CN116307193A - Virtual power plant double-layer optimization method considering refined demand response and electrolytic hydrogen production - Google Patents
Virtual power plant double-layer optimization method considering refined demand response and electrolytic hydrogen production Download PDFInfo
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Abstract
The invention discloses a virtual power plant double-layer optimization method considering refined demand response and electrolytic hydrogen production, which belongs to the field of power distribution network optimization scheduling and comprises the following steps: building a novel virtual power plant frame; establishing a refined demand response model to optimize an electric load curve and assist in distributed wind-solar grid-connected digestion; establishing an operation model for producing hydrogen by utilizing new energy electrolysis; the method comprises the steps of comprehensively considering the operation and hydrogen energy production requirements of a virtual power plant, establishing a scheduling model of the virtual power plant source-load-network-storage double-layer collaborative hydrogen production, wherein an upper model aims at the lowest operation cost of the virtual power plant, and a lower model aims at the maximum hydrogen energy production benefit; and solving by adopting a whale algorithm based on nonlinear convergence and self-adaptive weight. The invention introduces electrolytic hydrogen production equipment into an electric-thermal comprehensive energy system to form an electric-thermal-hydrogen multi-energy coupling system, and achieves the aim of improving the new energy consumption level from the aspects of improving the source-load flexibility, widening the wind-solar energy consumption path and the like.
Description
Technical Field
The invention relates to the field of power distribution network optimization scheduling, in particular to a virtual power plant double-layer optimization method considering refined demand response and electrolytic hydrogen production.
Background
The electric power system is a real-time balanced dynamic system, the production and the use of electric energy are synchronous, when the traditional thermal power unit is used for generating electricity, the generated power is stable and controllable, and with the deep advancement of the low-carbon environment-friendly concept in recent years, the new energy source takes a high proportion, but the new energy source represented by wind power and photovoltaic is greatly influenced by weather, and the generated power is poor in stability.
How to improve the new energy consumption level represented by wind power and distributed photovoltaic is a key ring of low-carbon transformation of an electric power system. The distribution network centralizes small conventional units, wind power, distributed photovoltaic, electric energy storage and other power generation electric equipment which are relatively dispersed, is difficult to centralize and dispatch, and is closely related to various electric loads. Along with the increasingly prominent effect of hydrogen energy in the aspects of promoting new energy consumption and accelerating the replacement of electric energy of terminal energy, when the peak value of the electricity generation of new energy such as wind power, photovoltaic and the like, the electric quantity is difficult to be completely consumed, and the condition of wind discarding and electricity discarding easily occurs. The residual electric quantity after the hydrogen energy is introduced can be used for electrolytic hydrogen production, the electric quantity is stored in a hydrogen form, and when new energy is used for generating electricity in a valley, the hydrogen is used for generating electricity, so that the power supply requirement is met. The peak clipping and valley filling of new energy are flexibly realized through hydrogen energy, so that the new energy power generation is more stable and efficient, the occurrence of the wind and electricity discarding situation is effectively reduced, and the hydrogen energy produced by electrolysis hydrogen production becomes a special power load on a power distribution side.
In terms of improving the capacity of new energy of a power distribution network, the current research starts with improving the source-load flexibility, for example, considering the establishment of a demand response mechanism, and exerting the initiative and enthusiasm of user participation, thereby improving the load response capacity; or to take into account the flexibility of using multiple energy storage to boost the source side. However, the current price type demand response model has limited load scheduling capability, cannot fully exert the flexibility of the load, and does not consider the novel load represented by the participation of electrolytic hydrogen production. The existing optimization configuration calculation of the power system adopts a whale optimization algorithm. The whale optimization algorithm is a novel intelligent optimization algorithm for the population, and has the advantages of simple operation, few parameters and strong capability of jumping out of local optimization. However, the traditional whale algorithm still has the defects of low solving precision, low convergence speed, easiness in sinking into local optimum and the like, so that when the power system is actually subjected to optimal configuration calculation, the calculated optimum solution has a certain deviation from the actual optimum solution.
The distribution network containing hydrogen energy production is a new form of future development, and has important significance in improving the level of new energy consumption and promoting the economic low carbon of the operation of the distribution network, and how to efficiently schedule the power generation electric equipment with dispersed power generation sides and realize the local economic stable operation of the distribution network.
Disclosure of Invention
The invention aims to solve the technical problem of providing a virtual power plant double-layer optimization method considering refined demand response and electrolytic hydrogen production, which further enhances source load flexibility by constructing a novel virtual power plant form and establishing a finer demand response model; meanwhile, the operation requirements of the operation of the power distribution network system and the internal hydrogen energy production are considered, a double-layer scheduling model is established, and the economic requirement of the operation of the power distribution network and the optimal benefit of the hydrogen energy production are realized.
In order to solve the technical problems, the invention adopts the following technical scheme: the virtual power plant double-layer optimization method considering refined demand response and electrolytic hydrogen production comprises the following steps:
step 1: determining a virtual power plant architecture and composition:
the virtual power plant comprises a conventional unit, a wind turbine generator, a distributed photovoltaic generator, a battery energy storage device and an electrolytic hydrogen production unit;
step 2: establishing a refined demand response model:
finely dividing a load response mode according to different response attributes of the load; establishing a refined demand response model aiming at different response modes;
step 3: establishing an electrolytic hydrogen production operation model:
taking the uncertainty of the output force of new energy hydrogen production and the safe operation characteristic of electrolytic hydrogen production equipment into consideration, and constructing an electrolytic hydrogen production operation model by combining other equipment at the power distribution network side;
step 4: establishing mathematical models and operation constraint conditions of a conventional unit, a wind turbine unit, a distributed photovoltaic generator unit, a battery energy storage device and an electrolytic hydrogen production unit;
step 5: constructing a scheduling model of virtual power plant source-load-network-storage double-layer collaborative hydrogen production:
the upper model aims at the lowest running cost of the virtual power plant, and the lower model aims at the maximum hydrogen energy production benefit;
step 6: acquiring the coal firing coefficient of a conventional unit, the capacity of a battery energy storage device, the output parameter of the battery energy storage device and the predicted power of a wind turbine unit and a distributed photovoltaic generator unit;
step 7: optimizing and solving a scheduling model of the virtual power plant source-load-network-storage double-layer collaborative hydrogen production by adopting a whale algorithm based on nonlinear convergence and self-adaptive weight;
step 8: outputting an optimized operation result of a scheduling model of the virtual power plant source-load-network-storage double-layer collaborative hydrogen production:
the wind power generation system comprises a conventional unit, a wind power unit, a distributed photovoltaic generator set, a battery energy storage device, a power output condition, an amount of abandoned wind and abandoned light, power purchase and power demand response and a front-back electric load condition.
The technical scheme of the invention is further improved as follows: the specific steps of establishing the refined demand response model in the step 2 are as follows:
step 21: finely classifying the load, and dividing the load into a rigid load, a transferable load influenced by price and an interruptible and translatable load influenced by excitation according to load response characteristics;
P L ＝P L1 +P L2 +P L3 (1)
P L1 、P L2 、P L3 respectively rigid, price-type and excitation-type electric loads
Step 22: establishing a price-driven load response model based on a Logistic function:
under the time-sharing electricity price mechanism, the electricity load is transferred from the peak electricity price time to the valley electricity price time; the scheduling period is divided into two categories according to the difference between the time-sharing electricity price and the original electricity price: s is a set of electricity price rise periods, s= { S 1 ,s 2 ,s 3 …s x M is a set m= { M of electricity price decrease periods 1 ,m 2 ,m 3 …m y X, y are the number of rise and fall periods;
s x the load shifted out due to the rise of electricity prices is distributed as shown in formula (2) in period M; m is m y The load distribution from the S period absorbed by the decrease in electricity prices is as shown in formula (3);
wherein: delta q To make the difference between the time-sharing electricity price and the original electricity price,
for the difference value between the electricity price in the y period and the original electricity price after the electricity price is reduced, < >>
The difference value between the power price after the power price rises in the period x and the original power price; f () is a load transfer function, formula (4) is a load transfer rate model, describing the relation between the load transfer rate and the electricity price change in each period, and dividing the load transfer rate reflection on the electricity price into a dead zone, a response zone and a saturation zone according to a Logistic function;
wherein: delta q A is dead zone threshold, K is slope of transfer rate curve of response zone, f max Maximum load transfer rate for saturation region, f max K+a is the inflection point of the saturation region;
in summary, the price driven load response model can be described as:
Step 23: building an incentive type demand response model based on a load aggregator:
the motivation type demand response is generally performed in a contract form, and a load aggregator signs up with a power grid company on behalf of a user and agrees to reduce the time limit and capacity of load and load transfer; the contracts are divided into two types, namely translatable load contracts and interruptible load shedding contracts; the total income of the load aggregator consists of the difference between the response compensation of the power grid operators and the response cost of the users, and the load aggregator responds with the maximum benefit of the load aggregator as the drive; the objective function is:
wherein N is T =24, response period of 1h; c is the profit of the load aggregator; p (P) t1 、P t2 Compensating for the response of a power grid and a load quotient unit in a t period in the power market;
a percentage of interruptible, translatable negative response in the t period;
N LC ∈{i-1,i,i+1,…} (11)
wherein:
flag for executing interruptible contract->
Executing a contract for 1 representing t period,/->
No contract is performed for period 1 representing t; />
The number of times of interruption is the maximum and minimum in the period; />
A duration time constraint for a period of time; />
Is an interruptible capacity constraint; n (N) LC A set of interruptible time periods;
N LS ∈{j-1,j,j+1,…} (16)
M LS ∈{k-1,k,k+1,…} (17)
wherein:
flag for translatable contract execution ++>
Executing a contract for 1 representing t period,/->
No contract is performed for period 1 representing t; />
Is the maximum and minimum translatable times in the period; />
A duration translation time constraint for a period of time; />
Is a translatable maximum capacity constraint; n (N) LS As translatable period set, M LS Is a set of translated time periods;
the incentive type demand response model is that,
wherein:
for t period of original excitation load, +.>
To the amount of load change due to enforcing the interruptible contract and the translatable contract.
The technical scheme of the invention is further improved as follows: the step 3 of establishing an electrolytic hydrogen production operation model specifically comprises the following steps: the combined hydrogen production model mainly taking wind power and energy storage as assistance consists of a scheduling layer and a hydrogen energy control layer;
the mathematical model of electrolytic hydrogen production is as follows:
P WTe +P ESe ＝P EHP (21)
m H for the quality of the hydrogen gas,
for producing hydrogen power by electricity, eta H1 ，η H2 ，/>
Respectively the electrolysis efficiency of the electrolytic tank, the efficiency of the hydrogen compressor and the heat value of the hydrogen; />
Maximum and minimum power of the electrolysis device for the period t; p (P) WTe 、P ESe The method is used for preparing hydrogen power for wind power and hydrogen power for energy storage.
The technical scheme of the invention is further improved as follows: and 5, constructing a scheduling model of the virtual power plant source-load-network-storage double-layer collaborative hydrogen production, wherein the objective function of an upper model is as follows:
the upper layer is a conventional unit regulation layer and consists of economic and environmental targets, and the economic targets are as follows: the economic cost of the conventional unit is the lowest; environmental objective: routine unit operation SO 2 、NO x The emission pollution cost is the lowest:
min(f 1 +f 2 ) (22)
wherein: f (f) 1 F is the coal cost of the conventional unit 2 The environmental pollution cost of the conventional unit is realized; t is a scheduling period, and the value is 24; n is the number of conventional units; a, a i 、b i 、c i Is the coal consumption coefficient of the conventional unit, u i Is a start-stop sign;
and (5) the electric power of the ith conventional unit in the period t.
Wherein: d (D) S 、D N Respectively SO 2 、NO x Is a pollution equivalent value of (2); w (W) s And W is equal to N SO generated for the operation of conventional units 2 、NO x A total amount; b is tax amount of pollution equivalent; j (J) m The unit price of the coal for fuel; omega S 、ω N SO produced by burning unit coal respectively 2 、NO x Quality.
The technical scheme of the invention is further improved as follows: and 5, constructing a scheduling model of the virtual power plant source-load-network-storage double-layer collaborative hydrogen production, wherein the operation constraint of an upper model is as follows:
(1) Power balance constraint
Equation (25) is an electric power balance constraint that does not account for network loss, where P Gi I electric output is provided for a conventional unit; p (P) WT The output of the wind turbine generator is output; p (P) PV The method is characterized by outputting force for a distributed photovoltaic generator set; p (P) ES Exerting force on the battery energy storage device; p (P) W Electric power purchased for an upper grid or other node; p (P) NL For the optimized electrical load; p (P) EHP Electric power consumed for electrolytic hydrogen production;
(2) Related unit operation constraint
1) Conventional unit operation constraint
Wherein: p (P) Gimin 、P Gimax 、
The power is t time period, minimum and maximum electric power of the ith conventional unit and power for climbing down and up;
2) Wind turbine generator system operation constraint
0≤P WT ≤P WTmax (27)
Wherein: p (P) WTmax The maximum output power of the wind turbine generator is obtained;
3) Distributed photovoltaic generator set operation constraint
0≤P PV ≤P PVmax (28)
Wherein: p (P) PVmax The maximum output power of the distributed photovoltaic generator set is obtained;
4) Battery energy storage device operation constraints
0≤|P ES |≤P ESmax ≤P ESW (29)
Wherein: p (P) ES The real-time power of the battery energy storage device is discharged if the power is larger than 0, and otherwise, the power is charged; p (P) ESmax Maximum charge and discharge power in unit time; p (P) ESW Rated power for electric energy storage;
5) Purchasing power line constraints
0≤P W ≤P Wmax (30)
Wherein: p (P) Wmax Maximum power purchased for an upper grid or other node.
The technical scheme of the invention is further improved as follows: and 5, constructing a scheduling model of the virtual power plant source-load-network-storage double-layer collaborative hydrogen production, wherein the objective function of a lower model is as follows:
the lower layer is an EHP regulation layer, and the maximum hydrogen energy benefit is taken as a target; the hydrogen energy benefit consists of hydrogen energy market income and hydrogen production cost, wherein the hydrogen production electricity is stored by a wind turbine generator and a battery, and the hydrogen energy benefit is the maximum equivalent to the lowest operation cost in the EHP rated operation state, as shown in a formula (31):
wherein: y is electricity price, S is energy storage mobilizing price; p (P) WTe For producing hydrogen power and P by wind power ESe Hydrogen production power stored by a battery.
The technical scheme of the invention is further improved as follows: and 5, constructing a scheduling model of the virtual power plant source-load-network-storage double-layer collaborative hydrogen production, wherein the operation constraint of a lower model is as follows:
0≤P WTe ≤P EHPmax (32)
0≤P ESe ≤P EHPmax (33)
P WTe +P ESe ＝P EHPmax (34)
formula (32) is the EHP hydrogen production wind power consumption power constraint, formula (33) is the EHP hydrogen production battery energy storage consumption power constraint, and formula (34) is the EHP hydrogen production total power constraint.
The technical scheme of the invention is further improved as follows: the whale algorithm with nonlinear convergence and self-adaptive weight in the step 7 specifically comprises the following steps: in the stage of surrounding the prey, changing the control parameter a from linear attenuation to nonlinear attenuation, and introducing an adaptive weight factor omega;
step 71, surrounding the prey stage, the mathematical model of which is specifically as follows:
D＝|CX * (T)-X(T)| (35)
wherein: t is the iteration number; x is X * (T) is the position of the current optimal solution; x (T) is the current position; c is a coefficient vector; d is the distance between the search agent and the target prey;
wherein: omega is an adaptive weight factor; max_t is the maximum iteration number;
X(T+1)＝ωX * (T)-A×D (37)
wherein: a is a coefficient vector;
A＝2ar 1 -a (38)
C＝2r 2 (39)
wherein: r is (r) 1 、r 2 Is [0-1]Random numbers in between; a is a control parameter which decreases nonlinearly with iteration number to 0, where
Step 72, in the stage of attacking the prey, whales spit out bubbles and travel to the target prey with spiral movement tracks, and the mathematical model of the individual position update is as follows:
X(T+1)＝D·e bl ·cos(2πl)+ωX * (T) (41)
wherein: b is a constant; l is a random vector between [0-1 ];
step 73, searching for a prey stage, enabling whale random walks to find food, and performing global exploration, wherein a mathematical model is as follows:
D＝|CX rand (T)-X(T)| (42)
X(T+1)＝ωX rand (T)-A×D (43)
wherein: x is X rand (T) randomly selecting a certain individual position from the current population.
By adopting the technical scheme, the invention has the following technical progress: by establishing a finer demand response model, finely classifying the load, and constructing a model which reflects fine electricity price, the capacity of dispatching the load is stronger, the flexibility of the load side is further improved, and the new energy grid-connected consumption is facilitated; by considering the hydrogen energy load of the power distribution network side, a novel virtual electric frame containing electrolytic hydrogen production is constructed; and from the economic efficiency of system operation and the benefit of hydrogen energy production, a double-layer scheduling model is built, so that the new energy is more stable and efficient to be absorbed, and the occurrence of the wind and light discarding situation is effectively reduced. By introducing a whale algorithm based on nonlinear convergence and self-adaptive weight to replace the traditional whale algorithm, the global searching capability and the local development capability can be better adjusted, the phenomenon that local optimum is easily trapped in later local development and early convergence occurs is prevented, and the optimization calculation of the adjusting method is more accurate.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art;
FIG. 1 is a schematic diagram of a power distribution network comprising a novel virtual power plant according to the present invention;
FIG. 2 is a schematic diagram of a wind-storage integrated hydrogen production structure;
FIG. 3 is a virtual power plant scheduling flow diagram;
FIG. 4 is a flowchart of an improved whale optimization algorithm of the present invention;
Detailed Description
The invention is further illustrated by the following examples:
as shown in fig. 3, the method for optimizing the double-layer of the virtual power plant, which considers the refined demand response and the electrolytic hydrogen production, comprises the following steps:
the novel virtual power plant considers novel energy devices appearing on the power distribution network side, namely an electrolytic hydrogen production unit, on the basis of a conventional thermal power unit, a wind turbine unit WT, a distributed photovoltaic generator unit PV and a battery energy storage device. The new energy is utilized to produce hydrogen, so that on one hand, the absorption way of the new energy can be widened, and the peak regulation effect is achieved; on the other hand, the method can promote clean low carbonization of hydrogen energy production and avoid a large amount of carbon dioxide generated by industrial hydrogen production.
Step 2, establishing a refined demand response model;
finely dividing a load response mode according to the response attribute of the load; establishing a refined demand response model according to different response modes;
step 21: the load is divided into a rigid load, a transferable load influenced by price and an interruptible, translatable load influenced by excitation according to the load response characteristics.
P L ＝P L1 +P L2 +P L3 (1)
Wherein: p (P) L1 、P L2 、P L3 Respectively rigid, price type and excitation type electric loads.
Step 22: establishing a price-driven load response model based on a Logistic function:
under the time-of-use electricity price mechanism, the electricity load is transferred from the peak electricity price period to the low electricity price period. Dividing the scheduling period into two types according to the difference between the time-of-use electricity price and the original electricity price, wherein: s is a set of electricity price rise periods, s= { S 1 ,s 2 ,s 3 …s x M is a set m= { M of electricity price decrease periods 1 ,m 2 ,m 3 …m y X, y are the number of rise and fall periods.
s x The load shifted out due to the rise of electricity prices is distributed as shown in formula (2) in period M; m is m y The load distribution from the S period absorbed by the decrease in electricity prices is as shown in formula (3).
Wherein: Δq is the difference between the established time-of-use electricity price and the original electricity price,
for the difference value between the electricity price in the y period and the original electricity price after the electricity price is reduced, < >>
The difference value between the power price after the power price rises in the period x and the original power price; f () is a load transfer function, formula (4) is a load transfer rate model, describing the relation between the load transfer rate and the electricity price change in each period, and dividing the load transfer rate reflection on the electricity price into a dead zone, a response zone and a saturation zone according to a Logistic function;
wherein: delta q A is dead zone threshold, K is slope of transfer rate curve of response zone, f max Maximum load transfer rate for saturation region, f max K+a is the inflection point of the saturation region;
in summary, the price driven load response model can be described as:
wherein:
for the original price load of t period, < >>
For the amount of load transferred as a result of implementation of time-of-use electricity prices, where when deltaq>At 0, the +>
When delta q<At 0, the +>
Step 23: building an incentive type demand response model based on load aggregators
The incentive type demand response is generally performed in a contract form, and a load aggregator signs up with a power grid company on behalf of a user, and agrees to reduce the time limit and capacity of load and load transfer. Contracts are divided into translatable load and interruptible (cut-down) load contracts. The total income of the load aggregator consists of the difference between the response compensation of the power grid operators and the response cost of the users, and the load aggregator responds with the greatest benefit of the load aggregator. The objective function is:
wherein N is T =24, response period is 1h. C is the profit of the load aggregator; p (P) t1 、P t2 Compensating for the response of a power grid and a load quotient unit in a t period in the power market;
the percentage is the interruptible, translatable negative response during period t.
N LC ∈{i-1,i,i+1,…} (11)
Wherein:
flag for executing interruptible contract->
Executing a contract for 1 representing t period,/->
No contract is performed for period 1 representing t; />
The number of times of interruption is the maximum and minimum in the period; />
A duration time constraint for a period of time; />
Is an interruptible capacity constraint; n (N) LC Is a set of interruptible time periods.
N LS ∈{j-1,j,j+1,…} (16)
M LS ∈{k-1,k,k+1,…} (17)
Wherein:
flag for translatable contract execution ++>
Executing a contract for 1 representing t period,/->
No contract is performed for period 1 representing t; />
Is the maximum and minimum translatable times in the period; />
A duration translation time constraint for a period of time; />
Is a translatable maximum capacity constraint; n (N) LS As translatable period set, M LS Is a set of translated periods.
In summary, the incentive type demand response model is that,
wherein:
for t period of original excitation load, +.>
To the amount of load change due to enforcing the interruptible contract and the translatable contract.
Step 3, establishing an electrolytic hydrogen production operation model:
taking the uncertainty of the output force of new energy hydrogen production and the safe operation characteristic of electrolytic hydrogen production equipment into consideration, and constructing an electrolytic hydrogen production operation model by combining other equipment at the power distribution network side;
the hydrogen is used as explosive gas, the production power control is very strict, and the safe operation requirement can be met only when the operation power environment is changed and the operation range of the EHU is 50-100% of the rated power by taking KZDQ-20/3.2 series hydrogen production equipment as an example. When the available power of wind power is lower than the lowest safe operation power of the hydrogen energy production equipment, the double selection of stopping or mobilizing other energy sources to maintain normal operation is faced, but on one hand, the service life of the equipment is influenced by the high-frequency start-stop, and on the other hand, the economy of system operation is reduced by the other convenience, otherwise, the system can be operated safely.
In combination with the analysis, the embodiment provides a combined hydrogen production model mainly comprising wind power and assisted by energy storage so as to ensure stable operation of hydrogen energy production, wherein the hydrogen energy production model comprises a scheduling layer and a hydrogen energy control layer, the scheduling layer is an upper control center, and after comprehensively considering factors of all parties, scheduling plans are arranged in an overall way to issue output instructions of all units. The hydrogen energy control layer is a lower control center, and the main task is to reasonably arrange a hydrogen energy production plan under the principle of safety and economy according to the issuing instruction of the scheduling layer. The wind-storage combined hydrogen production process comprises the following steps: 1. the scheduling layer transmits available wind power for hydrogen production to the hydrogen energy control layer; 2. the hydrogen energy production layer arranges a hydrogen energy production plan according to the available wind power and feeds back to the scheduling layer whether energy storage needs to be allocated for participation. Assuming that the hydrogen production equipment is in a non-stop state, the hydrogen production conditions possibly occurring during the process are wind-storage combined hydrogen production (available wind power cannot meet the minimum safe operating power of the hydrogen production equipment, and energy storage needs to be fed back to a scheduling layer for energy allocation), wind power hydrogen production, and a model is shown in figure 2
The mathematical model of electrolytic hydrogen production is as follows:
P WTe +P ESe ＝P EHP (21)
wherein: m is m H For the quality of the hydrogen gas,
for producing hydrogen power by electricity, eta H1 ，η H2 ，/>
Respectively the electrolysis efficiency of the electrolytic tank, the efficiency of the hydrogen compressor and the heat value of hydrogen; />
Maximum and minimum power of the electrolysis device for the period t; p (P) WTe 、P ESe Is hydrogen, wind power and energy storage power.
step 5, constructing a scheduling model of the virtual power plant source-load-network-storage double-layer collaborative hydrogen production, wherein the upper model aims at the lowest running cost of the virtual power plant, and the lower model aims at the maximum hydrogen energy production benefit;
the upper layer is a conventional unit regulation layer and consists of economic and environmental targets, and the economic targets are as follows: the economic cost of the conventional unit is the lowest; environmental objective: routine unit operation SO 2 、NO x The emission pollution cost is the lowest:
min(f 1 +f 2 ) (22)
wherein: f (f) 1 F is the coal cost of the conventional unit 2 The environmental pollution cost of the conventional unit is realized; t is a scheduling period, and the value is 24; n is the number of conventional units; a, a i 、b i 、c i Is the coal consumption coefficient of the conventional unit, u i Is a start-stop sign;
and (5) the electric power of the ith conventional unit in the period t.
Wherein: d (D) S 、D N Respectively SO 2 、NO x Is a pollution equivalent value of (2); w (W) s And W is equal to N SO generated for the operation of conventional units 2 、NO x A total amount; b is tax amount of pollution equivalent; j (J) m The unit price of the coal for fuel; omega S 、ω N SO produced by burning unit coal respectively 2 、NO x Quality.
Determining upper model operational constraints
(1) Power balance constraint
Equation (25) is an electric power balance constraint that does not account for network loss, where P Gi I electric output is provided for a conventional unit; p (P) WT The output of the wind turbine generator is output; p (P) PV The method is characterized by outputting force for a distributed photovoltaic generator set; p (P) ES Exerting force on the battery energy storage device; p (P) W Electric power purchased for an upper grid or other node; p (P) NL For the optimized electrical load; p (P) EHP Electric power consumed for electrolytic hydrogen production;
(2) Related unit operation constraint
1) Conventional unit operation constraint
Wherein: p (P) Gimin 、P Gimax 、
The power is the minimum and maximum electric power and the power of the descending and ascending slopes of the ith conventional thermal power generating unit in the t period. />
2) Wind turbine generator system operation constraint
0≤P WT ≤P WTmax (27)
Wherein: p (P) WTmax And outputting maximum power for the wind turbine.
3) Distributed photovoltaic operation constraints
0≤P PV ≤P PVmax (28)
Wherein: p (P) PVmax And outputting the maximum power for the distributed photovoltaic generator set.
4) Battery energy storage device operation constraints
0≤|P ES |≤P ESmax ≤P ESW (29)
Wherein: p (P) ES The real-time power of the battery energy storage device is discharged if the power is larger than 0, and otherwise, the power is charged; p (P) ESmax Maximum charge and discharge power in unit time; p (P) ESW Rated power for electric energy storage;
5) Purchasing power line constraints
、0≤P W ≤P Wmax (30)
Wherein: p (P) Wmax Maximum power purchased for an upper grid or other node.
Determining underlying model objective functions
The lower layer is an EHP regulation layer, and the maximum hydrogen energy benefit is taken as a target; the hydrogen energy benefit is composed of the market income of hydrogen energy and the hydrogen production cost, wherein the electricity used for producing hydrogen is from wind power and energy storage. The maximum hydrogen energy efficiency is equivalent to the lowest operating cost of the EHP in the rated operating state, as shown in formula (31):
wherein: y is electricity price, S is energy storage mobilizing price; p (P) WTe For producing hydrogen power and P by wind power ESe Hydrogen production power stored by a battery.
Determining underlying model operational constraints
0≤P WTe ≤P EHPmax (32)
0≤P ESe ≤P EHPmax (33)
P WTe +P ESe ＝P EHPmax (34)
Formula (32) is the EHP hydrogen production wind power consumption constraint, formula (33) is the EHP hydrogen production electric energy storage consumption constraint, and formula (34) is the EHP hydrogen production total power constraint.
And 6, acquiring the coal-fired coefficient of the conventional thermal power generating unit, the capacity of the battery energy storage device, the output parameter of the battery energy storage device and the predicted power of the wind turbine unit and the distributed photovoltaic generator unit.
Step 7, optimizing and solving a scheduling model of the virtual power plant source-load-network-storage double-layer collaborative hydrogen production by adopting a whale algorithm based on nonlinear convergence and self-adaptive weight;
as shown in fig. 4, the whale optimization algorithm is a novel group intelligent optimization algorithm, and has the advantages of simplicity in operation, few parameters and strong capability of jumping out of local optimization. However, the traditional whale algorithm still has the defects of low solving precision, low convergence speed, easy sinking into local optimum and the like, and in order to overcome the defects, the global searching capability and the local development capability are better adjusted, the phenomenon of easy sinking into local optimum and early convergence in the later local development is prevented, and the invention provides the whale algorithm based on nonlinear convergence and self-adaptive weight, which is different from the traditional whale algorithm in that: in the surrounding prey stage, the control parameter a is changed from linear attenuation to nonlinear attenuation, and an adaptive weight factor omega is introduced.
The surrounding prey stage has the following mathematical model:
D＝|CX * (T)-X(T)| (35)
wherein: t is the iteration number; x is X * (T) is the position of the current optimal solution; x (T) is the current position; c is a coefficient vector; d is the distance between the search agent and the target prey;
wherein: omega is an adaptive weight factor; max_t is the maximum iteration number;
X(T+1)＝ωX * (T)-A×D (37)
wherein: a is a coefficient vector;
A＝2ar 1 -a (38)
C＝2r 2 (39)
wherein: r is (r) 1 、r 2 Is [0-1]Random numbers in between; a is a control parameter which decreases nonlinearly with iteration number to 0, where
In the stage of attacking the prey, whales spit out bubbles and swim to the target prey with spiral movement tracks, and the mathematical model of the individual position update is as follows:
X(T+1)＝D·e bl ·cos(2πl)+ωX * (T) (41)
wherein: b is a constant; l is a random vector between [0-1 ].
Searching for prey stage, whale random walk foraging, and carrying out global exploration, wherein the mathematical model is as follows:
D＝|CX rand (T)-X(T)| (42)
X(T+1)＝ωX rand (T)-A×D (43)
wherein: x is X rand (T) randomly selecting a certain individual position from the current population.
And 8, outputting an optimized operation result of a scheduling model of the virtual power plant source-load-network-storage double-layer collaborative hydrogen production, wherein the optimized operation result comprises the power output condition, the waste wind and waste light quantity, the electricity purchasing power condition and the electricity load condition before and after the demand response of a conventional thermal power unit, a wind power unit, a distributed photovoltaic generator unit and a battery energy storage device.
The invention introduces electrolytic hydrogen production equipment into an electric-thermal comprehensive energy system to form an electric-thermal-hydrogen multi-energy coupling system, and achieves the aim of improving the new energy consumption level from the aspects of improving the source-load flexibility, widening the wind-solar energy consumption path and the like.
The above examples are only illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solution of the present invention should fall within the scope of protection defined by the claims of the present invention without departing from the spirit of the design of the present invention.
Claims (8)
1. The virtual power plant double-layer optimization method considering refined demand response and electrolytic hydrogen production is characterized by comprising the following steps:
step 1: determining a virtual power plant architecture and composition:
the virtual power plant comprises a conventional unit, a wind turbine generator, a distributed photovoltaic generator, a battery energy storage device and an electrolytic hydrogen production unit;
step 2: establishing a refined demand response model:
finely dividing a load response mode according to different response attributes of the load; establishing a refined demand response model aiming at different response modes;
step 3: establishing an electrolytic hydrogen production operation model:
taking the uncertainty of the output force of new energy hydrogen production and the safe operation characteristic of electrolytic hydrogen production equipment into consideration, and constructing an electrolytic hydrogen production operation model by combining other equipment at the power distribution network side;
step 4: establishing mathematical models and operation constraint conditions of a conventional unit, a wind turbine unit, a distributed photovoltaic generator unit, a battery energy storage device and an electrolytic hydrogen production unit;
step 5: constructing a scheduling model of virtual power plant source-load-network-storage double-layer collaborative hydrogen production:
the upper model aims at the lowest running cost of the virtual power plant, and the lower model aims at the maximum hydrogen energy production benefit;
step 6: acquiring the coal firing coefficient of a conventional unit, the capacity of a battery energy storage device, the output parameter of the battery energy storage device and the predicted power of a wind turbine unit and a distributed photovoltaic generator unit;
step 7: optimizing and solving a scheduling model of the virtual power plant source-load-network-storage double-layer collaborative hydrogen production by adopting a whale algorithm based on nonlinear convergence and self-adaptive weight;
step 8: outputting an optimized operation result of a scheduling model of the virtual power plant source-load-network-storage double-layer collaborative hydrogen production:
the wind power generation system comprises a conventional unit, a wind power unit, a distributed photovoltaic generator set, a battery energy storage device, a power output condition, an amount of abandoned wind and abandoned light, power purchase and power demand response and a front-back electric load condition.
2. The virtual power plant double-layer optimization method considering refined demand response and electrolytic hydrogen production according to claim 1, wherein the specific steps of establishing the refined demand response model in step 2 are as follows:
step 21: finely classifying the load, and dividing the load into a rigid load, a transferable load influenced by price and an interruptible and translatable load influenced by excitation according to load response characteristics;
P L ＝P L1 +P L2 +P L3 (1)
P L1 、P L2 、P L3 respectively rigid, price-type and excitation-type electric loads
Step 22: establishing a price-driven load response model based on a Logistic function:
under the time-sharing electricity price mechanism, the electricity load is transferred from the peak electricity price time to the valley electricity price time; the scheduling period is divided into two categories according to the difference between the time-sharing electricity price and the original electricity price: s is a set of electricity price rise periods, s= { S 1 ,s 2 ,s 3 …s x M is a set m= { M of electricity price decrease periods 1 ,m 2 ,m 3 …m y X, y are the number of rise and fall periods;
s x the load shifted out due to the rise of electricity prices is distributed as shown in formula (2) in period M; m is m y The load distribution from the S period absorbed by the decrease in electricity prices is as shown in formula (3);
wherein: Δq is the difference between the established time-of-use electricity price and the original electricity price,
is the difference value between the power price in the y period and the original power price after the power price is reduced,
the difference value between the power price after the power price rises in the period x and the original power price; f () is a load transfer function, equation (4) is a load transfer rate model describing the relationship between the load transfer rate and the change in electricity price for each period, and this equation will be based on a Logistic functionThe load transfer rate reflects electricity price and is divided into a dead zone, a response zone and a saturation zone;
wherein: Δq is the absolute value of the electricity price change quantity, a is the dead zone threshold value, K is the slope of the transfer rate curve of the response zone, and f max Maximum load transfer rate for saturation region, f max K+a is the inflection point of the saturation region;
in summary, the price driven load response model can be described as:
Step 23: building an incentive type demand response model based on a load aggregator:
the motivation type demand response is generally performed in a contract form, and a load aggregator signs up with a power grid company on behalf of a user and agrees to reduce the time limit and capacity of load and load transfer; the contracts are divided into two types, namely translatable load contracts and interruptible load shedding contracts; the total income of the load aggregator consists of the difference between the response compensation of the power grid operators and the response cost of the users, and the load aggregator responds with the maximum benefit of the load aggregator as the drive; the objective function is:
wherein N is T =24, response period of 1h; c is the profit of the load aggregator; p (P) t1 、P t2 Compensating for the response of a power grid and a load quotient unit in a t period in the power market;
a percentage of interruptible, translatable negative response in the t period;
N LC ∈{i-1,i,i+1,…} (11)
wherein:
flag for executing interruptible contract->
Executing a contract for 1 representing t period,/->
No contract is performed for period 1 representing t; />
The number of times of interruption is the maximum and minimum in the period; />
A duration time constraint for a period of time; />
Is an interruptible capacity constraint; n (N) LC A set of interruptible time periods;
N LS ∈{j-1,j,j+1,…} (16)
M LS ∈{k-1,k,k+1,…} (17)
wherein:
flag for translatable contract execution ++>
Executing a contract for 1 representing t period,/->
No contract is performed for period 1 representing t; />
Is the maximum and minimum translatable times in the period; />
A duration translation time constraint for a period of time; />
Is a translatable maximum capacity constraint; n (N) LS As translatable period set, M LS Is a set of translated time periods;
the incentive type demand response model is that,
wherein:
for t period of original excitation load, +.>
To the amount of load change due to enforcing the interruptible contract and the translatable contract.
3. The virtual power plant double-layer optimization method considering refined demand response and electrolytic hydrogen production according to claim 1, wherein the establishing an electrolytic hydrogen production operation model in step 3 specifically comprises: the combined hydrogen production model mainly taking wind power and energy storage as assistance consists of a scheduling layer and a hydrogen energy control layer;
the mathematical model of electrolytic hydrogen production is as follows:
P WTe +P ESe ＝P EHP (21)
m H for the quality of the hydrogen gas,
for producing hydrogen power by electricity, eta H1 ，η H2 ，/>
Respectively the electrolysis efficiency of the electrolytic tank, the efficiency of the hydrogen compressor and the heat value of the hydrogen; />
Maximum and minimum power of the electrolysis device for the period t; p (P) WTe 、P ESe The method is used for preparing hydrogen power for wind power and hydrogen power for energy storage.
4. The virtual power plant double-layer optimization method considering refined demand response and electrolytic hydrogen production according to claim 1, wherein the virtual power plant source-load-net-storage double-layer collaborative hydrogen production scheduling model constructed in the step 5 has an upper layer model objective function of:
the upper layer is a conventional unit regulation layer and consists of economic and environmental targets, and the economic targets are as follows: conventional machine setThe cost is lowest; environmental objective: routine unit operation SO 2 、NO x The emission pollution cost is the lowest:
min(f 1 +f 2 ) (22)
wherein: f (f) 1 F is the coal cost of the conventional unit 2 The environmental pollution cost of the conventional unit is realized; t is a scheduling period, and the value is 24; n is the number of conventional units; a, a i 、b i 、c i Is the coal consumption coefficient of the conventional unit, u i Is a start-stop sign;
and (5) the electric power of the ith conventional unit in the period t.
Wherein: d (D) S 、D N Respectively SO 2 、NO x Is a pollution equivalent value of (2); w (W) s And W is equal to N SO generated for the operation of conventional units 2 、NO x A total amount; b is tax amount of pollution equivalent; j (J) m The unit price of the coal for fuel; omega S 、ω N SO produced by burning unit coal respectively 2 、NO x Quality.
5. The virtual power plant double-layer optimization method considering refined demand response and electrolytic hydrogen production according to claim 1, wherein the virtual power plant source-load-net-storage double-layer collaborative hydrogen production scheduling model constructed in the step 5 is characterized in that the upper-layer model operation constraint is as follows:
(1) Power balance constraint
Equation (25) is an electric power balance constraint that does not account for network loss, where P Gi I electric output is provided for a conventional unit; p (P) WT The output of the wind turbine generator is output; p (P) PV The method is characterized by outputting force for a distributed photovoltaic generator set; p (P) ES Exerting force on the battery energy storage device; p (P) W Electric power purchased for an upper grid or other node; p (P) NL For the optimized electrical load; p (P) EHP Electric power consumed for electrolytic hydrogen production;
(2) Related unit operation constraint
1) Conventional unit operation constraint
Wherein: p (P) Gimin 、P Gimax 、
The power is t time period, minimum and maximum electric power of the ith conventional unit and power for climbing down and up;
2) Wind turbine generator system operation constraint
0≤P WT ≤P WTmax (27)
Wherein: p (P) WTmax The maximum output power of the wind turbine generator is obtained;
3) Distributed photovoltaic generator set operation constraint
0≤P PV ≤P PVmax (28)
Wherein: p (P) PVmax The maximum output power of the distributed photovoltaic generator set is obtained;
4) Battery energy storage device operation constraints
0≤|P ES |≤P ESmax ≤P ESW (29)
Wherein: p (P) ES The real-time power of the battery energy storage device is discharged if the power is larger than 0, and otherwise, the power is charged; p (P) ESmax Maximum charge and discharge power in unit time; p (P) ESW Rated power for electric energy storage;
5) Purchasing power line constraints
0≤P W ≤P Wmax (30)
Wherein: p (P) Wmax Maximum power purchased for an upper grid or other node.
6. The virtual power plant double-layer optimization method considering refined demand response and electrolytic hydrogen production according to claim 1, wherein the virtual power plant source-load-net-storage double-layer collaborative hydrogen production scheduling model constructed in the step 5 has the following objective function:
the lower layer is an EHP regulation layer, and the maximum hydrogen energy benefit is taken as a target; the hydrogen energy benefit consists of hydrogen energy market income and hydrogen production cost, wherein the hydrogen production electricity is stored by a wind turbine generator and a battery, and the hydrogen energy benefit is the maximum equivalent to the lowest operation cost in the EHP rated operation state, as shown in a formula (31):
wherein: y is electricity price, S is energy storage mobilizing price; p (P) WTe For producing hydrogen power and P by wind power ESe Hydrogen production power stored by a battery.
7. The virtual power plant double-layer optimization method considering refined demand response and electrolytic hydrogen production according to claim 1, wherein the virtual power plant source-load-net-storage double-layer collaborative hydrogen production scheduling model constructed in the step 5 is characterized in that the lower model operation constraint is as follows:
0≤P WTe ≤P EHPmax (32)
0≤P ESe ≤P EHPmax (33)
P WTe +P ESe ＝P EHPmax (34)
formula (32) is the EHP hydrogen production wind power consumption power constraint, formula (33) is the EHP hydrogen production battery energy storage consumption power constraint, and formula (34) is the EHP hydrogen production total power constraint.
8. The virtual power plant double-layer optimization method considering refined demand response and electrolytic hydrogen production according to claim 1, wherein the non-linear convergence and adaptive weight whale algorithm in step 7 specifically comprises: in the stage of surrounding the prey, changing the control parameter a from linear attenuation to nonlinear attenuation, and introducing an adaptive weight factor omega;
step 71, surrounding the prey stage, the mathematical model of which is specifically as follows:
D＝|CX * (T)-X(T)| (35)
wherein: t is the iteration number; x is X * (T) is the position of the current optimal solution; x (T) is the current position; c is a coefficient vector; d is the distance between the search agent and the target prey;
wherein: omega is an adaptive weight factor; max_t is the maximum iteration number;
X(T+1)＝ωX * (T)-A×D (37)
wherein: a is a coefficient vector;
A＝2ar 1 -a (38)
C＝2r 2 (39)
wherein: r is (r) 1 、r 2 Is [0-1]Random numbers in between; a is a control parameter which decreases nonlinearly with iteration number to 0, where
Step 72, in the stage of attacking the prey, whales spit out bubbles and travel to the target prey with spiral movement tracks, and the mathematical model of the individual position update is as follows:
X(T+1)＝D·e bl ·cos(2πl)+ωX * (T) (41)
wherein: b is a constant; l is a random vector between [0-1 ];
step 73, searching for a prey stage, enabling whale random walks to find food, and performing global exploration, wherein a mathematical model is as follows:
D＝|CX rand (T)-X(T)| (42)
X(T+1)＝ωX rand (T)-A×D (43)
wherein: x is X rand (T) randomly selecting a certain individual position from the current population.
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