CN117239793A - Energy storage planning method, device, computer equipment and storage medium - Google Patents
Energy storage planning method, device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to an energy storage planning method, an energy storage planning device, computer equipment and a storage medium. The method comprises the following steps: determining an energy storage planning cost value model and a net load fluctuation model in the power distribution network; acquiring an energy storage planning target amount according to an energy storage planning cost value model, and inputting the energy storage planning target amount into a net load fluctuation model to determine a target charging and discharging strategy according to the net load fluctuation model, wherein the energy storage planning target amount is the energy storage planning amount of an energy storage system when the cost value of the energy storage system is minimum, and the target charging and discharging strategy is the charging and discharging strategy of energy storage when the net load fluctuation in a power distribution network is minimum; and planning energy storage for the power distribution network according to the energy storage planning target quantity and the target charging and discharging strategy. By adopting the method, the net load fluctuation of the power distribution network can be smoothed, and the safe and stable operation of the power system is ensured.
Description
Technical Field
The present application relates to the field of energy storage planning technologies, and in particular, to an energy storage planning method, an energy storage planning device, a computer device, and a storage medium.
Background
Along with the continuous improvement of the permeability of renewable energy sources, the renewable energy sources represented by wind power and photovoltaic are combined into a power distribution network on a large scale to become a new running scene of a future power system. Renewable energy sources represented by wind power and photovoltaics have obvious randomness, intermittence and volatility, so that the traditional power distribution network gradually evolves into an active power distribution network with multiple uncertainty controllable sources, and therefore, higher requirements are provided for safe and stable operation of the power distribution network.
In order to reduce the impact of renewable energy grid connection on a power system, the energy storage planning is introduced at the power distribution network side, so that the problem of intermittent output of renewable energy sources can be relieved. In planning the energy storage access location, the energy storage planning amount and the corresponding operation strategy, economic, benefit, battery life and the like are often taken into main consideration.
However, the energy storage planning method is difficult to be compatible with the controllability and flexibility of the power system, so that the safety and stability of the operation of the power system are required to be improved.
Disclosure of Invention
Based on this, it is necessary to provide an energy storage planning method, apparatus, computer device and storage medium capable of increasing the controllability, flexibility and safety stability of the power system in view of the above technical problems.
In a first aspect, the present application provides a method of energy storage planning. The method comprises the following steps:
determining an energy storage planning cost value model and a net load fluctuation model in the power distribution network;
acquiring an energy storage planning target amount according to an energy storage planning cost value model, and inputting the energy storage planning target amount into a net load fluctuation model to determine a target charging and discharging strategy according to the net load fluctuation model, wherein the energy storage planning target amount is the energy storage planning amount of an energy storage system when the cost value of the energy storage system is minimum, and the target charging and discharging strategy is the charging and discharging strategy of energy storage when the net load fluctuation in a power distribution network is minimum;
And planning energy storage planning for the power distribution network according to the energy storage planning target quantity and the target charging and discharging strategy.
In one embodiment, the method includes obtaining an energy storage planning target amount according to an energy storage planning cost value model, inputting the energy storage planning target amount into a payload fluctuation model, and determining a target charge-discharge strategy according to the payload fluctuation model, and further includes:
under the condition that the net load fluctuation corresponding to the target charge-discharge strategy does not meet the preset condition, updating the energy storage planning cost value model by combining the penalty function to obtain an updated energy storage planning cost value model;
the energy storage planning target quantity is redetermined and updated according to the updated energy storage planning cost value model;
inputting the energy storage planning target quantity into a net load fluctuation model, and re-determining and updating the target charging and discharging strategy according to the net load model to obtain a net load fluctuation value corresponding to the updated target charging and discharging strategy;
and (3) carrying out iterative computation until the net load fluctuation value corresponding to the updated target charge-discharge strategy meets the preset condition.
In one embodiment, determining an energy storage planning cost model and a payload fluctuation model in a power distribution network includes:
determining energy storage construction nodes in the power distribution network according to new energy parameters and load power of the power distribution network;
And constructing an energy storage planning cost value model according to the energy storage construction nodes, and constructing a net load fluctuation model by adopting a scene analysis method according to the new energy parameters, the load power and the energy storage construction nodes.
In one embodiment, obtaining the energy storage planning target amount according to the energy storage planning cost value model includes:
initializing candidate values of a plurality of energy storage planning target quantities based on a half-mean method;
updating a candidate value of the energy storage planning target quantity by adopting an exponential change weight factor based on a preset random number;
obtaining optimal candidate values through the energy storage planning cost value model, and updating the candidate values of the energy storage planning target values again according to the optimal candidate values, wherein the optimal candidate values are the candidate values with the minimum energy storage system cost values corresponding to the candidate values of the energy storage planning target values;
and obtaining the target quantity of energy storage planning according to the candidate values of all the updated target energy storage sources.
In one embodiment, the energy storage planning target quantity is input into a payload fluctuation model to determine a target charge-discharge strategy according to the payload fluctuation model, and the method comprises the following steps:
linearizing the net load fluctuation model;
and determining a charging and discharging strategy when the net load fluctuation in the power distribution network is minimum by adopting a linear programming method according to the net load fluctuation model after linearization processing.
In one embodiment, the energy storage planning quantity includes energy storage power and energy storage capacity;
when the energy storage planning target quantity is obtained according to the energy storage planning cost value model, a first constraint condition is set, wherein the first constraint condition comprises constraint of energy storage power and constraint of energy storage capacity.
In one embodiment, the energy storage planning target quantity is input into a payload fluctuation model to determine a target charge-discharge strategy according to the payload fluctuation model, and the method further comprises: setting a second constraint on the payload fluctuation model; the second constraint condition comprises a power flow constraint, a branch transmission power constraint, a voltage constraint and an energy storage constraint of the power distribution network.
In a second aspect, the application further provides an energy storage planning device. The device comprises:
the determining module is used for determining an energy storage planning cost value model and a net load fluctuation model in the power distribution network;
the optimization module is used for acquiring an energy storage planning target amount according to the energy storage planning cost value model, inputting the energy storage planning target amount into the net load fluctuation model, and determining a target charging and discharging strategy according to the net load fluctuation model, wherein the energy storage planning target amount is the energy storage planning amount of the energy storage system when the cost value of the energy storage system is minimum, and the target charging and discharging strategy is the charging and discharging strategy of energy storage when the net load fluctuation in the power distribution network is minimum;
And the planning module is used for planning energy storage planning for the power distribution network according to the energy storage planning target quantity and the target charging and discharging strategy.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the energy storage planning method provided by the first aspect of the application when executing the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the energy storage planning method provided by the first aspect of the application.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the energy storage planning method provided by the first aspect of the application.
According to the energy storage planning method, the device, the computer equipment, the storage medium and the computer program product, the energy storage planning cost model and the net load fluctuation model in the power distribution network are determined, the energy storage planning quantity of the energy storage system when the cost value of the energy storage system is minimum is obtained according to the energy storage planning cost model, namely the energy storage planning target quantity, the energy storage planning target quantity is input into the net load fluctuation model, the charge and discharge strategy of energy storage when the net load fluctuation in the power distribution network is minimum is determined, namely the target charge and discharge strategy, and the energy storage is planned for the power distribution network according to the energy storage planning target quantity and the target charge and discharge strategy. The energy storage is required to be applied to the power distribution network on the premise of safety, the access position of the energy storage, the selection of the planning amount of the energy storage and the corresponding operation strategy are indispensible from the safe and stable operation density of the power distribution network, if the energy storage is not reasonably optimized and planned, the energy storage is equivalent to adding more disturbance power sources to the power distribution network, the traditional technology is mainly used for considering economy, benefit, battery life and the like when planning the energy storage and planning, and when large-scale renewable energy sources are accessed to the power distribution network, the adjustment capability of a power system is insufficient, so that the net load fluctuation of the power distribution network is larger, the power and electric quantity balance of a dispatching mechanism is not facilitated, and the problems of safety and stability and the like can be even caused.
In order to reduce impact of renewable energy sources on a power system in a power distribution network and increase controllability and flexibility of the power system, the embodiment of the application ensures that the net load fluctuation of the power distribution network is minimum under the energy storage planning scheme by optimizing a charging and discharging strategy of energy storage by determining an energy storage planning cost model and a net load fluctuation model and simultaneously considering economic factors and net load fluctuation factors of the power distribution network and combining the energy storage planning target quantity after determining the energy storage planning quantity when the cost of the energy storage system is minimum. According to the energy storage planning method provided by the embodiment of the application, energy storage is planned, so that impact of renewable energy sources in the power distribution network to the power system can be reduced, the problems of rapid decrease and rapid rise of net load of the power distribution network can be avoided, net load fluctuation of the power distribution network is smoothed, and electric power and electric quantity balance of the power system during energy source scheduling is facilitated, thereby improving controllability and flexibility of the power system and ensuring safe and stable operation of the power system.
Drawings
FIG. 1 is an application environment diagram of an energy storage planning method in one embodiment;
FIG. 2 is a flow chart of a method of energy storage planning in one embodiment;
FIG. 3 is a topology of a power distribution network node in one embodiment;
FIG. 4 is a block diagram of a two-layer model in one embodiment;
FIG. 5 is a flow diagram of a model for solving energy storage planning cost values in one embodiment;
FIG. 6 is a block diagram of an energy storage planning device in one embodiment;
fig. 7 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The energy storage planning method provided by the embodiment of the application can be applied to an application environment shown in figure 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
The method comprises the steps that a terminal 102 determines an energy storage planning cost value model and a net load fluctuation model in a power distribution network; acquiring an energy storage planning target amount according to an energy storage planning cost value model, and inputting the energy storage planning target amount into a net load fluctuation model to determine a target charging and discharging strategy according to the net load fluctuation model, wherein the energy storage planning target amount is the energy storage planning amount of an energy storage system when the cost value of the energy storage system is minimum, and the target charging and discharging strategy is the charging and discharging strategy of energy storage when the net load fluctuation in a power distribution network is minimum; and planning energy storage for the power distribution network according to the energy storage planning target quantity and the target charging and discharging strategy.
In one embodiment, as shown in fig. 2, an energy storage planning method is provided, and the method is applied to the terminal 102 in fig. 1 for illustration, and includes the following steps:
step 202, determining an energy storage planning cost value model and a net load fluctuation model in a power distribution network.
Wherein the part of the object or space that is drawn for determining the subject is called the energy storage system (Energy Storage System, ESS) when analyzing the energy storage plan. The energy storage system includes input and output of energy and substances, and energy conversion and storage devices. Energy storage systems often involve multiple energies, multiple devices, multiple substances, multiple processes, are complex energy systems that change over time, and require multiple indicators to describe their performance.
The energy storage planning cost value model in the embodiment of the application comprises a model for planning the energy storage planning quantity of the energy storage system according to the cost value of the energy storage system. The energy storage system cost value comprises investment cost value, operation maintenance cost value, line loss cost value and the like, and the energy storage planning quantity comprises energy storage capacity and energy storage power.
The net load fluctuation model in the embodiment of the application comprises a model for planning a charging and discharging strategy of energy storage according to net load fluctuation in a power distribution network. The net load fluctuation in the power distribution network is the adjustment requirement which needs to be met by the power distribution network, namely the total power consumption load minus the load after the output of the renewable new energy source, the fluctuation characteristic of the net load determines the requirement of the power distribution network on the adjustment capability, and the fluctuation of the net load is closely related to the power consumption load and the output characteristic of the new energy source. The charge-discharge strategy includes charging power and discharging power of the stored energy in different time periods.
Illustratively, basic information of the power distribution network, such as topology information, load information, planning construction nodes for energy storage of output data of grid-connected new energy sources, unit cost of energy storage planning amount and the like, is acquired first. And acquiring a calculation mode of the cost value of the energy system and a calculation mode of net load fluctuation in the power distribution network based on the basic information of the power distribution network. Constructing an energy storage planning cost value model in a calculation mode of the cost value of the energy system, wherein the energy storage planning cost value model takes the energy storage planning quantity of the energy storage system as an optimization variable and takes the cost value of the energy storage system as an objective function; and constructing a net load fluctuation model in a calculation mode of net load fluctuation in the power distribution network, wherein the net load fluctuation model takes a charging and discharging strategy of energy storage as an optimization variable and takes the net load fluctuation in the power distribution network as an objective function.
At present, the randomness and fluctuation of renewable energy sources such as wind power and photovoltaic are exposed to the defect of insufficient peak regulation capacity of an electric power system. The large-planning renewable energy is connected into the power distribution network, and the adjustment capability of the power system is insufficient, so that the net load fluctuation of the power distribution network is large, the power and electricity balance of a dispatching mechanism is not facilitated, and even the problems of safety, stability and the like can be caused. In order to reduce impact of renewable energy sources on a power system in a power distribution network and increase controllability and flexibility of the power system, when the energy storage is planned on the power distribution network side, an energy storage planning cost model and a net load fluctuation model are firstly determined, and economic performance and net load fluctuation dual factors in the power distribution network are researched to plan the energy storage.
And 204, obtaining an energy storage planning target quantity according to the energy storage planning cost value model, and inputting the energy storage planning target quantity into the net load fluctuation model to determine a target charge and discharge strategy according to the net load fluctuation model.
The target energy storage planning amount is the energy storage planning amount of the energy storage system when the cost value of the energy storage system is minimum, and the target charge-discharge strategy is the charge-discharge strategy of energy storage when the net load fluctuation in the power distribution network is minimum.
The energy storage planning target quantity is determined by the energy storage planning cost value model, namely the energy storage planning quantity when the energy storage system cost value is minimum, is input into the net load fluctuation model, namely the variable energy storage planning quantity in the net load fluctuation model is determined as the energy storage planning target quantity, and the net load fluctuation in the power distribution network under the energy storage planning target quantity and the target charge-discharge strategy is enabled to be minimum by optimizing the charge-discharge strategy of energy storage.
In one implementation, the energy storage planning cost value model is represented as an upper layer sub-model, the net load fluctuation model is represented as a lower layer sub-model, the upper layer sub-model and the lower layer sub-model form an energy storage planning double-layer model, the angle of the energy storage planning double-layer model on the power grid side is formed, the upper layer sub-model takes the minimum cost value of an energy storage system as a target, the lower layer sub-model takes the minimum net load fluctuation of a power distribution network as a target, the upper layer sub-model is a decision layer, namely decision energy storage planning quantity, and the lower layer sub-model is an operation strategy problem of energy storage planning, namely optimizing the charge and discharge strategy of energy storage. The upper layer sub-model transmits an optimization result of the energy storage planning quantity, namely an energy storage planning target quantity, to the lower layer sub-model, and the lower layer sub-model enables the net load fluctuation of the power distribution network to be minimum under the energy storage planning scheme through optimizing the charge and discharge strategy of energy storage after receiving the energy storage planning target quantity of the upper layer.
And step 206, planning energy storage for the power distribution network according to the energy storage planning target quantity and the target charging and discharging strategy.
After the target energy storage planning amount and the target charging and discharging strategy are obtained according to the energy storage planning cost value model and the net load fluctuation model, the energy storage planning amount of the power distribution network is planned through the target energy storage planning amount, and the operation strategy of the power distribution network energy storage is planned through the target charging and discharging strategy.
According to the energy storage planning method, the energy storage planning cost model and the net load fluctuation model in the power distribution network are determined, the energy storage planning quantity of the energy storage system when the cost value of the energy storage system is minimum is obtained according to the energy storage planning cost model, namely the energy storage planning target quantity, the energy storage planning target quantity is input into the net load fluctuation model, the charge and discharge strategy of energy storage when the net load fluctuation in the power distribution network is minimum is determined, namely the target charge and discharge strategy, and the power distribution network is subjected to energy storage planning according to the energy storage planning target quantity and the target charge and discharge strategy. The energy storage planning is applied to the power distribution network on the premise of safety, the access position of the energy storage planning, the selection of the energy storage planning quantity and the corresponding operation strategy are indistinguishable from the safe and stable operation of the power distribution network, if the energy storage planning is not reasonably planned, the energy storage planning is equivalent to adding more disturbance power sources to the power distribution network, the traditional technology is mainly used for considering economy, benefit, battery life and the like when the energy storage planning is carried out, when large-scale renewable energy sources are accessed to the power distribution network, the adjustment capability of a power system is insufficient, the net load fluctuation of the power distribution network is larger, the power and electric quantity balance of a dispatching mechanism is not facilitated, and the problems of safety and stability can be even caused. In order to reduce impact of renewable energy sources on a power system in a power distribution network and increase controllability and flexibility of the power system, the embodiment of the application ensures that the net load fluctuation of the power distribution network is minimum under the energy storage planning scheme by optimizing a charging and discharging strategy of energy storage by determining an energy storage planning cost model and a net load fluctuation model and simultaneously considering economic factors and net load fluctuation factors of the power distribution network and combining the energy storage planning target quantity after determining the energy storage planning quantity when the cost of the energy storage system is minimum. According to the energy storage planning method provided by the embodiment of the application, energy storage is planned, so that impact of renewable energy sources in the power distribution network to the power system can be reduced, the problems of rapid decrease and rapid rise of net load of the power distribution network can be avoided, net load fluctuation of the power distribution network is smoothed, and electric power and electric quantity balance of the power system during energy source scheduling is facilitated, thereby improving controllability and flexibility of the power system and ensuring safe and stable operation of the power system.
In one embodiment, determining an energy storage planning cost model and a payload fluctuation model in a power distribution network includes the steps of:
(A1) The method comprises the following steps And determining energy storage construction nodes in the power distribution network according to the new energy parameters and the load power of the power distribution network, and constructing an energy storage planning cost value model according to the energy storage construction nodes.
The new energy parameters comprise grid-connected nodes of new energy and output data of the new energy, such as annual output curves of wind power plants of all grid-connected nodes, the load power comprises a typical daily load curve, and the energy storage construction nodes refer to positions for accessing energy storage in the power distribution network.
It should be noted that there are generally a plurality of energy storage construction nodes, and according to information such as new energy parameters and load power, a plurality of nodes can be selected in the power distribution network to carry out energy storage planning, and an energy storage planning cost value model can be constructed according to energy storage planning cost values corresponding to all the energy storage construction nodes.
Illustratively, the optimization variable in the energy storage planning cost model is an energy storage planning quantity, the energy storage planning quantity comprises energy storage power and energy storage capacity, and the objective function in the energy storage planning cost model is an energy storage system cost value, and the energy storage system cost value can be obtained by adopting the following formula:
Wherein C is inv C is the cost value of the energy storage system p Is the cost value of unit energy storage power, C e The cost value of the unit energy storage capacity is represented by n, the number of energy storage construction nodes in the power distribution network is represented by P max，i Power for the ith stored energy, E max，i And gamma is the discount rate, and y is the full life cycle of the stored energy.
(A2) The method comprises the following steps And constructing a net load fluctuation model by adopting a scene analysis method in combination with new energy parameters, load power and energy storage construction nodes.
The scene analysis method is used for solving the problems of uncertainty of the pointer on the output power of the new energy source, randomness of unit outage and the like, considering different scenes possibly occurring, and adopting a multi-scene technology to process the influence of the uncertainty factors. The net load fluctuation model is a model for optimizing the actual operation strategy of the energy storage, and in order to simulate the operation condition of the energy storage more truly and reduce the calculated amount, the embodiment of the application adopts a scene analysis method to construct the net load fluctuation model.
Exemplary, a typical daily wind power output scene, a working day and a non-working day of 4 seasons are obtained to form a typical scene set.
Illustratively, from the angles of emergency power supply, daily operation of the power distribution network and the like, a wind power output scene and a daily load curve are obtained, and a typical scene set is formed.
In one implementation, at least one uncertainty factor in the energy storage operation is first determined, a probability distribution function is set according to the actual situation of the uncertainty factor, various situations of the uncertainty factor occurring in the future are simulated, and a typical scene containing the uncertainty factor is generated. For example, random sampling is performed through probability density functions obeyed by uncertain factors, clustering is performed through a K-means method aiming at sampling results of each uncertain factor, and then clustering results of all uncertain factors are combined randomly to generate a plurality of typical scenes, so that a typical scene set is obtained.
The payload fluctuation model aims at the minimum payload fluctuation of all typical scenes and takes a charge-discharge strategy as an optimization variable, wherein the absolute value sum of differences of the payload at two continuous moments can be used for representing the payload fluctuation situation under the corresponding typical scenes, and the objective function of the payload fluctuation model can be expressed as follows:
wherein F is 2 For the net load fluctuation values in all typical scenes, w is the number of typical scenes, P load，t+1，o For the net load power of the power distribution network at the t+1 time under the o-th typical scene, P load，t，o And (3) the net load power of the distribution network at the T moment in the o-th typical scene, wherein T represents the time section of the typical scene.
According to the embodiment of the application, the energy storage construction node in the power distribution network is determined through the new energy parameter and the load power of the power distribution network, the energy storage cost value model is constructed according to the energy storage construction node, and the net load fluctuation model is constructed by combining the new energy parameter, the load power and the energy storage construction node by adopting a scene analysis method, so that the actual situation of energy storage planning can be simulated more truly, and the calculated amount is reduced.
In one embodiment, when the energy storage planning target quantity is obtained according to the energy storage planning cost value model, a first constraint condition is set, wherein the first constraint condition comprises constraint of energy storage power and constraint of energy storage capacity.
The first constraint condition is a constraint condition in an energy storage planning cost value model, when the variable of the energy storage planning cost value model is optimized, the minimum cost value of the energy storage system is taken as a target, the constraint of energy storage power and the constraint of energy storage capacity are taken as constraint conditions, the energy storage planning quantity is optimized, and the energy storage planning target quantity is obtained.
Illustratively, the first constraint may be expressed as:
wherein P is max，i Power for the ith stored energy, E max，i For the capacity of the ith stored energy,P max，i andE maxi representing the lower power limit of the ith stored energy and the lower capacity limit of the ith stored energy respectively, And->The upper power limit of the ith stored energy and the upper capacity limit of the ith stored energy are respectively indicated.
The embodiment of the application sets a first constraint condition when the energy storage planning cost value model obtains the energy storage planning target quantity, wherein the first constraint condition comprises constraint of energy storage power and constraint of energy storage capacity, and when the variable of the energy storage planning cost value model is optimized through setting of the first constraint condition, the minimum cost value of the energy storage system is taken as a target, and the constraint of the energy storage power and the constraint of the energy storage capacity are taken as constraint conditions, so that the energy storage planning quantity is optimized, and the energy storage planning target quantity is obtained.
In one embodiment, the energy storage planning target quantity is input into a payload fluctuation model, so that when a target charge-discharge strategy is determined according to the payload fluctuation model, a second constraint condition is set, wherein the second constraint condition comprises a power flow constraint, a branch transmission power constraint, a voltage constraint and an energy storage constraint of the power distribution network.
The second constraint condition is a constraint condition in the net load fluctuation model, when the variable of the net load fluctuation model is optimized, the net load fluctuation of the power distribution network is taken as a target, the power flow constraint, the branch transmission power constraint, the voltage constraint and the energy storage constraint of the power distribution network are taken as constraint conditions, and the charge and discharge strategy of the energy storage is optimized, so that the target charge and discharge strategy is obtained.
In one implementation mode, the conventional power flow constraint is non-convex and nonlinear, which can cause the problem of the lower layer to be unable to be solved.
Illustratively, the linearized power flow constraint may be expressed as:
wherein, the collectionAnd aggregate epsilon represents the aggregate of distribution network nodes and the aggregate of lines, p node，j And q node，j Respectively represent the injected active power and reactive power of the node j, P ij And Q ij Representing the active and reactive power transmitted by the line starting from node i and ending at node j, +.>AndP ij respectively representing the upper limit value and the lower limit value of the line transmission power, v i Square of the voltage at node i, +.>Andv i represented as an upper limit value and a lower limit value of the square of the node voltage, respectively.
Illustratively, the energy storage charge and discharge may be limited by its power and capacity, and the energy storage constraint may be expressed as:
0≤P out，i，t ≤U i，t P max，i ； (9)
0≤P in，i，t ≤(1-U t，i )P max，i ； (10)
E i，t ＝E i，t-1 +λ out，i P out，i，t Δt-P in，i，t Δt/λ in，i ； (11)
0.2E max，i ≤E t，i ≤0.8E max，i ； (12)
E i，0 ＝E i，T ； (13)
wherein, the formula (9) and the formula (10) are discharge and charge power constraints of energy storage, P out，i，t And P in，i，t Respectively representing the charge power and the discharge power of the ith energy storage in the time period t, U t，i The energy storage device is a binary variable, wherein the variable is 1, and the energy storage device is in a discharging or idle state in a time period t, and the variable is 0, and the energy storage device is in a charging or idle state in the time period t; formula (11) -formula (12) represents an electrical energy constraint for storing energy at any time t, wherein E t，i For the ith energy stored at time t, lambda out，i And lambda (lambda) in，i The discharge efficiency and the charge efficiency of the stored energy are respectively represented, and Δt represents the time length of the time period t.
In order to ensure continuous operation of the stored energy, the electrical energy at the first time of the stored energy needs to be equal to the electrical energy at the last time, as shown in equation (13).
According to the embodiment of the application, the target quantity of the energy storage planning is input into the net load fluctuation model, when the target charge-discharge strategy is determined according to the net load fluctuation model, the second constraint condition is set, wherein the second constraint condition comprises the power flow constraint, the branch transmission power constraint, the voltage constraint and the energy storage constraint of the power distribution network, and the target charge-discharge strategy is obtained by setting the second constraint condition, taking the minimum net load fluctuation as the target and taking the power flow constraint, the branch transmission power constraint, the voltage constraint and the energy storage constraint of the power distribution network as constraint conditions when the variable of the net load fluctuation model is optimized.
In one embodiment, the energy storage planning target quantity is obtained according to the energy storage planning cost value model, and the energy storage planning target quantity is input into the payload fluctuation model to determine a target charge-discharge strategy according to the payload fluctuation model, and the method further comprises the following steps:
(B1) The method comprises the following steps And under the condition that the net load fluctuation corresponding to the target charge-discharge strategy does not meet the preset condition, updating the energy storage planning cost value model by combining the penalty function to obtain an updated energy storage planning cost value model, and re-determining and updating the energy storage planning target quantity according to the updated energy storage planning cost value model.
And after the net load fluctuation model receives the energy storage planning target quantity, the net load fluctuation of the power distribution network is minimized under the energy storage planning scheme by optimizing the charge and discharge strategy of the energy storage, so as to obtain the target charge and discharge strategy. And the net load fluctuation value corresponding to the target charge-discharge strategy is the minimum net load fluctuation value determined by taking the charge-discharge strategy as an optimization variable by combining the energy storage planning target quantity after the net load fluctuation model receives the energy storage planning target quantity.
In order to further smooth the net load fluctuation, the embodiment of the application judges whether the energy storage planning scheme, namely the current energy storage planning target quantity and the target charging and discharging strategy, is in accordance with the preset condition after obtaining the minimum net load fluctuation value of the power distribution network under the energy storage planning scheme, and if not, the energy storage planning cost value model is updated by combining the punishment function, so that the energy storage planning target quantity can be redetermined and updated according to the updated energy storage planning cost value model.
In one implementation, the payload fluctuation model represents an optimized operation problem of the energy storage plan, and a penalty function is returned to the energy storage plan cost value model according to the operation result. Under the condition that the net load fluctuation corresponding to the target charging and discharging strategy does not meet the preset condition, acquiring a punishment function according to the net load fluctuation condition under the energy storage planning scheme, and updating the energy storage planning cost value model by combining the punishment function.
For example, the preset condition may be expressed as that the value of the payload fluctuation at all times in all typical scenarios is smaller than or equal to the limit value of the payload fluctuation, that is, in the case where there is a value of the payload fluctuation at a time greater than the limit value of the payload fluctuation in the payload fluctuation corresponding to the target charge-discharge strategy, the payload fluctuation corresponding to the target charge-discharge strategy does not meet the preset condition. According to the net load fluctuation condition under the scheme, a penalty function is obtained, and the energy storage planning cost value model is updated by combining the penalty function.
Illustratively, the penalty function may be expressed as:
in the above formula, A represents the limit value of the net load fluctuation, cout t To indicate the variable, cout t When the value is 1, the net load fluctuation value from the time t to the time t+1 exceeds the limit value of the net load fluctuation, cout t A value of 0 indicates that the fluctuation of the payload from time t to time t+1 does not exceed the limit of the fluctuation of the payload, M being a penalty factor.
Equation (14) shows that the greater the number of times the net load fluctuation exceeds the limit, the greater the penalty function value η, indicating that the energy storage planning target amount determined by the energy storage planning cost model is not a preferred planning scheme.
In one implementation, updating the energy storage planning cost model in combination with the penalty function refers to updating an objective function in the energy storage planning cost model in combination with the penalty function, and the updated objective function F of the energy storage planning cost model 1 Can be expressed as:
min F 1 ＝C inv +η； (16)
wherein C is inv And representing the cost value of the energy storage system, wherein eta is a penalty function transferred from the net load fluctuation model to the energy storage planning cost value model.
(B2) The method comprises the following steps And inputting the energy storage planning target quantity into a net load fluctuation model, and re-determining and updating the target charging and discharging strategy according to the net load model to obtain a corresponding net load fluctuation value when the updated net load fluctuation is minimum.
The energy storage planning target amount in the step B2 is the energy storage planning target amount redetermined and updated in the step B1. After updating the objective function in the energy storage planning cost value model by combining the penalty function, determining the energy storage planning objective quantity by taking the minimum updated objective function as a target, inputting the energy storage planning objective quantity into the net load fluctuation model, taking the minimum net load fluctuation of the power distribution network as the target, re-determining and updating the target charging and discharging strategy, and obtaining the net load fluctuation corresponding to the minimum net load fluctuation of the power distribution network, namely the net load fluctuation corresponding to the target charging and discharging strategy.
(B3) The method comprises the following steps And (3) carrying out iterative computation until the net load fluctuation value corresponding to the updated target charge-discharge strategy meets the preset condition.
And B2, judging whether the payload fluctuation corresponding to the target charge-discharge strategy obtained in the step B2 meets preset conditions again, and executing the processes of the step B1 and the step B2 again under the condition that the payload fluctuation does not meet the preset conditions, and performing iterative calculation until the payload fluctuation value corresponding to the target charge-discharge strategy meets the preset conditions.
According to the embodiment of the application, whether the net load fluctuation corresponding to the target charge-discharge strategy meets the preset condition is judged, the energy storage planning cost value model is updated by combining the punishment function under the condition that the net load fluctuation corresponding to the target charge-discharge strategy does not meet the preset condition, the energy storage planning target quantity is redetermined and updated through the updated energy storage planning cost value model, the energy storage planning target quantity is transferred to the net load fluctuation model, the target charge-discharge strategy is redetermined and updated according to the net load model, whether the net load fluctuation corresponding to the target charge-discharge strategy meets the preset condition is judged, the scheme meeting the preset condition is found through iterative calculation, the problem of rapid decrease and rapid rise of the net load of the power distribution network is further relieved, the net load fluctuation of the power distribution network is smoothed, and the power and electricity balance of the power system during energy scheduling is facilitated, so that the controllability and the flexibility of the power system are improved, and the safe and stable operation of the power system is ensured.
In one embodiment, the process of obtaining the energy storage planning target volume from the energy storage planning cost model employs a multi-strategy modified adaptive ray foraging optimization algorithm (MSAMRFO).
The multi-strategy improved self-adaptive bated ray foraging optimization algorithm (MSAMRFO) is an improved method of a bated ray foraging optimization algorithm (MRFO) in the traditional technology. The method is characterized in that a baty ray foraging optimization algorithm (MRFO) is a foraging process imitating baty rays in the ocean, mathematical modeling is carried out aiming at different predation strategies, and a mathematical description is carried out on a mode of updating the positions of the baty ray individuals. MRFO has three predation strategies, chain foraging, screw foraging and tumbling foraging, respectively.
According to the embodiment of the application, a multi-strategy improved self-adaptive bate ray foraging optimization algorithm (MSAMRFO) is adopted to solve the energy storage planning cost model, and the energy storage planning quantity when the cost value of the energy storage system is minimum, namely the energy storage planning target quantity, is obtained.
In one implementation, a process for obtaining an energy storage planning target amount according to an energy storage planning cost model includes the steps of:
(C1) The method comprises the following steps A plurality of candidate values for the energy storage planning target quantity are initialized based on a half-mean method.
The candidate values of the plurality of energy storage planning target values refer to different energy storage planning amounts, the candidate values of the plurality of energy storage planning target values (the number of the candidate values is set as N) are divided into two sub-candidate sets in half, and the sub-candidate sets are respectively marked as a sub-candidate set 1 and a sub-candidate set 2. The sub-candidate set 1 adopts the same random initialization mode as the traditional MRFO algorithm, and the sub-candidate set 2 adopts a half-uniform initialization strategy to ensure the diversification of candidate values of the whole multiple energy storage planning target quantities. The mathematical model of the half-initialization strategy is:
Wherein p is low And p up Search space lower and upper limits, respectively representing candidate values for a plurality of energy storage planning target amounts, rand () represents a random function, p i，1 And p j，2 Represents the random initial value of the ith candidate value in the sub-candidate set 1 and the random initial value of the jth candidate value in the sub-candidate set 2 respectively, N is the number of candidate values, wherein,
(C2) The method comprises the following steps And updating the candidate value of the energy storage planning target quantity by adopting an exponential change weight factor based on a preset random number.
Each iteration is preset with a random number, an adaptive chained foraging strategy and an adaptive spiral foraging strategy are selected based on the size of the preset random number, and the candidate value of the energy storage planning target quantity is updated. And an exponential change weight factor is adopted, a Sigmoid function is introduced in the self-adaptive chained foraging and self-adaptive spiral foraging, and the original random step length is changed into a variable step length, so that the candidate value of the energy storage planning target quantity can better balance global optimization and local search, and the global optimization convergence speed can be accelerated.
For example, if the preset random number is greater than 0.5, the candidate value of the energy storage planning target amount is updated by adopting the adaptive chained foraging strategy, and the updating process can be expressed by adopting the following formula:
Wherein,a value representing the ith candidate value in the mth iteration of the d-th dimension, +.>The value of the optimal solution in the d dimension for the mth iteration is that beta and alpha are weight coefficients respectively.
For example, if the preset random number is smaller than 0.5 and larger than m/Iter (Iter is the maximum iteration number), the candidate value of the energy storage planning target amount is updated by adopting the first adaptive spiral foraging strategy, and the updating process can be expressed by adopting the following formula:
illustratively, if the preset random number is smaller than m/Iter (Iter is the maximum number of iterations), the candidate value of the energy storage planning target amount is updated by adopting the second adaptive spiral foraging strategy, and the updating process can be expressed by adopting the following formula:
wherein,values, UB, randomly generated for the d-th dimension of the mth iteration d And LB d Upper and lower values of the d-th dimension variable, r 2 Is [0,1]Random numbers in (a) and (b).
(C3) The method comprises the following steps And obtaining an optimal candidate value through the energy storage planning cost value model, and updating the candidate value of the energy storage planning target value again according to the optimal candidate value.
The optimal candidate value is the candidate value with the minimum energy storage system cost value corresponding to the candidate value of each energy storage planning target value. And aiming at the candidate value of any energy storage planning target quantity, acquiring the energy storage system cost value corresponding to the candidate value through an energy storage planning cost value model, and selecting the candidate value corresponding to the least energy storage system cost value from the candidate values as the optimal candidate value.
After the optimal candidate value is selected from the candidate values of the plurality of energy storage planning target values, the candidate value of the energy storage planning target value is updated again according to the optimal candidate value.
In one implementation, a tumbling predation approach may be employed to update the candidate value for the energy storage planning target amount again based on the optimal candidate value. The rolling predation method takes the current optimal candidate value as a rolling fulcrum, rolls the space positions represented by other candidate values to the other side which is in mirror image relation with the current position, and the mathematical model is expressed as follows:
wherein r is 3 And r 4 Respectively [0,1 ]]Random numbers in (a) and (b).
In one implementation, before updating the candidate values by the tumbling predation method, comparing the cost values of the energy storage systems corresponding to the candidate values before and after the tumbling predation method with respect to the candidate value of any energy storage planning target quantity. If the cost value of the energy storage system corresponding to the candidate value updated by adopting the rolling predation method is greater than or equal to the cost value of the energy storage system corresponding to the candidate value before being updated by adopting the rolling predation method, updating the candidate value by adopting the rolling predation method; if the cost value of the energy storage system corresponding to the candidate value updated by the tumbling predation method is smaller than the cost value of the energy storage system corresponding to the candidate value before the updating by the tumbling predation method, the candidate value is not updated again by the tumbling predation method, and the updating strategy of the single candidate value can be expressed as follows:
/>
Wherein fit () is an energy storage system cost value function corresponding to the candidate value.
(C4) The method comprises the following steps And obtaining the target quantity of energy storage planning according to the candidate values of all the updated target energy storage sources.
In order to prevent the local optimum solution from being trapped, judging whether the global optimum or the local optimum is reached or not by adopting an energy storage system cost value variance, a candidate average approach distance and a candidate approach degree threshold value, and if the local optimum is trapped, jumping out of the local optimum solution by mutation with a certain probability.
In one implementation, judging whether all candidate values fall into local optimum and whether global optimum is achieved according to energy storage system cost value variances, candidate value average approaching distances and candidate value approaching degree thresholds corresponding to candidate values of all updated energy storage planning target values; and if all the candidate values do not fall into the local optimum and reach the global optimum, taking the optimal candidate value as the energy storage planning quantity when the cost value of the energy storage system in the power distribution network is minimum, namely taking the optimal candidate value as the energy storage planning target quantity.
Illustratively, the energy storage system cost value variance may reflect the aggregation degree between the multiple candidate values, where the smaller the energy storage system cost value variance (close to 0) is, which indicates that the multiple candidate values are aggregated, that is, that the multiple candidate values find the optimal solution or fall into the local optimum, otherwise, that the multiple candidate values are in a random search state, and the energy storage system cost value variance may be defined by the following formula:
Wherein delta 2 To store the cost value variance, fit of the energy system i For the cost value of the energy storage system corresponding to the ith candidate value, fit avg Is the average value of cost values of the energy storage system of a plurality of candidate values, fit m To normalize the scaling factor, which acts to limit delta 2 Size, fit m The value of (2) is as follows:
fit m ＝max{1，max{|fit i -fit avg |}}； (27)
for example, the candidate average approach distance meandit may be expressed as:
wherein D is the dimension of the candidate value of the energy storage planning target quantity,the location of the historical optimal solution at the d-th dimension is the i-th candidate.
Illustratively, the candidate proximity threshold BorderDist may be expressed as:
and when the energy storage system cost value variance approaches 0 and the candidate average approach distance is greater than or equal to the candidate approach degree threshold, the global optimal solution, namely global convergence, is achieved. Otherwise, when the cost variance of the energy storage system approaches 0 and the average approach distance of the candidate values is smaller than the threshold value of the approach degree of the candidate values, the local optimal solution is trapped. The candidate value can be jumped out of the locally optimal solution by mutating in the following way: p to be trapped in a locally optimal solution by a certain probability ρ best Performing mutation, for example ρ=rand (0.2, 0.5), from [0,1]Random number r is randomly generated 5 If ρ is greater than r 5 Then the mutation operation is performed, otherwise the mutation operation is not performedAs such, the mutation operation can be expressed as:
/>
in the method, in the process of the application,is a random number subject to a standard normal distribution.
According to the embodiment of the application, a multi-strategy improved self-adaptive baton foraging optimization algorithm (MSAMRFO) is adopted, and the energy storage planning target quantity is obtained according to the energy storage planning cost model, so that the global optimizing capability is improved, and the convergence speed is accelerated. Firstly, in the aspect of initializing the candidate value of the energy storage planning target quantity, a half-uniform initialization strategy is provided to improve the diversity of the candidate value; secondly, a new index change weight factor is adopted, a Sigmoid function is introduced into chain search and spiral foraging by MSAMRFO, and the original random step length is changed into a variable step length, so that global optimization and local search can be balanced better among a plurality of candidate values, and meanwhile, the convergence speed of global optimization can be accelerated; and finally, judging whether the local optimum is met or not through the energy storage system cost value variance, the candidate value average approach distance and the candidate value approach degree threshold value, and mutating a plurality of candidate values which are met with the local optimum according to a certain mutation probability to enable the candidate values to jump out of the local optimum.
In one embodiment, inputting the energy storage planning target amount into the payload fluctuation model to determine the target charge-discharge strategy from the payload fluctuation model includes: linearizing the net load fluctuation model; and determining a charging and discharging strategy when the net load fluctuation in the power distribution network is minimum by adopting a linear programming method according to the net load fluctuation model after linearization processing.
Illustratively, linearizing the objective function and constraint conditions in the payload fluctuation model can be expressed as:
wherein,and->The payload fluctuation model can be converted into a linear model by converting the auxiliary variables into the formulas (31) to (34), and the objective function of the linear model is the formula (31), and the constraint conditions are the formulas (32) to (34) and the formulas (4) to (13).
In one implementation, after the linearization process is performed on the payload fluctuation model to convert the payload fluctuation model to a linear programming model, a solver may be used to determine a charge-discharge strategy when the payload fluctuation in the power distribution network is minimal.
According to the embodiment of the application, the net load fluctuation model is subjected to linearization, and the charge and discharge strategy when the net load fluctuation in the power distribution network is minimum is determined by adopting a linear programming method according to the net load fluctuation model after linearization, so that the solving speed is high, and the efficiency is higher.
In one embodiment, there is provided an energy storage planning method comprising the steps of:
(D1) The method comprises the following steps And determining an energy storage planning cost value model and a net load fluctuation model in the power distribution network according to the new energy parameters and the load power of the power distribution network.
According to the embodiment of the application, a 10kV power distribution network in a certain area is taken as an example, as shown in fig. 3, PV represents photovoltaic, WT represents wind power, ESS represents energy storage planning, the power distribution network comprises 45 nodes, 4 wind powers and 5 photovoltaic are connected, and grid-connected nodes and capacities of the wind powers and the photovoltaic are shown in table 1. The power distribution network plans are formed in the No. 2 node, the No. 7 node, the No. 23 node and the No. 40 node to build a distributed energy storage plan.
TABLE 1 grid-connected node and capacity for wind and photovoltaic
| New energy type | Grid-connected node | Capacity (kW) |
| Wind power generation | 2、7、11、15 | 2500 |
| Photovoltaic device | 19、22、29、30、40 | 2000 |
Along with the technology maturation and cost reduction of the novel energy storage mainly comprising electrochemical energy storage, the electrochemical energy storage has the advantages of high energy density, high charge and discharge speed, high efficiency and the like, so that the electrochemical energy storage is rapidly developed. In the embodiment of the application, a lithium battery is taken as an example, the maximum energy storage power of each energy storage plan is 5000KW, the minimum energy storage power is 500KW, the cost of the energy storage power is 3400 yuan/kW, the cost of the energy storage capacity is 340 yuan/(kW.h), the service life of the energy storage plan is 15 years, the survival rate is 3%, and the circulation efficiency is 90%.
And determining a double-layer model according to the related information of the power distribution network, such as topology information, load information, output data of new energy, planning construction nodes of energy storage and the like. As shown in fig. 4, the two-layer model includes an upper layer sub-model and a lower layer sub-model, wherein the upper layer sub-model is a planning problem of energy storage planning quantity, and aims at the minimum cost value of an energy storage system, takes energy storage power and energy storage capacity as optimization variables, and takes constraint of the energy storage power and constraint of the energy storage capacity as constraint conditions; the lower layer sub-model is an operation strategy problem of energy storage planning, and aims at the minimum fluctuation of net load of the power distribution network and the charge and discharge strategy of energy storage as an optimization variable.
Wherein, the objective function of the upper layer sub-model can be expressed as: min F 1 ＝C inv +η, wherein C inv And representing the cost value of the energy storage system, wherein eta is a penalty function transferred from the net load fluctuation model to the energy storage planning cost value model, and the initial value is a zero value function.
(D2) The method comprises the following steps And solving the upper layer sub-model by adopting a self-adaptive ray foraging optimization algorithm (MSAMRFO), and determining an energy storage planning target amount, wherein the energy storage planning target amount is the energy storage planning amount of the energy storage system when the cost value of the energy storage system is minimum.
In one implementation, as shown in FIG. 5, the upper layer sub-model is solved using an adaptive bata foraging optimization algorithm (MSAMRFO), and determining the energy storage planning target amount includes:
At step 502, a half-mean method initializes a candidate set.
Wherein the candidate set of values includes a plurality of candidate values for the energy storage planning target quantity. The candidate value set is divided into two sub-candidate sets in half, which are denoted as sub-candidate set 1 and sub-candidate set 2, respectively. The sub-candidate set 1 adopts the same random initialization mode as the traditional MRFO algorithm, and the sub-candidate set 2 adopts a half-even initialization strategy according to the formula (17) to ensure the diversification of candidate values of the whole multiple energy storage planning target quantities.
In step 504, the preset iteration number m is 1.
Step 506, calculating the cost value of the energy storage system corresponding to each candidate value in the candidate value set.
The cost value of the energy storage system corresponding to each candidate value can be calculated according to the formula (1).
Step 508, presetting a random number r 1 If r 1 If greater than 0.5, step 510 is performed, if r 1 Less than or equal to 0.5, step 512 is performed.
Step 510, adopting an adaptive chained foraging strategy to update the candidate value of the energy storage planning target quantity according to the formula (18).
Step 512, if r 1 Greater than m/Iter (Iter is the maximum number of iterations), step 514 is performed if r 1 Less than or equal to m/Iter, then step 516 is performed.
Step 514, adopting a first adaptive spiral foraging strategy to update the candidate value of the energy storage planning target quantity according to the formula (21).
Step 516, a second adaptive spiral foraging strategy is employed to update the candidate value of the energy storage planning target amount according to equation (22).
Step 518, using a tumbling predation method according to equation (24), and updating the candidate value of the energy storage planning target value again according to the optimal candidate value.
Step 520, comparing the cost values of the energy storage systems before and after updating according to the formula (25), and updating the candidate value set.
Step 522, determining whether the candidate value set falls into a local optimum, if so, executing step 526, and if not, executing step 524.
Step 524, determining whether the candidate value set is globally optimal, if so, executing step 534, and if not, executing step 530.
Step 526 of determining whether to sink to p of the locally optimal solution according to a certain probability ρ best The mutation is performed, step 528 is performed if the mutation operation is performed, and step 530 is performed if the mutation operation is not performed.
Step 528, performing mutation operation according to formula (30).
Step 530, comparing the current iteration number with the maximum iteration number, if the current iteration number is less than or equal to the maximum iteration number, executing step 532, and if the current iteration number is greater than the maximum iteration number, executing step 534.
Step 532, step 506 is performed after updating the current iteration number.
And 534, outputting the current optimal candidate value, and taking the optimal candidate value as an energy storage planning target quantity.
(D3) The method comprises the following steps And inputting the energy storage planning target quantity into a net load fluctuation model, and determining a target charging and discharging strategy by adopting a linear programming method, wherein the target charging and discharging strategy is a charging and discharging strategy of energy storage when the net load fluctuation of the power distribution network is minimum.
(D4) The method comprises the following steps And if the net load fluctuation corresponding to the target charging and discharging strategy meets the preset condition, planning the power distribution network according to the current target charging and discharging strategy and the energy storage planning target quantity.
(D5) The method comprises the following steps If the net load fluctuation corresponding to the target charge-discharge strategy does not meet the preset condition, determining a current punishment function according to the net load fluctuation condition, updating an energy storage planning cost value model by combining the punishment function, returning to the step D1, determining the energy storage planning target quantity again, and repeating iteration until the net load fluctuation corresponding to the target charge-discharge strategy meets the preset condition.
According to the embodiment of the application, the upper layer sub-model transmits the optimization result of the energy storage planning quantity, namely the energy storage planning target quantity, to the lower layer sub-model, and the lower layer sub-model enables the net load fluctuation of the power distribution network to be minimum under the energy storage planning scheme by optimizing the charge and discharge strategy of energy storage after receiving the upper layer energy storage planning target quantity. And judging whether the net load fluctuation meets the requirement, if not, introducing a penalty function to the upper model, otherwise, returning a zero penalty function, and then continuing iteration until an optimal scheme is found. According to the embodiment of the application, the energy storage planning at the power distribution network side is considered, the economic factors and the net load fluctuation factors are combined, and the net load fluctuation of the power distribution network is further smoothed through the punishment function, so that the impact of renewable energy sources in the power distribution network on a power system can be reduced, the problem of rapid decrease and rapid rise of the net load of the power distribution network can be avoided, the balance of electric power and electric quantity of the power system during energy scheduling is facilitated, the controllability and the flexibility of the power system are improved, and the safe and stable operation of the power system is ensured; solving an energy storage planning cost value model by adopting a multi-strategy improved adaptive ray foraging optimization algorithm, and firstly, providing a half-uniform initialization strategy to improve the diversity of candidate values in the aspect of initializing the candidate values of the energy storage planning target quantity; secondly, a new index change weight factor is adopted, a Sigmoid function is introduced into chain search and spiral foraging by MSAMRFO, and the original random step length is changed into a variable step length, so that global optimization and local search can be balanced better among a plurality of candidate values, and meanwhile, the convergence speed of global optimization can be accelerated; and finally, judging whether the local optimum is met or not through the energy storage system cost value variance, the candidate value average approach distance and the candidate value approach degree threshold value, and mutating a plurality of candidate values which are met with the local optimum according to a certain mutation probability to enable the candidate values to jump out of the local optimum.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides an energy storage planning device for realizing the energy storage planning method. The implementation of the solution provided by the apparatus is similar to the implementation described in the above method, so the specific limitation in the embodiments of the energy storage planning apparatus provided below may be referred to the limitation of the energy storage planning method hereinabove, and will not be repeated here.
In one embodiment, as shown in fig. 6, there is provided an energy storage planning apparatus, comprising: a determination module 602, an optimization module 604, and a planning module 606, wherein:
the determining module 602 is configured to determine an energy storage planning cost model and a payload fluctuation model in the power distribution network.
The optimizing module 604 is configured to obtain an energy storage planning target amount according to the energy storage planning cost value model, and input the energy storage planning target amount into the payload fluctuation model, so as to determine a target charging and discharging strategy according to the payload fluctuation model, where the energy storage planning target amount is an energy storage planning amount of the energy storage system when the cost value of the energy storage system is minimum, and the target charging and discharging strategy is a charging and discharging strategy of energy storage when the payload fluctuation in the power distribution network is minimum.
The planning module 606 is configured to plan an energy storage plan for the power distribution network according to the energy storage planning target amount and the target charging and discharging strategy.
In one embodiment, the optimization module 604 is configured to: if the net load fluctuation corresponding to the target charge-discharge strategy does not meet the preset condition, updating the energy storage planning cost value model by combining the penalty function to obtain an updated energy storage planning cost value model; the energy storage planning target quantity is redetermined and updated according to the updated energy storage planning cost value model; inputting the energy storage planning target quantity into a net load fluctuation model, and re-determining and updating the target charging and discharging strategy according to the net load model to obtain a net load fluctuation value corresponding to the updated target charging and discharging strategy; and (3) carrying out iterative computation until the net load fluctuation value corresponding to the updated target charge-discharge strategy meets the preset condition.
In one embodiment, the determining module 602 is configured to: determining energy storage construction nodes in the power distribution network according to new energy parameters and load power of the power distribution network; and constructing an energy storage planning cost value model according to the energy storage construction nodes, and constructing a net load fluctuation model by adopting a scene analysis method according to the new energy parameters, the load power and the energy storage construction nodes.
In one embodiment, the optimization module 604 is configured to: obtaining an energy storage planning target quantity according to the energy storage planning cost value model, comprising: initializing candidate values of a plurality of energy storage planning target quantities based on a half-mean method; updating a candidate value of the energy storage planning target quantity by adopting an exponential change weight factor based on a preset random number; obtaining optimal candidate values through the energy storage planning cost value model, and updating the candidate values of the energy storage planning target values again according to the optimal candidate values, wherein the optimal candidate values are the candidate values with the minimum energy storage system cost values corresponding to the candidate values of the energy storage planning target values; and obtaining the target quantity of energy storage planning according to the candidate values of all the updated target energy storage sources.
In one embodiment, the optimization module 604 is configured to: and inputting the energy storage planning target amount into a payload fluctuation model for determining a target charge-discharge strategy according to the payload fluctuation model, comprising: linearizing the net load fluctuation model; and determining a charging and discharging strategy when the net load fluctuation in the power distribution network is minimum by adopting a linear programming method according to the net load fluctuation model after linearization processing.
In one embodiment, the optimization module 604 is configured to: when the energy storage planning target quantity is obtained according to the energy storage planning cost value model, a first constraint condition is set, wherein the first constraint condition comprises constraint of energy storage power and constraint of energy storage capacity.
In one embodiment, the optimization module 604 is configured to: inputting the energy storage planning target quantity into a net load fluctuation model, and setting a second constraint condition when determining a target charging and discharging strategy according to the net load fluctuation model; the second constraint condition comprises a power flow constraint, a branch transmission power constraint, a voltage constraint and an energy storage constraint of the power distribution network.
The various modules in the energy storage planning device described above may be implemented in whole or in part in software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal. The computer program is executed by a processor to implement a method of energy storage planning.
It will be appreciated by those skilled in the art that the structure shown in FIG. 7 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (10)
1. An energy storage planning method, the method comprising:
determining an energy storage planning cost value model and a net load fluctuation model in the power distribution network;
acquiring an energy storage planning target amount according to the energy storage planning cost value model, and inputting the energy storage planning target amount into the net load fluctuation model to determine a target charge-discharge strategy according to the net load fluctuation model, wherein the energy storage planning target amount is the energy storage planning amount of an energy storage system when the cost value of the energy storage system is minimum, and the target charge-discharge strategy is the charge-discharge strategy of energy storage when the net load fluctuation in the power distribution network is minimum;
And planning energy storage for the power distribution network according to the energy storage planning target quantity and the target charging and discharging strategy.
2. The method of claim 1, wherein the obtaining an energy storage planning target amount from the energy storage planning cost model and inputting the energy storage planning target amount into the payload fluctuation model for determining a target charge-discharge strategy from the payload fluctuation model, further comprises:
under the condition that the net load fluctuation corresponding to the target charge-discharge strategy does not meet the preset condition, updating the energy storage planning cost value model by combining a penalty function to obtain an updated energy storage planning cost value model;
determining and updating the energy storage planning target quantity again according to the updated energy storage planning cost value model;
inputting the energy storage planning target quantity into the payload fluctuation model, and re-determining and updating the target charge-discharge strategy according to the payload model to obtain a payload fluctuation value corresponding to the updated target charge-discharge strategy;
and (3) carrying out iterative computation until the net load fluctuation value corresponding to the updated target charge-discharge strategy meets the preset condition.
3. The method of claim 1, wherein determining an energy storage planning cost model and a payload fluctuation model in a power distribution network comprises:
Determining energy storage construction nodes in the power distribution network according to the new energy parameters and the load power of the power distribution network;
and constructing the energy storage planning cost value model according to the energy storage construction node, and constructing the net load fluctuation model by adopting a scene analysis method according to the new energy parameter, the load power and the energy storage construction node.
4. The method of claim 1, wherein the obtaining an energy storage planning target amount from the energy storage planning cost model comprises:
initializing candidate values of a plurality of energy storage planning target quantities based on a half-mean method;
updating the candidate value of the energy storage planning target quantity by adopting an exponential change weight factor based on a preset random number;
acquiring an optimal candidate value through the energy storage planning cost value model, and updating the candidate value of the energy storage planning target value again according to the optimal candidate value, wherein the optimal candidate value is the candidate value with the minimum energy storage system cost value corresponding to the candidate value of the energy storage planning target value;
and obtaining the target quantity of energy storage planning according to the candidate values of all the updated target energy storage sources.
5. The method of claim 1, wherein the inputting the energy storage planning target amount into the payload fluctuation model to determine a target charge-discharge strategy from the payload fluctuation model comprises:
Linearizing the payload fluctuation model;
and determining a charge-discharge strategy when the net load fluctuation in the power distribution network is minimum by adopting a linear programming method according to the net load fluctuation model after linearization processing.
6. The method of claim 1, wherein the energy storage planning quantity comprises energy storage power and energy storage capacity;
when the energy storage planning target quantity is obtained according to the energy storage planning cost value model, a first constraint condition is set, and the first constraint condition comprises the constraint of the energy storage power and the constraint of the energy storage capacity.
7. The method of claim 1, wherein the and inputting the energy storage planning target amount into the payload fluctuation model for determining a target charge-discharge strategy from the payload fluctuation model, further comprising:
setting a second constraint on the payload fluctuation model; the second constraint condition comprises a power flow constraint, a branch transmission power constraint, a voltage constraint and an energy storage constraint of the power distribution network.
8. An energy storage planning apparatus, the apparatus comprising:
the determining module is used for determining an energy storage planning cost value model and a net load fluctuation model in the power distribution network;
The optimization module is used for acquiring an energy storage planning target amount according to the energy storage planning cost value model, inputting the energy storage planning target amount into the net load fluctuation model, and determining a target charging and discharging strategy according to the net load fluctuation model, wherein the energy storage planning target amount is the energy storage planning amount of an energy storage system when the cost value of the energy storage system is minimum, and the target charging and discharging strategy is the charging and discharging strategy of energy storage when the net load fluctuation in the power distribution network is minimum;
and the planning module is used for planning energy storage planning for the power distribution network according to the energy storage planning target quantity and the target charging and discharging strategy.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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