:
step 3.1: transforming a random optimization scheduling model of the electrolytic tank cluster into deterministic optimization through track sensitivity decomposition of the augmented state variable; the method specifically comprises the following steps:
step 3.1.1: variables in the random processTrack variable decomposed into reference values->And the track sensitivity variable +.>The following is shown:
wherein:and->Reference value trace variable and trace sensitivity variable forms, respectively, of state variables, < >>Expanding the step length of the item for the series; o (·) represents a higher order infinitesimal term; />And->A reference value trace variable and a trace sensitivity variable which are random variables respectively;
step 3.1.2: splitting equations (20) - (21) in the stochastic optimization scheduling model into a reference value trajectory equation and a trajectory sensitivity equation as shown below;
reference value trajectory equation:
wherein: the initial conditions are that
Trajectory sensitivity equation:
wherein: the initial conditions are thatGain coefficients in a system control law;
step 3.1.3: based on M t The trajectory sensitivity decomposition of (2) decomposing the objective function (19) as follows:
J＝J 0 +J 1 (31)
wherein:a trace variable of a reference value which is a state variable at the time T; />The track sensitivity variable form is a variable set; />Representing deriving each variable in the function;
step 3.1.4: in order to ensure that the optimal scheduling model is convex, the following relaxation treatment is carried out:
wherein:a trace variable form of a reference value of a variable set;
opportunity constraint (23) expression:
wherein: kappa (kappa) γ The constant term corresponds to the fractional number of the standard normal distribution gamma;
step 3.1.5: the deterministic optimization problem expressed in formulas (19) - (24) in the random optimization scheduling model is as follows:
min J＝J 0 +J 1 (37)
s.t.
step 3.2: rolling and solving random optimization scheduling models (37) - (46) in a model prediction control mode;
step 3.2.1: given an initial state x of a photovoltaic power generation hydrogen production system at an initial time t t 、And z t Giving a control period T; wherein (1)>The prediction error is the initial time;
step 3.2.2: predicting the photovoltaic output value at time t+1As input of model predictive control, solving a random optimization scheduling model shown in formulas (37) - (46) to obtain a state x of the system at the time t+1 t+1 、/>And z t+1 Control law
Step 3.2.3: repeating the step 3.2.2 until t>T is a T; outputting an optimal scheduling result x of the photovoltaic power generation hydrogen production system T ，z T And an objective function J.
The beneficial effects of the invention are as follows:
1) The invention provides modeling of photovoltaic output uncertainty based on an Embelliferae process model, unified modeling, analysis and control of a cluster scheduling model of an electrolytic cell under a random dynamics frame are realized, and an electrolytic cell cluster random optimization scheduling model considering the photovoltaic output uncertainty is established; the running state and power distribution of each electrolytic tank can be dynamically adjusted, high-efficiency electro-hydrogen production under the condition of uncertainty of photovoltaic output is realized, and the capability of absorbing fluctuating photovoltaic is improved.
2) According to the method, based on dynamic track sensitivity decomposition, the random optimization scheduling problem is converted into deterministic optimization, the objective function and the opportunity constraint of the random optimization scheduling model are decomposed, the random dynamics optimization problem is converted into deterministic second-order cone planning, and model prediction control rolling solution is adopted, so that the defects of high calculation complexity and low solution efficiency of a traditional random sampling simulation-based method are effectively overcome.
Drawings
FIG. 1 is a schematic flow chart of a random optimization scheduling method of an electrolytic cell of a photovoltaic hydrogen production system based on an Italian grape vine process.
Fig. 2 is an overall structure of a photovoltaic power generation hydrogen production system.
FIG. 3 shows the operation of each cell in the example for 96 periods.
Figure 4 is a graph of photovoltaic output and cell cluster power for the example.
Detailed Description
The invention will now be described in further detail with reference to the drawings and to specific examples. The invention models the uncertainty of the photovoltaic output based on the Embectomy process, can realize random optimization scheduling of the electrolytic tank cluster under the condition of accounting for the uncertainty of the photovoltaic output, realizes high-efficiency electro-hydrogen production, and improves the capability of absorbing fluctuating photovoltaic. Firstly, building a photovoltaic power generation hydrogen production system model, wherein the photovoltaic power generation hydrogen production system model comprises an Italian procedure model of photovoltaic output, an electrolytic cell cluster scheduling model and a hydrogen storage tank model; then taking the highest expected economic benefit and energy utilization rate under the influence of uncertainty of photovoltaic output prediction error as an objective function, and utilizing an affine strategy to design a control law of a system, and establishing a random optimization scheduling model of the electrolytic tank cluster based on the model; and finally, based on the track sensitivity decomposition, transforming the random optimization control model into a deterministic optimization problem, and adopting a model predictive control mode to roll and optimize the solution. As shown in fig. 1, the detailed procedure is as follows:
step 1: and establishing a photovoltaic power generation hydrogen production system model, wherein the photovoltaic power generation hydrogen production system model comprises an Italian procedure model of photovoltaic output, an electrolytic cell cluster scheduling model and a hydrogen storage tank model.
The photovoltaic power generation hydrogen production system is composed of units such as photovoltaic power generation, an electrolytic cell cluster, a hydrogen buffer tank and the like, and the whole structure is shown in figure 2.
1.1 Italian vine process model with photovoltaic output
Wherein: p (P) t PV The actual output of the photovoltaic power is provided; p (P) t PV,pred The photovoltaic output predicted value;is the prediction error.
The model of the process of the aedes with prediction error is modeled as:
wherein: w (W) t Representing a standard wiener process;is a drift term; />Is a diffusion term; a is the time constant of the cloud shadow change; b is the average value recovery target of the drift term; t is t r 、t s Sunrise and sunset times respectively; the sine function represents the solar altitude; c represents the uncertainty intensity; d and e represent the variation intervals of irradiation intensity.
1.2 Cluster scheduling model for electrolytic cells
Logical constraint of single cell operating state conversion:
wherein: n represents the nth electrolytic cell in the electrolytic cell cluster;three operating states of the electrolytic cell are respectively: production, standby and shutdown; />The start-up and shut-down operation of the electrolyzer, respectively, < >>Indicating the transition of the electrolyzer from the standby state to the operating state; the variables are represented by binary variables; the subscripts t-1 and t-2 denote the last scheduling period and the last scheduling period, respectively, when the scheduling period is t.
Physical constraints of the cell clusters themselves:
wherein: n is the total number of electrolytic cells; p (P) n,t The power of the nth electrolytic cell at the t moment; p (P) max 、P min Respectively restricting the upper and lower limits of power of a single electrolytic tank in a production state; p (P) SB The constant power of the auxiliary machine is used for a single electrolytic tank in a standby state; f (F) n,t Hydrogen production for the nth cell; a is that 1 、A 2 、A 3 The constant is determined by the characteristics of the electrolytic cell.
1.3 hydrogen buffer tank storage and capacity constraints are:
wherein:the hydrogen buffer tank is used for storing the hydrogen; f (F) t out Supplying hydrogen for downstream application for the hydrogen buffer tank outlet flow;and->The upper limit and the lower limit of the available storage interval of the hydrogen buffer tank are respectively; />The climbing rate for hydrogen gas discharge; Δt is the scheduling step size; />The upper limit and the lower limit of the climbing rate of the hydrogen discharge are respectively set.
Step 2: and establishing a random optimization scheduling model of the electrolytic tank cluster by taking the highest expected economic benefit and the highest energy utilization rate under the influence of uncertainty of the photovoltaic output prediction error as objective functions based on a photovoltaic power generation hydrogen production system model and by utilizing an affine strategy to design a control law of the system.
2.1, taking the highest expected economic benefit and energy utilization efficiency of the electrolytic cell cluster under the influence of uncertainty of photovoltaic output prediction error as an objective function:
wherein:is shown in the initial state x of the system 0 And prediction error initial value +.>The following conditions are expected; />C PV The electricity-measuring cost is respectively hydrogen selling price and electricity purchasing cost for the photovoltaic power station; c (C) SU 、C SD Operating costs for starting and stopping the electrolyzer respectively; alpha and beta are cost conversion coefficients, the quadratic term corresponding to alpha represents maximum utilization energy, and the quadratic term corresponding to beta represents that the reserve of the hydrogen buffer tank is as close to an initial value as possible after the whole scheduling period; />Is an augmented state variable, wherein: />Representing state variables +.>Representing a control variable; q is a constant coefficient matrix.
2.2, expressing inequality constraint in the photovoltaic power generation hydrogen production system as probability form, constructing opportunity constraint, and expressing in vector form:
wherein: gamma represents the tolerance size; pr []Representing the probability;coefficient vector representing inequality constraint, +.>An upper limit value that is an inequality constraint; omega shape C A set of all constraints.
2.3, adopting an affine strategy to design a control law of the system, and parameterizing a control command into an affine function of a photovoltaic output prediction error:
wherein:is a constant term, K t Is a gain coefficient matrix.
2.4, objective function, constraint condition and control function established by the simultaneous, random optimization scheduling model of the electrolytic cell cluster is as follows:
s.t.
wherein:is an integer variable; phi (phi) i Coefficients that are integer variables; />Is the upper bound.
Step 3: based on the track sensitivity decomposition, a random optimization scheduling model is transformed into a deterministic optimization problem, and the deterministic optimization problem is solved in a rolling way by adopting an MPC (MPC) mode, wherein the method comprises the following steps of:
3.1 transformation of random optimization scheduling models (19) - (24) into deterministic optimization by trajectory sensitivity decomposition of augmented state variables
First, variables in the random process are to be usedTrack variable decomposed into reference values->And the track sensitivity variable +.>The following is shown:
to this end, formulas (20) - (21) in the stochastic optimization scheduling model can be split into reference value trajectory equations (27) - (28) and trajectory sensitivity equations (29) - (30) as shown below.
Reference value trajectory equations of formulas (20) - (21):
wherein: the initial conditions are that
The trajectory sensitivity equations of formulas (20) - (21):
wherein: the initial conditions are thatIs the gain factor in the system control law.
Then, based on the pair M t The trajectory sensitivity decomposition of (2) decomposing the objective function (19) as follows:
J＝J 0 +J 1 (31)
wherein:
in order to ensure that the optimal scheduling model is convex, the following relaxation treatment is carried out:
and opportunity constraint (23) may express:
to this end, the stochastic optimization scheduling models (19) - (24) may be expressed as deterministic optimization problems as follows:
min J＝J 0 +J 1 (37)
s.t.
3.2 Rolling solution of random optimization scheduling models (37) - (46) in the form of model predictive control
Step 1): given an initial state x of a photovoltaic power generation hydrogen production system at an initial time t t ，z t Given a control period T.
Step 2): predicting the photovoltaic output value at time t+1As input of model predictive control, solving a random optimization scheduling model shown in formulas (37) - (46) to obtain a state x of the system at the time t+1 t+1 ，/>z t+1 Control law
Step 3): repeating step 2 until t>T. Outputting an optimal scheduling result x of the photovoltaic power generation hydrogen production system T ，z T And an objective function J.
4. Example analysis
In the example, a photovoltaic power station in a certain region of southwest of Sichuan is selected, 4 alkaline electrolytic tanks are arranged in a hydrogen production factory to form a cluster, and the optimal scheduling is carried out for 24 hours, wherein the scheduling step length is 15min.
The embodiment builds a random optimization scheduling model of the electrolytic cell based on a Wolfram Mathematica platform and adopts a Mosek solver to solve.
The 96-period operation state of each electrolytic tank in the example is shown in figure 3; the photovoltaic output and cell cluster power for the example are shown in figure 4.
Therefore, the method for randomly optimizing and scheduling the electrolytic cell clusters of the photovoltaic hydrogen production system based on the Italian procedure can dynamically adjust the running states of the electrolytic cells, effectively realize high-efficiency electric hydrogen production under the condition of uncertainty of photovoltaic output and fully absorb fluctuating photovoltaic output.
Claims (6)
1. A random optimization scheduling method for a photovoltaic power generation hydrogen production cluster based on an illite process is characterized by comprising the following steps:
step 1: establishing a photovoltaic power generation hydrogen production system model, wherein the photovoltaic power generation hydrogen production system model comprises an Italian procedure model of photovoltaic output, an electrolytic cell cluster scheduling model and a hydrogen storage tank model;
step 2: taking the highest expected economic benefit and the highest energy utilization rate under the influence of uncertainty of photovoltaic output prediction errors as objective functions, and establishing a random optimization scheduling model of the electrolytic tank cluster based on a photovoltaic power generation hydrogen production system model and by utilizing a control law of an affine strategy design system;
step 3: based on the track sensitivity decomposition, a random optimization control model is transformed into a deterministic optimization problem, and model predictive control mode is adopted to roll optimization solution.
2. The method for randomly optimizing and scheduling the photovoltaic power generation hydrogen production clusters based on the ember process according to claim 1, wherein the ember process model of the photovoltaic output is as follows:
wherein: p (P) t PV The actual output of the photovoltaic power is provided; p (P) t PV,pred The photovoltaic output predicted value;is a prediction error;
the model of the process of the aedes with prediction error is modeled as:
wherein: w (W) t Representing a standard wiener process;is a drift term; />Is a diffusion term; a is the time constant of the cloud shadow change; b is the average value recovery target of the drift term; t is t r 、t s Sunrise and sunset times respectively; the sine function represents the solar altitude; c represents the uncertainty intensity; d and e represent the variation intervals of irradiation intensity.
3. The method for randomly optimizing and scheduling the photovoltaic power generation hydrogen production clusters based on the ember process according to claim 2, wherein the electrolytic cell cluster scheduling model comprises the following steps:
logical constraint of single cell operating state conversion:
wherein: n represents the nth electrolytic cell in the electrolytic cell cluster;and->Respectively representing electrolytic cellsThree operating states: production, standby and shutdown; />And->The start-up and shut-down operation of the electrolyzer, respectively, < >>Indicating the transition of the electrolyzer from the standby state to the operating state; the variables are represented by binary variables; subscripts t-1 and t-2 represent a last scheduling period and a further last scheduling period, respectively, when the scheduling period is t;
physical constraints of the cell clusters themselves:
wherein: n is the total number of electrolytic cells; p (P) n,t The power of the nth electrolytic cell at the t moment; p (P) max 、P min Respectively restricting the upper and lower limits of power of a single electrolytic tank in a production state; p (P) SB The constant power of the auxiliary machine is used for a single electrolytic tank in a standby state; f (F) n,t Hydrogen production for the nth cell; a is that 1 、A 2 、A 3 The constant is determined by the characteristics of the electrolytic cell.
4. The method for randomly optimizing and scheduling the photovoltaic power generation hydrogen production clusters based on the ember process according to claim 3, wherein the hydrogen storage tank model comprises the following steps:
the hydrogen buffer tank storage capacity and capacity constraint are:
wherein:the storage capacity of the hydrogen buffer tank is t; />Supplying hydrogen for downstream application for the hydrogen buffer tank outlet flow;and->The upper limit and the lower limit of the available storage interval of the hydrogen buffer tank are respectively; />The climbing rate for hydrogen gas discharge; Δt is the scheduling step size; />The upper limit and the lower limit of the climbing rate of the hydrogen discharge are respectively set.
5. The random optimization scheduling method for the photovoltaic power generation hydrogen production cluster based on the ember process according to claim 4, wherein the step 2 is specifically:
step 2.1: the objective function is:
wherein:is shown in the initial state x of the system 0 And prediction error initial value +.>The following conditions are expected; />And C PV The electricity-measuring cost is respectively hydrogen selling price and electricity purchasing cost for the photovoltaic power station; c (C) SU And C SD Operating costs for starting and stopping the electrolyzer respectively; alpha and beta are cost conversion coefficients, the quadratic term corresponding to alpha represents maximum utilization energy, and the quadratic term corresponding to beta represents that the reserve of the hydrogen buffer tank is as close to an initial value as possible after the whole scheduling period; />And->The hydrogen buffer tank storage capacity at the moment of the tail end T and the hydrogen buffer tank storage capacity at the moment of the initial time are respectively; />Is an augmented state variable, wherein: />Representing state variables +.>Representing a control variable; q is a constant coefficient matrix; t is the scheduling period, x T A state variable at the end T moment;
step 2.2: inequality constraint in a photovoltaic power generation hydrogen production system is expressed as a probability form, opportunity constraint is constructed, and the inequality constraint is expressed as a vector form:
wherein: gamma represents the tolerance size; pr []Representing the probability;coefficient vector representing inequality constraint, +.>An upper limit value that is an inequality constraint; omega shape C A set of all constraints;
step 2.3: and (3) parameterizing a control command into an affine function of a photovoltaic output prediction error by adopting a control law of an affine strategy design system:
wherein:is a constant term, K t Is a gain coefficient matrix; therefore will->Reduced to->
Step 2.4: the established objective function, constraint condition and control function are combined, and a random optimization scheduling model of the electrolytic cell cluster is as follows:
s.t.
wherein:is an integer variable; phi (phi) i Coefficients that are integer variables; />Is a constraint upper limit; A. b, C and D are coefficient matrices of state variables, control variables, random variables, and integer variables, respectively.
6. The random optimization scheduling method for the photovoltaic power generation hydrogen production cluster based on the emblic process according to claim 5, wherein the step 3 is specifically:
step 3.1: transforming a random optimization scheduling model of the electrolytic tank cluster into deterministic optimization through track sensitivity decomposition of the augmented state variable; the method specifically comprises the following steps:
step 3.1.1: variables in the random processTrack variable decomposed into reference values->And the track sensitivity variable +.>The following is shown:
wherein:and->Reference value trace variable and trace sensitivity variable forms, respectively, of state variables, < >>Expanding the step length of the item for the series; o (·) represents a higher order infinitesimal term; />And->A reference value trace variable and a trace sensitivity variable which are random variables respectively;
step 3.1.2: splitting equations (20) - (21) in the stochastic optimization scheduling model into a reference value trajectory equation and a trajectory sensitivity equation as shown below;
reference value trajectory equation:
)
wherein: the initial conditions are that
Trajectory sensitivity equation:
wherein: the initial conditions are thatFor controlling the systemGain coefficients in the law;
step 3.1.3: based on M t The trajectory sensitivity decomposition of (2) decomposing the objective function (19) as follows:
J＝J 0 +J 1 (31)
wherein:a trace variable of a reference value which is a state variable at the time T; /> The track sensitivity variable form is a variable set; />Representing deriving each variable in the function;
step 3.1.4: in order to ensure that the optimal scheduling model is convex, the following relaxation treatment is carried out:
wherein:a trace variable form of a reference value of a variable set;
opportunity constraint (23) expression:
wherein: kappa (kappa) γ The constant term corresponds to the fractional number of the standard normal distribution gamma;
step 3.1.5: the deterministic optimization problem expressed in formulas (19) - (24) in the random optimization scheduling model is as follows:
min J＝J 0 +J 1 (37)
s.t.
step 3.2: rolling and solving random optimization scheduling models (37) - (46) in a model prediction control mode;
step 3.2.1: given an initial state x of a photovoltaic power generation hydrogen production system at an initial time t t 、And z t Giving a control period T; wherein (1)>The prediction error is the initial time;
step 3.2.2: predicting the photovoltaic output value at time t+1As input of model predictive control, solving a random optimization scheduling model shown in formulas (37) - (46) to obtain a state x of the system at the time t+1 t+1 、/>And z t+1 Control law
Step 3.2.3: repeating the step 3.2.2 until t>T is a T; output photovoltaic power generation systemOptimal scheduling results x for hydrogen systems T ，z T And an objective function J.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310589172.6A CN116540545A (en) | 2023-05-23 | 2023-05-23 | Photovoltaic power generation hydrogen production cluster random optimization scheduling method based on ember process |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310589172.6A CN116540545A (en) | 2023-05-23 | 2023-05-23 | Photovoltaic power generation hydrogen production cluster random optimization scheduling method based on ember process |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116540545A true CN116540545A (en) | 2023-08-04 |
Family
ID=87454122
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310589172.6A Pending CN116540545A (en) | 2023-05-23 | 2023-05-23 | Photovoltaic power generation hydrogen production cluster random optimization scheduling method based on ember process |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116540545A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117408489A (en) * | 2023-11-08 | 2024-01-16 | 无锡混沌能源技术有限公司 | Grid-connected photovoltaic hydrogen production coupling system energy optimization scheduling method |
-
2023
- 2023-05-23 CN CN202310589172.6A patent/CN116540545A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117408489A (en) * | 2023-11-08 | 2024-01-16 | 无锡混沌能源技术有限公司 | Grid-connected photovoltaic hydrogen production coupling system energy optimization scheduling method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112467807B (en) | Day-ahead optimal scheduling method and system for multi-energy power system | |
| CN107622324A (en) | A kind of robust environmental economy dispatching method for considering more microgrid energy interactions | |
| CN109103929B (en) | Power distribution network economic optimization scheduling method based on improved dynamic kriging model | |
| CN110400056B (en) | Method and device for cascade hydropower day-ahead optimized scheduling based on wind-light space-time characteristics | |
| CN112103994A (en) | Layered coordination control method and device for wind-hydrogen coupling system based on MPC | |
| CN109474007B (en) | Energy internet scheduling method based on big data cloud technology | |
| CN102510108A (en) | Method for calculating maximum wind power installed capacity of district power network | |
| CN114744687A (en) | Energy regulation and control method and system of virtual power plant | |
| CN110543966A (en) | day-ahead scheduling optimization method for micro-energy grid with combined supply of electricity, heat and gas | |
| CN107947166B (en) | Dispatching method and device when a kind of multipotency microgrid change based on dynamic matrix control | |
| CN110867907B (en) | Power system scheduling method based on multi-type power generation resource homogenization | |
| CN112671035A (en) | Virtual power plant energy storage capacity configuration method based on wind power prediction | |
| CN116540545A (en) | Photovoltaic power generation hydrogen production cluster random optimization scheduling method based on ember process | |
| CN115423282A (en) | Electricity-hydrogen-storage integrated energy network multi-objective optimization scheduling model based on reward and punishment stepped carbon transaction | |
| CN116402210A (en) | Multi-objective optimization method, system, equipment and medium for comprehensive energy system | |
| CN113363976B (en) | Scene graph-based wind-solar-water complementary power generation system medium-term optimization scheduling method | |
| CN115099590A (en) | Active power distribution network economic optimization scheduling method and system considering light load uncertainty | |
| Hu et al. | Adaptive time division power dispatch based on numerical characteristics of net loads | |
| CN117040027B (en) | Coordination optimization method and device for rural virtual power plant | |
| CN116742812A (en) | Energy storage capacity coordination optimization configuration method and system for multi-energy complementary power generation system | |
| CN116979611A (en) | Hierarchical optimization scheduling method for source network load storage | |
| CN116418001A (en) | Reservoir group multi-energy complementary scheduling method and system for coping with uncertainty of new energy | |
| CN111799842B (en) | Multi-stage power transmission network planning method and system considering flexibility of thermal power generating unit | |
| CN115313508A (en) | Microgrid energy storage optimal configuration method, device and storage medium | |
| CN114188942A (en) | Power grid dispatching method comprising large-scale new energy base |
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 |