CN116341241A - Hybrid energy storage multi-time scale optimization scheduling strategy under power market scheduling demand - Google Patents
Hybrid energy storage multi-time scale optimization scheduling strategy under power market scheduling demand Download PDFInfo
- Publication number
- CN116341241A CN116341241A CN202310291823.3A CN202310291823A CN116341241A CN 116341241 A CN116341241 A CN 116341241A CN 202310291823 A CN202310291823 A CN 202310291823A CN 116341241 A CN116341241 A CN 116341241A
- Authority
- CN
- China
- Prior art keywords
- day
- time
- energy storage
- scheduling
- power
- 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 177
- 238000005457 optimization Methods 0.000 title claims description 63
- 238000004364 calculation method Methods 0.000 claims abstract description 39
- 238000011217 control strategy Methods 0.000 claims abstract description 5
- 238000000034 method Methods 0.000 claims description 22
- 238000007599 discharging Methods 0.000 claims description 19
- 230000005540 biological transmission Effects 0.000 claims description 14
- 230000009194 climbing Effects 0.000 claims description 14
- 239000000243 solution Substances 0.000 claims description 12
- 238000005259 measurement Methods 0.000 claims description 7
- 238000010248 power generation Methods 0.000 claims description 7
- 238000010977 unit operation Methods 0.000 claims description 7
- 238000013499 data model Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 238000003860 storage Methods 0.000 claims description 6
- 238000012986 modification Methods 0.000 claims description 4
- 230000004048 modification Effects 0.000 claims description 4
- 230000014759 maintenance of location Effects 0.000 claims description 3
- 238000013486 operation strategy Methods 0.000 claims description 3
- 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 claims description 2
- 238000002347 injection Methods 0.000 claims description 2
- 239000007924 injection Substances 0.000 claims description 2
- 238000013178 mathematical model Methods 0.000 claims description 2
- 230000004044 response Effects 0.000 claims description 2
- 230000035945 sensitivity Effects 0.000 claims description 2
- 238000006467 substitution reaction Methods 0.000 claims description 2
- 230000002459 sustained effect Effects 0.000 claims description 2
- 230000006399 behavior Effects 0.000 abstract description 3
- 230000002035 prolonged effect Effects 0.000 abstract description 2
- 230000007547 defect Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
-
- 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
- G06Q10/04—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
- 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
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Game Theory and Decision Science (AREA)
- Evolutionary Computation (AREA)
- Entrepreneurship & Innovation (AREA)
- General Engineering & Computer Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Geometry (AREA)
- Development Economics (AREA)
- Computer Hardware Design (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a hybrid energy storage multi-time scale optimal scheduling strategy under the power market scheduling requirement, which comprises the steps of firstly carrying out day-ahead optimal scheduling, then carrying out intra-day hour-level optimal scheduling, wherein the calculation frequency is 96 times per day, and the step length of predicted data is 15 minutes. And carrying out real-time optimized scheduling, wherein the calculation frequency is calculated once every 5 minutes, and the corresponding predicted data step length is 5 minutes. And finally, integrating the results of day-ahead optimal scheduling, day-in-day hour scheduling and real-time scheduling operation to obtain an actual control strategy of the thermal power unit and the hybrid energy storage. According to the invention, by introducing control of the charge-discharge switching times of the energy storage, effective energy storage investment is realized, and the service life of the energy storage equipment is prolonged. Through coordination of three stages of day-time, efficient and economical management of the charge and discharge behaviors of the hybrid energy storage is achieved.
Description
Technical Field
The invention belongs to the technical field of power control, and particularly relates to a hybrid energy storage multi-time scale optimization scheduling strategy under power market scheduling requirements.
Background
With the large-scale integration of renewable energy sources represented by wind and light into a power grid, the strong volatility and uncertainty of the renewable energy sources bring great challenges to the safe and stable operation of the power grid. The energy storage device has the capability of storing and releasing electric energy, and can effectively improve the operation risk brought to the power grid by source side resource fluctuation. At present, the investment cost of single type energy storage equipment is high, so that a fully reasonable marketized and optimized operation strategy of energy storage resources is developed, and the service life of energy storage is very necessary while the energy storage requirement is reduced. In the prior art, energy storage participates in the dispatching of the electric power market, and independent researches on the time period before or in the day are mainly focused, so that the consideration of the multi-time scale cooperative technology is less developed. Due to the energy timing coupling relationship of the stored energy, the power requirements at future moments need to be considered as much as possible when optimizing the energy storage strategy. However, it is not trivial to optimize control strategy calculations for long periods of time. Therefore, the design of the multi-time scale cooperative control strategy with long-time operation and rapid calculation requirements can remarkably make up the defects of the existing strategy. In addition, in the existing operation scheduling model, the calculation of the energy storage life in the coordinated optimization operation is mostly developed by adopting a raindrop counting method, but the operability of the mode is relatively weak. Therefore, the method adopts a mode of controlling the charge and discharge times with higher operability.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides a hybrid energy storage multi-time scale optimization scheduling strategy under the power market scheduling requirement.
And the multi-time scale optimization strategy is used for prolonging the service life of energy storage while guaranteeing the energy storage economy. Therefore, the hybrid energy storage multi-time scale optimization scheduling strategy under the power market scheduling requirement provided by the invention mainly comprises four steps:
in the first step, the scheduling is optimized before the day, the calculation frequency is 1 time per day, and the step size of the predicted data is 1 hour.
The day-ahead optimization scheduling aims at minimizing the total day-ahead scheduling operation cost, and based on the renewable energy source forecast output and load demand information obtained in the day-ahead, the system operation constraint, the thermal power unit operation constraint, the hybrid energy storage system operation constraint and the renewable energy source operation constraint are comprehensively considered, and the start-stop state, the energy type energy storage charging and discharging strategy and the positive and negative standby schemes of the day-ahead thermal power unit are obtained through optimization calculation;
and secondly, carrying out daily hour-level optimized scheduling, wherein the calculation frequency is 96 times a day, and the step size of the predicted data is 15 minutes.
Taking a starting and stopping state of a thermal power unit, an energy type energy storage charging and discharging strategy and positive and negative standby schemes of the thermal power unit which are optimized and scheduled in the future and a future as determined values, optimizing and obtaining a thermal power unit power strategy and energy type energy storage operation power and capacity value which minimize the expected power operation cost of the system under the condition that the system daily operation constraint, the thermal power unit daily operation constraint, the hybrid energy storage daily operation constraint and the renewable energy source daily operation constraint are met based on renewable energy source prediction output and load demand prediction data which are obtained by daily short-term prediction and take 15 minutes as step length;
and thirdly, optimizing scheduling in real time, wherein the calculation frequency is calculated once every 5 minutes, and the corresponding predicted data step length is 5 minutes.
And taking a result obtained by day-ahead optimal scheduling as a determined value, taking a result obtained by day-in-hour scheduling as a reference value, taking the deviation between the minimum and the reference value as a target, and expanding and optimizing to calculate the operating power of the thermal power unit, the energy-type energy storage operating power and the power-type energy storage operating power by combining with real-time scheduling operation constraint.
And fourthly, integrating the results of day-ahead optimal scheduling, day-in-day hour scheduling and real-time scheduling operation to obtain an actual control strategy of the thermal power generating unit and the hybrid energy storage.
The invention has the following beneficial effects:
the multi-time scale random scheduling model provided by the scheme has the following advantages:
1) The proposed multi-time-scale hybrid energy storage collaborative optimization model facing the power market dispatching requirement is more in line with the market regulation control law;
2) By introducing the control of the charge-discharge switching times of the energy storage, the effective energy storage investment is realized, and the service life of the energy storage equipment is prolonged.
3) Through coordination of three stages of day-time, efficient and economical management of the charge and discharge behaviors of the hybrid energy storage is achieved.
Detailed Description
Along with the continuous approximation of the running time, the prediction accuracy of the output of the renewable energy source is obviously improved. The multi-time scale optimization strategy is adopted to obtain the running schemes under different time scales in a stepwise manner, so that the running schemes approach to a better scheme step by step. The implementation details of the multi-time-scale optimization strategy established by the scheme are as follows:
step 1, optimizing and scheduling before the day;
day-ahead optimal schedule is 10 per day: 00pm is used for carrying out scheduling plan measurement about the next day, and the calculated data step length is 1 hour. The scheme aims at determining the start-up and stop state of the thermal power generating unit, the energy type energy storage charging and discharging strategy and the positive and negative standby scheme of the thermal power generating unit required by the optimization scheduling of the hour level in the next day through day-ahead optimization calculation. The specific optimization strategy model is as follows:
1.1 an objective function;
the day-ahead optimization scheduling aims at minimizing the running cost of the system generator and the energy storage, and the objective function can be written as a formula (1) and is mainly divided into two parts: the first part is the start-up and shut-down costs of a conventional thermal power plant, and the second part is the power costs of thermal power generation and HESS.
Wherein T represents the time T, T day Represents a set of day-ahead optimal scheduling time, G represents a thermal power unit G, G represents a set of all thermal power units, G r Representing a conventional thermal power generating unit with a fast response capability, E representing energy storage devices E, E representing all energy storage devices, S representing scenes S, S day Representing a set of all potential scenarios generated by a day-ahead prediction,
representing the probability of occurrence of scene s in the day-ahead prediction,/->
And->
The starting and stopping costs of the thermal power generating unit are respectively. K (K) g,t And G g,t Respectively representing the starting and stopping states of the thermal power generating unit, < ->
And p g,t,s The unit operation cost and the power of the thermal power generating unit are respectively. />
And->
Representing the cost of discharging and charging, respectively, of the energy storage unit power,/->
And->
Respectively representing the discharge and charge power of the stored energy.
1.2 constraint conditions;
constraint conditions of the day-ahead optimization scheduling model comprise system operation constraint, thermal power unit operation constraint, hybrid energy storage system operation constraint and renewable energy source operation constraint;
a) System operation constraints.
The system operation firstly needs to ensure the balance of the electric power and the electric quantity of all equipment in the system, namely the balance among a thermal power unit, an energy storage unit, a renewable energy unit and a user load, as shown in a formula (2). Meanwhile, the system operation also needs to ensure that the capacity constraint of the transmission line of the system is met, namely, the sum of the power flowing through the transmission line of all equipment needs to be within the limit range of the maximum capacity of the transmission line, as shown in a formula (3). On the other hand, the system operation needs to ensure that the standby power is satisfied, and the standby is divided into positive and negative standby as shown in the formula (4).
Wherein p is r,t,s Representing renewable energy power generation, r represents a renewable energy unit r, and REN represents a collection of renewable energy units. P is p d,t,s Representing the daily forecast of the load, D representing the load node D, and D representing the set of loads,
is the maximum power capacity of line l, SF l,b Is the coefficient of sensitivity of the power flow through line l to the power injection of power transmission node B, B representing power transmission node B and B representing the set of all power transmission nodes. />
And->
Respectively representing the positive power standby of the thermal power unit g and the total positive power standby of the system, +.>
And->
Respectively representing the reserve of the negative power of the thermal power generating unit g and the total reserve of the negative power of the system.
b) And (5) operating constraint of the thermal power generating unit.
Formula (5) is the output constraint of the thermal power generating unit, formula (6) is the climbing constraint of the thermal power generating unit, the left side is the negative climbing upper limit, the right side is the positive climbing upper limit, formula (7) is the minimum time limit constraint of starting and stopping of the thermal power generating unit respectively, and formula (8) is the relation among the running state, starting state and stopping state of the thermal power generating unit.
Wherein,,
and->
Respectively the minimum and the maximum of the power of the thermal power generating unit. X is x g,t 、x g,t' And x g,t-1 Respectively representing state variables of the thermal power generating unit g at the time t, t' and t-1, p g,t-1,s The power of thermal power unit g at time t-1 in scene s is shown. />
And->
Is the limit of the positive climbing capacity and the negative climbing capacity of the thermal power generating unit, and is +.>
And->
Representing the start-up and shut-down power of the thermal power generating unit. QDs g And GT g Is the minimum start-up and shut-down duration of the thermal power generating unit.
c) Hybrid energy storage system operating constraints.
The hybrid energy storage system operating constraints of the day-ahead stage include two parts, a conventional operating constraint (equations (9-12)) and a cycle number limiting constraint (equations (13-16)) model. Wherein, the conventional operation constraint aims at two types of energy storage of a power type and an energy type, and the cycle number limiting constraint model aims at energy type energy storage only. The formula (9) represents the limit constraint of the charge and discharge power of the energy storage, the formula (10) represents the limit constraint of the running state of the energy storage, the formula (11) represents the limit constraint of the capacity update of the energy storage, the formula (12) represents the limit constraint of the capacity limit of the energy storage, the formula (13) represents the limit of the minimum duration of the charge and discharge of the energy storage, the formula (14) represents the limit of the maximum number of times of the charge and discharge of the energy storage, and the formula (15) and the formula (16) represent the logic relationship between various state descriptions of the energy storage.
E e,o,s ＝e e,T,s (12)
Wherein,,
and->
Respectively maximum and minimum values of rated power of energy storage e->
And->
Respectively the charge and discharge state of the energy store e at time t under the scene s, +.>
And->
The charge and discharge efficiencies of the energy storage e, respectively. e, e e,t-1,s And e e,t-1,s Respectively representing the capacities of the energy storage E at the time t and the time t-1 under the scene s, E e,o,s And E is e,T,s Representing the capacity of the stored energy e at time 0 and time T under the scene s, respectively. />
And->
Representing the maximum and minimum values of the rated capacity of the stored energy e. CT and DT represent the minimum sustained charge and discharge time of the energy store, < >>
Represents the maximum number of cycles in the day of the energy store ee, < >>
And->
Respectively represent the charge and discharge states of the energy store ee at time k, +.>
And->
Representing the switching of the energy store ee from the other state to the charge state and the discharge state, respectively, +.>
And->
The energy storage ee representing the energy is switched from the charge state and the discharge state to the other state, +.>
And->
The charge and discharge states of the stored energy e at time t-1, respectively. Δt (delta t) day Representing a single time step of day-ahead optimal scheduling phase.
d) Renewable energy source operation constraints.
Equation (17) is the output power limit constraint of the renewable energy unit.
Wherein,,
representing the predicted power value of the renewable energy unit r at the moment t under the scene s in the day-ahead optimal scheduling stage.
The solution of the day-ahead optimal scheduling strategy model is based on Matlab compilation, and a Gurobi solution is adopted, wherein the MILP gap is set to be 0.1%. And solving a data model, calculating to obtain a start-stop state, an energy-type energy storage charging and discharging strategy and a positive standby scheme and a negative standby scheme of the thermal power generating unit before the day aiming at the thermal power generating unit, and storing the result.
Step 2, optimizing and dispatching the hour level in the day;
the intra-day hour level optimized schedule is developed 1 hour in advance with respect to scheduling plan measurement for the next 4 hours, and the calculated data step size is 15 minutes. (i.e., a measurement of the 2:00am-6:00am schedule run scheme is developed at 1:00 am). The scheme aims at determining a power strategy of the thermal power generating unit in the day and an operation power reference standard of energy type energy storage through optimization calculation in the day.
The specific optimal scheduling model is as follows:
2.1 an objective function;
in the day hour level optimizing and scheduling stage, the stage aims at minimizing the running cost of the thermal power generating unit and the energy storage, but the variable of the stage is only the running power of the thermal power generating unit and the energy storage, and the method can be specifically written as follows:
wherein t is i The starting calculation time of the hour-of-day level optimization schedule is represented,
basic prediction scene 0,S for representing hour-of-day level optimized scheduling ind Representing all potential scene sets generated by intra-day prediction, < >>
Representing the probability of occurrence of scene s under intra-day prediction, T ind Optimizing the time length of the schedule for the hours within the day,/->
Representing scene +.>
Power value of next time ti, +.>
And->
Respectively represent the energy storage e in the day hour level optimized dispatching stage in the scene +.>
The discharge and charge power values at the next instant ti, < ->
Representing the power value of time t under scene s of thermal power generating unit g obtained in the day hour level optimized scheduling stage,/->
And->
And respectively representing the discharge power value and the charge power value of the energy storage e at the time t under the scene s in the hour-in-day level optimal scheduling stage.
2.2 constraint conditions;
the intra-day hour-level optimization scheduling is the same as the constraint of the pre-day optimization scheduling model, and also comprises four parts, namely a system intra-day operation constraint, a thermal power unit intra-day operation constraint, a hybrid energy storage intra-day operation constraint and a renewable energy source intra-day operation constraint, and specifically:
a) System daily operation constraint
The day-ahead scheduling model and the day-ahead scheduling model are mainly embodied in different time scales in the system operation constraint level, and all parameters of the day-ahead scheduling model are replaced by the day-ahead optimal operation parameters.
The operation of the hour-of-day optimized dispatching system also needs to ensure the balance of electric power and electric quantity between the internal charge storages of the system, as shown in a formula (19). On the other hand, the intra-day hour-level optimized schedule also needs to satisfy transmission line constraints, as shown in equation (20). The reserve is reserved for the day before the day, so the consideration of the reserve capacity is not carried out in the day.
Wherein,,
day power generation amount of renewable energy unit r at time t under scene s, +.>
The intra-day predicted value of the load d at time t below the scene s is indicated.
b) And (5) the daily operation constraint of the thermal power generating unit.
Different from a day-ahead scheduling model, the day-in-hour-level optimized scheduling strategy is further developed based on a start-up and stop state result of the thermal power unit obtained by day-ahead scheduling. Thus, the generator operation constraint portion of the hour-of-day level optimization schedule contains only constraints (5) and (6), two constraints that limit power. Similarly, the calculation in this section also needs to replace all the day-ahead scheduling model parameters with the day-hour-level optimized scheduling parameters, which is specifically as follows:
equation (21) is the output constraint of the thermal power unit in the intra-day hour stage scheduling, and equation (22) is the climbing constraint of the thermal power unit in the intra-day hour stage scheduling.
Wherein,,
and the daily hour level operation power value of the thermal power generating unit g at the time t-1 under the scene s is represented.
c) Hybrid energy storage day-to-day operational constraints.
The energy type energy storage determines the charge and discharge state of the energy type energy storage in the day-ahead stage, so that a circulation constraint model does not need to be built again, and the charge and discharge state behavior is used as a parameter to calculate in the day-ahead stage. Thus, the energy storage operation constraint portion of the intra-day optimization model contains only equations (10) - (12). Similarly, in the calculation of the part, the calculation result of the day-ahead scheduling model needs to be used as an operation parameter operated in the day, and the specific substitution is as follows:
wherein,,
and->
Respectively the charging and discharging state of the power energy storage pe at time t under the scene s, +.>
And->
The capacities of the stored energy e at time t-1 and time t under the scene s are shown, respectively. Δt (delta t) ind A single time step representing an hour-of-day level optimized schedule.
d) Renewable energy daily operation constraint;
the power constraint of the renewable energy unit of the daily hour-level optimized scheduling is as shown in a formula (26):
wherein,,
and the predicted power value of the renewable energy unit r obtained in the day hour level optimal scheduling stage at the time t under the scene s is represented.
The solution of the intra-day hour-level optimized scheduling strategy is also based on Matlab compilation, and is based on the solution of a Gurobi solver, wherein MILP gap is set to be 0.1%. And solving a data model, calculating and obtaining a thermal power unit power strategy for minimizing the expected power operation cost of the system, and an energy-type energy storage operation power value and a capacity value at each moment, and storing data.
Step 3, optimizing and dispatching in real time;
the real-time optimized scheduling strategy is to expand the scheduling plan measurement and calculation for the following 2 hours in advance by 5 minutes, and the calculated data step length is 5 minutes. (i.e., a measurement of 1:00am-3:00am schedule run at 0:55 am). The scheme aims at determining the adjustment quantity of the running state of the thermal power unit, the adjustment quantity of energy-type energy storage power and the running scheme of power-type energy storage through real-time optimization calculation. The specific model is as follows:
3.1 objective function
The goal of real-time optimized scheduling mainly includes two aspects: on the one hand, it is desirable to minimize the running costs of the system, i.e. the running costs of the generator and the energy storage, and on the other hand, it is also desirable to be able to match as much as possible with the global optimum calculated on a longer time scale in the day, in order to reduce the influence of unknown information. The specific objective function can be written as:
wherein t is r S represents the initial computing time of real-time dispatching operation 0r Basic prediction scene representing real-time optimized scheduling, S real Representing a set of all potential scenarios generated by real-time optimized scheduling predictions,
representing the probability of occurrence of the real-time optimal scheduling phase scene s,T r optimizing the time window length of the schedule for real time, +.>
And->
Respectively represent real-time optimization stage scenes S 0r At the time t r Real-time optimized dispatching power of thermal power generating unit g, discharging power of energy storage e and charging power of energy storage e, < >>
And->
Respectively represent real-time optimization stage scenes S 0r At the time t r Discharge power and charge power of energy store ee, +.>
And->
Respectively representing real-time optimized dispatching power, discharging power of energy storage e and charging power of energy storage e of thermal power generating unit g at moment t under real-time optimized stage scene S +.>
And->
Respectively representing the discharge power and the charging power of the energy-type energy storage Ee at the moment t under the real-time optimization stage scene S, wherein the subscript Ee represents the energy-type energy storage Ee, the Ee represents the collection of the energy-type energy storage, and alpha and beta are respectively the deviation power penalty coefficients of the thermal power generating unit and the energy-type energy storage, respectively>
Respectively represent the in-scene ++obtained based on the hour-of-day level optimized scheduling calculation>
At the time t r The power of the thermal power generating unit g, the discharge power of the energy-type energy storage ee and the charging power.
3.2 constraint conditions;
the real-time optimization scheduling is the same as the scheduling model constraint in the day before and in the day, and also comprises four parts, namely a system real-time operation constraint, a thermal power unit real-time operation constraint, a hybrid energy storage real-time operation constraint and a renewable energy source real-time operation constraint, and specifically, the four parts are as follows:
a) The system runs in real time.
The real-time optimized scheduling has consistency with the mathematical model of the first two stages, but in the calculation process, the corresponding parameters are required to be replaced by related data acquired in real time, and the method specifically comprises the following steps:
optimizing scheduling system operation in real time also requires ensuring power balance between the internal charge stores of the system, as shown in equation (28). On the other hand, real-time optimized scheduling also needs to meet transmission line constraints, as shown in equation (29).
Wherein,,
representing the real-time power generation of renewable energy units r at time t under scene s, +.>
Representing the real-time predictive value of the load d at time t under the scene s.
b) And (5) real-time operation constraint of the thermal power generating unit.
The same as the intra-day scheduling model, the corresponding intra-day parameters need to be replaced by real-time parameters in the calculation of the part, and the specific replacement is as follows:
equation (30) is the output constraint of the thermal power generating unit in the real-time optimal scheduling stage, and equation (31) is the climbing constraint of the thermal power generating unit in the real-time optimal scheduling stage.
Wherein,,
and the power value of the real-time optimal scheduling phase of the thermal power unit g at the time t-1 under the scene s is represented.
c) Hybrid energy storage real-time operation constraints.
The real-time optimal run phase constraint of hybrid energy storage consists of two parts: 1) Conventional operational constraints; 2) And (5) operating deviation constraint. Its conventional operating constraints are as follows:
wherein,,
and->
Respectively, real-time optimizing scheduling stageThe charging and discharging state of the power energy storage pe at time t under the scene s, +.>
And->
The capacities of the energy storage e at the time t-1 and the time t under the scene s in the real-time optimal scheduling stage are respectively represented. Δt (delta t) real The single time step size of the scheduling stage is optimized in real time.
And the operation deviation constraint refers to the deviation of the energy state of the energy type energy storage relative to the daily hour level optimization operation. This is because the energy storage is used as the energy-limited device, if the deviation between the energy state and the expected operation scheme is too large, the subsequent operation strategy cannot be executed, so that a capacity limiting model constraint is added on the basis of the daily energy storage scheduling operation model.
In the method, in the process of the invention,
at t r Capacity of energy storage at moment, +.>
Is the energy storage ee calculated in the intra-day schedule in the scene->
At the time t r Is a function of the capacity of the battery. a, a ee,r The method is a real-time scheduling stage, and the maximum capacity deviation ratio of energy type energy storage is calculated.
d) Renewable energy real-time operation constraints;
the same as the intra-day scheduling model, the corresponding intra-day parameters need to be replaced by real-time parameters in the calculation of the part, and the specific modification is as follows:
the power constraint of the renewable energy unit of real-time optimization scheduling is as shown in formula (36):
wherein,,
and the predicted power value of the renewable energy unit r at the moment t under the scene s is obtained by representing the real-time optimal scheduling stage.
The solution of the real-time optimized scheduling strategy is also based on Matlab compilation, and is based on the solution of a Gurobi solver, wherein MILP gap is set to be 0.1%. And solving a data model to calculate and obtain the operating power of the thermal power unit, the operating power of the energy storage, the operating power of the power storage and the power of the energy unit.
Step 4, integrating three-stage scheduling strategies;
in the actual implementation process, parameters to be determined include the following aspects:
1) The operation part of the thermal power generating unit, the starting and stopping state and the positive and negative standby schemes of the thermal power generating unit are obtained in the optimized operation in the future; the power value of the generator is operated, a primary result is obtained in an hour-level optimization scheduling stage in the day, secondary optimization is carried out in a real-time scheduling stage, and the operation power of the thermal power generating unit obtained in the real-time optimization stage is taken as an actual execution scheme;
2) And the energy storage operation part is used for controlling the charge and discharge strategy of the energy storage based on the scheme obtained in the operation of the day-ahead optimal scheduling stage, obtaining a preliminary result in the day-ahead hour-level optimal scheduling stage, adjusting the preliminary result in the real-time optimal scheduling stage, obtaining the new energy storage operation power and the optimal result of the power storage operation power, and taking the group of results as the actual implementation scheme.
3) And the renewable energy unit part takes the power of the energy unit which can be obtained by real-time optimization stage scheduling as an actual operation scheme.
The foregoing is a further detailed description of the invention in connection with specific/preferred embodiments, and it is not intended that the invention be limited to such description. It will be apparent to those skilled in the art that several alternatives or modifications can be made to the described embodiments without departing from the spirit of the invention, and these alternatives or modifications should be considered to be within the scope of the invention.
The invention, in part not described in detail, is within the skill of those skilled in the art.
Claims (5)
1. The hybrid energy storage multi-time scale optimization scheduling strategy under the power market scheduling requirement is characterized by mainly comprising the following four steps:
the first step, optimizing and scheduling in the day before, wherein the calculation frequency is 1 time per day, and the step length of predicted data is 1 hour;
the day-ahead optimization scheduling aims at minimizing the total day-ahead scheduling operation cost, and based on the renewable energy source forecast output and load demand information obtained in the day-ahead, the system operation constraint, the thermal power unit operation constraint, the hybrid energy storage system operation constraint and the renewable energy source operation constraint are comprehensively considered, and the start-stop state, the energy type energy storage charging and discharging strategy and the positive and negative standby schemes of the day-ahead thermal power unit are obtained through optimization calculation;
step two, intra-day hour-level optimized scheduling, wherein the calculation frequency is 96 times per day, and the step length of predicted data is 15 minutes;
taking a starting and stopping state of a thermal power unit, an energy type energy storage charging and discharging strategy and positive and negative standby schemes of the thermal power unit which are optimized and scheduled in the future and a future as determined values, optimizing and obtaining a thermal power unit power strategy and energy type energy storage operation power and capacity value which minimize the expected power operation cost of the system under the condition that the system daily operation constraint, the thermal power unit daily operation constraint, the hybrid energy storage daily operation constraint and the renewable energy source daily operation constraint are met based on renewable energy source prediction output and load demand prediction data which are obtained by daily short-term prediction and take 15 minutes as step length;
thirdly, optimizing scheduling in real time, wherein the calculation frequency is calculated once every 5 minutes, and the corresponding predicted data step length is 5 minutes;
taking a result obtained by day-ahead optimal scheduling as a determined value, taking a result obtained by day-in-hour scheduling as a reference value, taking the deviation between the minimum value and the reference value as a target, and unfolding and optimizing to calculate the operating power of the thermal power unit, the energy-type energy storage operating power and the power-type energy storage operating power by combining with real-time scheduling operation constraint;
and fourthly, integrating the results of day-ahead optimal scheduling, day-in-day hour scheduling and real-time scheduling operation to obtain an actual control strategy of the thermal power generating unit and the hybrid energy storage.
2. The power market scheduling demand hybrid energy storage multi-time scale optimal scheduling strategy according to claim 1, wherein the day-ahead optimal scheduling concrete method is as follows;
day-ahead optimal schedule is 10 per day: carrying out scheduling plan calculation about the next day at 00pm, wherein the calculated data step length is 1 hour; the scheme aims at determining the start-up and stop states of the thermal power generating unit, the energy-type energy storage charging and discharging strategy and the positive and negative standby schemes of the thermal power generating unit required by the optimization scheduling of the hour level in the next day through day-ahead optimization calculation; the specific optimization strategy model is as follows:
1.1 an objective function;
the day-ahead optimization scheduling aims at minimizing the running cost of the system generator and the energy storage, and the objective function can be written as a formula (1) and is mainly divided into two parts: the first part is the starting and closing cost of a conventional thermal power generating unit, and the second part is the power cost of thermal power generation and HESS;
wherein T represents the time T, T day Represents a set of day-ahead optimal scheduling time, G represents a thermal power unit G, G represents a set of all thermal power units, G r Representing a conventional thermal power generating unit with a fast response capability, E representing energy storage devices E, E representing all energy storage devices, S representing scenes S, S day Representing a set of all potential scenarios generated by a day-ahead prediction,
representing the probability of occurrence of scene s in the day-ahead prediction,/->
And->
The starting and stopping costs of the thermal power generating unit are respectively; k (K) g,t And G g,t Respectively representing the starting and stopping states of the thermal power generating unit, < ->
And p g,t,s The unit operation cost and the power of the thermal power unit are respectively; />
And->
Representing the cost of discharging and charging, respectively, of the energy storage unit power,/->
And->
Respectively representing the discharge power and the charge power of the stored energy;
1.2 constraint conditions;
constraint conditions of the day-ahead optimization scheduling model comprise system operation constraint, thermal power unit operation constraint, hybrid energy storage system operation constraint and renewable energy source operation constraint;
a) System operation constraints;
firstly, ensuring the balance of electric power and electric quantity of all equipment in the system, namely the balance among a thermal power unit, an energy storage unit, a renewable energy unit and a user load, wherein the balance is shown in a formula (2); meanwhile, the system operation also needs to ensure that the capacity constraint of the transmission line of the system is satisfied, namely the sum of the power flowing through the modified line of all equipment needs to be within the limit range of the maximum capacity of the line, as shown in a formula (3); on the other hand, the system operation needs to ensure that the standby power is satisfied, and the standby is divided into positive standby and negative standby as shown in a formula (4);
wherein p is r,t,s Representing renewable energy generating capacity, r represents a renewable energy unit r, and REN represents a collection of renewable energy units; p is p d,t,s Representing the daily forecast of the load, D representing the load node D, and D representing the set of loads,
is the maximum power capacity of line l, SF l,b Is the sensitivity coefficient of the power flow through line l to the power injection of the power transmission node B, B representing the power transmission node B and B representing the set of all power transmission nodes; />
And->
Respectively representing the positive power standby of the thermal power unit g and the total positive power standby of the system, +.>
And->
Respectively representing the reserve of the negative power of the thermal power unit g and the total reserve of the negative power of the system;
b) Thermal power generating unit operation constraint;
the formula (5) is the output constraint of the thermal power generating unit, the formula (6) is the climbing constraint of the thermal power generating unit, the left side is the negative climbing upper limit, the right side is the positive climbing upper limit, the formula (7) is the minimum time limit constraint of starting and stopping of the thermal power generating unit respectively, and the formula (8) is the relation among the running state, the starting state and the stopping state of the thermal power generating unit;
wherein,,
and->
Respectively, thermal power generating unitMinimum and maximum power; x is x g,t 、x g,t' And x g,t-1 Respectively representing state variables of the thermal power generating unit g at the time t, t' and t-1, p g,t-1,s The power of the thermal power generating unit g at the moment t-1 under the scene s is represented; />
And
is the limit of the positive climbing capacity and the negative climbing capacity of the thermal power generating unit, and is +.>
And->
Representing the starting and stopping power of the thermal power generating unit; QDs g And GT g Is the minimum start-up and shut-down duration time of the thermal power generating unit;
c) Hybrid energy storage system operational constraints;
the operation constraint of the hybrid energy storage system in the day-ahead stage comprises two parts of a conventional operation constraint and a cycle number limit constraint model; the conventional operation constraint aims at two types of energy storage of a power type and an energy type, and the cycle number limiting constraint model aims at the energy type energy storage only; the formula (9) represents the limit constraint of the charge and discharge power of the energy storage, the formula (10) represents the limit constraint of the running state of the energy storage, the formula (11) represents the limit constraint of the capacity update of the energy storage, the formula (12) represents the limit constraint of the capacity limit of the energy storage, the formula (13) represents the limit of the minimum duration of the charge and discharge of the energy storage, the formula (14) represents the limit of the maximum number of times of the charge and discharge of the energy storage, and the formula (15) and the formula (16) represent the logic relationship between various state descriptions of the energy storage;
E e,o,s ＝e e,T,s (12)
wherein,,
and->
Respectively maximum and minimum values of rated power of energy storage e->
And->
Respectively the charge and discharge state of the energy store e at time t under the scene s, +.>
And->
Charging and discharging efficiencies of the energy storage e respectively; e, e e,t-1,s And e e,t-1,s Respectively representing the capacities of the energy storage E at the time t and the time t-1 under the scene s, E e,o,s And E is e,T,s Representing the capacity of the energy storage e at time 0 and time T under the scene s, respectively; />
And->
Representing the maximum and minimum values of the rated capacity of the stored energy e; CT and DT represent the minimum sustained charge and discharge time of the energy store, < >>
Represents the maximum number of cycles in the day of the energy store ee, < >>
And->
Respectively represent the charge and discharge states of the energy store ee at time k, +.>
And->
Representing the switching of the energy store ee from the other state to the charge state and the discharge state, respectively, +.>
And->
The energy storage ee representing the energy is switched from the charge state and the discharge state to the other state, +.>
And->
Respectively charging and discharging states of the energy storage e at a time t-1; Δt (delta t) day A single time step length representing a day-ahead optimal scheduling stage;
d) Renewable energy source operation constraints;
equation (17) is the output power limit constraint of the renewable energy unit;
wherein,,
the predicted output value of the renewable energy unit r at the moment t under the scene s represents the day-ahead optimal scheduling stage;
solving a day-ahead optimal scheduling strategy model based on Matlab compiling, and adopting Gurobi to solve, wherein MILP gap is set to be 0.1%; and solving a data model, calculating to obtain a start-stop state, an energy-type energy storage charging and discharging strategy and a positive standby scheme and a negative standby scheme of the thermal power generating unit before the day aiming at the thermal power generating unit, and storing the result.
3. The hybrid energy storage multi-time scale optimal scheduling strategy under the power market scheduling requirement according to claim 2, wherein the specific method of the intra-day hour-level optimal scheduling is as follows;
the intra-day hour level optimized scheduling is that scheduling plan measurement and calculation related to the following 4 hours are developed 1 hour in advance, and the calculated data step length is 15 minutes; the scheme aims at determining a power strategy of a thermal power generating unit in the day and an operation power reference standard of energy type energy storage through optimization calculation in the day; the specific optimal scheduling model is as follows:
2.1 an objective function;
in the day hour level optimizing and scheduling stage, the stage aims at minimizing the running cost of the thermal power generating unit and the energy storage, but the variable of the stage is only the running power of the thermal power generating unit and the energy storage, and the method can be specifically written as follows:
wherein t is i The starting calculation time of the hour-of-day level optimization schedule is represented,
basic prediction scene 0,S for representing hour-of-day level optimized scheduling ind Representing all potential scene sets generated by intra-day prediction, < >>
Representing the probability of occurrence of scene s under intra-day prediction, T ind Optimizing the time length of the schedule for the hours within the day,/->
Representing scene +.>
Power value of next time ti, +.>
And->
Respectively representDay hour level optimizing and scheduling phase energy storage e in scene +.>
The discharge and charge power values at the next instant ti, < ->
Representing the power value of time t under scene s of thermal power generating unit g obtained in the day hour level optimized scheduling stage,/->
And->
Respectively representing the discharge and charge power values of the energy storage e at time t under the scene s in the hour-in-day level optimized scheduling stage;
2.2 constraint conditions;
the intra-day hour-level optimization scheduling is the same as the constraint of the pre-day optimization scheduling model, and also comprises four parts, namely a system intra-day operation constraint, a thermal power unit intra-day operation constraint, a hybrid energy storage intra-day operation constraint and a renewable energy source intra-day operation constraint, and specifically:
a) System daily operation constraint
The day-ahead scheduling model and the day-ahead scheduling model are mainly embodied in different time scales on the system operation constraint level, and all parameters of the day-ahead scheduling model are replaced by the day-ahead optimal operation parameters;
the operation of the daily hour-level optimal scheduling system also needs to ensure the balance of electric power and electric quantity between the internal charge storages of the system, as shown in a formula (19); on the other hand, the daily hour-level optimized scheduling also needs to meet the constraint of the power transmission line, as shown in a formula (20); the standby is reserved in the day before the day, so that the consideration of the standby capacity is not carried out in the day any more;
wherein,,
day power generation amount of renewable energy unit r at time t under scene s, +.>
A daily predictive value representing the load d at time t under the scene s;
b) The daily operation constraint of the thermal power generating unit;
different from a day-ahead scheduling model, the day-in-hour-level optimized scheduling strategy is further developed based on a start-stop state result of the thermal power unit obtained by day-ahead scheduling; thus, the generator operation constraint part of the hour-of-day level optimization schedule contains only constraints (5) and (6), two constraints limiting power; similarly, the calculation in this section also needs to replace all the day-ahead scheduling model parameters with the day-hour-level optimized scheduling parameters, which is specifically as follows:
the formula (21) is the output constraint of the thermal power unit in the intra-day hour stage scheduling, and the formula (22) is the climbing constraint of the thermal power unit in the intra-day hour stage scheduling;
wherein,,
the daily hour level operation power value of the thermal power unit g at the time t-1 under the scene s is represented;
c) Hybrid energy storage intra-day operational constraints;
the energy type energy storage determines the charge and discharge state of the energy type energy storage in the day-ahead stage, so that a circulation constraint model does not need to be built again, and the charge and discharge state behavior is used as a parameter to calculate in the day-ahead stage; thus, the energy storage operation constraint part of the intra-day optimization model only contains formulas (10) - (12); similarly, in the calculation of the part, the calculation result of the day-ahead scheduling model needs to be used as an operation parameter operated in the day, and the specific substitution is as follows:
wherein,,
and->
Respectively the charging and discharging state of the power energy storage pe at time t under the scene s, +.>
And->
The capacities of the energy storage e at the time t-1 and the time t under the scene s are respectively represented; Δt (delta t) ind A single moment step length representing intra-day hour level optimized scheduling;
d) Renewable energy daily operation constraint;
the power constraint of the renewable energy unit of the daily hour-level optimized scheduling is as shown in a formula (26):
wherein,,
the predicted force value of the renewable energy unit r obtained in the day hour level optimal scheduling stage at the time t under the scene s is represented;
the method is the same as the solution of the day-ahead optimal scheduling strategy, the solution of the day-in-hour-level optimal scheduling strategy is also based on Matlab compiling, and the solution is conducted by using a Gurobi solver, wherein MILP gap is set to be 0.1%; and solving a data model, calculating and obtaining a thermal power unit power strategy for minimizing the expected power operation cost of the system, and an energy-type energy storage operation power value and a capacity value at each moment, and storing data.
4. The hybrid energy storage multi-time scale optimal scheduling strategy under the power market scheduling requirement according to claim 3, wherein the specific real-time optimal scheduling method is as follows;
the real-time optimized scheduling strategy is to expand scheduling plan measurement and calculation for 2 hours in advance by 5 minutes, and the calculated data step length is 5 minutes; the scheme aims at determining the adjustment quantity of the running state of the thermal power unit, the adjustment quantity of energy-type energy storage power and the running scheme of power-type energy storage through real-time optimization calculation; the specific model is as follows:
3.1 objective function
The goal of real-time optimized scheduling mainly includes two aspects: on the one hand, it is necessary to minimize the running cost of the system, i.e. the running cost of the generator and the energy storage, and on the other hand, it is also desirable to be able to match as much as possible with the global optimum calculated at a longer time scale in the day, so as to reduce the influence of unknown information; the specific objective function can be written as:
wherein t is r S represents the initial computing time of real-time dispatching operation 0r Basic prediction scene representing real-time optimized scheduling, S real Representing a set of all potential scenarios generated by real-time optimized scheduling predictions,
representing the probability of occurrence of a scene s in a real-time optimal scheduling stage, T r Optimizing the time window length of the schedule for real time, +.>
And->
Respectively represent real-time optimization stage scenes S 0r At the time t r Real-time optimized dispatching power of thermal power generating unit g, discharging power of energy storage e and charging power of energy storage e, < >>
And->
Respectively represent real-time optimization stage scenes S 0r At the time t r The discharge power and the charge power of the energy store ee,
and->
Respectively representing real-time optimized dispatching power, discharging power of energy storage e and charging power of energy storage e of thermal power generating unit g at moment t under real-time optimized stage scene S +.>
And->
Respectively represent the discharge power and the charge power of the energy storage ee at the moment t under the real-time optimization stage scene S, and the subscript ee represents the energy storage E e Representing a collection of energy type energy storage, wherein alpha and beta are deviation power penalty coefficients of a thermal power unit and the energy type energy storage respectively, and +.>
Respectively represent the in-scene ++obtained based on the hour-of-day level optimized scheduling calculation>
At the time t r The power of the thermal power generating unit g, the discharge power of the energy-type energy storage ee and the charging power;
3.2 constraint conditions;
the real-time optimization scheduling is the same as the scheduling model constraint in the day before and in the day, and also comprises four parts, namely a system real-time operation constraint, a thermal power unit real-time operation constraint, a hybrid energy storage real-time operation constraint and a renewable energy source real-time operation constraint, and specifically, the four parts are as follows:
a) The system runs the constraint in real time;
the real-time optimized scheduling has consistency with the mathematical model of the first two stages, but in the calculation process, the corresponding parameters are required to be replaced by related data acquired in real time, and the method specifically comprises the following steps:
the real-time optimal scheduling system is operated, so that the balance of electric power and electric quantity between the internal charge storages of the system is ensured, as shown in a formula (28); on the other hand, real-time optimized scheduling also needs to meet transmission line constraints, as shown in formula (29);
wherein,,
representing the real-time power generation of renewable energy units r at time t under scene s, +.>
A real-time predicted value representing the load d at time t under the scene s;
b) Real-time operation constraint of the thermal power generating unit;
the same as the intra-day scheduling model, the corresponding intra-day parameters need to be replaced by real-time parameters in the calculation of the part, and the specific replacement is as follows:
equation (30) is the output constraint of the thermal power generating unit in the real-time optimal scheduling stage, and equation (31) is the climbing constraint of the thermal power generating unit in the real-time optimal scheduling stage;
wherein,,
the power value of the real-time optimal scheduling stage of the thermal power unit g at the moment t-1 under the scene s is represented;
c) Hybrid energy storage real-time operation constraints;
the real-time optimal run phase constraint of hybrid energy storage consists of two parts: 1) Conventional operational constraints; 2) Operating deviation constraint; its conventional operating constraints are as follows:
wherein,,
and->
Respectively optimizing the charge and discharge states of the power type energy storage pe at the time t under the scene s in real time,/for the scheduling stage>
And->
Respectively representing the capacities of the energy storage e at the time t-1 and the time t under the scene s in the real-time optimal scheduling stage; Δt (delta t) real A single moment step length of a scheduling stage is optimized in real time;
and the operation deviation constraint refers to the deviation of the energy state of the energy storage relative to the daily hour level optimization operation; the energy storage is used as energy-limited equipment, if the deviation between the energy state and the expected operation scheme is too large, the subsequent operation strategy cannot be executed, so that a capacity limiting model constraint is added on the basis of a daily energy storage scheduling operation model;
in the method, in the process of the invention,
at t r Capacity of energy storage at moment, +.>
Is the energy storage ee calculated in the intra-day schedule in the scene->
At the time t r Is a capacity of (2); a, a ee,r The method is a real-time scheduling stage, and the maximum capacity deviation ratio of energy storage is the maximum capacity deviation ratio of energy storage;
d) Renewable energy real-time operation constraints;
the same as the intra-day scheduling model, the corresponding intra-day parameters need to be replaced by real-time parameters in the calculation of the part, and the specific modification is as follows:
the power constraint of the renewable energy unit of real-time optimization scheduling is as shown in formula (36):
wherein,,
the predicted output value of the renewable energy unit r at the moment t under the scene s is obtained in the real-time optimized scheduling stage;
the method is the same as the solution of the strategies in the first two stages, the solution of the real-time optimized scheduling strategy is also based on Matlab compiling, and the solution is conducted by using a Gurobi solver, wherein MILP gap is set to be 0.1%; and solving a data model to calculate and obtain the operating power of the thermal power unit, the operating power of the energy storage, the operating power of the power storage and the power of the energy unit.
5. The hybrid energy storage multi-time scale optimized scheduling strategy under power market scheduling requirements according to claim 4, wherein the fourth specific method is as follows;
in the actual implementation process, parameters to be determined include the following aspects:
1) The operation part of the thermal power generating unit, the starting and stopping state and the positive and negative standby schemes of the thermal power generating unit are obtained in the optimized operation in the future; the power value of the generator is operated, a primary result is obtained in an hour-level optimization scheduling stage in the day, secondary optimization is carried out in a real-time scheduling stage, and the operation power of the thermal power generating unit obtained in the real-time optimization stage is taken as an actual execution scheme;
2) The energy storage operation part, the charge and discharge strategy of the energy storage is based on the scheme obtained in the operation of the optimization scheduling stage before the day, the operation power and the capacity value of the energy storage are both obtained in the optimization scheduling stage of the hour level in the day, the preliminary result is adjusted in the real-time optimization scheduling stage, the new optimization results of the operation power of the energy storage and the operation power of the power type energy storage are obtained, and the group of results are used as the actual implementation scheme;
3) And the renewable energy unit part takes the power of the energy unit which can be obtained by real-time optimization stage scheduling as an actual operation scheme.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310291823.3A CN116341241A (en) | 2023-03-23 | 2023-03-23 | Hybrid energy storage multi-time scale optimization scheduling strategy under power market scheduling demand |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310291823.3A CN116341241A (en) | 2023-03-23 | 2023-03-23 | Hybrid energy storage multi-time scale optimization scheduling strategy under power market scheduling demand |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116341241A true CN116341241A (en) | 2023-06-27 |
Family
ID=86894441
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310291823.3A Pending CN116341241A (en) | 2023-03-23 | 2023-03-23 | Hybrid energy storage multi-time scale optimization scheduling strategy under power market scheduling demand |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116341241A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116613750A (en) * | 2023-07-18 | 2023-08-18 | 山东大学 | Integrated scheduling method, system, terminal equipment and medium for power system |
| CN117853273A (en) * | 2024-03-06 | 2024-04-09 | 国网北京市电力公司 | Park-level distributed energy system optimization method, device, equipment and medium |
| CN117853273B (en) * | 2024-03-06 | 2024-06-07 | 国网北京市电力公司 | Park-level distributed energy system optimization method, device, equipment and medium |
-
2023
- 2023-03-23 CN CN202310291823.3A patent/CN116341241A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116613750A (en) * | 2023-07-18 | 2023-08-18 | 山东大学 | Integrated scheduling method, system, terminal equipment and medium for power system |
| CN116613750B (en) * | 2023-07-18 | 2023-10-13 | 山东大学 | Integrated scheduling method, system, terminal equipment and medium for power system |
| CN117853273A (en) * | 2024-03-06 | 2024-04-09 | 国网北京市电力公司 | Park-level distributed energy system optimization method, device, equipment and medium |
| CN117853273B (en) * | 2024-03-06 | 2024-06-07 | 国网北京市电力公司 | Park-level distributed energy system optimization method, device, equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Abdalla et al. | Integration of energy storage system and renewable energy sources based on artificial intelligence: An overview | |
| Cao et al. | Battery energy storage sizing based on a model predictive control strategy with operational constraints to smooth the wind power | |
| CN108667052B (en) | Multi-type energy storage system planning configuration method and system for virtual power plant optimized operation | |
| Pourmousavi et al. | Real-time energy management of a stand-alone hybrid wind-microturbine energy system using particle swarm optimization | |
| CN102289566B (en) | Multiple-time-scale optimized energy dispatching method for micro power grid under independent operation mode | |
| CN107565607B (en) | Micro-grid multi-time scale energy scheduling method based on real-time electricity price mechanism | |
| CN110311421A (en) | Micro-capacitance sensor Multiple Time Scales energy management method based on Demand Side Response | |
| CN112713618B (en) | Active power distribution network source network load storage cooperative optimization operation method based on multi-scene technology | |
| CN108964103B (en) | Microgrid energy storage configuration method considering schedulability of microgrid system | |
| CN107370171B (en) | Large-scale energy storage optimal configuration and coordination control method in independent microgrid | |
| CN105139147A (en) | Economic scheduling method for micro-grid system | |
| CN113541205B (en) | Cluster learning-based low-carbon CSP system collaborative optimization method and device | |
| CN104795830A (en) | Controlling method of tracing planned contribution of electricity generation with various energy-storing systems | |
| CN111293682A (en) | Multi-microgrid energy management method based on cooperative model predictive control | |
| CN116341241A (en) | Hybrid energy storage multi-time scale optimization scheduling strategy under power market scheduling demand | |
| CN115423207A (en) | Wind storage virtual power plant online scheduling method and device | |
| CN115313516A (en) | Photovoltaic power generation and energy storage microgrid combined random optimization operation strategy | |
| CN116050637A (en) | Comprehensive energy virtual power plant optimal scheduling method and system based on time-of-use electricity price | |
| Belloni et al. | A stochastic optimal control solution to the energy management of a microgrid with storage and renewables | |
| CN117559526A (en) | Router-simulated energy regulation and control method based on optical storage and charging integrated charging station | |
| CN111582599A (en) | Energy storage equipment day active power scheduling method based on virtual power plant peak shaving | |
| CN104281984A (en) | Power supply method for microgrid economical operation | |
| Lin et al. | Hybrid energy storage system control and capacity allocation considering battery state of charge self-recovery and capacity attenuation in wind farm | |
| CN114418453A (en) | Micro-grid multi-time scale energy management system based on electric power market | |
| CN114336703A (en) | Automatic cooperative control method for large-scale wind-solar power storage station |
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 |