CN118487301A - Energy storage application control optimization method, device and storage medium - Google Patents
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Abstract
The application discloses an energy storage application control optimization method, a device and a storage medium, wherein the method comprises the following steps: basic data in the area of the power grid are obtained; according to the basic data, a load short-term prediction curve and a renewable energy short-term power prediction curve are obtained in a prediction mode; generating a commercial power demand planning curve of a day-ahead time scale according to the load short-term prediction curve and the renewable energy short-term power prediction curve; building a green electricity comprehensive evaluation index according to the renewable energy short-term power prediction curve and the mains supply demand planning curve; and establishing an optimization model by combining green electricity comprehensive evaluation indexes with a penalty function, and carrying out dynamic optimization in a daily stage with the aim of minimizing the optimization model to generate an energy storage charging and discharging demand curve and a commercial power demand curve. The application effectively improves the utilization efficiency of regional green electricity.
Description
Technical Field
The invention relates to the technical field of power control, in particular to an energy storage application control optimization method, an energy storage application control optimization device and a storage medium.
Background
The green power is simply called green power, which refers to power from renewable energy sources, and in the process of producing power, the carbon dioxide emission amount of the power is zero or approaches zero, and compared with the traditional energy sources such as thermal power generation, the generated power has lower pollution to the environment. At present, green electric power is mainly divided into five types of solar power generation, hydroelectric power generation, wind power generation, biomass energy power generation and geothermal power generation according to power sources.
The development and utilization of green electricity is a direct path for energy conversion and also brings about a plurality of opportunities and challenges. Clean energy power generation such as wind power, photovoltaic and the like has volatility and intermittence, large-scale grid-connected power generation brings challenges to safe and stable operation of a power grid, and flexible regulation resources such as energy storage and the like with power bidirectional regulation capability are widely focused. In order to improve the grid-connected capacity of renewable energy sources, the real-time balance of the power grid is quickly adjusted by utilizing the space-time translation capacity of the stored energy.
Disclosure of Invention
The invention aims to provide an energy storage application control optimization method, an energy storage application control optimization device and a storage medium, so as to solve the problem that green electricity utilization efficiency is low due to fluctuation and intermittence of green electricity in the prior art.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the present application discloses a method for optimizing energy storage application control, comprising:
basic data in the area of the power grid are obtained; wherein the base data includes electrical load curve data and renewable energy generation data;
Predicting and obtaining a load short-term prediction curve P load (t) and a renewable energy short-term power prediction curve P gen (t) according to the basic data;
generating a commercial power demand planning curve Pgrid (t) of a day-ahead time scale according to the load short-term prediction curve Pload (t) and the renewable energy short-term power prediction curve Pgen (t);
Building a green electricity comprehensive evaluation index according to the renewable energy short-term power prediction curve Pgen (t) and the mains supply demand planning curve Pgrid (t); and (3) establishing an optimization model by combining green electricity comprehensive evaluation indexes with a penalty function, and carrying out dynamic optimization at a daily stage with the aim of minimizing the optimization model to generate an energy storage charge-discharge demand curve Pess (t) and a commercial electricity demand curve Pgrid (t').
Further, the objective function of the optimization model is:
f＝min(Φ+M×|Z|)；
Wherein, phi is green electricity comprehensive evaluation index, Z is penalty function, M is penalty function coefficient.
Further, the dynamic optimization in the intra-day stage is carried out with the aim of minimizing the optimization model, and the power constraint of the power grid and the constraint of energy storage equipment are used as conditions; the grid power constraint includes: a power balance equation constraint and a mains power output constraint;
the electric power balance equation constraint:
Pgrid(t)+Z+Pgen(t)+Pess(t)＝Pload(t)；
Wherein Z represents an unbalanced power penalty function, and z=p grid(t)'-Pgrid(t);Pess (t) is the charge and discharge power of the energy storage device in the optimization period;
the mains power output constraint:
0≤Pgrid(t)；
The energy storage device constraints:
Pess.min≤Pess(t)≤Pess.max；
Eess.min≤Eess(t)≤Eess.max；
Wherein, P ess.min and P ess.max are respectively the upper limit and the lower limit of the charge and discharge power of the energy storage device; e ess.min and E ess.max are respectively the upper limit and the lower limit of the charge and discharge capacity of the energy storage device; e ess (t) is the energy storage capacity at each moment in the optimization period.
Further, the expression of the green electricity comprehensive evaluation index is:
wherein lambda 1,λ2,λ3 is the comprehensive coefficient, and the value is 1.1-2; beta is the sum of the power generation utilization ratio of renewable energy sources, Chi (t) is the power generation utilization ratio of renewable energy, and N is the time point of the optimized period; the ratio of the fluctuation range of the power generated by the renewable energy source to the average value; η is the product of the power generation utilization ratio of renewable energy sources, Γ la is the time duty ratio of the forward statistics renewable energy power generation utilization ratio which is more than 50%,Γ sm is the time duty ratio of negative statistics renewable energy power generation utilization less than 50%,
Further, the method comprises the steps of,
The expression of the renewable energy power generation utilization ratio χ (t) is as follows:
The ratio of the fluctuation range to the average value of the renewable energy generation power The expression of (2) is:
wherein Pgreen _max is the maximum value of renewable energy power generation, pgreen _min is the minimum value of renewable energy power generation, pgreen _ave is the average value of renewable energy power generation,
Further, the expression of the day-ahead time scale of the utility power demand planning curve Pgrid (t) is:
Pgrid(t)＝Pload(t)-Pgen(t)。
Further, the method further comprises the step of generating a charge and discharge control instruction for controlling the energy storage converter according to an energy storage charge and discharge demand curve Pess (t); and controlling the mains supply scheduling according to a mains supply demand curve Pgrid (t)'.
Further, the renewable energy sources include solar energy, wind energy, water energy, biomass energy, and geothermal energy.
In a second aspect, the present application discloses an energy storage application control optimizing device, comprising:
The acquisition module is used for acquiring basic data in the area range of the power grid; wherein the base data includes electrical load curve data and renewable energy generation data;
the prediction module is used for predicting and obtaining a load short-term prediction curve Pload (t) and a renewable energy short-term power prediction curve Pgen (t) according to the basic data;
the day-ahead planning module is used for generating a commercial power demand planning curve Pgrid (t) of a day-ahead time scale according to the load short-term prediction curve P load (t) and the renewable energy short-term power prediction curve Pgen (t);
The daily optimization module is used for establishing a green electricity comprehensive evaluation index according to the renewable energy short-term power prediction curve Pgen (t) and the mains supply demand planning curve Pgrid (t), establishing an optimization model by using the green electricity comprehensive evaluation index and the penalty function, and carrying out dynamic optimization at a daily stage with the aim of minimizing the optimization model to generate an energy storage charging and discharging demand curve Pess (t) and a mains supply demand curve Pgrid (t)'.
In a third aspect, the present application discloses a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the energy storage application control optimization method according to any of the first aspects.
According to the technical scheme, the invention has the beneficial effects that:
According to the method, an optimization model is established by combining green electricity comprehensive evaluation indexes with penalty functions, dynamic optimization at a daily stage is performed with the aim of minimizing the optimization model, an energy storage charge-discharge demand curve Pess (t) and a commercial electricity demand curve Pgrid (t)', the power generation utilization condition of distributed renewable energy sources in a power grid is quantitatively analyzed and evaluated, meanwhile, the demand power of the power grid area for the commercial electricity is stabilized as much as possible, the demand curves of the energy storage and the commercial electricity are optimized, a specific demand curve is obtained, charge-discharge control can be guided through the obtained curve, and the green electricity utilization efficiency of the area is effectively improved;
According to the application, a penalty function is considered in the generation process of the demand curve, so that the optimization model can realize real-time balance of power as much as possible under the condition of meeting constraint conditions, the stability and reliability of a power system are improved, the problem that a power unbalance value is easy to occur when the charge and discharge power of an energy storage system is physically limited is solved, and the electric utilization efficiency is further improved.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of a two-stage optimization model time scale according to the present invention;
fig. 3 is a flow chart of the method of the present invention.
Detailed Description
The invention is further described in connection with the following detailed description, in order to make the technical means, the creation characteristics, the achievement of the purpose and the effect of the invention easy to understand.
The application takes a day-ahead and day-ahead two-stage method as an optimization framework, comprehensively considers the uncertainty of electricity demand and the uncertainty of renewable energy power generation in the power grid from the aspects of distributed renewable energy power generation and load prediction in the power grid, takes energy storage as an energy buffer, realizes the optimal utilization of the power grid power generation resources, and improves the utilization rate of renewable energy power generation to the greatest extent on the premise of keeping the stable operation of the power grid. The method specifically comprises two aspects of a day-ahead planning stage and an intra-day dynamic optimization stage of power generation and power utilization. In a day-ahead planning stage, according to the prediction data of the distributed renewable energy power generation and power consumption load in the regional power grid range, determining the day-ahead power generation (renewable energy power generation capacity plan and mains supply access capacity plan) and power consumption (load power consumption plan) arrangement by combining with the real-time balance principle of power grid operation power; in the daytime, the corresponding green electricity comprehensive evaluation index is used as a target, the power constraint of the power grid and the energy storage equipment is used as a condition, the power generation and electricity utilization data are dynamically updated according to the real-time operation data of the power grid, the charge and discharge power curve of the energy storage is optimally arranged, the energy storage power bidirectional flow capacity is utilized to control the energy storage converter to complete optimal control and application, the distributed power generation utilization rate in the power grid is improved, and the consumption level of clean energy of the power grid is further improved.
The main idea of the invention is as follows: the method comprises the steps of forming a basic database by acquiring power grid operation data (including renewable energy power generation data and load power consumption data); based on the basic data, new energy power generation power prediction curve data and power consumption power prediction curve data of a day-ahead time scale are obtained by utilizing a time sequence prediction method, so that a mains supply demand power curve of the day-ahead time scale is calculated; and combining the established green electricity evaluation model, taking a short-term prediction result as a basis, taking the minimum comprehensive index combined with a penalty function as a target, taking the power constraint of power grid operation and the constraint of energy storage equipment as conditions, utilizing methods such as dynamic programming and the like to perform optimization solution on the daily time scale on the integral power curve of the energy storage to obtain a charge-discharge control power curve of the energy storage, and finally completing the optimization control on the energy storage power through the control of the energy storage converter, thereby achieving the effects of improving the green electricity utilization condition of the power grid, improving the energy storage control efficiency and improving the operation stability of the power grid.
Specifically, the application discloses an energy storage application control optimization method, which comprises the following steps:
Step 1, basic data in a power grid area range is obtained; wherein the base data includes electrical load curve data and renewable energy generation data.
In step 1, electrical load curve data are acquired as follows: power load data is collected over a period of time (past hours, days, weeks) in the area, reflecting the change in consumer power demand over time. The renewable energy power generation data are obtained as follows: and collecting historical output data and real-time output prediction data of renewable energy power generation facilities such as wind power, solar energy, water energy, geothermal energy and the like in the same time period so as to analyze randomness and fluctuation of the renewable energy power generation facilities.
And 2, predicting and obtaining a load short-term prediction curve P load (t) and a renewable energy short-term power prediction curve P gen (t) according to the basic data.
In this step, a short-term prediction is performed on the future power grid load demand and the renewable energy power generation amount by using a time series analysis technology of statistics and machine learning (such as an autoregressive model AR, a moving average model MA, an autoregressive moving average model ARMA, an autoregressive integral moving average model ARIMA, a long-term memory network LSTM, and the like), so as to form a prediction curve of a certain period (such as several hours to several days in the future).
And 3, generating a commercial power demand planning curve Pgrid (t) of a day-ahead time scale according to the load short-term prediction curve Pload (t) and the renewable energy short-term power prediction curve Pgen (t).
In this step, the mains demand planning curve Pgrid (t) of the day-ahead time scale is the difference between the load short-term prediction curve Pload (t) and the renewable energy short-term power prediction curve Pgen (t).
Step 4, building a green electricity comprehensive evaluation index according to the renewable energy short-term power prediction curve Pgen (t) and the mains supply demand planning curve Pgrid (t); and (3) establishing an optimization model by combining green electricity comprehensive evaluation indexes with a penalty function, and carrying out dynamic optimization at a daily stage with the aim of minimizing the optimization model to generate an energy storage charge-discharge demand curve Pess (t) and a commercial electricity demand curve Pgrid (t').
In the step, a green electricity comprehensive evaluation index reflecting the aspects of green electricity consumption degree, energy structure optimization, carbon emission reduction and the like is constructed by combining a renewable energy short-term power prediction curve Pgen (t) and a planned mains supply demand curve Pgrid (t). The green electricity comprehensive evaluation index is integrated into an optimization model, and a penalty function is added, so that the utilization rate of renewable energy sources is improved as much as possible and the dependence on fossil energy sources is reduced while the supply and demand balance of a power grid is met.
And dynamically optimizing in the real-time scheduling process in the day by utilizing an optimization model, and searching for the optimal scheduling strategy. In the process, the charging and discharging behaviors of the energy storage device are determined without considering the requirement of the mains supply, and an energy storage charging and discharging requirement curve Pess (t) is generated. The energy storage charge-discharge demand curve Pess (t) reflects the charge or discharge state of the energy storage system at each moment in the day, and is used for stabilizing the fluctuation of the output of renewable energy sources and ensuring the stable operation of the power grid. The optimized commercial power demand curve Pgrid (t)' is a finer and more efficient power grid outsourcing demand plan finally determined after the energy storage regulation and control function is fully considered.
Step 5, generating a charge and discharge control instruction for controlling the energy storage converter according to the energy storage charge and discharge demand curve Pess (t); and controlling the mains supply scheduling according to a mains supply demand curve Pgrid (t)'.
In this step, the energy storage charge-discharge demand curve Pess (t) depicts, at different points in time, whether the energy storage system should perform a charge or discharge operation, and the corresponding power level. For example, when the value of Pess (t) is positive, it indicates that the energy storage system should absorb the excess electric quantity of the power grid at the moment to charge; and when the Pess (t) value is negative, the energy storage system is indicated to release the stored electric energy, and the electric energy is discharged to the power grid so as to help balance the supply and the demand of the power grid. According to the curve, a specific instruction aiming at the energy storage converter can be made, and the working mode and the output power of the energy storage converter are guided to be adjusted in different time intervals, so that the energy storage system can be efficiently utilized and flexibly responded.
The energy storage converter (PCS) is a power electronic device, and is used for connecting an energy storage system (such as a battery energy storage system) with a power grid or a load to realize ac/dc bidirectional conversion and control of electric energy. Its main functions and working principles include: electric energy conversion: the energy storage converter can convert Alternating Current (AC) of a power grid into Direct Current (DC) to charge the energy storage system, and can also convert the direct current stored by the energy storage system into alternating current to be fed back to the power grid or directly supplied to an alternating current load. Bidirectional energy flow: the PCS has bidirectional working capacity, can absorb and store redundant electric energy from a power grid in an energy storage mode, and releases the stored electric energy back to the power grid or is used by an internal independent load in a power supply mode. And (3) power adjustment: according to the background control instruction, the PCS can accurately control charging and discharging power, participate in active power and reactive power adjustment of the power grid, and contribute to improving the quality of electric energy and stabilizing the frequency and voltage of the power grid.
The utility power demand curve Pgrid (t)' is the final power grid dispatching demand formed after factors such as renewable energy output prediction, energy storage system regulation and control and the like are considered. It represents the total power demand that the grid needs to meet from a conventional power source, purchase external power, or allocate internal resources (including energy storage systems) at each time node. The power dispatching department generates refined operation instructions according to the curve, including but not limited to: and (3) dispatching generating sets of a thermal power plant, a hydropower station and the like to increase or decrease the power generation output according to the demand, or arranging electricity purchasing transaction to supplement the shortage part, so that the actual power supply curve is ensured to be as close to the demand planning curve of Pgrid (t)' as possible, and the safe and stable operation and the power supply quality of a power grid are ensured. Meanwhile, the overall dispatching effect is further optimized by combining the charge and discharge control instructions of the energy storage system.
In sum, through the established optimization model, from two stages of daily planning and daily dynamic optimization, the output process of energy storage is optimally controlled, so that the purposes of optimizing energy storage control and reducing the carbon emission of power grid operation are achieved, meanwhile, the renewable energy grid-connected consumption level can be improved, the energy storage application value is improved, and the green low-carbon transformation of power grid energy is promoted.
The application is illustrated by the following specific examples.
Example 1
According to the application, a green electricity utilization evaluation model is established, the power generation utilization condition of the distributed renewable energy sources in the power grid is quantitatively analyzed and evaluated, the optimal green electricity utilization effect is taken as a target, meanwhile, the demand power of the power grid region for the commercial power is stabilized as much as possible, the power constraint of the power grid operation and the energy storage equipment constraint are taken as conditions, the dynamic programming algorithm is utilized to optimize the demand curves of the energy storage and the commercial power, the specific energy storage equipment control instruction is obtained, the control process is completed through the energy storage converter device, the energy storage control efficiency is improved, the purpose of improving the regional green electricity utilization efficiency is achieved, and the power assisting is developed for the low carbon of the energy sources in China.
The control optimization method comprises the following specific steps:
step 1: and acquiring and obtaining the electric load curve data condition in the power grid area range, and the renewable energy source power generation data condition.
Step 2: according to the data obtained in the step 1, a time sequence prediction method is adopted, and a load short-term prediction curve P load (t) and a renewable energy short-term power prediction curve P gen (t) are calculated.
Step 3: and (3) obtaining corresponding mains supply planning according to the short-term prediction curve obtained in the step (2). And generating a commercial power demand planning curve Pgrid (t) of a day-ahead time scale according to the real-time balance principle of the active power of the power grid.
Step 4: and combining the related data of the steps, establishing an optimization model based on the green electricity comprehensive evaluation model, and establishing a dynamic optimization method in the day based on the index model to obtain an optimization result in the day stage.
Step 5: and (3) generating equipment control instructions according to the energy storage charge-discharge control curve obtained in the step (4), completing charge-discharge control of energy storage through an energy storage converter device, and simultaneously completing a dispatching process of the commercial power according to the updated demand curve of the commercial power in the step (4).
The green electricity comprehensive evaluation model is an index model established for analyzing green electricity characteristics and the utilization condition thereof, and specifically comprises two main types of characteristic indexes of the green electricity and characteristic indexes of the green electricity utilization, namely an index model established according to related amplitude characteristics and time characteristics, and is specifically as follows:
(1) Green electricity self characteristic index
Maximum value of renewable energy power generation:
Pgreen_max＝max(Pgen(t))；
minimum value of renewable energy power generation:
Pgreen_min＝min(Pgen(t))；
Average value of renewable energy power generation:
Ratio of renewable energy generated power fluctuation range to average value:
Wherein N is the time point number in the optimization period.
(2) Green electricity utilization characteristic index
Renewable energy power generation utilization ratio:
The sum of the renewable energy power generation utilization ratios:
the product of the renewable energy power generation utilization ratio:
Marking and counting the renewable energy power generation utilization ratio state at a 50% demarcation point:
the time ratio of the forward statistics renewable energy power generation utilization ratio is more than 50 percent:
the negative statistical renewable energy power generation utilization rate is less than 50 percent:
Based on the related indexes, calculating a green electricity comprehensive evaluation index:
wherein, lambda 1, lambda 2 and lambda 3 are comprehensive coefficients, and the value is 1.1-2. The larger the green electricity comprehensive index is, the better the renewable energy power generation and utilization effect is.
The method is based on a result of daily optimization stage, aims at combining a green electricity comprehensive evaluation index and a comprehensive index generated by a related penalty function to be minimum, takes energy storage as an energy buffer, considers physical constraint of energy storage equipment, sets a corresponding penalty function (so as to prevent the extreme scene requirement that the utility power is required to be additionally scheduled to meet the operation requirement because the real-time power balance of a power grid cannot be met in the capacity range of the energy storage equipment), takes the comprehensive index objective function to be minimum, and performs dynamic planning optimization in the daily stage on the basis of the daily planning result to generate an energy storage charge-discharge requirement curve Pess (t) and an updated utility power requirement curve P grid (t)'. The real-time balance in the running process of the power grid is realized by utilizing the power bidirectional charging and discharging function of energy storage, so that the power generation grid-connected utilization of renewable energy sources is realized. The specific optimization model is as follows:
1) Objective function
f＝min(Φ+M×|Z|)；
Wherein Z is a penalty function, and when real-time balance of power cannot be realized in the energy storage physical constraint range, the imbalance value of the power is penalized, M is a corresponding penalty function coefficient, the value is larger, and 10≡4 can be set according to requirements.
It should be noted that, the penalty function can be set to meet the power grid operation requirement under the circumstance that the difference between the daily planning and the daily real-time operation condition is large (i.e. under the extreme scenes such as burst high-power requirement, etc.), and the power grid operation requirement is mainly realized by urgently adjusting the mains supply scheduling plan.
2) Constraint conditions
On the condition of power constraint and energy storage equipment constraint of power grid operation
① The power balance equation constraint:
Pgrid(t)+Z+Pgen(t)+Pess(t)＝Pload(t)；
In the formula, the unbalanced power represented by the penalty function Z is calculated as follows: z=p grid(t)'-Pgrid (t).
② Inequality constraint:
Mains power output constraints:
0≤Pgrid(t)；
Energy storage device constraints:
Pess.min≤Pess(t)≤Pess.max；
Eess.min≤Eess(t)≤Eess.max；
Wherein: p ess.min and P ess.max are respectively the upper limit and the lower limit of the charge and discharge power of the energy storage device (wherein P ess.min represents the maximum power of the whole chargeable energy storage device, the value is negative; P ess.max represents the maximum power of the dischargeable energy storage device, the value is positive); e ess.min and E ess.max are respectively the upper limit and the lower limit of the charge and discharge capacity of the energy storage device; e ess (t) is the energy storage electric quantity at each moment in the optimization period; p ess (t) is the charge and discharge power of the energy storage device in the optimized period.
In summary, the method starts from two time scales of day-before-day, stabilizes the power demand of the power grid region on the mains supply, considers the demand of an extreme scene by adding a penalty function into an objective function, and improves the stability of the power grid. On the basis of improving the utilization rate of energy storage control, the energy storage optimization control method for further improving the clean energy consumption level of the power grid provides further support for the excavation of the operation scene of energy storage and promotes the low-carbon development of energy sources in China. The method comprises the steps of forming a basic database by acquiring power grid operation data (including renewable energy power generation data and load power consumption data); based on basic data, obtaining new energy short-term power prediction curve data and system load short-term prediction curve data by using a time sequence prediction method, so as to calculate and obtain a commercial power demand power curve of a day-ahead time scale; and combining the established comprehensive evaluation model of green electricity utilization, taking a short-term prediction result as a basis, taking the minimum comprehensive index as a target, taking the power constraint of power grid operation and the energy storage equipment constraint as conditions, carrying out optimization solution on the daily time scale on the whole energy storage power curve by utilizing methods such as dynamic programming and the like to obtain an energy storage charge-discharge control power curve, and finally completing the optimization control on the energy storage power through the energy storage converter control, thereby achieving the effects of improving the green electricity utilization condition of the power grid, improving the energy storage control efficiency and improving the operation stability of the power grid.
Example 2
Based on the same inventive concept as embodiment 1, this embodiment discloses an energy storage application control optimizing device, including: the acquisition module is used for acquiring basic data in the area range of the power grid; wherein the base data includes electrical load curve data and renewable energy generation data.
And the prediction module is used for predicting and obtaining a load short-term prediction curve Pload (t) and a renewable energy short-term power prediction curve Pgen (t) according to the basic data.
And the day-ahead planning module is used for generating a commercial power demand planning curve Pgrid (t) of a day-ahead time scale according to the load short-term prediction curve P load (t) and the renewable energy short-term power prediction curve Pgen (t).
The daily optimization module is used for establishing a green electricity comprehensive evaluation index according to the renewable energy short-term power prediction curve Pgen (t) and the mains supply demand planning curve Pgrid (t), establishing an optimization model by using the green electricity comprehensive evaluation index and the penalty function, and carrying out dynamic optimization at a daily stage with the aim of minimizing the optimization model to generate an energy storage charging and discharging demand curve Pess (t) and a mains supply demand curve Pgrid (t)'.
Example 3
The present embodiment provides a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification. In some possible embodiments, the various aspects of the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the invention as described in the "exemplary methods" section of this specification, when said program product is run on the terminal device.
The program product for implementing the above-described method according to an embodiment of the present invention may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can output, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
It will be appreciated by those skilled in the art that the present invention can be carried out in other embodiments without departing from the spirit or essential characteristics thereof. Accordingly, the above disclosed embodiments are illustrative in all respects, and not exclusive. All changes that come within the scope of the invention or equivalents thereto are intended to be embraced therein.
Claims (10)
1. A method for energy storage application control optimization, comprising:
basic data in the area of the power grid are obtained; wherein the base data includes electrical load curve data and renewable energy generation data;
Predicting and obtaining a load short-term prediction curve P load (t) and a renewable energy short-term power prediction curve P gen (t) according to the basic data;
generating a commercial power demand planning curve Pgrid (t) of a day-ahead time scale according to the load short-term prediction curve Pload (t) and the renewable energy short-term power prediction curve Pgen (t);
Building a green electricity comprehensive evaluation index according to the renewable energy short-term power prediction curve Pgen (t) and the mains supply demand planning curve Pgrid (t); and (3) establishing an optimization model by combining green electricity comprehensive evaluation indexes with a penalty function, and carrying out dynamic optimization at a daily stage with the aim of minimizing the optimization model to generate an energy storage charge-discharge demand curve Pess (t) and a commercial electricity demand curve Pgrid (t').
2. The energy storage application control optimization method according to claim 1, wherein the objective function of the optimization model is:
f＝min(Φ+M×|Z|)；
Wherein, phi is green electricity comprehensive evaluation index, Z is penalty function, M is penalty function coefficient.
3. The energy storage application control optimization method according to claim 1, wherein the dynamic optimization at the intra-day stage is performed with the aim of minimizing an optimization model, and the power constraint of a power grid and the constraint of energy storage equipment are used as conditions; the grid power constraint includes: a power balance equation constraint and a mains power output constraint;
the electric power balance equation constraint:
Pgrid(t)+Z+Pgen(t)+Pess(t)＝Pload(t)；
Wherein Z represents an unbalanced power penalty function, and z=p grid(t)'-Pgrid(t);Pess (t) is the charge and discharge power of the energy storage device in the optimization period;
the mains power output constraint:
0≤Pgrid(t)；
The energy storage device constraints:
Pess.min≤Pess(t)≤Pess.max；
Eess.min≤Eess(t)≤Eess.max；
Wherein, P ess.min and P ess.max are respectively the upper limit and the lower limit of the charge and discharge power of the energy storage device; e ess.min and E ess.max are respectively the upper limit and the lower limit of the charge and discharge capacity of the energy storage device; e ess (t) is the energy storage capacity at each moment in the optimization period.
4. The energy storage application control optimization method according to claim 1, wherein the expression of the green electricity comprehensive evaluation index is:
wherein lambda 1,λ2,λ3 is the comprehensive coefficient, and the value is 1.1-2; beta is the sum of the power generation utilization ratio of renewable energy sources, Chi (t) is the power generation utilization ratio of renewable energy, and N is the time point of the optimized period; the ratio of the fluctuation range of the power generated by the renewable energy source to the average value; η is the product of the power generation utilization ratio of renewable energy sources, Γ la is the time duty ratio of the forward statistics renewable energy power generation utilization ratio which is more than 50%,Γ sm is the time duty ratio of negative statistics renewable energy power generation utilization less than 50%,
5. The energy storage application control optimization method as defined in claim 4, wherein,
The expression of the renewable energy power generation utilization ratio χ (t) is as follows:
The ratio of the fluctuation range to the average value of the renewable energy generation power The expression of (2) is:
wherein Pgreen _max is the maximum value of renewable energy power generation, pgreen _min is the minimum value of renewable energy power generation, pgreen _ave is the average value of renewable energy power generation,
6. The energy storage application control optimization method according to claim 1, wherein the expression of the day-ahead time scale of the utility power demand planning curve Pgrid (t) is:
Pgrid(t)＝Pload(t)-Pgen(t)。
7. The energy storage application control optimization method according to claim 1, further comprising generating a charge-discharge control instruction for controlling the energy storage converter according to an energy storage charge-discharge demand curve pest (t); and controlling the mains supply scheduling according to a mains supply demand curve Pgrid (t)'.
8. The energy storage application control optimization method of claim 1, wherein the renewable energy sources include solar energy, wind energy, water energy, biomass energy, and geothermal energy.
9. An energy storage application control optimizing apparatus, comprising:
The acquisition module is used for acquiring basic data in the area range of the power grid; wherein the base data includes electrical load curve data and renewable energy generation data;
the prediction module is used for predicting and obtaining a load short-term prediction curve Pload (t) and a renewable energy short-term power prediction curve Pgen (t) according to the basic data;
the day-ahead planning module is used for generating a commercial power demand planning curve Pgrid (t) of a day-ahead time scale according to the load short-term prediction curve P load (t) and the renewable energy short-term power prediction curve Pgen (t);
The daily optimization module is used for establishing a green electricity comprehensive evaluation index according to the renewable energy short-term power prediction curve Pgen (t) and the mains supply demand planning curve Pgrid (t), establishing an optimization model by using the green electricity comprehensive evaluation index and the penalty function, and carrying out dynamic optimization at a daily stage with the aim of minimizing the optimization model to generate an energy storage charging and discharging demand curve Pess (t) and a mains supply demand curve Pgrid (t)'.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the energy storage application control optimization method according to any one of claims 1-8.
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