CN115600757A - Coordination optimization method and system for offshore wind power sharing energy storage participation spot market trading - Google Patents
Coordination optimization method and system for offshore wind power sharing energy storage participation spot market trading Download PDFInfo
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Abstract
The invention discloses a coordination optimization method and a system for offshore wind power sharing energy storage to participate in spot market trading, wherein the coordination optimization method comprises the following steps: acquiring parameters of an offshore wind power cluster and self-distribution energy storage; generating offshore wind power output information by using a scene method; establishing a profit model of the offshore wind farm participating in spot market transaction based on the parameters and the wind power output information; establishing an optimized operation model and constraint conditions for participating in spot market trading of the offshore wind power cluster based on the shared energy storage based on the profit model with the maximum yield of the combined operation of the offshore wind power and the shared energy storage as an objective function; and carrying out optimization solution on the optimized operation model of the offshore wind power cluster participating in spot market trading to obtain the optimal charge and discharge power of the shared energy storage. The invention integrates the offshore wind power plants to form an alliance, and jointly dispatches in a shared energy storage mode, thereby improving the income of the whole offshore wind power cluster.
Description
Technical Field
The invention belongs to the technical field of offshore wind power, and particularly relates to a coordination optimization method, a coordination optimization system, coordination optimization equipment and coordination optimization medium for sharing stored energy of offshore wind power to participate in spot market trading.
Background
In recent years, with increasing energy crisis and environmental issues, renewable energy sources such as wind power have drawn more and more attention. Compared with onshore wind power, offshore wind power has the characteristics of being close to an electrical load center, no land resource occupation of an offshore wind turbine, small output fluctuation, higher efficiency of the offshore wind turbine and the like. The large-scale application of offshore wind power can effectively deal with energy crisis problems and environmental problems.
Aiming at the problem of large-scale offshore wind power consumption, the energy storage system is one of effective methods for solving the problem, and stores electric energy at the output peak stage of the offshore wind power and releases the electric energy at the output valley stage so as to obtain more electric energy benefits. However, the investment cost of the current energy storage system is still high, especially for a large-scale energy storage system, the method only depends on the energy storage system to participate in the electric energy transaction to improve the economic benefit, the cost recovery year limit of the energy storage system is long, and the utilization rate of the energy storage system is low. The energy storage system can effectively participate in the frequency modulation auxiliary service due to the characteristics of quick adjustment and the like, and becomes a high-quality frequency modulation resource. Through a proper energy management strategy and a proper control strategy of the energy storage power station, the offshore wind power and the energy storage power station can jointly participate in peak-shaving frequency modulation auxiliary service.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a coordination optimization method for offshore wind power sharing energy storage to participate in spot market trading. The method disclosed by the invention has the advantages that the stored energy of the offshore wind power plant is subjected to alliance sharing based on the idea of cooperation, and the stored energy of the offshore wind power plant participate in power grid dispatching together, so that the consumption level of the offshore wind power can be effectively improved, and the economic benefit of the offshore wind power can be improved.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
a coordination optimization method for offshore wind power sharing energy storage participation spot market trading comprises the following steps:
acquiring various parameters of offshore wind power and self-distribution energy storage; generating wind power output information by using a scene method;
establishing a profit model of the offshore wind farm participating in spot market transaction based on the parameters and the wind power output information; establishing an optimized operation model and constraint conditions for participating in spot market trading of the offshore wind power cluster based on the shared energy storage based on the profit model with the maximum yield of the combined operation of the offshore wind power and the shared energy storage as an objective function;
and carrying out optimization solution on the optimization operation model based on constraint conditions to obtain the target charge and discharge power of the shared energy storage.
As a further improvement of the present invention, the parameters include: rated power, energy storage capacity, energy storage maximum charge-discharge power, energy storage SOC interval, construction cost and maximum charge-discharge frequency of the wind power plant.
As a further improvement of the present invention, the generating wind power output information by using the scene method includes:
forecasting the wind speed information according to the autoregressive moving average model;
adopting Latin hypercube layered sampling to sample the wind speed prediction error, and assuming that the probability of each sample is equal;
reducing the prediction error scene sample set by adopting a backward reduction technology, and combining similar scenes to generate an offshore wind power output scene;
according to the relation between the wind speed and the wind power, giving a wind power limited output scene and the probability of a corresponding scene of an offshore wind power output scene;
and taking the wind power limited output scene and the probability of the corresponding scene as wind power output information.
As a further improvement of the present invention, the method for establishing the revenue model of the offshore wind farm participating in the spot market transaction includes the following steps:
the day ahead market revenue model is:
the real-time market revenue model is:
in the formula: t is the number of segments of deltat encompassed by the union period, S is the total number of offshore wind power output scenes, gamma s Is the probability of scene s, W A,t Total revenue for alliance A to participate in spot market transactions at time t, E i,t The total income of the offshore wind farm i in the time period t is specifically expressed as follows:
in the formula:
for the return of offshore wind farms in the day-ahead market,
for the revenue of offshore wind farms on the real-time market,
in order to obtain the price of the product in the day,
for the bid value of the offshore wind farm in the market today,
for actual generated power, λ + 、λ - Respectively a positive penalty coefficient and a negative penalty coefficient;
the energy storage cycle life cost model is:
in the formula, C cycle The energy storage cycle life cost, N the energy storage construction cost,
and (4) storing energy in the t period for equivalent cycle times.
As a further improvement of the present invention, the maximum combined operating yield of offshore wind power and shared energy storage is an objective function, and the objective function is:
Max(E A -C cycle )
in the formula, C cycle For energy storage cycle life costs, E A A future market revenue model;
the shared energy storage provides charging and discharging power for the offshore wind farm according to the power generation error state of the offshore wind farm;
counting the power generation error state of the offshore wind power plant:
ΔP t ＝P real -P d
in the formula,. DELTA.P t For the power generation error of the offshore wind farm at time t, P real For actual generated power, P d Is the power bid in the market at the day-ahead.
As a further improvement of the present invention, the constraint conditions include:
and wind power bidding power constraint:
in the formula (I), the compound is shown in the specification,
the bidding power of the wind power plant in the market before the day is shown, and Pt and max are rated power of the wind power plant;
positive and negative deviation assessment price constraint:
0<λ + <1
λ - >1
in the formula, λ + 、λ - Positive deviation examination price coefficient and negative deviation examination price technology of wind power supplier day-ahead bidding output and actual output respectively;
and power balance constraint:
in the formula P i,t Charging and discharging power, P, provided to the new energy power station i for a time period t for shared energy storage d,i,t The power is required for charging and discharging of the new energy power station i in the t period;
energy storage charge and discharge power constraint:
-P max ≤P i,t ≤P max
P max ＝min{P c ,P m,i,t }
P m,i,t ＝(S SOC,i,i-1 -S SOC,min )C i η dis /Δt
in the formula P max Maximum charge-discharge power for energy storage, P c Rated power for energy storage, P m,i,t The average power S corresponding to the available electric quantity of the new energy power station i in the time period t when the available electric quantity is completely discharged in the time period t SOC,min Lower limit value of self-distribution energy storage charge state of new energy power station, C i Rated capacity, η, for energy storage dis Charge-discharge efficiency;
energy storage and charge quantity restraint:
S SOC,min ≤S soc,i,t ≤S SOC,max
in the formula, S SOC,min 、S SOC,max Respectively a lower limit value and an upper limit value of the energy storage charge state;
energy storage charging and discharging state constraint:
in the formula, beta ch 、β dis The variables are respectively the charge and discharge state variables of the energy storage system, wherein 0 represents charge and 1 represents discharge.
As a further improvement of the present invention, the performing an optimization solution on the optimized operation model of the shared energy storage-based offshore wind power cluster participating in spot market trading to obtain a target charge and discharge power of the shared energy storage includes:
summarizing power generation information and energy storage states of offshore wind power plants;
randomly generating N groups of charge and discharge power schemes which are provided for each offshore wind power plant by sharing energy storage at a time t;
and carrying out iterative optimization on the optimized operation model by preset iteration times to obtain a target charge and discharge power scheme which is finally provided for each offshore wind farm by the shared energy storage at the time period t.
A coordination optimization system for offshore wind power sharing energy storage participation spot market trading comprises:
the acquisition module is used for acquiring various parameters of offshore wind power and self-distribution energy storage; generating wind power output information by using a scene method;
the modeling module is used for establishing a profit model of the offshore wind farm participating in spot market trading based on the parameters and the wind power output information; establishing an optimized operation model and constraint conditions for participating in spot market trading of the offshore wind power cluster based on the shared energy storage based on the profit model by taking the maximum joint operation profit of the offshore wind power and the shared energy storage as an objective function;
and the solving module is used for carrying out optimization solving on the optimized operation model based on the constraint condition to obtain the target charge and discharge power of the shared energy storage.
An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the offshore wind power shared energy storage participation spot market trading coordination optimization method when executing the computer program.
A computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the offshore wind power shared energy storage participation spot market trading coordination optimization method.
Compared with the prior art, the invention has the following beneficial effects:
the invention relates to a coordination optimization method for offshore wind power sharing energy storage participation spot market trading, which aims at the maximum income of an offshore wind power and sharing energy storage system to construct an optimized operation model for sharing energy storage participation offshore wind power cluster alliance to participate in spot market; and generating an optimal charge and discharge power scheme provided for each offshore wind farm. The invention integrates the offshore wind farms to form an alliance, and jointly dispatches in a form of sharing energy storage, thereby improving the benefit of the whole offshore wind farm cluster. According to the method, the stored energy of the offshore wind power plant is subjected to union sharing based on the idea of cooperation, and the stored energy of the offshore wind power plant participate in power grid dispatching together, so that the consumption level of the offshore wind power can be effectively improved, and the economic benefit of the offshore wind power can be improved.
Drawings
Fig. 1 is a flowchart of a coordination optimization method for participating in spot market trading by offshore wind power sharing energy storage provided by the invention;
FIG. 2 is a diagram of an offshore wind farm and shared derating topology;
FIG. 3 shows a flow of optimizing, solving, sharing, and providing the shared energy storage to the optimal charging and discharging power of the offshore wind farm by using a chaotic quantum genetic algorithm;
FIG. 4 is a coordination optimization system for participating in spot market trading of offshore wind power sharing energy storage according to the invention;
fig. 5 is a schematic diagram of an electronic device according to the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention provides a coordination optimization method for offshore wind power sharing energy storage participation spot market trading. According to the method, the stored energy of the offshore wind power plant is subjected to union sharing based on the idea of cooperation, and the stored energy of the offshore wind power plant participate in power grid dispatching together, so that the consumption level of the offshore wind power can be effectively improved, and the economic benefit of the offshore wind power can be improved. The specific scheme is as follows:
a coordination optimization method for offshore wind power sharing energy storage participation spot market trading comprises the following steps:
(1) Selecting an offshore wind farm cluster in a certain coastal region, and acquiring parameters of an offshore wind farm and self-distribution energy storage;
(2) Generating wind power output information by using a scene method;
(3) Establishing a profit model of the offshore wind farm participating in spot market trading, wherein the profit model comprises a day-ahead market profit model, a real-time market profit model and an energy storage cycle life cost model;
(4) Establishing an optimized operation model of participating in spot market trading of the offshore wind power plant cluster based on the shared energy storage by taking the maximum combined operation yield of the offshore wind power and the shared energy storage as an objective function;
(5) Establishing model constraint conditions including energy storage charging and discharging state constraint, shared energy storage power balance constraint, capacity constraint and energy storage charge quantity constraint;
(6) Based on the established optimized operation model of the offshore wind power shared energy storage participating in the spot market transaction, the chaotic quantum genetic algorithm is adopted to carry out optimized solution, and the charge and discharge power of the shared energy storage target is obtained.
The method of the present invention will be described in detail with reference to specific examples.
As shown in fig. 1, for a flowchart of a coordination optimization method for offshore wind power sharing energy storage participation spot market trading provided by the present invention, the coordination optimization method for offshore wind power sharing energy storage participation spot market trading provided by the present invention includes:
selecting a plurality of offshore wind power plants with self-distributed energy storage to form an alliance, and obtaining predicted output values of the offshore wind power plants and various parameters of the self-distributed energy storage;
establishing an optimized operation model of sharing energy storage participation offshore wind power cluster alliance participation spot market with the goal of taking the maximum profit of the offshore wind power and the shared energy storage system as a target;
the method comprises the steps of establishing an energy storage cycle life cost model by considering energy storage cycle life cost; the offshore wind power cluster reports information such as daily power generation shortage, power generation surplus, self-distribution energy storage and the like to the shared energy storage platform, and generates an optimal charging and discharging power scheme provided for each offshore wind power plant by taking the maximum combined operation yield of the offshore wind power and the shared energy storage as a target function. The invention integrates the offshore wind farms to form an alliance, and jointly dispatches in a form of sharing energy storage, thereby improving the benefit of the whole offshore wind farm cluster.
The steps are described as follows:
step 1, selecting an offshore wind farm cluster in a certain coastal region, and acquiring parameters of an offshore wind farm and self-distribution energy storage, wherein the parameters comprise: rated power, energy storage capacity, maximum energy storage charge-discharge power, energy storage SOC interval, construction cost and maximum charge-discharge frequency of the wind power plant.
Step 2, generating an offshore wind power output scene, and generating wind power output information by using a scene method; the scene method is specifically expressed as follows:
predicting wind speed information according to an autoregressive moving average model (ARMA);
adopting Latin hypercube layered sampling to sample the wind speed prediction error, and assuming that the probability of each sample is equal;
reducing the prediction error scene sample set by adopting a backward reduction technology, and combining similar scenes to generate an offshore wind power output scene;
and according to the relation between the wind speed and the wind power, giving out the wind power limited output scene of the offshore wind power output scene and the probability of the corresponding scene.
Step 3, establishing a profit model of the offshore wind farm participating in spot market trading, wherein the profit model comprises a day-ahead market profit model, a real-time market profit model and an energy storage cycle life cost model, and the profit model is specifically expressed as follows:
the day ahead market revenue model is:
the real-time market revenue model is:
in the formula: t is the number of segments of delta T contained in the alliance period, S is the total number of offshore wind power output scenes, and gamma is s Is the probability of scene s, W A,t Total revenue for alliance A to participate in spot market transactions at time t, E i,t The total income of the offshore wind farm i in the time period t is specifically expressed as follows:
in the formula:
for the return of offshore wind farms in the day-ahead market,
for the revenue of offshore wind farms on the real-time market,
in order to obtain the price of the product in the day,
for the bid value of the offshore wind farm in the market today,
for actual generated power, λ + 、λ - Respectively a positive penalty coefficient and a negative penalty coefficient;
the energy storage cycle life cost model is:
in the formula C cycle The energy storage cycle life cost, N the energy storage construction cost,
the energy storage equivalent cycle times of the energy storage in the t-th time period are obtained;
and 4, constructing an optimized operation model of the offshore wind power plant cluster participating in spot market trading by taking the maximum combined operation yield of offshore wind power and shared energy storage as an objective function, wherein the optimized operation model is specifically expressed as follows:
the objective function is:
Max(E A -C cycle )
the shared energy storage provides charge and discharge power for the offshore wind farm according to the power generation error state of the offshore wind farm, and the specific expression is as follows:
and (3) uniting the power generation error states of the offshore wind power plants:
ΔP t ＝P real -P d
in the formula,. DELTA.P t For the power generation error of the offshore wind farm at time t, P real For actual generated power, P d Power bid for market at the day-ahead;
as shown in fig. 2, it is a topological diagram of an offshore wind farm and a shared energy storage platform.
In the state 1 and the state delta P > 0, when the power generation is excessive, the shared energy storage platform provides charging power for the offshore wind power plant, and when the power generation excess power exceeds the power supplied to the external shared energy storage, the residual power is used for self-distribution energy storage charging; when the power supplied to the external shared energy storage exceeds the power generation excess power, the self-distribution energy storage is consumed to supply external energy storage requirements;
in the state 2, the delta P is less than 0, the power generation is in shortage, the shared energy storage platform provides discharge power for the offshore wind farm,
when the self-distribution energy storage can completely make up the power generation shortage, the shared energy storage provides charging power for the power station; when the self-distribution energy storage cannot completely make up the power generation shortage, the shared energy storage provides discharge power for the power station;
step 5, establishing model constraint conditions, including energy storage charging and discharging state constraint, shared energy storage power balance constraint, capacity constraint and energy storage charge quantity constraint;
wind power bidding power constraint:
in the formula (I), the compound is shown in the specification,
the bidding power of the wind power plant in the market before the day is shown, and Pt and max are rated power of the wind power plant.
Positive and negative deviation checking price constraint:
the positive and negative deviation electricity examination price refers to the day-ahead clearing price setting, meets the constraint that the negative deviation examination price is larger than the day-ahead market clearing price, and the positive deviation examination price is smaller than the day-ahead market clearing price, and is specifically expressed as follows:
0<λ + <1
λ - >1
in the formula, λ + 、λ - Positive deviation examination price coefficient and negative deviation examination price technology of wind power supplier day-ahead bidding output and actual output respectively;
and (3) power balance constraint:
in the formula P i,t Charging and discharging power, P, provided to the new energy power station i for a time period t for shared energy storage d,i,t The power is required for charging and discharging of the new energy power station i in the t period;
energy storage charge and discharge power constraint:
-P max ≤P i,t ≤P max
P max ＝min{P c ,P m,i,t }
P m,i,t ＝(S SOC,i,i-1 -S SOC,min )C i η dis /Δt
in the formula P max Maximum charge-discharge power for energy storage, P c Rated power for energy storage, P m,i,t The average power S corresponding to the condition that the available electric quantity of the new energy power station i in the t period is completely discharged in the t period SOC,min For new energy power stationLower limit of charge state of the auxiliary energy storage, C i Rated capacity, η, for energy storage dis Charge-discharge efficiency;
energy storage and charge quantity restraint:
S SOC,min ≤S soc,i,t ≤S SOC,max
in the formula, S SOC,min 、S SOC,max Respectively a lower limit value and an upper limit value of the energy storage charge state;
energy storage charging and discharging state constraint:
in the formula, beta ch 、β dis Respectively representing charge and discharge state variables of the energy storage system, wherein 0 represents charge, and 1 represents discharge;
and step 6, based on the established offshore wind power shared energy storage and spot market optimized operation model, carrying out optimized solving by using a chaotic quantum genetic algorithm to obtain the optimal charge and discharge power of the shared energy storage, and as shown in the attached figure 3, carrying out optimized solving on the optimal charge and discharge power flow provided for the offshore wind power plant by the shared energy storage for the chaotic quantum genetic algorithm.
As shown in fig. 3, the specific expression is:
summarizing the power generation information and the energy storage state of each offshore wind farm;
randomly generating N groups of charge and discharge power schemes which are provided for each offshore wind power plant by sharing energy storage at a time t;
and optimizing to obtain a charge-discharge power scheme which is finally provided for each offshore wind power plant by sharing energy storage at a time t through the iteration optimization of the maximum iteration times.
The iteration optimization through the maximum iteration times (the maximum iteration times are preset) comprises the following steps:
initializing a population and calculating the alliance income of the initial population;
comparing the fitness of the current population, and searching a global optimal scheme;
updating population individuals by using a rotation quantum gate;
and the maximum iteration times are reached, and the optimal charge and discharge power of the shared energy storage target is obtained.
And if the maximum iteration times are not reached, returning to the step of initializing the population and carrying out iterative calculation again.
As shown in fig. 4, the present invention further provides a coordination optimization system for offshore wind power sharing energy storage to participate in spot market trading, comprising:
the acquisition module is used for acquiring parameters of offshore wind power and self-distribution energy storage; generating wind power output information by using a scene method;
the modeling module is used for establishing a profit model of the offshore wind farm participating in spot market trading based on the parameters and the wind power output information; establishing an optimized operation model and constraint conditions for participating in spot market trading of the offshore wind power cluster based on the shared energy storage based on the profit model with the maximum yield of the combined operation of the offshore wind power and the shared energy storage as an objective function;
and the solving module is used for carrying out optimization solving on the optimized operation model based on the constraint condition to obtain the target charge and discharge power of the shared energy storage.
As shown in fig. 5, the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the offshore wind power shared energy storage participation spot market trading coordination optimization method when executing the computer program.
The coordination optimization method for the offshore wind power sharing energy storage participation spot market trading comprises the following steps:
acquiring parameters of an offshore wind farm cluster and self-distribution energy storage; generating wind power output information by using a scene method;
establishing a profit model of the offshore wind farm participating in spot market trading based on various parameters and wind power output information; establishing an optimized operation model of the offshore wind power plant cluster participating in spot market trading with the maximum of the combined operation income of the offshore wind power and the shared energy storage as an objective function, and establishing a income model constraint condition;
and based on a yield model constraint condition, carrying out optimization solution on the optimized operation model of the offshore wind farm cluster participating in spot market trading to obtain the optimal charge and discharge power of the shared energy storage.
The invention also provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the offshore wind power shared energy storage participation spot market trading coordination optimization method.
The coordination optimization method for the offshore wind power sharing energy storage participation spot market trading comprises the following steps:
acquiring parameters of an offshore wind farm cluster and self-distribution energy storage; generating wind power output information by using a scene method;
establishing a profit model of the offshore wind farm participating in spot market trading based on various parameters and wind power output information; establishing an optimized operation model of the offshore wind power plant cluster participating in spot market trading with the maximum of the combined operation income of the offshore wind power and the shared energy storage as an objective function, and establishing a income model constraint condition;
and based on the income model constraint condition, carrying out optimization solution on the optimized operation model of the offshore wind farm cluster participating in spot market trading to obtain the optimal charge and discharge power of the shared energy storage.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A coordination optimization method for offshore wind power sharing energy storage participation spot market trading is characterized by comprising the following steps:
acquiring various parameters of offshore wind power and self-distribution energy storage; generating wind power output information by using a scene method;
establishing a profit model of the offshore wind farm participating in spot market transaction based on the parameters and the wind power output information; establishing an optimized operation model and constraint conditions for participating in spot market trading of the offshore wind power cluster based on the shared energy storage based on the profit model by taking the maximum joint operation profit of the offshore wind power and the shared energy storage as an objective function;
and carrying out optimization solution on the optimization operation model based on constraint conditions to obtain the target charge and discharge power of the shared energy storage.
2. The offshore wind power shared energy storage participation spot market trading coordination optimization method according to claim 1, wherein the parameters comprise: rated power, energy storage capacity, energy storage maximum charge-discharge power, energy storage SOC interval, construction cost and maximum charge-discharge frequency of the wind power plant.
3. The offshore wind power shared energy storage participation spot market trading coordination optimization method according to claim 1, wherein the generating wind power output information by using a scene method comprises:
predicting wind speed information according to the autoregressive moving average model;
adopting Latin hypercube hierarchical sampling to sample the wind speed prediction error, and assuming that the probability of each sample is equal;
reducing the prediction error scene sample set by adopting a backward reduction technology, and combining similar scenes to generate an offshore wind power output scene;
according to the relation between the wind speed and the wind power, giving a wind power limited output scene of an offshore wind power output scene and the probability of the corresponding scene;
and taking the wind power limited output scene and the probability of the corresponding scene as wind power output information.
4. The offshore wind power shared energy storage participation spot market trading coordination optimization method according to claim 1, wherein a profit model of an offshore wind farm participating spot market trading is established, the profit model comprises a day-ahead market profit model, a real-time market profit model and an energy storage cycle life cost model, and specifically comprises:
the day ahead market revenue model is:
the real-time market revenue model is:
in the formula: t is the number of segments of delta T contained in the alliance period, S is the total number of offshore wind power output scenes, and gamma is s Is the probability of scene s, W A,t Total revenue for alliance A to participate in spot market transactions at time t, E i,t The total income of the offshore wind farm i in the time period t is specifically expressed as follows:
in the formula:
for the return of offshore wind farms in the day-ahead market,
for the revenue of offshore wind farms in the real-time market,
in order to obtain the price of the product in the day,
for the bid value of the offshore wind farm in the market today,
for actual generated power, λ + 、λ - Respectively a positive penalty coefficient and a negative penalty coefficient;
the energy storage cycle life cost model is:
in the formula, C cycle The energy storage cycle life cost, N the energy storage construction cost,
and (4) storing energy in the t period for equivalent cycle times.
5. The offshore wind power shared energy storage participation spot market trading coordination optimization method according to claim 1, wherein the maximum yield of the offshore wind power and shared energy storage combined operation is an objective function, and the objective function is as follows:
Max(E A -C cycle )
in the formula, C cycle For energy storage cycle life costs, E A A future market revenue model;
the shared energy storage provides charging and discharging power for the offshore wind farm according to the power generation error state of the offshore wind farm;
counting the power generation error state of the offshore wind power plant:
ΔP t ＝P real -P d
in the formula,. DELTA.P t For the power generation error of the offshore wind farm at time t, P real For actual generated power, P d Is the power bid in the market at the day-ahead.
6. The offshore wind power shared energy storage participation spot market trading coordination optimization method according to claim 1, wherein the constraint condition comprises:
and wind power bidding power constraint:
in the formula (I), the compound is shown in the specification,
the bidding power of the wind power plant in the market before the day is shown, and Pt and max are rated power of the wind power plant;
positive and negative deviation checking price constraint:
0<λ + <1
λ - >1
in the formula, λ + 、λ - Positive deviation assessment price coefficients and negative deviation assessment price technologies of the wind power supplier day-ahead bidding output and actual output are respectively adopted;
and power balance constraint:
in the formula P i,t Charging and discharging power, P, provided to the new energy power station i for a time period t for shared energy storage d,i,t The power is required for charging and discharging of the new energy power station i in the t period;
energy storage charge and discharge power constraint:
-P max ≤P i,t ≤P max
P max ＝min{P c ,P m,i,t }
P m,i,t ＝(S SOC,i,i-1 -S SOC,mon )C i η dis /Δt
in the formula P max Maximum charge-discharge power for energy storage, P c Rated power for energy storage, P m,i,t The average power S corresponding to the available electric quantity of the new energy power station i in the time period t when the available electric quantity is completely discharged in the time period t SOC,min Lower limit value of self-distribution energy storage charge state of new energy power station, C i Rated capacity, η, for energy storage dis Charge-discharge efficiency;
energy storage and charge quantity restraint:
S SOC,min ≤S soc,i,t ≤S SOC,max
in the formula, S SOC,min 、S SOC,max Respectively a lower limit value and an upper limit value of the energy storage charge state;
energy storage charging and discharging state constraint:
in the formula, beta ch 、β dis The variables are respectively the charge and discharge state variables of the energy storage system, wherein 0 represents charge and 1 represents discharge.
7. The offshore wind power shared energy storage and spot market transaction coordination optimization method according to claim 1, wherein the optimizing and solving the optimized operation model of the offshore wind power cluster based on shared energy storage and participating in spot market transaction to obtain the target charge and discharge power of the shared energy storage comprises:
summarizing power generation information and energy storage states of offshore wind power plants;
randomly generating N groups of charge and discharge power schemes which are provided for each offshore wind power plant by sharing energy storage at a time t;
and carrying out iterative optimization on the optimized operation model by preset iteration times to obtain a target charge and discharge power scheme which is finally provided for each offshore wind farm by the shared energy storage at the time period t.
8. The utility model provides an offshore wind power sharing energy storage participates in spot market trade and coordinates optimizing system which characterized in that includes:
the acquisition module is used for acquiring parameters of offshore wind power and self-distribution energy storage; generating wind power output information by using a scene method;
the modeling module is used for establishing a profit model of the offshore wind farm participating in spot market trading based on the parameters and the wind power output information; establishing an optimized operation model and constraint conditions for participating in spot market trading of the offshore wind power cluster based on the shared energy storage based on the profit model with the maximum yield of the combined operation of the offshore wind power and the shared energy storage as an objective function;
and the solving module is used for carrying out optimization solving on the optimized operation model based on the constraint condition to obtain the target charge and discharge power of the shared energy storage.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the offshore wind power shared energy storage participation spot market trading coordination optimization method of any one of claims 1-7 when executing the computer program.
10. A computer-readable storage medium storing a computer program which, when executed by a processor, carries out the steps of the offshore wind power shared energy storage participation spot market trading coordination optimization method of any one of claims 1 to 7.
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| CN118539418A (en) * | 2024-05-14 | 2024-08-23 | 中能智新科技产业发展有限公司 | Power parameter determination method, device and equipment of power system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117791656A (en) * | 2023-12-28 | 2024-03-29 | 中国长江电力股份有限公司 | Multi-scenario application-oriented shared energy storage optimization control method |
| CN118539418A (en) * | 2024-05-14 | 2024-08-23 | 中能智新科技产业发展有限公司 | Power parameter determination method, device and equipment of power system |
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