CN114825388A - New energy comprehensive consumption scheduling method based on source network load-storage cooperation - Google Patents
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Abstract
The utility model belongs to the technical field of electric power system, concretely relates to new energy comprehensive consumption scheduling method based on source network load-storage cooperation, which comprises the following steps: acquiring power grid operation data at the current moment; predicting the power grid load power at the next moment based on the acquired operation data; judging the power unbalance of the power grid according to the obtained power grid load power; when the power of the power grid is unbalanced, on the premise of ensuring that both the wind power and the photovoltaic are in the maximum output state, judging whether the energy storage system meets the power difference of the power grid, and if so, adjusting the charging and discharging power of the energy storage system; otherwise, calculating the secondary power difference of the power grid on the basis of adjusting the charging and discharging power of the energy storage system, and adjusting the load of the power controllable user to meet the secondary power difference of the power grid so as to realize comprehensive consumption scheduling of new energy.
Description
Technical Field
The disclosure belongs to the technical field of power systems, and particularly relates to a new energy comprehensive consumption scheduling method based on source network load-storage cooperation.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The power system gradually develops towards a novel power system taking new energy as a main body, and the load and storage integration and the multi-energy complementation of a power source network are actively promoted. In order to realize the renewable energy consumption capability, the source-grid-load-storage integrated project can fully exert the load-side regulation response capability, develop the effect analysis of the regulation and support requirements of the large power grid, coordinate and balance all levels of power supplies in a development area, and promote the grid-source-load-storage collaborative development and operation. With the implementation of the distributed photovoltaic strategy, distributed photovoltaic, energy storage, controllable load and the like are widely connected to the power distribution network, and due to the lack of cooperativity with power grid dispatching, power distribution network planning and power distribution network upgrading and transformation, negative effects are brought to high-quality power supply of users, safe and reliable operation of power distribution facilities, power grid peak shaving and the like. Meanwhile, considering that the cost of the energy storage system is high and the adjustment means of the power distribution network is limited, how to ensure the safe and economic operation of the power distribution network and reduce the operation cost of energy storage, controllable load, comprehensive energy and the like is a problem which needs to be solved urgently by current power grid enterprises and new energy development enterprises.
Different from a centralized large power grid, after the source network charge storage is connected into the power distribution network, the autonomous cooperative operation capacity of the distribution network, the multiple power distribution networks and the regional power distribution network is mainly considered, various distributed schedulable resources in the regional source network charge storage system are coordinated comprehensively, a multi-objective power optimization strategy is realized, and flexible optimization operation of various energy situations is met. The traditional control is designed by taking the lowest power supply cost, the optimal new energy consumption and the like as optimization targets, various flexible power utilization modes of power users are neglected, the balance optimization of the benefits of multiple sources of source network load storage is not facilitated, and the maximization of economic and social benefits of the whole source network load storage is not realized.
Considering the continuous access of comprehensive energy, adjustable load and the like, the conventional research has single consideration on flexibility measures, and is difficult to meet the flexibility requirement of high-proportion new energy grid-connected consumption, and the traditional grid frame has complex flexible modeling and the consumption model is difficult to solve efficiently. The single control strategy is often accompanied by inherent limitations, for example, the response speed of the single control strategy is limited due to a large calculation amount of centralized control, and the regulation and control cost of the distributed control strategy cannot be effectively reduced due to lack of information interaction, so that the rapidity and the economy of regulation and control are difficult to be considered by the single control strategy.
Disclosure of Invention
In order to solve the problems, the utility model provides a new energy comprehensive consumption scheduling method based on source network load-storage cooperation, according to the fixed load demand of power consumer, guarantee the full consumption of the maximum output of wind-powered electricity generation and photovoltaic, according to the system power balance condition, adjust the regulation capacity of the controllable user load of power in real time, adjust the charge and discharge state quantity of the energy storage system, realize the power balance of the whole power system, effectively reduce the fluctuation and the running cost of load.
According to some embodiments, the scheme of the disclosure provides a new energy comprehensive consumption scheduling method based on source network load-storage cooperation, and the following technical scheme is adopted:
a new energy comprehensive consumption scheduling method based on source network load-storage cooperation comprises the following steps:
acquiring power grid operation data at the current moment;
predicting the power grid load power at the next moment based on the acquired operation data;
judging the power unbalance of the power grid according to the obtained power grid load power;
when the power of the power grid is unbalanced, on the premise of ensuring that both the wind power and the photovoltaic are in the maximum output state, judging whether the energy storage system meets the power difference of the power grid, and if so, adjusting the charging and discharging power of the energy storage system; otherwise, calculating the secondary power difference of the power grid on the basis of adjusting the charging and discharging power of the energy storage system, and adjusting the load of the power controllable user to meet the secondary power difference of the power grid so as to realize comprehensive consumption scheduling of new energy.
As a further technical limitation, when there is no grid power imbalance, the existing operating states of the wind power, photovoltaic, energy storage systems and power controllable users in the grid are maintained.
As a further technical limitation, in the process of predicting the power grid load power at the next moment based on the acquired operation data, iterative optimization is performed according to different time scales, and based on the operation rules of the wind power, the photovoltaic power, the power controllable users and the energy storage system, the operation power prediction of the wind power, the photovoltaic power, the power controllable users and the energy storage system at the next moment is performed simultaneously within a preset sampling calculation time period in consideration of the difference of inertia response time of the power electronic equipment, so that the prediction of the power grid load power at the next moment is realized.
Furthermore, in the process of predicting the wind power, based on historical data information of the wind power output, considering the influence of wind power uncertainty factors, predicting probability interval information of the wind power output at the next moment by adopting a probability prediction mode, and providing a predicted value of the wind power at the next moment through the maximum probability; when the next moment occurs, comparing the actual value of the wind power with the predicted value of the wind power at the next moment, and optimizing wind power prediction;
in the process of predicting the photovoltaic power, based on historical data information of photovoltaic output, considering the influence of uncertain photovoltaic factors, predicting probability interval information of the photovoltaic output at the next moment by adopting a probability prediction mode, and providing a predicted value of the photovoltaic power at the next moment through the maximum probability; and when the next moment occurs, comparing the actual photovoltaic power value with the predicted photovoltaic power value at the next moment, and optimizing photovoltaic power prediction.
Specifically, on the basis of wind power prediction and photovoltaic power prediction, secondary correction is performed on wind power and photovoltaic power under the same time scale according to the time-space complementary characteristics of wind power and photovoltaic power and based on a historical new energy superposition power rule, and the accuracy of new energy output superposition is improved.
Furthermore, in the process of predicting the power of the energy storage system, the power of the energy storage system is predicted by adopting the battery charge state of the energy storage system, and the residual charge-discharge power of the energy storage system, namely the predicted value of the available charge-discharge power of the energy storage system at the next moment, is calculated based on the maximum rated available capacity of the battery of the energy storage system and the charge-discharge operating power of the energy storage system at the current moment.
Furthermore, in the process of predicting the power of the electric power controllable user, the prediction value of the power of the electric power controllable user is obtained by superposing the prediction of the load of the fixed electric power user and the prediction of the controllable load interval range and carrying out statistical analysis according to the space-time characteristics.
As a further technical limitation, in the process of judging the power imbalance of the power grid, calculating the generated power and the consumed power inside the power grid based on the obtained predicted value of the load power of the power grid at the next moment, and judging the power imbalance of the power grid by comparing the balance condition between the generated power and the consumed power; the power generation power comprises wind power, photovoltaic power and discharge power of the energy storage system, and the power utilization power comprises charging power of the energy storage system and power controllable user power.
As a further technical limitation, the objective function of the comprehensive consumption scheduling of the new energy is as follows:
wherein, alpha is a penalty factor,
and f2 is a controllable load power value to be adjusted in the power grid.
Furthermore, the constraint conditions of the objective function comprise node voltage constraint, energy storage system charge and discharge constraint and controllable load constraint.
Compared with the prior art, the beneficial effect of this disclosure is:
according to the method, through effective modeling of various flexible resources, the controllable load and the time-space complementary characteristics of the new energy output and the load are fully excavated and coordinated, the peak-valley difference of a power grid is reduced, and the maximum output of the new energy is ensured; through the scheduling of various resources, the peak valley is obviously different, the peak clipping and valley filling change is obvious after optimization, the new energy consumption condition is improved, the load peak valley difference is obviously reduced, and the load fluctuation and the operation cost are effectively reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a flowchart of a new energy comprehensive consumption scheduling method based on source network load-store coordination in an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a wind power-photovoltaic-energy storage access grid in an embodiment of the present disclosure;
FIG. 3 is a graphical illustration of a typical daily load prediction in an embodiment of the disclosure;
fig. 4 is a schematic diagram of an optimized scheduling result in the embodiment of the present disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
Examples
The embodiment of the disclosure introduces a new energy comprehensive consumption scheduling method based on source network load storage cooperation.
The day-ahead scheduling is to predict the new energy output and the load capacity of the scheduling day through historical data and weather forecast of the scheduling day, and then to make a scheduling day plan according to the historical data. The balance of the whole power system is maintained, power output of wind power, photovoltaic power, thermal power and the like is needed, losses of power grid lines, transformers and the like are superposed, meanwhile, according to the fixed load demand of a power consumer, full consumption of the maximum output of the wind power and the photovoltaic power is guaranteed, according to the power balance condition of the system, the adjusting capacity of the load of the power controllable consumer is adjusted in real time, the charging and discharging state quantity of the energy storage system is adjusted, and finally the power balance of the whole power system is achieved.
As shown in fig. 1, a new energy comprehensive consumption scheduling method based on source network load-store cooperation includes the following steps:
step S01: acquiring power grid operation data at the current moment, and respectively predicting the power of wind power, photovoltaic power, power load and an energy storage system at the next moment;
step S02: on the basis of power prediction, power load of a power grid is obtained, power generation and power utilization balance conditions in the power grid are calculated, and whether the power unbalance problem of the power grid exists at the next moment or not is judged.
Step S03: if the power balance problem does not exist, the wind power, the photovoltaic, the energy storage, the power users and the like maintain the original running state; if the power balance problem exists, calculating a power deviation value which needs to be adjusted currently, and judging whether the energy storage charging and discharging capacity meets the current power deviation requirement or not on the premise of keeping the maximum output states of the wind power and the photovoltaic;
step S04: if the energy storage charging and discharging capacity can meet the current power deviation requirement, the energy storage system is preferentially selected to carry out charging and discharging power adjustment;
step S05: if the energy storage charging and discharging capacity can not meet the current power deviation requirement, calculating the power difference which is not supplemented on the basis of preferentially selecting an energy storage system for charging and discharging power adjustment, and performing secondary adjustment by using the controllable load;
step S06: and after the controllable load adjustment is finished, judging whether the power of the power grid at the current moment reaches power balance, if so, enabling the system to reach a balanced state, and if not, continuing returning to the step S01 to perform iterative adjustment optimization.
In step S01, all power predictions are iteratively optimized according to different time scales, according to respective operating rule characteristics of wind power, photovoltaic power, power load, and stored energy, considering inertia response time differences of power electronic devices (photovoltaic power generation belongs to a device without inertia or with very weak inertia, wind power has a certain buffering property, power load belongs to a fixed conventional common load and a controllable load with a certain time scale, and stored energy is controlled according to a charge-discharge rate of a battery), a sampling calculation period of 1 minute is adopted as a whole system, and a next power condition of one minute is predicted by simultaneously calculating a balance condition of the wind power, photovoltaic power, power load, and stored energy operating state at the current time, and specific prediction processes are respectively as follows:
(1) wind power prediction, on the basis of wind power output historical data, the influence of weather forecast uncertainty factors is considered, a probability prediction method is adopted, probability interval information of wind power output at the next moment is predicted, a wind power prediction value at the next moment is provided according to the maximum probability, meanwhile, after the next moment occurs, the actual wind power output information is compared with the predicted power output, a wind power model is optimized in return, and model prediction accuracy is improved.
(2) And photovoltaic power prediction, namely predicting probability interval information of photovoltaic output at the next moment by adopting a probability prediction method based on historical photovoltaic output data and combining the influence of weather forecast uncertainty factors, providing a photovoltaic power prediction value at the next moment according to the maximum probability, and comparing actual photovoltaic output information with the predicted power output after the next moment occurs, thereby optimizing a photovoltaic power model and improving the model prediction accuracy.
On the basis of wind power prediction and photovoltaic power prediction, wind power and photovoltaic power under the same time scale are secondarily corrected on the basis of a historical new energy superposition power rule according to the characteristic of space-time complementation of wind power and photovoltaic power, and the accuracy of new energy output superposition is improved.
(3) And (3) power load power prediction, wherein a superposition mode of fixed power user load prediction and controllable load interval range prediction is adopted, and statistical analysis is carried out according to the time-space characteristics.
(4) And predicting the energy storage charging and discharging power, namely predicting by adopting the battery charge state of an energy storage system, connecting the battery charge state with an energy storage battery management system to be consistent, deducting the residual charging and discharging power after the charging and discharging operation power of the energy storage battery at the current moment by using the rated available maximum capacity of the energy storage battery, and thus obtaining the predicted available charging and discharging power at the next moment.
In step S02, after the power deviation is determined, the safe operation capacity range of each device is considered, and then a control policy is issued to each device, where the specific calculation flow is as follows: on the basis of power prediction of wind power, photovoltaic, energy storage, load and the like, the difference between the generated power in the system and the power required by a user is calculated, if the generated power is inconsistent with the power value required by the user after deducting network loss, the power generation and the power utilization are unbalanced, the problem of unbalanced power exists in the power grid, and the controllable load, the energy storage charging and discharging power and the like are timely adjusted according to the power difference on the premise of first meeting the requirements of maintaining the maximum power output of new energy and continuously supplying the load of a conventional fixed power user, so that the power balance of the power generation and the power utilization is ensured, and the safe and stable operation of the whole system is realized.
In step S05, when the charging and discharging power of the energy storage system cannot adjust the power imbalance of the power grid, a secondary difference of the power grid power needs to be introduced, that is, the secondary difference of the power grid power is obtained by a difference between a power value of the power imbalance of the power grid and the charging and discharging power of the energy storage system, and the secondary difference of the power grid power is satisfied by load adjustment of the power controllable user, so as to implement comprehensive consumption scheduling of new energy.
The aim of scheduling in the day is to use the new energy power generation amount in the maximum amount and priority, and simultaneously maintain the controllable units in the power grid and the adjustment amount of scheduling in the day before in a minimum state. The objective function is:
wherein, alpha is a penalty factor,
and f2 is a controllable load power value to be adjusted in the power grid. f2 represents the deviation of the power generation power and the power utilization power in the power grid and the controllable load power which needs to be adjusted, and f2 is minimum, which represents the minimum amount of multi-flexibility resource coordination scheduling.
Constraint conditions of the objective function scheduled in the day are mainly constrained by technical indexes of the active power distribution network, and the constraint conditions comprise node voltage, power balance, energy storage charging and discharging, controllable load and the like.
The node voltage constraint is:
wherein,
are respectively node voltage U i Lower limit and upper limit of (1).
The power balance constraint is：
Wherein,
the wind power output at the current time t,
the wind power output at the current time t,
the charging/discharging output of the stored energy at the current time t,
the power output is the power output at the current moment t,
for the current time t the grid loss,
for the ordinary consumer power load at the present time t,
and controlling the power load of the user at the current time t.
The energy storage charging and discharging constraint conditions are as follows:
wherein,
for the minimum charge/discharge capacity of the energy storage system at the present time t,
the maximum capacity of the energy storage system to charge/discharge at the present time t,
the state of charge of the energy storage system at the current time t,
the minimum state of charge of the energy storage system at the current time t,
and the maximum charge state of the energy storage system at the current time t.
The controllable load constraint conditions are as follows:
wherein,
for the minimum regulation capacity of the controllable load at the current time t,
and the maximum regulation capacity of the controllable load at the current time t.
In order to verify the feasibility of the proposed scheduling method, taking an IEEE 33 node system as an example, as shown in fig. 2, photovoltaic, energy storage and controllable loads are all connected to a node 6, wind power, energy storage and controllable loads are all connected to a node 31, a live-wire generator set and a power consumer load in a power grid maintain normal operation states, no power exchange is performed between the power generation set and a superior power grid, and power balance of power generation and power utilization in the power grid is maintained.
Load prediction of conventional loads of wind power, photovoltaic and electric power is carried out, measurement and calculation are carried out according to typical load days, and prediction results are shown in a graph 3. As can be seen from fig. 3, the load of the distribution network has a distinct peak-to-valley variation characteristic with time, wherein the peak is mainly concentrated at about 12 o 'clock to 14 o' clock, and the load valley time is mainly concentrated at midnight and early morning.
The long-time scale is based on 1h as a unit, an optimized scheduling scheme is formulated 24h every day, and the optimized scheduling result is shown in fig. 4.
And determining the charging and discharging capacity of the controllable load and the stored energy under the day-ahead scheduling according to the minimum condition of maximally ensuring the maximum consumption of wind power and photovoltaic, ensuring the continuous power supply of the conventional load of the power and reasonably adjusting the capacity of the controllable load. By comparing fig. 3 and fig. 4, in the load curves before and after optimization, through scheduling of various resources, the peak-to-valley difference exists, the peak-to-valley change is obvious after optimization, the new energy consumption condition is improved, the load peak-to-valley difference is obviously reduced, and the accuracy and reliability of the optimization model are verified.
According to the method, the multiple resources are scheduled, the peak valley is obviously different, the peak load shifting change is obvious after optimization, the new energy consumption condition is improved, the load peak valley difference is obviously reduced, and the load fluctuation and the operation cost are effectively reduced.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (10)
1. A new energy comprehensive consumption scheduling method based on source network load-storage cooperation is characterized by comprising the following steps:
acquiring power grid operation data at the current moment;
predicting the power grid load power at the next moment based on the acquired operation data;
judging the power unbalance of the power grid according to the obtained power grid load power;
when the power of the power grid is unbalanced, on the premise of ensuring that both the wind power and the photovoltaic are in the maximum output state, judging whether the energy storage system meets the power difference of the power grid, and if so, adjusting the charging and discharging power of the energy storage system; otherwise, calculating the secondary power difference of the power grid on the basis of adjusting the charging and discharging power of the energy storage system, and adjusting the load of the power controllable user to meet the secondary power difference of the power grid so as to realize comprehensive consumption scheduling of new energy.
2. The source grid charge-storage coordination-based new energy comprehensive consumption scheduling method as claimed in claim 1, characterized in that when there is no power imbalance of the power grid, the existing operating states of wind power, photovoltaic, energy storage systems and power controllable users in the power grid are maintained.
3. The source-grid-storage-collaboration-based new energy comprehensive consumption scheduling method as claimed in claim 1, wherein iterative optimization is performed according to different time scales in a process of predicting the grid load power at the next moment based on the acquired operation data, and the prediction of the operation power of the wind power, the photovoltaic, the power controllable users and the energy storage system at the next moment is simultaneously performed in a preset sampling calculation time period by considering the difference of inertia response time of power electronic equipment based on the operation rules of the wind power, the photovoltaic, the power controllable users and the energy storage system, so that the prediction of the grid load power at the next moment is realized.
4. The source-grid-charge-storage-cooperation-based new energy comprehensive consumption scheduling method as claimed in claim 3, characterized in that in the prediction process of the wind power, based on historical data information of the wind power output, the influence of wind power uncertainty factors is considered, the probability interval information of the wind power output at the next moment is predicted in a probability prediction mode, and the predicted value of the wind power at the next moment is provided through the maximum probability; and when the next moment occurs, comparing the actual value of the wind power with the predicted value of the wind power at the next moment, and optimizing the prediction of the wind power.
5. The source-network-load-storage-cooperation-based new energy comprehensive consumption scheduling method as claimed in claim 3, wherein in the photovoltaic power prediction process, based on historical data information of photovoltaic output, the influence of photovoltaic uncertainty factors is considered, the probability interval information of the photovoltaic output at the next moment is predicted in a probability prediction mode, and a predicted value of the photovoltaic power at the next moment is provided through the maximum probability; and when the next moment occurs, comparing the actual photovoltaic power value with the predicted photovoltaic power value at the next moment, and optimizing photovoltaic power prediction.
6. The source network charge-storage coordination-based new energy comprehensive consumption scheduling method as claimed in claim 3, characterized in that in the energy storage system power prediction process, the battery charge state of the energy storage system is used to perform power prediction of the energy storage system, and based on the maximum rated available capacity of the battery of the energy storage system and the charge-discharge operating power of the energy storage system at the current moment, the remaining charge-discharge power of the energy storage system, that is, the predicted value of the available charge-discharge power of the energy storage system at the next moment, is calculated.
7. The source network load-storage cooperation-based new energy comprehensive consumption scheduling method as claimed in claim 3, characterized in that in the process of power controllable user power prediction, a predicted value of power controllable user power is obtained by superposing fixed power user load prediction and controllable load interval range prediction and performing statistical analysis according to space-time characteristics.
8. The source grid load-storage coordination-based new energy comprehensive absorption scheduling method as claimed in claim 1, characterized in that in the process of judging power imbalance of the power grid, the generated power and the consumed power in the power grid are calculated based on the obtained predicted value of the power grid load power at the next moment, and the judgment of the power imbalance of the power grid is performed by comparing the balance condition between the generated power and the consumed power; the power generation power comprises wind power, photovoltaic power and discharge power of the energy storage system, and the power utilization power comprises charging power of the energy storage system and power controllable user power.
9. The method for integrated new energy consumption scheduling based on source network load-store coordination as claimed in claim 1, wherein the objective function of the integrated new energy consumption scheduling is:
wherein, alpha is a penalty factor,
and f2 is a controllable load power value to be adjusted in the power grid.
10. The source-grid-charge-storage-cooperation-based new energy comprehensive consumption scheduling method as claimed in claim 9, wherein the constraint conditions of the objective function include node voltage constraint, energy storage system charge-discharge constraint and controllable load constraint.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115173415A (en) * | 2022-09-07 | 2022-10-11 | 华电电力科学研究院有限公司 | Comprehensive energy system and optimal regulation and control method |
| CN117254497A (en) * | 2023-11-20 | 2023-12-19 | 国网山东省电力公司枣庄供电公司 | Method and system for processing electric digital data for user side distributed energy system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115173415A (en) * | 2022-09-07 | 2022-10-11 | 华电电力科学研究院有限公司 | Comprehensive energy system and optimal regulation and control method |
| CN117335454A (en) * | 2023-09-04 | 2024-01-02 | 国网湖北省电力有限公司随州供电公司 | Method and system for energy storage configuration in source network and charge storage integrated regional power grid |
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| CN117254497A (en) * | 2023-11-20 | 2023-12-19 | 国网山东省电力公司枣庄供电公司 | Method and system for processing electric digital data for user side distributed energy system |
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