CN109583647B - Multi-user sharing method and power supply system for energy storage products - Google Patents
Multi-user sharing method and power supply system for energy storage products Download PDFInfo
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Abstract
The technical scheme of the invention discloses a multi-user sharing method of an energy storage product, which comprises the following steps: s1: collecting the electricity demand of a user in the past and the current energy storage quota state; s2: forming a daily quota/charging and discharging plan through an energy storage system state model established based on a Markov chain according to the electricity demand and the current energy storage quota state; s3: judging whether the stored energy is remained or not according to the daily quota/charging and discharging plan, if yes, performing the step S4, and if not, directly performing the step S5; s4: collecting the instant demands of users and determining whether to update the daily quota/charge-discharge plan; s5: when the electricity price enters the valley, the electricity demand is not received, the charging and discharging program which is still performed is continuously performed until the charging and discharging program is finished, and the energy storage power supply system enters the settlement, charging, operation and maintenance links. The multi-user sharing method for the energy storage product can realize multi-user sharing of the energy storage product.
Description
Technical Field
The invention relates to the field of distributed energy, in particular to an energy storage product multi-user sharing method and a power supply system.
Background
The existing energy storage products are all single-user products, namely, an energy storage container solution is provided for users with parks or single-span, but when a plurality of clients share the parks or public buildings, the problems of attribution and occupation of the energy storage products are considered, and the solution cannot be provided for the clients. Meanwhile, some customers wish to use the energy storage product in a leasing way instead of spending the cost to buy a large amount of fixed assets, so that a method for supplying power to the energy storage product based on a sharing economic mode needs to be explored.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, the technical problem to be solved by the technical scheme of the present invention is that the existing energy storage products cannot be shared when in use.
In order to solve the technical problems, the technical scheme of the invention provides a multi-user sharing method of an energy storage product, which comprises the following steps:
s1: collecting the electricity demand of a user in the past and the current energy storage quota state;
s2: forming a daily quota/charging and discharging plan through an energy storage system state model established based on a Markov chain according to the electricity demand and the current energy storage quota state;
s3: judging whether the stored energy is remained or not according to the daily quota/charging and discharging plan, if yes, performing the step S4, and if not, directly performing the step S5;
s4: collecting the instant demands of users and determining whether to update the daily quota/charge-discharge plan;
s5: when the electricity price enters the valley, the electricity demand is not received, the charging and discharging program which is still performed is continuously performed until the charging and discharging program is finished, and the energy storage power supply system enters the settlement, charging, operation and maintenance links.
Further, in step S2, the daily quota/charging schedule includes a daily schedule state sequence, a daily schedule action sequence, a daily total report and an optimal path, where the daily schedule state sequence is an ordered combination of a series of states, the daily schedule action sequence is an ordered combination of a series of actions, the actions correspond to a report value, the daily schedule action sequence corresponds to a daily total report value, a total report experience value exists in a path for transferring an action from one state to another, and the path corresponding to the maximum total report experience value is the optimal path.
Further, the actions include at least one of accepting an order, rejecting an order, charging an offline, full power online, and operating maintenance offline.
Further, in step S4, the method responds to the instant demand of the customer according to the total return experience value and decides whether to update the daily quota/charging/discharging schedule, which is specifically as follows:
s41: respectively calculating the total return experience value of the current quota/charging and discharging plan formed after the instant demand is accepted and the total return experience value of the current quota/charging and discharging plan formed after the instant demand is not accepted;
s42: comparing the magnitudes of the two total return experience values in S41:
if the total return experience value of the current quota/charge-discharge plan formed after the instant demand is received is smaller than the total return experience value of the current quota/charge-discharge plan formed after the instant demand is not received, the instant demand is not received;
if the total return experience value of the current quota/charge-discharge plan formed after the instant demand is received is greater than or equal to the total return experience value of the current quota/charge-discharge plan formed after the instant demand is not received, the instant demand is received, the daily quota/charge-discharge plan is updated, and the current quota/charge-discharge plan is obtained.
Further, a policy of a return value is selected according to an operation target, wherein the operation target comprises at least one of meeting customer requirements and optimizing economy, and the policy of the return value is specifically as follows:
when the operation target is to meet the requirement of the customer, the return value is a function related to the satisfaction degree of the customer or related to the order taking quantity; when the operation target is the optimal economy, the return value is a function related to the electric charge or the income; when the operation target is the customer demand and the economy is optimal, the return value is the result of weighted summation of the return value when the customer demand is satisfied and the return value when the economy is optimal.
Further, the model of the total return experience value is as follows:
Q(S，A)＝(1-α)×Q(S，A)+α×[R+γ×MAX Q(S′，A′)]
wherein Q (S, A) is the total reported experience value obtained by a series of changes of obtaining a reported value R and transferring to the state S' after taking action A in the state S; s is a state; a is action; alpha is the learning rate; gamma is the discount factor. The choice of α and γ affects the model's preference in empirical values and heuristics.
From the model of the return experience values, it is known that the return experience value of the previous state to the subsequent state is related to the experience value of the subsequent state, and the experience value of the subsequent state to the next subsequent state is also related to the experience value of the next subsequent state, so the algorithm is essentially an iterative algorithm, i.e. the final result is accumulated back from the final result of the loop.
In a preferred embodiment of the present invention, the model of the total return experience value is established by an iterative method, and the specific process is as follows:
(1) Zeroing the total return experience value or giving a random value to carry out first training;
(2) Randomly selecting a strategy of a return value and initializing a model of the total return experience value, wherein the initialization comprises setting the return value and the initial state of the action;
(3) Expanding a plurality of steps backwards from the initial state, and selecting action sequences which possibly occur in the plurality of steps and corresponding state sequences, wherein each action sequence corresponds to a total return experience value;
(4) Comparing and selecting a state sequence with the maximum total return experience value;
(5) The state sequence with the maximum total return experience value is transferred from the initial state to the next state and the total return experience value transferred at the time is updated;
(6) Randomly giving a model of the total return experience value to trigger events, repeating the operation steps (3) - (6), and replacing the initial states in the steps (3) and (5) with the current state when repeating the operation, wherein the trigger events comprise at least one of the following: new order, start/end of charge-discharge action, and battery failure;
(7) Repeating the steps (2) - (6), and replacing the initial state in the steps (3) and (5) with the current state when repeating the operation until all the total reported experience values are converged.
Further, the state is an array vector of a set 1 x M:
S＝[B 1 ,B 2 ,…,B m ]；
wherein S is a state; m is the number of the battery packs connected with the bus; b (B) m A state quantity which is the m-th group battery string;
B m ∈{C m ，ST m }, wherein C m Representing the remaining capacity of the m-th battery pack, ST m Representing the mth group of battery packs as non-powered states, transitions between the state quantities being driven by at least one of the following events: order, start/end of charge-discharge action, and battery failure.
Further, the C m Is the first phase is 0, the tolerance is
The number of items is->
Is equal to the arithmetic progression of:
wherein C is total,m The total rated capacity of the battery pack of the m-th string; n is a positive number that can be divided by 100. The accuracy of the modeling described above depends on the value of n, and if n=10, the interval representing the state of charge is chosen to be a 10% interval of charge, then each string of battery packs will have 13 state quantities (including 2 non-powered modes). If n=5, the interval representing the state of charge is selected to be a 5% interval of charge, then each battery string will have 23 state amounts (including 2 non-power modes). As n becomes smaller, the number of elements of the state quantity set becomes multiplied, which may lead to an excessively large calculation of the optimization problem without solution in extreme cases. When M.ltoreq.10 groups, the value of n is preferably 1<n is less than or equal to 5; when M > 10 groups, n is preferably 2.ltoreq.n.ltoreq.10.
Further, ST m ≤0，ST m e.I, I is an integer. Wherein ST is m The values of (a) can be given various practical meanings (non-power-on state), such as ST m = -1 represents that the battery is in charge state, ST m = -2 indicates that the battery is in maintenance down-line state, and so on.
In a preferred embodiment, assuming that a total of M groups of battery strings are connected to the bus bar, the rated capacity of each group of battery is C, and the state quantity-1 representsThe battery is in a charged state and the state quantity-2 indicates that the battery is in a maintenance offline state. One possible state S i = (C, 0.5C,0.3C, …, -1, -1, -2, -2), this state represents the battery string B 1 In a fully charged state, battery string B 2 Remaining 50% of the charge, battery string B 3 Remaining 30% of the charge, battery string B M-3 And B M-2 In a charged state, battery string B M-1 And B M In a maintenance offline state.
The technical scheme of the invention describes a state sequence through a Markov chain, and transitions between states are described through total return experience values. At each step of the Markov chain, the current state may also be maintained by selecting to change from one state to another based on the magnitude of the total reported empirical value.
State sequence L of the invention s Is an ordered combination of a series of states, i.e. L s ＝[S L1 ,S L2 ,…]。L s Each element in (a) is M in common in the model (100/n)+3 One of the states. The state sequence represents a time-series representation of the operational state of each battery pack over a period of operational time (typically a natural day).
The first element of the state sequence is the initial state S init Expressed is a sequence L s Is a starting point of (c). The choice of the starting state of the invention depends on several aspects: 1) Maintenance planning for each battery pack; 2) The degradation of each battery pack; 3) The amount of agreement for the medium and long term user or the reservation client is charged to the charge and discharge plan at a later date and the selected state is set as the initial state.
The invention also provides a multi-user shared energy storage power supply system, which adopts the sharing method and comprises an energy storage module, a control module, a server and a metering module, wherein the energy storage module is connected in parallel with an energy storage bus and is connected with a user through the metering module to supply power to the user, the control module controls the power supply process of the energy storage module, and the server controls the whole power supply system in real time.
Compared with the prior art, the multi-user sharing method for the energy storage product can realize multi-user sharing of the energy storage product.
Drawings
Fig. 1 is a flow chart of a multi-user sharing method of an energy storage product according to embodiment 1 of the present invention;
FIG. 2 is a state model diagram of an energy storage system according to embodiment 1 of the present invention;
fig. 3 is a schematic structural diagram of a multi-user shared energy storage power supply system according to embodiment 2 of the present invention.
Detailed Description
Example 1
As shown in fig. 1, the multi-user sharing method for the energy storage product according to the embodiment of the invention includes the following steps:
s1: collecting the electricity demand of a user in the past and the current energy storage quota state;
s2: forming a daily quota/charging and discharging plan through an energy storage system state model established based on a Markov chain according to the electricity demand and the current energy storage quota state;
s3: judging whether the stored energy is remained or not according to the daily quota/charging and discharging plan, if yes, performing the step S4, and if not, directly performing the step S5;
s4: collecting the instant demands of users and determining whether to update the daily quota/charge-discharge plan;
s5: when the electricity price enters the valley, the electricity demand is not received, the charging and discharging program which is still performed is continuously performed until the charging and discharging program is finished, and the energy storage power supply system enters the settlement, charging, operation and maintenance links.
As shown in fig. 2, the state model of the energy storage system according to the embodiment of the present invention is built based on a markov chain, which describes a state sequence in which transitions between states (indicated by the letter S in the figure) are described by total return experience values. At each step of the Markov chain, the selection is made to change from one state to another, depending on the magnitude of the total return experience value (represented by the letter Q), and the current state may also be maintained. Selecting slave state S, e.g. according to the size of Q j Change to state S m Or can maintain the current state S j 。
Wherein, the Q value is calculated according to a model of the total return experience value:
Q(S，A)＝(1-α)×Q(S，A)+α×[R+γ×MAX Q(S′，A′)]；
wherein Q (S, A) is the total reported experience value obtained by a series of changes of obtaining a reported value R and transferring to the state S' after taking action A in the state S; s is a state; a is action; alpha is the learning rate; gamma is the discount factor. The choice of α and γ affects the model's preference in empirical values and heuristics.
The model of the total return experience value is established by an iteration method, and the specific process is as follows:
(1) Zeroing the total return experience value or giving a random value to carry out first training;
(2) Randomly selecting a strategy of a return value and initializing a model of the total return experience value, wherein the initialization comprises setting the return value and the initial state of the action;
(3) Expanding a plurality of steps backwards from the initial state, and selecting action sequences which possibly occur in the plurality of steps and corresponding state sequences, wherein each action sequence corresponds to a total return experience value;
(4) Comparing and selecting a state sequence with the maximum total return experience value;
(5) Transferring from the initial state to the next state and updating the total return experience value transferred at this time in the state sequence with the maximum total return experience value;
(6) Randomly giving a model of the total return experience value to trigger events, repeating the operation steps (3) - (6), and replacing the initial states in the steps (3) and (5) with the current state when repeating the operation, wherein the trigger events comprise at least one of the following: new order, start/end of charge-discharge action, and battery failure;
(7) Repeating the steps (2) - (6), and replacing the initial state in the steps (3) and (5) with the current state when repeating the operation until all the total reported experience values are converged.
In step S2 of the embodiment of the present invention, the daily quota/filling plan includes a daily planned state sequence, a daily planned action sequence, a daily planned total return and an optimal path, wherein the daily planned state sequence is an ordered combination of a series of states, the daily planned action sequence is an ordered combination of a series of actions, the actions correspond to a return value, the daily planned action sequence corresponds to a daily planned total return value, a total return experience value exists in a path for transferring an action from one state to another state, and the path corresponding to the maximum total return experience value is the optimal path.
In step S4, the method responds to the instant demand of the customer according to the total return experience value and decides whether to update the daily quota/charging/discharging schedule, which is specifically as follows:
s41: respectively calculating the total return experience value of the current quota/charging and discharging plan formed after the instant demand is accepted and the total return experience value of the current quota/charging and discharging plan formed after the instant demand is not accepted;
s42: comparing the magnitudes of the two total return experience values in S41:
if the total return experience value of the current quota/charge-discharge plan formed after the instant demand is received is smaller than the total return experience value of the current quota/charge-discharge plan formed after the instant demand is not received, the instant demand is not received;
if the total return experience value of the current quota/charge-discharge plan formed after the instant demand is received is greater than or equal to the total return experience value of the current quota/charge-discharge plan formed after the instant demand is not received, the instant demand is received, the daily quota/charge-discharge plan is updated, and the current quota/charge-discharge plan is obtained.
The multi-user sharing method of the energy storage product of the invention is further described below with reference to specific examples.
It is assumed that there are a parallel battery 1, a battery 2 and a battery 3 in the system. The rated capacity of the battery pack 1 is C1, the rated capacity of the battery pack 2 is C2, and the rated capacity of the battery pack 3 is C3.
At 1 day and night of a month, the system collects the power consumption demands and the current energy storage quota state of two long-term users AA and BB, and the energy storage system state model obtains an action sequence and a state sequence which can meet orders of the two users and a total return experience value of a path for one state to take one action to transfer to the other state.
By the day of the month 2, if two business orders have occupied all time periods for all three batteries, then the current day order is not accepted. Otherwise, when the new order CC is obtained, calculating the total return experience value of the current day quota/charge-discharge plan formed after the new order CC is accepted and the total return experience value of the current day quota/charge-discharge plan formed after the new order CC is not accepted respectively, and if the total return experience value of the current day quota/charge-discharge plan formed after the new order CC is accepted is smaller than the total return experience value of the current day quota/charge-discharge plan formed after the new order CC is not accepted, not accepting the new order CC;
if the total return experience value of the current quota/charge-discharge plan formed after receiving the new order CC is greater than or equal to the total return experience value of the current quota/charge-discharge plan formed after not receiving the new order CC, receiving the new order CC and updating the current quota/charge-discharge plan, and updating the action sequence, the state sequence and the return value of the response to obtain the current quota/charge-discharge plan.
If yes, updating the response behavior sequence, the state sequence and the total return experience value of the path for taking one action to transfer to the other state by one state, and obtaining the current quota/charging and discharging plan. And settling all the accepted orders until the settlement time period.
Example 2
As shown in fig. 3, the multi-user shared energy storage power supply system of the embodiment of the invention adopts the multi-user shared method of the energy storage product of the embodiment 2 to operate, and mainly comprises an energy storage module, a control module, a server and a metering module, wherein the energy storage module is connected in parallel with an energy storage bus and connected with a user through the metering module to supply power to the user, the control module controls the power supply process of the energy storage module, and the server controls the whole power supply system in real time. The energy storage module of the embodiment of the invention is a plurality of groups of battery strings.
While specific embodiments of the invention have been described in detail, it will be appreciated that those skilled in the art, upon attaining an understanding of the principles of the invention, may readily make numerous modifications and variations to the present invention. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (7)
1. The multi-user sharing method for the energy storage product is characterized by comprising the following steps of:
s1: collecting the electricity demand of a user in the past and the current energy storage quota state;
s2: forming a daily quota/charging and discharging plan through an energy storage system state model established based on a Markov chain according to the electricity demand and the current energy storage quota state; the daily quota/charging and discharging plan comprises a daily plan state sequence, a daily plan action sequence, a daily plan total return and an optimal path, wherein the daily plan state sequence is an ordered combination of a series of states, the daily plan action sequence is an ordered combination of a series of actions, the actions correspond to a return value, the daily plan action sequence corresponds to the daily plan total return value, a total return experience value exists in a path for transferring one action from one state to the other, and the path corresponding to the maximum total return experience value is the optimal path;
s3: judging whether the stored energy is remained or not according to the daily quota/charging and discharging plan, if yes, performing the step S4, and if not, directly performing the step S5;
s4: collecting the instant demand of a user, responding to the instant demand according to the total return experience value, and determining whether to update a daily quota/charging and discharging plan; the step S4 specifically comprises the following steps: s41: respectively calculating the total return experience value of the current quota/charging and discharging plan formed after the instant demand is accepted and the total return experience value of the current quota/charging and discharging plan formed after the instant demand is not accepted; s42: comparing the magnitudes of the two total return experience values in S41: if the total return experience value of the current quota/charge-discharge plan formed after the instant demand is received is smaller than the total return experience value of the current quota/charge-discharge plan formed after the instant demand is not received, the instant demand is not received; if the total return experience value of the current quota/charge-discharge plan formed after the instant demand is received is greater than or equal to the total return experience value of the current quota/charge-discharge plan formed after the instant demand is not received, receiving the instant demand and updating the daily quota/charge-discharge plan to obtain the current quota/charge-discharge plan;
the model of the total return experience value is established by an iteration method, and the specific process is as follows:
(1) Zeroing the total return experience value or giving a random value to carry out first training;
(2) Randomly selecting a strategy of a return value and initializing a model of the total return experience value, wherein the initialization comprises setting the return value and the initial state of the action;
(3) Expanding a plurality of steps backwards from the initial state, and selecting action sequences which possibly occur in the plurality of steps and corresponding state sequences, wherein each action sequence corresponds to a total return experience value;
(4) Comparing and selecting a state sequence with the maximum total return experience value;
(5) Transferring from the initial state to the next state and updating the total return experience value transferred at this time in the state sequence with the maximum total return experience value;
(6) Randomly giving a model of the total return experience value to trigger events, repeating the operation steps (3) - (6), and replacing the initial states in the steps (3) and (5) with the current state when repeating the operation, wherein the trigger events comprise at least one of the following: new order, start/end of charge-discharge action, and battery failure;
(7) Repeating the steps (2) - (6), and replacing the initial states in the steps (3) and (5) with the current state when repeating the operation until all the total return experience values are converged;
s5: when the electricity price enters the valley, the electricity demand is not received, the charging and discharging program which is still performed is continuously performed until the charging and discharging program is finished, and the energy storage power supply system enters the settlement, charging, operation and maintenance links.
2. The energy storage product multiuser sharing method according to claim 1, wherein the model of the total return experience value is as follows:
Q(S，A)＝(1-α)×Q(S，A)+α×[R+γ×MAX Q(S′，A′)]
wherein Q (S, A) is the total reported experience value obtained by a series of changes of obtaining a reported value R and transferring to the state S' after taking action A in the state S; s is a preamble state; a is the action corresponding to the preamble state S; s ' is the subsequent state, A ' is the action corresponding to the subsequent state S '; alpha is the learning rate; gamma is the discount factor.
3. The energy storage product multi-user sharing method of claim 2, wherein the policy of the return value is selected according to an operation objective, wherein the operation objective comprises at least one of meeting customer requirements and optimizing economy, and the policy of the return value is specifically as follows:
when the operation target is to meet the requirement of the customer, the return value is a function related to the satisfaction degree of the customer or related to the order taking quantity; when the operation target is the optimal economy, the return value is a function related to the electric charge or the income; when the operation target is the customer demand and the economy is optimal, the return value is the result of weighted summation of the return value when the customer demand is satisfied and the return value when the economy is optimal.
4. The method of claim 2, wherein the state is an array vector of 1 x M:
S＝[B 1 ,B 2 ,…,B m ]；
wherein S is a state; m is the number of the battery packs connected with the bus; b (B) m A state quantity which is the m-th group battery string;
B m ∈{C m ，ST m }, wherein C m Representing the remaining capacity of the m-th battery pack, ST m Representing the mth group of battery packs as non-powered states, transitions between the state quantities being driven by at least one of the following events: order, start/end of charge-discharge action, and battery failure.
5. Energy storage product multiuser sharing as defined in claim 4The method is characterized in that the C m Is characterized by the first term being 0 and the tolerance being (n/100) gammaC total,m Arithmetic progression with 1+ (100/n) terms:
wherein C is total,m The total rated capacity of the battery pack of the m-th string; n is a positive number that can be divided by 100.
6. The energy storage product multiuser sharing method according to claim 1, wherein the actions include at least one of accepting an order, rejecting an order, charging off-line, full-power on-line, and operation maintenance off-line.
7. The multi-user shared energy storage power supply system is characterized by comprising an energy storage module, a control module, a server and a metering module, wherein the energy storage module is connected in parallel with an energy storage bus and is connected with a user through the metering module to supply power to the user, the control module controls the power supply process of the energy storage module, and the server controls the whole power supply system in real time.
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