CN113077166A - Community energy storage scheduling method based on Markov decision process - Google Patents

Community energy storage scheduling method based on Markov decision process Download PDF

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CN113077166A
CN113077166A CN202110411473.0A CN202110411473A CN113077166A CN 113077166 A CN113077166 A CN 113077166A CN 202110411473 A CN202110411473 A CN 202110411473A CN 113077166 A CN113077166 A CN 113077166A
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孙宏斌
孙勇
郭庆来
李宝聚
王彬
李振元
邓莉荣
吕项羽
潘昭光
李德鑫
张璇
王佳蕊
阳天舒
张懿夫
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State Grid Jilin Electric Power Corp
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Abstract

The invention belongs to the technical field of power grid dispatching, and particularly relates to a community energy storage dispatching method based on a Markov decision process. The method comprises the steps of firstly establishing a community energy storage scheduling method based on a Markov decision process, rewriting a community energy storage management model into a Bellman equation, and solving the Bellman equation equivalent to the community energy storage scheduling model by using a threshold method of an optimal strategy to obtain a scheduling result of the community energy storage equipment. The community energy storage scheduling method based on the Markov decision process establishes an energy storage management model taking the energy storage as a price maker under the condition of considering market uncertainties such as renewable energy power generation, price and demand. The situation that large-scale energy storage participates in the market to influence the price, namely, the price is used as a price maker is considered, so that the situation is more practical, and the community based on production consumers can be better guided to obtain benefits by utilizing the energy storage.

Description

Community energy storage scheduling method based on Markov decision process
Technical Field
The invention belongs to the technical field of power grid dispatching, and particularly relates to a community energy storage dispatching method based on a Markov decision process.
Background
In recent years, community energy storage has become one of advanced smart grid technologies, and brings benefits to the power system in terms of reliability, quality, economy and controllability. The community energy storage system can be a single energy storage system or a group of energy storage systems with scattered geographic positions, but can be coordinated in the form of a virtual power plant. The community energy storage system is located close to the consumer and the distributed energy sources, and can be used as an inventory to relieve the uncertainty of the random distributed energy sources and promote the further consumption of the distributed energy sources.
The existing community energy storage management method generally determines the operation, scale and value of energy storage based on an inventory theory. In the face of market uncertainty such as renewable energy generation, price and demand, existing practices generally assume that stored energy is the price recipient and has no impact on market price. And the scale of the energy storage and market admission policies is continuously enlarged to encourage the energy storage to participate in the market and to participate in price making, so that the price maker is formed. It is assumed that the storage of energy is the price acceptor is no longer applicable. There is a complex interaction of energy storage as a price maker with the electricity market: on the one hand, energy storage utilizes market price differences for energy arbitrage. On the other hand, it eases price variation by reducing peak loads and increasing off-peak loads, thereby reducing arbitrage opportunities. In order to ensure the profit of the energy storage of the price maker owned by the community, the invention solves the problem of community energy storage management from the following aspects: 1) interaction between energy storage actions (charging, discharging) and uncertain market environments; 2) energy storage decision structures affecting the electricity market; 3) when a community is composed of different market participants (e.g., consumers, producers, or producer consumers), there is an advantageous method of operation for the community.
The existing method for setting up community energy storage management usually determines the operation, scale and value of energy storage based on inventory theory, and especially under the condition of facing market uncertainty such as renewable energy power generation, price and demand, the existing method usually assumes that the energy storage is a price acceptor and has no influence on market price. With the continuous enlargement of the scale of the policies of energy storage and market admission, large-scale energy storage participates in the market and price making, and becomes a price maker. Assuming that energy storage is the price taker, it reduces the revenue of community energy storage management and is no longer applicable.
a. The existing method for formulating the community energy storage management is complex in actual use, large in calculation amount, long in calculation time and not practical.
b. Currently, the commonly used stochastic dynamic programming uses a finite layer model, which is plagued by "cursing" dimensions. They require highly discretized states in the computation to obtain the optimal solution using reverse inference. It is difficult to obtain an optimal solution because the complexity of reverse inference grows exponentially as the state size increases.
Disclosure of Invention
The invention aims to provide a community energy storage scheduling method based on a Markov decision process, which is established by taking community energy storage as a price maker to perform community energy storage scheduling in a real-time market under the condition of considering uncertainty such as renewable energy power generation, price and demand.
The invention provides a community energy storage scheduling method based on a Markov decision process.
The invention provides a community energy storage scheduling method based on a Markov decision process, which has the advantages that:
1. the community energy storage scheduling method based on the Markov decision process establishes an energy storage management model taking the energy storage as a price maker under the condition of considering market uncertainties such as renewable energy power generation, price and demand. The situation that large-scale energy storage participates in the market to influence the price, namely, the price is used as a price maker is considered, so that the situation is more practical, and the community based on production consumers can be better guided to obtain benefits by utilizing the energy storage.
2. The community energy storage scheduling method simultaneously optimizes energy arbitrage and social total income, and can obtain more economic benefits compared with the prior art which only optimizes energy arbitrage.
3. Compared with the conventional random dynamic programming algorithm, the community energy storage scheduling method has the advantages that the related scheduling model and the threshold solving algorithm of the optimal strategy avoid the discretization state, the complexity cannot increase exponentially along with the increase of the state size, and the optimal solution can be obtained quickly.
Detailed Description
The invention provides a community energy storage scheduling method based on a Markov decision process.
The community energy storage scheduling method based on the Markov decision process specifically comprises the following steps:
(1) establishing a community energy storage scheduling model as follows:
Figure BDA0003024286870000031
satisfies the following conditions: xT+1＝X1 (1b)
0≤ut≤C-Xt (1c)
0≤wt≤Xt (1d)
Xt+1＝Xt+ut-wt (1e)
Wherein the content of the first and second substances,
Figure BDA0003024286870000032
for calculation of the expected value, rTEnergy storage Total revenue function, r, for the Community at scheduling time TtThe method comprises the steps that a community energy storage total income function of a scheduling time T comprises arbitrage profits and net social total income of community energy storage equipment in a charging state and a discharging state, X is an energy storage level, and T is the number of scheduling time periods in a scheduling period;
the constraint condition (1b) represents that the community energy storage is recovered to the initial energy storage level of the scheduling period T at the end of the last phase of the current scheduling period T, utRepresenting the charging energy, w, of the scheduled time t of the community energy storage devicetThe discharge energy of the dispatching time t of the community energy storage equipment is represented, C represents the maximum energy value which can be stored by the community energy storage equipment, C is obtained from a factory nameplate of the community energy storage equipment, and utAnd wtRespectively limited by constraint conditions (1c) and (1d), wherein the constraint condition (1e) establishes the energy storage level X of the community energy storage equipment at the t +1 scheduling momentt+1And Xt、utAnd wtThe relationship of (1);
the above-mentioned total profit function r of community energy storagetThe expression of (a) is:
rt(ut,wt,REt)＝gd(wt,REt)-gc(ut,REt)(2a)
Figure BDA0003024286870000033
Figure BDA0003024286870000034
wherein REtRepresenting the total power of renewable energy sources in the community at the scheduled time t, rt(ut,wt,REt) Indicating that the renewable energy source is charging energy u at the scheduling time ttDischarge energy of wtTotal power of renewable energy source is REtA time community energy storage total income function; subscripts c and d represent the charge and discharge states, g, respectivelyd(wt,REt) Shows the profit of community energy storage discharge, gc(ut,REt) Indicating the cost of the community's stored energy discharge, ptIndicating the clearing price, η, of the market without community energy storage device participationcEfficiency of charging, η, for community energy storage devicesdDischarge efficiency, η, for community energy storage devicesc、ηd∈(0,1)，pc,tAnd pd,tRespectively representing the market electricity prices a of the community energy storage equipment at the scheduling time t during charging and dischargingtFor scheduling the maximum load of the community at time t, b for the price elasticity of the community load, ballFor flexibility in overall price, ballObtained by fitting a curve according to historical data of community load capacity and market electricity price,
Figure BDA0003024286870000041
bureau around predicted price for total power supply curveThe slope of partial linearization is obtained according to historical data of market electricity price; (2b) w in (2c)c(ut,REt)、Wd(wt,REt) Respectively show under community energy storage equipment charged state and community energy storage equipment discharged state, the influence of community energy storage to the net income of community, the expression is:
Figure BDA0003024286870000042
Figure BDA0003024286870000043
wherein D ist(p) represents a load function related to the market price p, ptRepresenting the electricity price clearing price, p, of the market without community energy storage devices participating0,tRepresenting the predicted initial market price, p, without community renewable energy and energy storage device participationc,tAnd pd,tThe market electricity price of the community energy storage equipment during charging and discharging is respectively represented, and the expressions are respectively as follows:
Figure BDA0003024286870000044
Figure BDA0003024286870000045
Figure BDA0003024286870000046
(2) and (2) rewriting the community energy storage management model in the step (1) into a Bellman equation, so that the community energy storage scheduling model is equivalent to:
Figure BDA0003024286870000047
Figure BDA0003024286870000048
wherein, the renewable energy resource REtIs a Markov process, Vt(Xt,REt) Indicating a community energy storage level X for a limited time range starting from the scheduling time T to the end of the scheduling period TtWhen the charging energy is utDischarge energy of wtTotal power of renewable energy source is REtThe best expected revenue obtained by the community,
Figure BDA0003024286870000049
which is indicative of the calculation of the expected value,
in the above community energy storage scheduling model, VT(XT,RET) Is the ultimate profit obtained by the community, if X1＞XTCharging the community energy storage energy to X in the scheduling period T1I.e. uT＝X1－XT，wT0, if X1≤XTDischarge of community stored energy to X1I.e. wT＝XT－X1,uT＝0；
(3) Solving the community energy storage scheduling model in the step (2) by using a threshold method of an optimal strategy to obtain a scheduling result of the community energy storage equipment, wherein the scheduling result is as follows: the optimal charging energy and the optimal discharging energy of community energy storage under different scheduling time, total power of different renewable energy sources and different community energy storage levels comprise the following steps:
(3-1) during initialization, setting community energy storage level X1Is a constant value, X1The value range of (a) is 0 to the maximum energy value C which can be stored by the community energy storage equipment, and the value of the invention is C/2 in one embodiment, namely, half of the maximum energy value which can be stored by the community energy storage equipment;
(3-2) setting discrete state number N of the electricity storage state of the community energy storage equipmentSOCAnd number of discrete states N of renewable energyRES，NSOCAnd NRESThe scheduling precision is set by a user according to the scheduling precision, the larger the set value is, the more precise the scheduling precision is, but the calculated amount is also greatly increased, and the value is 20 in one embodiment of the invention;
(3-3) setting a scheduling time T as T;
(3-4) setting the community energy storage level Xt＝1
(3-5) setting an initial value RE of the total power of the renewable energy sourcest＝1
(3-6) judging T, if T is equal to T, performing the step (3-7), and if T is smaller than T, performing the step (3-8);
(3-7) energy storage level X to community1Making a judgment if X1Greater than XtThen use the formula
Figure BDA0003024286870000051
Calculated to obtain the RE in renewable energytMarginal profit value h from t scheduling timet(Xt,REt) Proceeding with step (3-11), if X1Less than or equal to XtThen use the formula
Figure BDA0003024286870000052
Calculated to obtain the RE in renewable energytMarginal profit value h from t scheduling timet(Xt,REt) Carrying out the step (3-11);
(3-8) Total Power RE of renewable energy sources at the time of t schedulingtOn renewable energy REtMarginal profit value h from t scheduling timet(Xt,REt) Integrating to obtain an expected marginal profit value h from the scheduling time tt(Xt)；
(3-9) respectively calculating the total power RE of the renewable energy sources at the t scheduling time by using the following formulatThe energy storage level of the community is XtOptimal charging energy of t-scheduling time of community energy storage equipment
Figure BDA0003024286870000053
And at t schedulingTotal power of renewable energy at all times REtThe energy storage level of the community is XtThe optimal discharge energy of the community energy storage equipment at the t scheduling moment is
Figure BDA0003024286870000054
Figure BDA0003024286870000061
(3-10) calculating to obtain the RE in the renewable energy source by using the following formulatMarginal profit value h from t scheduling timet(Xt,REt)：
Figure BDA0003024286870000062
(3-11) reacting REt＝REt+1, repeating steps (3-6) - (3-11) until the renewable energy resource REtAnd the number of discrete states N of the renewable energy source set in the step (3-2)RESEqual;
(3-12) reacting Xt＝Xt+1, repeating steps (3-6) - (3-12) until the community energy storage level XtDiscrete state number N of the power storage state set in the step (3-2)SOCEqual;
(3-13) repeating steps (3-6) to (3-13) with t-1 until t-1;
(3-14) outputting a scheduling result of the community energy storage equipment, namely, the total power RE of the renewable energy sources at the scheduling time T-1 to T-Tt1 to REt＝NRESThe energy storage level of the community is Xt1 to Xt＝NSOCOptimal charging energy corresponding to community energy storage equipment
Figure BDA0003024286870000063
And the total power of the renewable energy sources is RE under the scheduling time of T1 to T Tt1 to REt＝NRESThe energy storage level of the community is Xt1 to Xt＝NSOCAnd then, the community energy storage equipment corresponds to the optimal discharge energy.

Claims (2)

1. A community energy storage scheduling method based on a Markov decision process is characterized by comprising the following steps: firstly, a community energy storage scheduling method based on a Markov decision process is established, a community energy storage management model is rewritten into a Bellman equation, the Bellman equation equivalent to the community energy storage scheduling model is solved by using a threshold method of an optimal strategy, and a scheduling result of the community energy storage equipment is obtained.
2. The markov decision process based community energy storage scheduling method of claim 1, wherein the method comprises the steps of:
(1) establishing a community energy storage scheduling model as follows:
Figure FDA0003024286860000011
satisfies the following conditions: xT+1＝X1 (1b)
0≤ut≤C-Xt (1c)
0≤wt≤Xt (1d)
Xt+1＝Xt+ut-wt (1e)
Wherein the content of the first and second substances,
Figure FDA0003024286860000012
for calculation of the expected value, rTEnergy storage Total revenue function, r, for the Community at scheduling time TtThe method comprises the steps that a community energy storage total income function of a scheduling time T comprises arbitrage profits and net social total income of community energy storage equipment in a charging state and a discharging state, X is an energy storage level, and T is the number of scheduling time periods in a scheduling period;
the constraint condition (1b) represents that the community energy storage is recovered to the initial energy storage level of the scheduling period T at the end of the last phase of the current scheduling period T，utRepresenting the charging energy, w, of the scheduled time t of the community energy storage devicetThe discharge energy of the dispatching time t of the community energy storage equipment is represented, C represents the maximum energy value which can be stored by the community energy storage equipment, C is obtained from a factory nameplate of the community energy storage equipment, and utAnd wtRespectively limited by constraint conditions (1c) and (1d), wherein the constraint condition (1e) establishes the energy storage level X of the community energy storage equipment at the t +1 scheduling momentt+1And Xt、utAnd wtThe relationship of (1);
the above-mentioned total profit function r of community energy storagetThe expression of (a) is:
rt(ut,wt,REt)＝gd(wt,REt)-gc(ut,REt) (2a)
Figure FDA0003024286860000021
Figure FDA0003024286860000022
wherein REtRepresenting the total power of renewable energy sources in the community at the scheduled time t, rt(ut,wt,REt) Indicating that the renewable energy source is charging energy u at the scheduling time ttDischarge energy of wtTotal power of renewable energy source is REtA time community energy storage total income function; subscripts c and d represent the charge and discharge states, g, respectivelyd(wt,REt) Shows the profit of community energy storage discharge, gc(ut,REt) Indicating the cost of the community's stored energy discharge, ptIndicating the clearing price, η, of the market without community energy storage device participationcEfficiency of charging, η, for community energy storage devicesdDischarge efficiency, η, for community energy storage devicesc、ηd∈(0,1)，pc,tAnd pd,tRespectively representing the charging time and the discharging time of the community energy storage equipment at the scheduling time tMarket price of time, atFor scheduling the maximum load of the community at time t, b for the price elasticity of the community load, ballFor flexibility in overall price, ballObtained by fitting a curve according to historical data of community load capacity and market electricity price,
Figure FDA0003024286860000023
obtaining a slope of local linearization around a predicted price for a total power supply curve according to historical data of market electricity prices; (2b) w in (2c)c(ut,REt)、Wd(wt,REt) Respectively show under community energy storage equipment charged state and community energy storage equipment discharged state, the influence of community energy storage to the net income of community, the expression is:
Figure FDA0003024286860000024
Figure FDA0003024286860000025
wherein D ist(p) represents a load function related to the market price p, ptRepresenting the electricity price clearing price, p, of the market without community energy storage devices participating0,tRepresenting the predicted initial market price, p, without community renewable energy and energy storage device participationc,tAnd pd,tThe market electricity price of the community energy storage equipment during charging and discharging is respectively represented, and the expressions are respectively as follows:
Figure FDA0003024286860000026
Figure FDA0003024286860000031
Figure FDA0003024286860000032
(2) and (2) rewriting the community energy storage management model in the step (1) into a Bellman equation, so that the community energy storage scheduling model is equivalent to:
Figure FDA0003024286860000033
Figure FDA0003024286860000034
wherein, the renewable energy resource REtIs a Markov process, Vt(Xt,REt) Indicating a community energy storage level X for a limited time range starting from the scheduling time T to the end of the scheduling period TtWhen the charging energy is utDischarge energy of wtTotal power of renewable energy source is REtThe best expected revenue obtained by the community,
Figure FDA0003024286860000035
representing an expected value calculation;
in the above community energy storage scheduling model, VT(XT,RET) Is the ultimate profit obtained by the community, if X1＞XTCharging the community energy storage energy to X in the scheduling period T1I.e. uT＝X1－XT，wT0, if X1≤XTDischarge of community stored energy to X1I.e. wT＝XT－X1,uT＝0；
(3) Solving the community energy storage scheduling model in the step (2) by using a threshold method of an optimal strategy to obtain a scheduling result of the community energy storage equipment, wherein the method comprises the following steps:
(3-1) during initialization, setting community energy storage level X1Is a constant value, X1The value range of (A) is 0 to the maximum energy value C which can be stored by the community energy storage equipment;
(3-2) setting discrete state number N of the electricity storage state of the community energy storage equipmentSOCAnd number of discrete states N of renewable energyRES，NSOCAnd NRESThe integer is a positive integer and is set by a user according to scheduling precision;
(3-3) setting a scheduling time T as T;
(3-4) setting the community energy storage level Xt＝1
(3-5) setting an initial value RE of the total power of the renewable energy sourcest＝1
(3-6) judging T, if T is equal to T, performing the step (3-7), and if T is smaller than T, performing the step (3-8);
(3-7) energy storage level X to community1Making a judgment if X1Greater than XtThen use the formula
Figure FDA0003024286860000036
Calculated to obtain the RE in renewable energytMarginal profit value h from t scheduling timet(Xt,REt) Proceeding with step (3-11), if X1Less than or equal to XtThen use the formula
Figure FDA0003024286860000037
Calculated to obtain the RE in renewable energytMarginal profit value h from t scheduling timet(Xt,REt) Carrying out the step (3-11);
(3-8) Total Power RE of renewable energy sources at the time of t schedulingtOn renewable energy REtMarginal profit value h from t scheduling timet(Xt,REt) Integrating to obtain an expected marginal profit value h from the scheduling time tt(Xt)；
(3-9) respectively calculating the total power RE of the renewable energy sources at the t scheduling time by using the following formulatThe energy storage level of the community is XtThe maximum of t scheduling moments of the community energy storage equipmentOptimal charging energy
Figure FDA0003024286860000041
And total power of renewable energy at t scheduling time is REtThe energy storage level of the community is XtThe optimal discharge energy of the community energy storage equipment at the t scheduling moment is
Figure FDA0003024286860000042
Figure FDA0003024286860000043
(3-10) calculating to obtain the RE in the renewable energy source by using the following formulatMarginal profit value h from t scheduling timet(Xt,REt)：
Figure FDA0003024286860000044
(3-11) reacting REt＝REt+1, repeating steps (3-6) - (3-11) until the renewable energy resource REtAnd the number of discrete states N of the renewable energy source set in the step (3-2)RESEqual;
(3-12) reacting Xt＝Xt+1, repeating steps (3-6) - (3-12) until the community energy storage level XtDiscrete state number N of the power storage state set in the step (3-2)SOCEqual;
(3-13) repeating steps (3-6) to (3-13) with t-1 until t-1;
(3-14) outputting a scheduling result of the community energy storage equipment, namely, the total power RE of the renewable energy sources at the scheduling time T-1 to T-Tt1 to REt＝NRESThe energy storage level of the community is Xt1 to Xt＝NSOCOptimal charging energy corresponding to community energy storage equipment
Figure FDA0003024286860000045
And the total power of the renewable energy sources is RE under the scheduling time of T1 to T Tt1 to REt＝NRESThe energy storage level of the community is Xt1 to Xt＝NSOCAnd then, the community energy storage equipment corresponds to the optimal discharge energy.
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