CN114372638A - Scheduling method and device for user side energy storage real-time participation demand response - Google Patents
Scheduling method and device for user side energy storage real-time participation demand response Download PDFInfo
- Publication number
- CN114372638A CN114372638A CN202210042290.0A CN202210042290A CN114372638A CN 114372638 A CN114372638 A CN 114372638A CN 202210042290 A CN202210042290 A CN 202210042290A CN 114372638 A CN114372638 A CN 114372638A
- Authority
- CN
- China
- Prior art keywords
- energy storage
- power
- user
- time
- storage battery
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004146 energy storage Methods 0.000 title claims abstract description 82
- 230000004044 response Effects 0.000 title claims abstract description 43
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000005457 optimization Methods 0.000 claims abstract description 53
- 238000005096 rolling process Methods 0.000 claims abstract description 19
- 230000005611 electricity Effects 0.000 claims abstract description 18
- 238000012937 correction Methods 0.000 claims abstract description 16
- 238000003860 storage Methods 0.000 claims description 55
- 238000007599 discharging Methods 0.000 claims description 16
- 238000004364 calculation method Methods 0.000 claims description 14
- 238000012546 transfer Methods 0.000 claims description 5
- 150000001875 compounds Chemical class 0.000 claims description 4
- 238000013500 data storage Methods 0.000 claims description 3
- 230000002349 favourable effect Effects 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 6
- 238000004590 computer program Methods 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 4
- 230000008901 benefit Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000014509 gene expression Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000000750 progressive effect Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 1
- 230000004931 aggregating effect Effects 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/06—Power analysis or power optimisation
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Educational Administration (AREA)
- Computer Hardware Design (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a scheduling method and device for user side energy storage real-time participation in demand response, and belongs to the technical field of demand response scheduling optimization. The invention relates to a scheduling method facing user side energy storage real-time participation demand response, which comprises the steps of constructing a day-ahead optimization scheduling model and an active power output prediction model; optimizing and controlling the user side energy storage and the transferable load through a day-ahead optimization scheduling model, minimizing the sum of the electricity purchasing cost and the energy storage battery loss cost of a user, and outputting a day-ahead scheduling result of the net load power to an active power output prediction model; the active output prediction model takes the day-ahead scheduling result as a real-time optimization scheduling initial value, and calculates the active output of real-time rolling optimization scheduling according to the control variable increment; and finally, the active power output is subjected to feedback correction, the interference of external uncertain input is corrected in time, the multi-stage coordination, the stage-by-stage refinement and the feedback correction are realized, the influence caused by load prediction uncertainty is minimized, and the scheme is detailed and is favorable for popularization and use.
Description
Technical Field
The invention relates to a scheduling method and device for user side energy storage real-time participation in demand response, and belongs to the technical field of demand response scheduling optimization.
Background
The current demand response can realize peak clipping, valley filling and clean energy consumption from the demand side, and is beneficial to relieving the situation of the current shortage of power supply in China.
The current application situation of user side energy storage-demand response is mainly that a mixed integer linear programming model is established by taking the comfort level of household appliances as constraint, and demand side response optimization scheduling is realized based on an energy storage device; the electricity consumption cost is taken as constraint, a convex optimization model is constructed, and the electricity consumption cost of a user is reduced; and establishing a planning model to realize the maximization of the benefit of the user and the like in an economic optimization operation mode under the application scene of considering the energy storage device participating in the power grid interaction.
Further, chinese patent (CN111738621B) discloses a method for participating in demand response by demand-side adjustable resource time-share scale aggregation, which specifically includes: dividing the demand side adjustable resources into small-level adjustable resources, minute-level adjustable resources, second-level adjustable resources and millisecond-level adjustable resources according to the adjustment performance; the adjustable resource aggregator or all parties combine the adjustable resources with different time scales to construct multi-time scale response capability, and correspondingly participate in power demand response with different time scales. The method for aggregating the adjustable resources on the demand side in the time scale to participate in the demand response, which is provided by the invention, considers the adjustment performance of the adjustable resources, and realizes the fine control of the adjustable load and the optimal utilization of the adjustable resources.
However, the model and the common feature of the prior art are that the uncertainty of the load is not taken into account. After reporting the participation demand response and winning the bid, the user must respond according to the bid amount, otherwise, the user faces deviation assessment. However, due to uncertainty of load prediction, most users cannot respond according to the actual bid amount, which causes serious economic loss.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a method for constructing a day-ahead optimization scheduling model and an active power output prediction model; optimizing and controlling the user side energy storage and the transferable load through a day-ahead optimization scheduling model, minimizing the sum of the electricity purchasing cost and the energy storage battery loss cost of a user, and outputting a day-ahead scheduling result of the net load power to an active power output prediction model; the active output prediction model takes the day-ahead scheduling result as a real-time optimization scheduling initial value, and calculates the active output of real-time rolling optimization scheduling according to the control variable increment; and finally, the active power output is subjected to feedback correction, so that the effects caused by multistage coordination, stepwise refinement and feedback correction are realized, the influence caused by load prediction uncertainty is minimized, the scheme is detailed, feasible and convenient to realize, and the scheduling method and the scheduling device facing the user side energy storage and participating in demand response in real time are beneficial to popularization and use.
In order to achieve the purpose, one technical scheme of the invention is as follows:
a scheduling method for user side energy storage real-time participation demand response,
the method comprises the following steps:
firstly, acquiring historical data;
the historical data at least comprises user side energy storage data, transferable load data, user electricity purchasing cost data and energy storage battery loss cost data;
secondly, constructing a day-ahead optimized scheduling model according to the historical data in the first step;
the day-ahead optimization scheduling model optimally controls the energy storage and the transferable load at the user side, so that the sum of the electricity purchasing cost and the energy storage battery loss cost of a user is minimum, and a day-ahead scheduling result of the net load power is output;
thirdly, constructing an active power output prediction model according to the charge and discharge power of the storage battery and the transferable load increment of the user,
the active output prediction model takes the day-ahead scheduling result in the second step as a real-time optimization scheduling initial value, and calculates the active output of real-time rolling optimization scheduling according to the control variable increment;
the control variable increment comprises a storage battery charging power increment, a storage battery discharging power increment and a user transferable load increment;
and fourthly, performing feedback correction on the active power output in the third step to realize multi-time scale participating closed-loop control.
According to the method, through continuous exploration and test, a day-ahead optimization scheduling model and an active power output prediction model are constructed; optimizing and controlling the user side energy storage and the transferable load through a day-ahead optimization scheduling model, minimizing the sum of the electricity purchasing cost and the energy storage battery loss cost of a user, and outputting a day-ahead scheduling result of the net load power to an active power output prediction model; the active output prediction model takes the day-ahead scheduling result as a real-time optimization scheduling initial value, and calculates the active output of real-time rolling optimization scheduling according to the control variable increment; and finally, performing feedback correction on the active power output to realize closed-loop control.
Furthermore, the optimal scheduling method based on the model predictive control solves the problem of scheduling power fluctuation when the energy storage at the user side participates in demand response by utilizing the idea of rolling optimization of the model predictive control, and simultaneously introduces feedback correction to correct the interference of external uncertain input in time, thereby realizing multi-level coordination, progressive refinement, feedback correction and minimized influence caused by load prediction uncertainty.
Furthermore, the scheduling method of the invention can make the actual amount of the user meet the requirement of the user on the scalar, so that the user can respond according to the actual scalar, and the requirement of the demand response is met.
As a preferable technical measure:
in the second step, the calculation formula of the objective function of the day-ahead optimization scheduling model is as follows:
in the formula, Pact(t)、PDR(t)、Pbat(t)、Pshift(t) respectively representing user net load power, user participation demand response power, energy storage battery charging and discharging power and transferable load power in a t period; t represents the total scheduling time period number; ρ (t) and ρDRRespectively representing the time-of-use electricity price and the demand response income.
As a preferable technical measure:
the unit charging and discharging loss cost calculation method of the energy storage battery comprises the following steps:
in the formula, CwA cost coefficient for the capacity of the energy storage battery; n is a radical ofaThe number of charge and discharge cycles for the energy storage life cycle; ddodThe energy storage charging and discharging depth.
As a preferable technical measure:
in order to ensure the electricity utilization experience of users and the safe work of storage batteries, the day-ahead scheduling plan needs to meet certain constraint conditions.
The constraint conditions of the day-ahead optimization scheduling model comprise energy storage equipment charge-discharge depth constraint, energy storage battery SOC period balance constraint, power balance constraint and load transfer constraint.
As a preferable technical measure:
the constraint formula of the energy storage device charge-discharge depth constraint is as follows:
wherein S (t +1) represents the SOC value of the battery during the period t; σ represents the self-discharge rate of the storage battery; v represents the battery capacity; etac、ηdRespectively representing the charge and discharge efficiency of the storage battery; pc(t)、Pd(t) represents the charging and discharging power of the storage battery in the period of t respectively; Δ t represents the time length of one scheduling period; smax、SminRespectively representing the upper and lower limits of the battery SOC.
The constraint formula of the SOC period balance constraint of the energy storage battery is as follows:
S(0)＝S(T) (4)
the constraint formula of the charge and discharge power constraint of the energy storage battery is as follows:
in the formula, Pd，max(t)、Pd，min(t) respectively representing the upper and lower limits of the discharge power of the storage battery; pc，max(t)、Pc，minAnd (t) represents the upper limit and the lower limit of the charging power of the storage battery respectively.
The constraint formula for the power balance constraint is as follows:
Pact(t)+Pc(t)-Pd(t)＝PL(t) (6)
in the formula, PLAnd (t) represents the original load demand of the user in the period of t and the transferable load value of the user in the period of t.
The constraint formula for the load shifting constraint is as follows:
in the formula (I), the compound is shown in the specification,λrepresents the user transferable load scaling factor, and is in the range of 0, 1]。
As a preferable technical measure:
and the day-ahead scheduling result is the output power of the storage battery or the transferable load power.
As a preferable technical measure:
the active power output prediction model in the third step has the following calculation formula:
in the formula, P0(t) is the initial value of the output power of the storage battery or the transferable load power at the time t; n represents a prediction step size; deltau (t + k | t) represents the control variable increment in the period (t + i-1, t + i) predicted at time t,
the calculation formula of the control variable increment is as follows:
Δu(t+k|t)＝[ΔPc(t+k|t)，ΔPd(t+k|t)，ΔPshift(t+k|t)] (9)
in the formula,. DELTA.Pc(t+k|t)、ΔPd(t+k|t)、ΔPshift(t + k | t) represents the battery charge power increment, battery discharge power increment, and user transferable load increment, respectively;
the active power output prediction model obtains user net load power according to user original load requirements in a t period and user transferable load values in the t period, and the user net load power is calculated as follows:
in the formula, I is a unit row vector; delta PL(t + k | t) is the predicted load predicted power increment for the (t + k-1, t + k) time period, which is the disturbance input variable;
selecting user net load power as an output variable, wherein the calculation formula is as follows:
Y(t+i|t)＝Pact(t+i|t) (11)
in the formula, Pact(t + i | t) represents the predicted user payload power at a future time t + i at time t.
As a preferable technical measure:
performing intraday real-time rolling optimization scheduling by using an active power output prediction model, wherein a calculation formula of an objective function is as follows:
in the formula (I), the compound is shown in the specification,
the reference value of the output variable at the time of t + i is obtained from the day-ahead scheduling result;
in the real-time rolling optimization scheduling, the constraint conditions comprise energy storage equipment charging and discharging depth constraint, energy storage battery SOC period balance constraint, power balance constraint, load transfer constraint, storage battery charging power control variable constraint and transferable load control variable constraint;
the constraint formulas of the storage battery charging power control variable constraint, the storage battery charging power control variable constraint and the transferable load control variable constraint are as follows:
in the formula,. DELTA.Pc，minAnd Δ Pd，minRespectively representing the minimum value of the storage battery charging power control variable and the storage battery charging power control variable; delta Pc，max、ΔPd，maxAnd Δ Pshift，maxRespectively representing the maximum values of the storage battery charging power control variable, the storage battery charging power control variable and the transferable load control variable; delta Pc(t+i|t)、ΔPd(t+i|t)、ΔPshift(t + i | t) represents a battery charge power control variable, and a transferable load control variable, respectively.
As a preferable technical measure:
in the fourth step, the feedback correction method is as follows:
in order to minimize the influence caused by prediction error, the actual net load power value measured at a certain time is used as the initial value of the active output prediction model at the time to carry out new optimization and realize closed-loop control, and the calculation formula is as follows:
Pact(t+1)＝Pmeasure(t+1) (14)
in the formula, Pmeasure(t +1) represents the actual net load power value measured at the time of t + 1; pactAnd (t +1) represents the initial value of the active power output prediction model at the time t + 1.
In order to achieve the purpose, the other technical scheme of the invention is as follows:
a scheduling device for user-side energy storage and real-time participation in demand response,
it includes:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, the one or more programs cause the one or more processors to implement a scheduling method for user-oriented energy storage real-time participation in demand response as described above.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, through continuous exploration and test, a day-ahead optimization scheduling model and an active power output prediction model are constructed; optimizing and controlling the user side energy storage and the transferable load through a day-ahead optimization scheduling model, minimizing the sum of the electricity purchasing cost and the energy storage battery loss cost of a user, and outputting a day-ahead scheduling result of the net load power to an active power output prediction model; the active output prediction model takes the day-ahead scheduling result as a real-time optimization scheduling initial value, and calculates the active output of real-time rolling optimization scheduling according to the control variable increment; and finally, performing feedback correction on the active power output to realize closed-loop control.
Furthermore, the optimal scheduling method based on model predictive control provided by the invention solves the problem of scheduling power fluctuation when the energy storage at the user side participates in demand response by utilizing the idea of model predictive control rolling optimization, simultaneously introduces feedback correction, corrects the interference of external uncertain input in time, realizes multi-level coordination, progressive refinement and feedback correction, minimizes the influence caused by load prediction uncertainty, has detailed scheme, is feasible, is convenient to realize and is beneficial to popularization and use.
Drawings
FIG. 1 is a flow chart of the real-time rolling optimization scheduling of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
On the contrary, the invention is intended to cover alternatives, modifications, equivalents and alternatives which may be included within the spirit and scope of the invention as defined by the appended claims. Furthermore, in the following detailed description of the present invention, certain specific details are set forth in order to provide a better understanding of the present invention. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details.
A scheduling method for user side energy storage real-time participation demand response,
the method comprises the following steps:
firstly, acquiring historical data;
the historical data at least comprises user side energy storage data, transferable load data, user electricity purchasing cost data and energy storage battery loss cost data;
secondly, constructing a day-ahead optimized scheduling model according to the historical data in the first step;
the day-ahead optimization scheduling model optimally controls the energy storage and the transferable load at the user side, so that the sum of the electricity purchasing cost and the energy storage battery loss cost of a user is minimum, and a day-ahead scheduling result of the net load power is output;
thirdly, constructing an active power output prediction model according to the charge and discharge power of the storage battery and the transferable load increment of the user,
the active output prediction model takes the day-ahead scheduling result in the second step as a real-time optimization scheduling initial value, and calculates the active output of real-time rolling optimization scheduling according to the control variable increment;
the control variable increment comprises a storage battery charging power increment, a storage battery discharging power increment and a user transferable load increment;
and fourthly, performing feedback correction on the active power output in the third step to realize closed-loop control.
The invention relates to a scheduling method for user side energy storage real-time participation demand response, which is a method for realizing closed-loop control by constructing model prediction.
Furthermore, the prediction model is a model for describing the dynamic behavior of the device, future control input information is obtained through the currently known historical input information of the device and the optimization of a controlled object, the prediction model only pays attention to the fact that the model can predict the future dynamic output value of the device according to the input information, and does not pay attention to the structural expression form of the active power output prediction model.
The invention discloses a specific embodiment of a day-ahead optimization scheduling model, which comprises the following steps:
the day-ahead optimization scheduling model is combined with the day-ahead load prediction power to optimally control the user side energy storage and the transferable load on the premise of not influencing the normal production activity of the user, so that the sum of the electricity purchasing cost of the user and the loss cost of the energy storage battery is minimum.
The objective function of the day-ahead optimization scheduling model is
In the formula, Pact(t)、PDR(t)、Pbat(t)、Pshift(t) respectively representing user net load power, user participation demand response power, energy storage battery charging and discharging power and transferable load power in a t period; t represents the total scheduling time period number; ρ (t) and ρDRRespectively representing the time-of-use electricity price and the demand response income.
The method for calculating the unit charge-discharge loss cost of the energy storage battery comprises the following steps:
in the formula, CwA cost coefficient for the capacity of the energy storage battery; n is a radical ofaThe number of charge and discharge cycles for the energy storage life cycle; ddodThe energy storage charging and discharging depth.
The invention relates to a specific embodiment of optimizing scheduling constraint in the day ahead:
in order to ensure the electricity utilization experience of the user and the safe operation of the storage battery, the day-ahead scheduling plan needs to satisfy the following constraint conditions.
Energy storage device charge-discharge depth constraint
Wherein S (t +1) represents the SOC value of the battery during the period t; σ represents the self-discharge rate of the storage battery; v represents the battery capacity; etac、ηdRespectively representing the charge and discharge efficiency of the storage battery; pc(t)、Pd(t) represents the charging and discharging power of the storage battery in the period of t respectively; Δ t represents the time length of one scheduling period; smax、SminRespectively representing the upper and lower limits of the battery SOC.
SOC period balance constraint of energy storage battery
S(0)＝S(T)(4)
Energy storage battery charge and discharge power constraint
In the formula, Pd，max(t)、Pd，min(t) respectively representing the upper and lower limits of the discharge power of the storage battery; pc，max(t)、Pc，minAnd (t) represents the upper limit and the lower limit of the charging power of the storage battery respectively.
Power balance constraint
Pact(t)+Pc(t)-Pd(t)＝PL(t) (6)
In the formula, PLAnd (t) represents the original load demand of the user in the period of t and the transferable load value of the user in the period of t.
Load transfer constraints
In the formula, lambda represents the proportion coefficient of the load which can be transferred by the user, and lambda belongs to [0, 1 ].
One specific embodiment of the predictive model of the invention:
the active power output of real-time rolling optimization scheduling is predicted by solving the charge and discharge power of the storage battery and the transferable load increment of a user, and the active power output prediction model is
In the formula, P0(t) is the initial value of the output power of the storage battery or the transferable load power at the time t; n represents a prediction step size; Δ u (t + k | t) represents the control variable increment in the period (t + i-1, t + i) predicted at time t, as shown in equation (9)
Δu(t+k|t)＝[ΔPc(t+k|t)，ΔPd(t+k|t)，ΔPshift(t+k|t)] (9)
In the formula,. DELTA.Pc(t+k|t)、ΔPd(t+k|t)、ΔPshift(t + k | t) represents the battery charge/discharge power increment and the user transferable load increment, respectively.
The active power output prediction model of the output variable obtained according to the formulas (6) and (8) is shown as the formula (10)
In the formula, I is a unit row vector; delta PL(t + k | t) is predicted (t + k-1, t + k)The load of the segment predicts the power increment as the disturbance input variable.
Selecting user net load power as output variable
Y(t+i|t)＝Pact(t+i|t) (11)
In the formula, Pact(t + i | t) represents the predicted user payload power at a future time t + i at time t.
The invention discloses a specific embodiment of real-time rolling optimization within a day:
the objective function and constraint conditions of the intra-day real-time rolling optimization comprise the following contents:
in the formula (I), the compound is shown in the specification,
the reference value of the output variable at the time t + i is obtained from the day-ahead scheduling result.
In the real-time rolling optimization scheduling, in addition to the constraint conditional expressions (2) to (7), the following constraint conditions need to be satisfied
In the formula,. DELTA.Pc，minAnd Δ Pd，minRespectively representing the minimum value of the storage battery charging power control variable and the storage battery charging power control variable; delta Pc，max、ΔPd，maxAnd Δ Pshift，maxRespectively representing the maximum values of the battery charge power control variable, the battery charge power control variable and the transferable load control variable.
One specific embodiment of the feedback correction of the present invention:
in order to minimize the impact of prediction errors, a feedback link is required to implement closed-loop control, and a new optimization process is established on the basis of actual measurement values, i.e., the order is given
Pact(t+1)＝Pmeasure(t+1) (14)
In the formula, Pmeasure(t +1) represents the actual net load power value measured at the time of t + 1; pactAnd (t +1) represents the initial value of the active power output prediction model at the time t + 1.
The flow of the real-time rolling optimization scheduling is shown in fig. 1.
An embodiment of a device to which the method of the invention is applied:
a scheduling device for user-side energy storage real-time participation in demand response comprises:
one or more processors;
storage means for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the scheduling method for user-oriented energy storage real-time participation in demand response.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A scheduling method for user side energy storage real-time participation demand response is characterized in that,
the method comprises the following steps:
firstly, acquiring historical data;
the historical data at least comprises user side energy storage data, transferable load data, user electricity purchasing cost data and energy storage battery loss cost data;
secondly, constructing a day-ahead optimized scheduling model according to the historical data in the first step;
the day-ahead optimization scheduling model optimally controls the energy storage and the transferable load at the user side, so that the sum of the electricity purchasing cost and the energy storage battery loss cost of a user is minimum, and the current scheduling result of the net load power is output;
thirdly, constructing an active power output prediction model according to the charge and discharge power of the storage battery and the transferable load increment of the user,
the active output prediction model takes the day-ahead scheduling result in the second step as a real-time optimization scheduling initial value, and calculates the active output of real-time rolling optimization scheduling according to the control variable increment;
the control variable increment comprises a storage battery charging power increment, a storage battery discharging power increment and a user transferable load increment;
and fourthly, performing feedback correction on the active power output in the third step to realize multi-time scale participating closed-loop control.
2. The scheduling method for user-oriented energy storage real-time participation in demand response as claimed in claim 1,
in the second step, the calculation formula of the objective function of the day-ahead optimization scheduling model is as follows:
in the formula, Pact(t)、PDR(t)、Pbat(t)、Pshift(t) respectively representing user net load power, user participation demand response power, energy storage battery charging and discharging power and transferable load power in a t period; t represents the total scheduling time period number; ρ (t) and ρDRRespectively representing the time-of-use electricity price and the demand response income.
3. The scheduling method for user-oriented energy storage real-time participation in demand response as claimed in claim 2,
the unit charging and discharging loss cost calculation method of the energy storage battery comprises the following steps:
in the formula, CwA cost coefficient for the capacity of the energy storage battery; n is a radical ofaThe number of charge and discharge cycles for the energy storage life cycle; ddodThe energy storage charging and discharging depth.
4. The scheduling method for user-oriented energy storage real-time participation in demand response as claimed in claim 3,
the constraint conditions of the day-ahead optimization scheduling model comprise energy storage equipment charge-discharge depth constraint, energy storage battery SOC period balance constraint, power balance constraint and load transfer constraint.
5. The scheduling method for user-oriented energy storage real-time participation in demand response as claimed in claim 4,
the constraint formula of the energy storage device charge-discharge depth constraint is as follows:
wherein S (t +1) represents the SOC value of the battery during the period t; σ represents the self-discharge rate of the storage battery; v represents the battery capacity; etac、ηdRespectively representing the charge and discharge efficiency of the storage battery; pc(t)、Pd(t) represents the charging and discharging power of the storage battery in the period of t respectively; Δ t represents the time length of one scheduling period; smax、SminRespectively representing the upper limit and the lower limit of the SOC of the storage battery;
the constraint formula of the SOC period balance constraint of the energy storage battery is as follows:
S(0)＝S(T) (4)
the constraint formula of the charge and discharge power constraint of the energy storage battery is as follows:
in the formula, Pd，max(t)、Pd，min(t) respectively representing the upper and lower limits of the discharge power of the storage battery; pc，max(t)、Pc，min(t) respectively representing the upper and lower limits of the charging power of the storage battery;
the constraint formula for the power balance constraint is as follows:
Pact(t)+Pc(t)-Pd(t)＝PL(t) (6)
in the formula, PL(t) represents the original load demand of the user in the t period and the transferable load value of the user in the t period;
the constraint formula for the load shifting constraint is as follows:
in the formula, lambda represents the proportion coefficient of the load which can be transferred by the user, and lambda belongs to [0, 1 ].
6. The scheduling method for user-oriented energy storage real-time participation in demand response as claimed in claim 1,
and the day-ahead scheduling result is the output power of the storage battery or the transferable load power.
7. The scheduling method of real-time participation in demand response to user-oriented energy storage according to claim 6,
the active power output prediction model in the third step has the following calculation formula:
in the formula, P0(t) is the initial value of the output power of the storage battery or the transferable load power at the time t; n represents a prediction step size; deltau (t + k | t) represents the control variable increment in the period (t + i-1, t + i) predicted at time t,
the calculation formula of the control variable increment is as follows:
Δu(t+k|t)＝[ΔPc(t+k|t)，ΔPd(t+k|t)，ΔPshift(t+k|t)] (9)
in the formula,. DELTA.Pc(t+k|t)、ΔPd(t+k|t)、ΔPshift(t + k | t) represents the battery charge power increment, battery discharge power increment, and user transferable load increment, respectively;
the active power output prediction model obtains user net load power according to user original load requirements in a t period and user transferable load values in the t period, and the user net load power is calculated as follows:
in the formula, I is a unit row vector; delta PL(t + k | t) is the predicted load predicted power increment for the (t + k-1, t + k) time period, which is the disturbance input variable;
selecting user net load power as an output variable, wherein the calculation formula is as follows:
Y(t+i|t)＝Pact(t+i|t) (11)
in the formula, Pact(t + i | t) represents the predicted user payload power at a future time t + i at time t.
8. The scheduling method of real-time participation in demand response to energy storage facing user side of claim 7,
performing intraday real-time rolling optimization scheduling by using an active power output prediction model, wherein a calculation formula of an objective function is as follows:
in the formula (I), the compound is shown in the specification,
the reference value of the output variable at the time of t + i is obtained from the day-ahead scheduling result;
in the real-time rolling optimization scheduling, the constraint conditions comprise energy storage equipment charging and discharging depth constraint, energy storage battery SOC period balance constraint, power balance constraint, load transfer constraint, storage battery charging power control variable constraint and transferable load control variable constraint;
the constraint formulas of the storage battery charging power control variable constraint, the storage battery charging power control variable constraint and the transferable load control variable constraint are as follows:
in the formula,. DELTA.Pc，minAnd Δ Pd，minRespectively representing the minimum value of the storage battery charging power control variable and the storage battery charging power control variable; delta Pc，max、ΔPd，maxAnd Δ Pshift，maxRespectively representing the maximum values of the storage battery charging power control variable, the storage battery charging power control variable and the transferable load control variable; delta Pc(t+i|t)、ΔPd(t+i|t)、ΔPshift(t + i | t) represents a battery charge power control variable, and a transferable load control variable, respectively.
9. The scheduling method for user-oriented energy storage real-time participation in demand response as claimed in claim 1,
in the fourth step, the feedback correction method is as follows:
and (3) taking the actual net load power value measured at a certain moment as an initial value of the active output prediction model at the moment, carrying out new optimization, and realizing closed-loop control, wherein the calculation formula is as follows:
Pact(t+1)＝Pmeasure(t+1) (14)
in the formula, Pmeasure(t +1) represents the actual net load power value measured at the time of t + 1; pactAnd (t +1) represents the initial value of the active power output prediction model at the time t + 1.
10. A scheduling device for user-side energy storage and real-time participation in demand response is characterized in that,
it includes:
one or more processors;
storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a method of scheduling user-side energy storage real-time participation in demand responses as recited in any of claims 1-9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210042290.0A CN114372638A (en) | 2022-01-14 | 2022-01-14 | Scheduling method and device for user side energy storage real-time participation demand response |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210042290.0A CN114372638A (en) | 2022-01-14 | 2022-01-14 | Scheduling method and device for user side energy storage real-time participation demand response |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114372638A true CN114372638A (en) | 2022-04-19 |
Family
ID=81143242
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210042290.0A Pending CN114372638A (en) | 2022-01-14 | 2022-01-14 | Scheduling method and device for user side energy storage real-time participation demand response |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114372638A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115936260A (en) * | 2023-01-13 | 2023-04-07 | 智光研究院(广州)有限公司 | Virtual power plant user energy storage scheduling method and device, electronic equipment and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140058571A1 (en) * | 2012-08-27 | 2014-02-27 | Nec Laboratories America, Inc. | Multi-objective energy management methods for micro-grids |
| CN107425534A (en) * | 2017-08-25 | 2017-12-01 | 电子科技大学 | A kind of micro-capacitance sensor dispatching method based on optimization accumulator cell charging and discharging strategy |
| CN110311421A (en) * | 2019-07-12 | 2019-10-08 | 燕山大学 | Micro-capacitance sensor Multiple Time Scales energy management method based on Demand Side Response |
| WO2019196375A1 (en) * | 2018-04-13 | 2019-10-17 | 华南理工大学 | Demand side response-based microgrid optimal unit and time-of-use electricity price optimization method |
| US20200033933A1 (en) * | 2015-06-30 | 2020-01-30 | China Electric Power Research Institute Company Limited | Active power distribution network multi-time scale coordinated optimization scheduling method and storage medium |
| AU2020102245A4 (en) * | 2019-01-08 | 2020-10-29 | Nanjing Institute Of Technology | A grid hybrid rolling dispatching method considering congestion and energy storage tou price |
| CN112966854A (en) * | 2021-02-07 | 2021-06-15 | 深圳供电局有限公司 | User side energy storage device configuration method and device, computer equipment and storage medium |
| CN113131503A (en) * | 2021-04-25 | 2021-07-16 | 山东电工电气集团有限公司 | Energy storage power station energy management method based on SOC consistency of multiple battery packs |
-
2022
- 2022-01-14 CN CN202210042290.0A patent/CN114372638A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140058571A1 (en) * | 2012-08-27 | 2014-02-27 | Nec Laboratories America, Inc. | Multi-objective energy management methods for micro-grids |
| US20200033933A1 (en) * | 2015-06-30 | 2020-01-30 | China Electric Power Research Institute Company Limited | Active power distribution network multi-time scale coordinated optimization scheduling method and storage medium |
| CN107425534A (en) * | 2017-08-25 | 2017-12-01 | 电子科技大学 | A kind of micro-capacitance sensor dispatching method based on optimization accumulator cell charging and discharging strategy |
| WO2019196375A1 (en) * | 2018-04-13 | 2019-10-17 | 华南理工大学 | Demand side response-based microgrid optimal unit and time-of-use electricity price optimization method |
| AU2020102245A4 (en) * | 2019-01-08 | 2020-10-29 | Nanjing Institute Of Technology | A grid hybrid rolling dispatching method considering congestion and energy storage tou price |
| CN110311421A (en) * | 2019-07-12 | 2019-10-08 | 燕山大学 | Micro-capacitance sensor Multiple Time Scales energy management method based on Demand Side Response |
| CN112966854A (en) * | 2021-02-07 | 2021-06-15 | 深圳供电局有限公司 | User side energy storage device configuration method and device, computer equipment and storage medium |
| CN113131503A (en) * | 2021-04-25 | 2021-07-16 | 山东电工电气集团有限公司 | Energy storage power station energy management method based on SOC consistency of multiple battery packs |
Non-Patent Citations (1)
| Title |
|---|
| 王建波等: "用户侧储能参与需求响应的多时间尺度优化调度策略研究", 电气工程学报, vol. 16, no. 3, 30 September 2021 (2021-09-30), pages 115 - 122 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115936260A (en) * | 2023-01-13 | 2023-04-07 | 智光研究院(广州)有限公司 | Virtual power plant user energy storage scheduling method and device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105846461B (en) | Control method and system for large-scale energy storage power station self-adaptive dynamic planning | |
| CN110086187B (en) | Energy storage peak regulation day-ahead optimization scheduling method considering load characteristics | |
| CN107248751A (en) | A kind of energy storage station dispatch control method for realizing distribution network load power peak load shifting | |
| CN110210709B (en) | Frequency modulation instruction scheduling method considering life attenuation and capacity balance bidding of energy storage power station | |
| Han et al. | Constructing prosumer coalitions for energy cost savings using cooperative game theory | |
| CN108551176B (en) | Energy storage battery system capacity configuration method combined with energy storage balancing technology | |
| CN110380405B (en) | Micro-grid operation method based on cooperative optimization of demand response and energy storage | |
| CN112636331B (en) | Dynamic economic dispatching distributed optimization method and system for smart power grid | |
| CN112001528A (en) | Optimal bidding method and system for wind storage combined participation in energy-frequency modulation market | |
| CN114256836B (en) | Capacity optimization configuration method for shared energy storage of new energy power station | |
| CN114707776B (en) | Carbon emission double control-based low-carbon energy consumption optimization system and method | |
| CN104408663A (en) | User demand response dispatching system and method of smart grid | |
| CN113162055B (en) | Coordination control method and system for reactive voltage of energy storage and new energy power station | |
| CN115423207A (en) | Wind storage virtual power plant online scheduling method and device | |
| CN114725969B (en) | Electric automobile load aggregation method based on continuous tracking of wind power curve | |
| CN114372638A (en) | Scheduling method and device for user side energy storage real-time participation demand response | |
| CN105098839B (en) | A kind of wind-electricity integration coordination optimizing method based on the uncertain output of wind-powered electricity generation | |
| CN113011101B (en) | Control method and system for energy storage to participate in frequency modulation auxiliary service optimization | |
| CN111030149A (en) | Micro-grid power supply method based on composite energy storage device and electronic equipment | |
| CN116995682B (en) | Adjustable load participation active power flow continuous adjustment method and system | |
| CN117057634B (en) | Low-carbon operation optimization method and system for participation of energy storage power station in electric power spot market | |
| CN116739146A (en) | Method for V2G price excitation mechanism considering battery loss | |
| CN115940225A (en) | Direct control type virtual power plant energy storage station operation method and device considering peak load | |
| CN116091092A (en) | Power grid energy storage operation method based on elastic energy state | |
| CN115758723A (en) | Polymerization regulation and control method and device for direct control type virtual power plant |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |