CN116646987A - Multi-resource cooperative scheduling method, device, equipment and storage medium - Google Patents

Multi-resource cooperative scheduling method, device, equipment and storage medium Download PDF

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CN116646987A
CN116646987A CN202310627007.5A CN202310627007A CN116646987A CN 116646987 A CN116646987 A CN 116646987A CN 202310627007 A CN202310627007 A CN 202310627007A CN 116646987 A CN116646987 A CN 116646987A
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period
constraint
load
unit
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李利利
孙大雁
陶洪铸
项中明
庞腊成
曹荣章
罗治强
王超
张志�
吴华华
徐立中
张思
李永刚
昌力
吴炳祥
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State Grid Zhejiang Electric Power Co Ltd
NARI Nanjing Control System Co Ltd
State Grid Electric Power Research Institute
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State Grid Zhejiang Electric Power Co Ltd
NARI Nanjing Control System Co Ltd
State Grid Electric Power Research Institute
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Publication of CN116646987A publication Critical patent/CN116646987A/en
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    • 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
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • G—PHYSICS
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    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • 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
    • 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]
    • 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

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Abstract

The invention discloses a multi-resource collaborative scheduling method, a device, equipment and a storage medium, which are used for predicting new energy unit data in power supply side data and rigid load data in load side data to obtain prediction data, and constructing a power balance decision scene according to the prediction data; according to the power source side data and the load side data of the power balance decision, an optimal scheduling model of multi-resource cooperation is established; solving a multi-resource collaborative optimization scheduling model under different power balance decision scenes to obtain optimal output results of a plurality of power balance decision scenes, calculating Euclidean distances between loads and new energy output of the power grid and the different power balance decision scenes according to real-time prediction, selecting a power balance decision scene with the minimum Euclidean distance, and taking the solving result of the power balance decision scene as a formal scheduling plan. The invention is helpful to promote the supply protection and the elimination of the scheduling decision requirement under the complicated background of the power scheduling scene and the scheduling object.

Description

Multi-resource cooperative scheduling method, device, equipment and storage medium
Technical Field
The invention relates to a multi-resource cooperative scheduling method, a device, equipment and a storage medium, belonging to the technical field of power system scheduling automation.
Background
The new energy power generation ratio is continuously improved, the randomness and fluctuation characteristics are increasingly outstanding, meanwhile, the new energy fluctuation and the load peak-valley difference are frequently increased in extreme weather, and the novel power system development faces the problems of insufficient balanced supporting capability, high difficulty in power supply guarantee and the like. Under the trend that the proportion of a stable and reliable traditional power supply installation machine gradually decreases, the power grid protection supply and the digestion promotion requirements are mutually interwoven, various flexibility adjustment resources on the power generation side and the load side are required to be comprehensively coordinated, and the overall adjustment capability of the power system is improved.
Because the novel power system has the characteristic of balanced scene diversification, the power grid dispatching relates to various objects such as primary energy sources, multi-class power sources, load response and the like. The traditional scheduling technology has the defects of consideration of potential risks caused by high-proportion new energy, generally builds an optimal scheduling model of specific resources based on a single balance scene, has the defects of consideration of power balance decisions under multiple scenes, has the adaptability of the optimal scheduling model, is difficult to meet the requirements of supply conservation and scheduling decision promotion under the complicated background of scheduling scenes and scheduling objects, and cannot fully exert the balanced supporting capability of the cooperative scheduling of the resources of multiple types of source loads.
Therefore, how to perform coordinated scheduling of multiple resources with the goal of power balance in multiple scenarios is a technical problem that needs to be solved by those skilled in the art.
Disclosure of Invention
The purpose is as follows: in order to overcome the defects in the prior art, the invention provides a multi-resource collaborative scheduling method, a device, equipment and a storage medium, which are used for constructing a power balance decision scene by considering new energy power generation and load demand fluctuation factors, carrying out scheduling decision of various resources based on the multi-power balance decision scene, and improving power grid scheduling guarantee supply and digestion promotion capability in the multi-power balance decision scene.
The technical scheme is as follows: in order to solve the technical problems, the invention adopts the following technical scheme:
in a first aspect, a multi-resource cooperative scheduling method includes the following steps:
and acquiring power supply side data of the power balance decision.
And acquiring load side data of the power balance decision.
And predicting according to the new energy unit data in the power supply side data and the rigid load data in the load side data, obtaining prediction data, and constructing a power balance decision scene according to the prediction data.
And establishing an optimal scheduling model of multi-resource cooperation according to the power source side data and the load side data of the power balance decision.
Solving a multi-resource collaborative optimization scheduling model under different power balance decision scenes to obtain optimal output results of thermal power units, new energy units, energy storage units and flexible loads of the power balance decision scenes, calculating Euclidean distances according to real-time prediction of the power grid and the loads and new energy output of the different power balance decision scenes, selecting a power balance decision scene with the minimum Euclidean distance, and taking the solving result of the power balance decision scene as a formal scheduling plan.
Further, the constructing a power balance decision scene according to the prediction data specifically includes:
generating a plurality of power balance decision scenarios according to the new energy unit prediction data, including: the new energy prediction standard scene, the new energy prediction high-rise scene with 10 percent of floating up, and the new energy prediction low-rise scene with 10 percent of floating down.
Generating a plurality of power balance decision scenarios from the rigid load prediction data, comprising: a reference scene of system rigid load prediction, a high load scene of 10% floating up of system rigid load prediction, and a low load scene of 10% floating down of system rigid load prediction.
Further, the calculation formula of the multi-resource collaborative optimization scheduling model is as follows:
Wherein T is the number of optimal scheduling time periods, I is the number of thermal power units comprising coal-fired units and gas-fired units, and C i Is the running cost of the thermal power unit i, P i,t For the active output of the thermal power generating unit i in the period t, S i Is the starting cost, y of the thermal power generating unit i i,t A sign for changing the state from shutdown to startup of the thermal power unit i; s is the number of energy storage units, C s For the scheduling cost of the energy storage unit s, P s,t Active output of the energy storage unit s in a period t; w is the number of new energy units comprising wind power and photovoltaic, C w The electricity discarding cost of the new energy unit w is P w,t The electric power is discarded for the new energy unit w in the period t; m is the number of flexible loads, C m For the adjustment cost of the flexible load m, P m,t The power is adjusted for the flexible load m during the period t.
Further, the constraint condition of the multi-resource collaborative optimization scheduling model includes: thermal power generating unit constraint, energy storage unit constraint, new energy unit constraint, flexible load constraint and system operation constraint.
The thermal power generating unit constraint specifically comprises an output range constraint, a climbing rate constraint, a minimum starting and stopping time constraint and an electric energy generation capacity constraint, and the calculation formula is as follows:
P i,min ≤P i,t ≤P i,max
-Δ i ≤P i,t -P i,t-1 ≤Δ i
wherein P is i,max And P i,min Respectively the upper limit and the lower limit of the output power of the thermal power unit i, P i,t For the active output of the thermal power generating unit i in the period t, P i,t-1 For the active output of the thermal power generating unit i in the period t-1, delta i For the maximum value of the climbing rate of the thermal power unit i in each period, u i,t Is the start-stop state of the thermal power generating unit i in the period t, y i,τ A sign for changing the state of the thermal power unit i from shutdown to startup in a period tau, z i,τ Is a mark for changing the state of the thermal power unit i from start-up to stop in the period tau, UT i And DT (DT) i Respectively the minimum start-up time and the minimum stop time of the thermal power unit i, E i,min And E is i,max The upper limit and the lower limit of the generating capacity of the unit i are respectively.
The energy storage unit constraint specifically comprises: the power accumulation constraint, the charging power constraint, the discharging power constraint, the electric quantity accumulation constraint and the electric quantity limit value constraint are calculated according to the following formula:
E s,min ≤E s,t ≤E s,max
wherein P is s,t For the active output of the energy storage unit s in the period t,and->Respectively discharging power and charging power of the energy storage unit s in t period, P s d,max And P s d,min Respectively the upper limit and the lower limit of the discharge power of the energy storage unit s, P s c,max And P s c ,min Respectively the upper limit and the lower limit of the charging power of the energy storage unit s, alpha s Is the charging efficiency coefficient beta of the energy storage unit s s For the discharge efficiency coefficient of the energy storage unit s, E s,t For accumulating electric quantity of the energy storage unit s in t period E s,t-1 For accumulating electric quantity of the energy storage unit s in t-1 period, E s,max And E is s,min The upper limit and the lower limit of the accumulated electric quantity of the energy storage unit s are respectively.
The new energy unit constraint specifically comprises an output range constraint, and the calculation formula is as follows:
wherein P is w,t The electric power is discarded by the new energy unit w in the period t,and the predicted power of the new energy unit w in the period t is obtained.
The flexible load constraint specifically includes: the load adjusting range and the load availability constraint are calculated according to the following formula:
wherein P is m,t For the regulated power of the flexible load m during period t,and->Respectively flexible loadsm is at the upper and lower limits of adjustment of t period, D m,t Baseline load for flexible load m in period t, < >>And->The upper and lower limits of the flexible load m in the t period are respectively available.
The system operation constraints specifically include: the power balance constraint, the system standby constraint and the power grid safety constraint are calculated according to the following formula:
wherein L is t R is the total rigid load demand of the system in the period t t For the standby demand of the system in period t, l p、respectively the lower limit and the upper limit of the tide of the first power transmission section, N is a power grid computing node set, P n,t Calculating the power generation power of a node n for a period t power grid, l n,t Calculating the load power of a node n for a period t power grid, S n,l,t And calculating the sensitivity of the injection power of the node n to the first transmission section for the period t power grid.
Further, the euclidean distance has the following calculation formula:
wherein: d (D) q Decision scene q for power grid real-time prediction and power balanceIs used for the distance of euclidean distance,predicting the load demand for the grid in real time during period t +.>Load demand for power balance decision scenario q in period t, < >>Predicting the sum of new energy output in time t for the power grid in real time, +.>And the new energy output sum of the power balance decision scene q in the period t is obtained.
In a second aspect, a multi-resource cooperative scheduling apparatus includes the following modules:
a power supply side data acquisition module: and the power supply side data is used for acquiring the power balance decision.
Load side data acquisition module: load side data for acquiring power balance decisions.
The power balance decision scene construction module: the method is used for predicting the rigid load data in the new energy unit data and the load side data according to the power supply side data, obtaining prediction data and constructing a power balance decision scene according to the prediction data.
The optimal scheduling model building module: and the multi-resource collaborative optimal scheduling model is established according to the power supply side data and the load side data of the power balance decision.
And (3) an optimal scheduling module: the method comprises the steps of solving a multi-resource collaborative optimization scheduling model under different power balance decision scenes, obtaining optimal output results of thermal power units, new energy units, energy storage units and flexible loads of the power balance decision scenes, calculating Euclidean distances according to real-time prediction of a power grid and loads and new energy output of the different power balance decision scenes, selecting a power balance decision scene with the minimum Euclidean distance, and taking the solving result of the power balance decision scene as a formal scheduling plan.
Further, the constructing a power balance decision scene according to the prediction data specifically includes:
generating a plurality of power balance decision scenarios according to the new energy unit prediction data, including: the new energy prediction standard scene, the new energy prediction high-rise scene with 10 percent of floating up, and the new energy prediction low-rise scene with 10 percent of floating down.
Generating a plurality of power balance decision scenarios from the rigid load prediction data, comprising: a reference scene of system rigid load prediction, a high load scene of 10% floating up of system rigid load prediction, and a low load scene of 10% floating down of system rigid load prediction.
Further, the calculation formula of the multi-resource collaborative optimization scheduling model is as follows:
wherein T is the number of optimal scheduling time periods, I is the number of thermal power units comprising coal-fired units and gas-fired units, and C i Is the running cost of the thermal power unit i, P i,t For the active output of the thermal power generating unit i in the period t, S i Is the starting cost, y of the thermal power generating unit i i,t A sign for changing the state from shutdown to startup of the thermal power unit i; s is the number of energy storage units, C s For the scheduling cost of the energy storage unit s, P s,t Active output of the energy storage unit s in a period t; w is the number of new energy units comprising wind power and photovoltaic, C w The electricity discarding cost of the new energy unit w is P w,t The electric power is discarded for the new energy unit w in the period t; m is the number of flexible loads, C m For the adjustment cost of the flexible load m, P m,t The power is adjusted for the flexible load m during the period t.
Further, the constraint condition of the multi-resource collaborative optimization scheduling model includes: thermal power generating unit constraint, energy storage unit constraint, new energy unit constraint, flexible load constraint and system operation constraint.
The thermal power generating unit constraint specifically comprises an output range constraint, a climbing rate constraint, a minimum starting and stopping time constraint and an electric energy generation capacity constraint, and the calculation formula is as follows:
P i,min ≤P i,t ≤P i,max
-Δ i ≤P i,t -P i,t-1 ≤Δ i
wherein P is i,max And P i,min Respectively the upper limit and the lower limit of the output power of the thermal power unit i, P i,t For the active output of the thermal power generating unit i in the period t, P i,t-1 For the active output of the thermal power generating unit i in the period t-1, delta i For the maximum value of the climbing rate of the thermal power unit i in each period, u i,t Is the start-stop state of the thermal power generating unit i in the period t, y i,τ A sign for changing the state of the thermal power unit i from shutdown to startup in a period tau, z i,τ Is a mark for changing the state of the thermal power unit i from start-up to stop in the period tau, UT i And DT (DT) i Respectively the minimum start-up time and the minimum stop time of the thermal power unit i, E i,min And E is i,max The upper limit and the lower limit of the generating capacity of the unit i are respectively.
The energy storage unit constraint specifically comprises: the power accumulation constraint, the charging power constraint, the discharging power constraint, the electric quantity accumulation constraint and the electric quantity limit value constraint are calculated according to the following formula:
E s,min ≤E s,t ≤E s,max
wherein P is s,t For the active output of the energy storage unit s in the period t,and->Respectively discharging power and charging power of the energy storage unit s in t period, P s d,max And P s d,min Respectively the upper limit and the lower limit of the discharge power of the energy storage unit s, P s c,max And P s c ,min Respectively the upper limit and the lower limit of the charging power of the energy storage unit s, alpha s Is the charging efficiency coefficient beta of the energy storage unit s s For the discharge efficiency coefficient of the energy storage unit s, E s,t For accumulating electric quantity of the energy storage unit s in t period E s,t-1 For accumulating electric quantity of the energy storage unit s in t-1 period, E s,max And E is s,min The upper limit and the lower limit of the accumulated electric quantity of the energy storage unit s are respectively.
The new energy unit constraint specifically comprises an output range constraint, and the calculation formula is as follows:
wherein P is w,t The electric power is discarded by the new energy unit w in the period t,and the predicted power of the new energy unit w in the period t is obtained.
The flexible load constraint specifically includes: the load adjusting range and the load availability constraint are calculated according to the following formula:
wherein P is m,t For the regulated power of the flexible load m during period t, And->The upper limit and the lower limit of the flexible load m in the t period are respectively adjusted, D m,t Baseline load for flexible load m in period t, < >>And->The upper and lower limits of the flexible load m in the t period are respectively available.
The system operation constraints specifically include: the power balance constraint, the system standby constraint and the power grid safety constraint are calculated according to the following formula:
wherein L is t R is the total rigid load demand of the system in the period t t For the standby demand of the system in period t, l p、respectively the lower limit and the upper limit of the tide of the first power transmission section, N is a power grid computing node set, P n,t Calculating the power generation power of a node n for a period t power grid, l n,t Calculating the load power of a node n for a period t power grid, S n,l,t And calculating the sensitivity of the injection power of the node n to the first transmission section for the period t power grid.
Further, the euclidean distance has the following calculation formula:
wherein: d (D) q The euclidean distance to the power grid real-time prediction and power balance decision scenario q,predicting the load demand for the grid in real time during period t +.>Load demand for power balance decision scenario q in period t, < >>Predicting the sum of new energy output in time t for the power grid in real time, +.>And the new energy output sum of the power balance decision scene q in the period t is obtained.
In a third aspect, a computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements a multi-resource co-scheduling method according to any of the first aspects.
In a fourth aspect, a computer device comprises:
and the memory is used for storing the instructions.
A processor configured to execute the instructions, so that the computer device performs an operation of a multi-resource co-scheduling method according to any one of the first aspect.
The beneficial effects are that: the multi-resource collaborative scheduling method, the device, the equipment and the storage medium provided by the invention are beneficial to improving the supply protection and the consumption of scheduling decision-making requirements under the complicated background of power scheduling scenes and scheduling objects. The method of the invention has the following characteristics and functions:
1) And constructing diversified power balance decision scenes based on fluctuation factors of new energy power generation and load demands, and improving adaptability of scheduling decisions.
2) And (3) considering various objects such as primary energy, multi-class power sources, load response and the like, constructing a multi-resource collaborative optimal scheduling model, expanding the space of resource optimal configuration and improving the power balance capability.
3) And carrying out power balance decision of a multi-power balance decision scene based on the multi-resource collaborative optimization scheduling model, guiding power grid scheduling operation, and improving power grid scheduling and protection supply and digestion promotion capabilities in the multi-power balance decision scene.
Drawings
Fig. 1 is a flow chart of a multi-resource cooperative scheduling method according to the present invention.
Fig. 2 is a schematic structural diagram of a multi-resource cooperative scheduling apparatus according to the present invention.
Detailed Description
The invention will be further described with reference to specific examples.
As shown in fig. 1, a first embodiment of a multi-resource cooperative scheduling method includes the following steps:
and step 1, acquiring power supply side data of a power balance decision.
Further, in one embodiment, the power source side data includes thermal power generating unit, energy storage unit and new energy unit data; wherein, thermal power generating unit and energy storage unit are adjustable resources, and new energy unit is non-adjustable resource.
And step 2, acquiring load side data of the power balance decision.
Further, in one embodiment, the load side data includes flexible load, rigid load data; wherein the flexible load is an adjustable resource and the rigid load is a non-adjustable resource.
And 3, predicting according to the new energy unit data in the power supply side data and the rigid load data in the load side data, obtaining prediction data, and constructing a power balance decision scene according to the prediction data.
Further, in one embodiment, new energy unit data and rigid load prediction data are obtained according to new energy unit data in power source side data and rigid load data in load side data of a power balance decision, and a plurality of power balance decision scenes are generated.
The new energy unit prediction data generates a plurality of power balance decision scenes, including: the new energy prediction standard scene, the new energy prediction high-rise scene with 10 percent of floating up, and the new energy prediction low-rise scene with 10 percent of floating down; the rigid load prediction data generates a plurality of power balance decision scenarios, including: a reference scene of system rigid load prediction, a high load scene of 10% floating up of system rigid load prediction, and a low load scene of 10% floating down of system rigid load prediction.
And 4, establishing a multi-resource collaborative optimal scheduling model according to the power supply side data and the load side data of the power balance decision.
Further, in an embodiment, the objective function of the multi-resource collaborative optimization scheduling model is the minimum sum of various adjustable power supply costs, adjustable load costs and new energy power-saving costs, and specifically includes a starting cost and an operation cost of the thermal power generating unit, an adjustment cost of the energy storage unit, a power-saving cost of the new energy generating unit, and an adjustment cost of a flexible load, and the calculation formula of the multi-resource collaborative optimization scheduling model minF is as follows:
wherein T is the number of optimal scheduling time periods, I is the number of thermal power units comprising coal-fired units and gas-fired units, and C i Is the running cost of the thermal power unit i, P i,t For the active output of the thermal power generating unit i in the period t, S i Is the thermal power unit iStarting up cost, y i,t A sign for changing the state from shutdown to startup of the thermal power unit i; s is the number of energy storage units, C s For the scheduling cost of the energy storage unit s, P s,t Active output of the energy storage unit s in a period t; w is the number of new energy units comprising wind power and photovoltaic, C w The electricity discarding cost of the new energy unit w is P w,t The electric power is discarded for the new energy unit w in the period t; m is the number of flexible loads, C m For the adjustment cost of the flexible load m, P m,t The power is adjusted for the flexible load m during the period t.
Constraint conditions comprise thermal power unit constraint, energy storage unit constraint, new energy unit constraint, flexible load constraint and system operation constraint.
The thermal power generating unit constraint specifically comprises an output range constraint, a climbing rate constraint, a minimum starting and stopping time constraint and an electric energy generation capacity constraint, and the model is as follows:
P i,min ≤P i,t ≤P i,max
-Δ i ≤P i,t -P i,t-1 ≤Δ i
wherein P is i,max And P i,min Respectively the upper limit and the lower limit of the output power of the thermal power unit i, P i,t For the active output of the thermal power generating unit i in the period t, P i,t-1 For the active output of the thermal power generating unit i in the period t-1, delta i For the maximum value of the climbing rate of the thermal power unit i in each period, u i,t Is the start-stop state of the thermal power generating unit i in the period t, y i,τ For the thermal power generating unit i in the period tau, whether the state is changed from shutdown to startupSign, z i,τ Is a mark for changing the state of the thermal power unit i from start-up to stop in the period tau, UT i And DT (DT) i Respectively the minimum start-up time and the minimum stop time of the thermal power unit i, E i,min And E is i,max The upper limit and the lower limit of the generating capacity of the unit i are respectively.
The energy storage unit constraint specifically comprises: the power accumulation constraint, the charging power constraint, the discharging power constraint, the electric quantity accumulation constraint and the electric quantity limit constraint are as follows:
E s,min ≤E s,t ≤E s,max
wherein P is s,t For the active output of the energy storage unit s in the period t,and->Respectively discharging power and charging power of the energy storage unit s in t period, P s d,max And P s d,min Respectively the upper limit and the lower limit of the discharge power of the energy storage unit s, P s c,max And P s c ,min Respectively the upper limit and the lower limit of the charging power of the energy storage unit s, alpha s Is the charging efficiency coefficient beta of the energy storage unit s s For the discharge efficiency coefficient of the energy storage unit s, E s,t At t for energy storage unit sCumulative electric quantity of segment E s,t-1 For accumulating electric quantity of the energy storage unit s in t-1 period, E s,max And E is s,min The upper limit and the lower limit of the accumulated electric quantity of the energy storage unit s are respectively.
The new energy unit constraint specifically comprises an output range constraint, and the model is as follows:
Wherein P is w,t The electric power is discarded by the new energy unit w in the period t,and the predicted power of the new energy unit w in the period t is obtained.
The flexible load constraint specifically includes: load adjustment range, load availability constraint, model as follows:
wherein P is m,t For the regulated power of the flexible load m during period t,and->The upper limit and the lower limit of the flexible load m in the t period are respectively adjusted, D m,t Baseline load for flexible load m in period t, < >>And->The upper and lower limits of the flexible load m in the t period are respectively available.
The system operation constraints specifically include: the power balance constraint, the system standby constraint and the power grid safety constraint are as follows:
wherein L is t R is the total rigid load demand of the system in the period t t For the standby demand of the system in period t, l p、respectively the lower limit and the upper limit of the tide of the first power transmission section, N is a power grid computing node set, P n,t Calculating the power generation power of a node n for a period t power grid, l n,t Calculating the load power of a node n for a period t power grid, S n,l,t And calculating the sensitivity of the injection power of the node n to the first transmission section for the period t power grid.
And 5, solving a multi-resource collaborative optimization scheduling model under different power balance decision scenes to obtain optimal output results of thermal power units, new energy units, energy storage units and flexible loads of the power balance decision scenes, calculating Euclidean distances according to real-time prediction of a power grid and loads and new energy output of the different power balance decision scenes, selecting a power balance decision scene with the minimum Euclidean distance, and taking the solving result of the power balance decision scene as a formal scheduling plan.
The Euclidean distance calculation formula is:
wherein: d (D) q The euclidean distance to the power grid real-time prediction and power balance decision scenario q,predicting the load demand for the grid in real time during period t +.>Load demand for power balance decision scenario q in period t, < >>Predicting the sum of new energy output in time t for the power grid in real time, +.>And the new energy output sum of the power balance decision scene q in the period t is obtained.
As shown in fig. 2, a multi-resource cooperative scheduling apparatus according to a second embodiment includes the following modules:
a power supply side data acquisition module: and the power supply side data is used for acquiring the power balance decision.
Further, in one embodiment, the power source side data includes thermal power generating unit, energy storage unit and new energy unit data; wherein, thermal power generating unit and energy storage unit are adjustable resources, and new energy unit is non-adjustable resource.
Load side data acquisition module: load side data for acquiring power balance decisions.
Further, in one embodiment, the load side data includes flexible load, rigid load data; wherein the flexible load is an adjustable resource and the rigid load is a non-adjustable resource.
The power balance decision scene construction module: the method is used for predicting the rigid load data in the new energy unit data and the load side data according to the power supply side data, obtaining prediction data and constructing a power balance decision scene according to the prediction data.
Further, in one embodiment, new energy unit data and rigid load prediction data are obtained according to new energy unit data in power source side data and rigid load data in load side data of a power balance decision, and a plurality of power balance decision scenes are generated.
The new energy unit prediction data generates a plurality of power balance decision scenes, including: the new energy prediction standard scene, the new energy prediction high-rise scene with 10 percent of floating up, and the new energy prediction low-rise scene with 10 percent of floating down; the rigid load prediction data generates a plurality of power balance decision scenarios, including: a reference scene of system rigid load prediction, a high load scene of 10% floating up of system rigid load prediction, and a low load scene of 10% floating down of system rigid load prediction.
The optimal scheduling model building module: and the multi-resource collaborative optimal scheduling model is established according to the power supply side data and the load side data of the power balance decision.
Further, in an embodiment, the objective function of the multi-resource collaborative optimization scheduling model is the minimum sum of various adjustable power supply costs, adjustable load costs and new energy power-saving costs, and specifically includes a starting cost and an operation cost of the thermal power generating unit, an adjustment cost of the energy storage unit, a power-saving cost of the new energy generating unit, and an adjustment cost of a flexible load, and the calculation formula of the multi-resource collaborative optimization scheduling model minF is as follows:
Wherein T is the number of optimal scheduling time periods, I is the number of thermal power units comprising coal-fired units and gas-fired units, and C i Is the running cost of the thermal power unit i, P i,t For the active output of the thermal power generating unit i in the period t, S i Is the starting cost, y of the thermal power generating unit i i,t A sign for changing the state from shutdown to startup of the thermal power unit i; s is the number of energy storage units, C s For the scheduling cost of the energy storage unit s, P s,t Active output of the energy storage unit s in a period t; w is the number of new energy units comprising wind power and photovoltaic, C w The electricity discarding cost of the new energy unit w is P w,t The electric power is discarded for the new energy unit w in the period t; m is the number of flexible loads, C m For the adjustment cost of the flexible load m, P m,t The power is adjusted for the flexible load m during the period t.
Constraint conditions comprise thermal power unit constraint, energy storage unit constraint, new energy unit constraint, flexible load constraint and system operation constraint.
The thermal power generating unit constraint specifically comprises an output range constraint, a climbing rate constraint, a minimum starting and stopping time constraint and an electric energy generation capacity constraint, and the model is as follows:
P i,min ≤P i,t ≤P i,max
-Δ i ≤P i,t -P i,t-1 ≤Δ i
wherein P is i,max And P i,min Respectively the upper limit and the lower limit of the output power of the thermal power unit i, P i,t For the active output of the thermal power generating unit i in the period t, P i,t-1 For the active output of the thermal power generating unit i in the period t-1, delta i For the maximum value of the climbing rate of the thermal power unit i in each period, u i,t Is the start-stop state of the thermal power generating unit i in the period t, y i,τ A sign for changing the state of the thermal power unit i from shutdown to startup in a period tau, z i,τ Is a mark for changing the state of the thermal power unit i from start-up to stop in the period tau, UT i And DT (DT) i Respectively the minimum start-up time and the minimum stop time of the thermal power unit i, E i,min And E is i,max The upper limit and the lower limit of the generating capacity of the unit i are respectively.
The energy storage unit constraint specifically comprises: the power accumulation constraint, the charging power constraint, the discharging power constraint, the electric quantity accumulation constraint and the electric quantity limit constraint are as follows:
/>
E s,min ≤E s,t ≤E s,max
wherein P is s,t For the active output of the energy storage unit s in the period t,and->Respectively discharging power and charging power of the energy storage unit s in t period, P s d,max And P s d,min Respectively the upper limit and the lower limit of the discharge power of the energy storage unit s, P s c,max And P s c ,min Respectively the upper limit and the lower limit of the charging power of the energy storage unit s, alpha s Is the charging efficiency coefficient beta of the energy storage unit s s For the discharge efficiency coefficient of the energy storage unit s, E s,t For accumulating electric quantity of the energy storage unit s in t period E s,t-1 For accumulating electric quantity of the energy storage unit s in t-1 period, E s,max And E is s,min The upper limit and the lower limit of the accumulated electric quantity of the energy storage unit s are respectively.
The new energy unit constraint specifically comprises an output range constraint, and the model is as follows:
wherein P is w,t Set w is t for new energyThe electric power is discarded in the period of time,and the predicted power of the new energy unit w in the period t is obtained.
The flexible load constraint specifically includes: load adjustment range, load availability constraint, model as follows:
wherein P is m,t For the regulated power of the flexible load m during period t,and->The upper limit and the lower limit of the flexible load m in the t period are respectively adjusted, D m,t Baseline load for flexible load m in period t, < >>And->The upper and lower limits of the flexible load m in the t period are respectively available.
The system operation constraints specifically include: the power balance constraint, the system standby constraint and the power grid safety constraint are as follows:
wherein L is t R is the total rigid load demand of the system in the period t t For the standby demand of the system in period t, l p、respectively the lower limit and the upper limit of the tide of the first power transmission section, N is a power grid computing node set, P n,t Calculating the power generation power of a node n for a period t power grid, l n,t Calculating the load power of a node n for a period t power grid, S n,l,t And calculating the sensitivity of the injection power of the node n to the first transmission section for the period t power grid.
And (3) an optimal scheduling module: the method comprises the steps of solving a multi-resource collaborative optimization scheduling model under different power balance decision scenes, obtaining optimal output results of thermal power units, new energy units, energy storage units and flexible loads of the power balance decision scenes, calculating Euclidean distances according to real-time prediction of a power grid and loads and new energy output of the different power balance decision scenes, selecting a power balance decision scene with the minimum Euclidean distance, and taking the solving result of the power balance decision scene as a formal scheduling plan.
Further, in one embodiment, the euclidean distance calculation formula is:
wherein: d (D) q The euclidean distance to the power grid real-time prediction and power balance decision scenario q,predicting the load demand for the grid in real time during period t +.>Load demand for power balance decision scenario q in period t, < >>Predicting in real time a time period t for a power gridNew energy output sum of ∈10->And the new energy output sum of the power balance decision scene q in the period t is obtained.
A third embodiment is a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a multi-resource co-scheduling method as described in any of the first embodiments.
A fourth embodiment is a computer device comprising:
and the memory is used for storing the instructions.
A processor configured to execute the instructions to cause the computer device to perform the operations of a multi-resource co-scheduling method according to any one of the first embodiments.
Examples:
the invention discloses a multi-resource cooperative scheduling method. The following is a preferred embodiment of the present invention, which includes an evaluation process of a provincial grid day-ahead multi-scenario power balance decision employing the method of the present invention, and its features, objects, and advantages can be seen from the description of the embodiments.
Taking a multi-resource cooperative scheduling decision of a provincial power grid as an example, based on the data of a power supply side and a load side, a multi-resource short-term power balance decision and real-time cooperative scheduling for multiple scenes are developed.
Firstly, generating a plurality of power balance decision scenes in a short-term planning time scale, taking prediction errors of new energy prediction and rigid load prediction into consideration according to new energy unit prediction data and rigid load prediction data, and respectively floating 10% on the basis of the prediction values of the new energy prediction and the rigid load prediction to obtain 6 power balance scenes as boundaries of a multi-resource short-term power balance decision.
Secondly, according to power source side data and load side data of the power balance decision, an optimized scheduling model with multi-resource cooperation is established, an objective function is the minimum sum of the power generation cost of the thermal power unit, the energy storage unit adjustment cost, the new energy unit electricity discarding cost and the flexible load adjustment cost, and constraint conditions comprise thermal power unit constraint, energy storage unit constraint, new energy unit constraint, flexible load constraint and system operation constraint.
And then, obtaining optimal output results of the thermal power units, the new energy units, the energy storage units and the flexible loads in a plurality of power balance decision scenes through model solving under different power balance decision scenes, and taking the optimal output results as a feasibility scheme of multi-resource collaborative scheduling.
And finally, in a real-time operation time scale, selecting a power balance decision scene with the minimum Euclidean distance with the real-time prediction data of the power grid according to the real-time prediction data of the new energy unit and the rigid load prediction data of the power grid, and taking the solving result of the power balance decision scene as a formal plan of the thermal power unit, the new energy unit, the energy storage unit and the flexible load to issue each plant station for execution.
And the scheduling decision results of different scenes are analyzed, and the balance capacity of the power grid can be effectively improved through multi-resource collaborative optimization scheduling. Under the reference scenes of new energy prediction and rigid load prediction, the supply capacity of a system power supply side meets the power consumption requirement of a load side, and the power supply side optimally schedules, finely decides the power generation plans of units such as coal, gas, energy storage and the like, and meets the load balance requirement; under the conditions of large new energy and low rigidity load, the supply capacity of the system power supply side is larger than the power consumption requirement of the load side, and the power supply side cannot meet the load balance requirement on the basis of optimal scheduling, so that the new energy is needed to be abandoned; under the conditions of small new energy and high rigidity load, the supply capacity of the system power supply side is smaller than the power consumption requirement of the load side, and on the basis of the power supply side optimal scheduling, the response plan of the flexible load is finely decided through the load side optimal scheduling, so that the load balance requirement is met.
By programming the multi-resource scheduling plans of a plurality of power balance decision scenes in advance, when a large deviation occurs between the short-term basic prediction and the real-time latest prediction of the power grid, the power balance decision scene closest to the real-time prediction can be rapidly identified through the correlation measurement based on Euclidean distance, so that the multi-resource scheduling plan which is more matched with the real-time prediction is obtained, and the real-time balance requirement of the power grid is met.
The method is suitable for multi-resource collaborative scheduling calculation of multi-scene power balance decision, does not need a large amount of manpower, can meet the requirement of practical application in calculation speed, effectively solves the problems that the traditional power balance scheduling calculation needs a large amount of manpower, depends on experience and is low in efficiency, and has wide popularization prospect.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.

Claims (12)

1. A multi-resource cooperative scheduling method is characterized in that: the method comprises the following steps:
acquiring power supply side data of a power balance decision;
acquiring load side data of a power balance decision;
predicting the rigid load data in the new energy unit data and the load side data in the power side data to obtain prediction data, and constructing a power balance decision scene according to the prediction data;
according to the power source side data and the load side data of the power balance decision, an optimal scheduling model of multi-resource cooperation is established;
Solving a multi-resource collaborative optimization scheduling model under different power balance decision scenes to obtain optimal output results of a thermal power unit, a new energy unit, an energy storage unit and a flexible load under a plurality of power balance decision scenes, calculating Euclidean distances according to real-time prediction of a power grid and the output of the load and the new energy of the different power balance decision scenes, selecting a power balance decision scene with the minimum Euclidean distance, and taking the solving result of the power balance decision scene as a formal scheduling plan.
2. The multi-resource co-scheduling method of claim 1, wherein: the construction of the power balance decision scene according to the prediction data specifically comprises the following steps:
generating a plurality of power balance decision scenarios according to the new energy unit prediction data, including: the new energy prediction standard scene, the new energy prediction high-rise scene with 10 percent of floating up, and the new energy prediction low-rise scene with 10 percent of floating down;
generating a plurality of power balance decision scenarios from the rigid load prediction data, comprising: a reference scene of system rigid load prediction, a high load scene of 10% floating up of system rigid load prediction, and a low load scene of 10% floating down of system rigid load prediction.
3. The multi-resource co-scheduling method of claim 2, wherein: the computing formula of the multi-resource collaborative optimal scheduling model minF is as follows:
wherein T is the number of optimal scheduling time periods, I is the number of thermal power units comprising coal-fired units and gas-fired units, and C i Is the running cost of the thermal power unit i, P i,t For the active output of the thermal power generating unit i in the period t, S i Is the starting cost, y of the thermal power generating unit i i,t A sign for changing the state from shutdown to startup of the thermal power unit i; s is the number of energy storage units, C s For the scheduling cost of the energy storage unit s, P s,t Active output of the energy storage unit s in a period t; w is the number of new energy units comprising wind power and photovoltaic, C w The electricity discarding cost of the new energy unit w is P w,t The electric power is discarded for the new energy unit w in the period t; m is the number of flexible loads, C m For the adjustment cost of the flexible load m, P m,t The power is adjusted for the flexible load m during the period t.
4. The multi-resource co-scheduling method of claim 3, wherein: the constraint condition of the multi-resource collaborative optimization scheduling model comprises the following steps: thermal power unit constraint, energy storage unit constraint, new energy unit constraint, flexible load constraint and system operation constraint;
The thermal power generating unit constraint specifically comprises an output range constraint, a climbing rate constraint, a minimum starting and stopping time constraint and an electric energy generation capacity constraint, and the calculation formula is as follows:
P i,min ≤P i,t ≤P i,max
-Δ i ≤P i,t -P i,t-1 ≤Δ i
wherein P is i,max And P i,min Respectively the upper limit and the lower limit of the output power of the thermal power unit i, P i,t For the active output of the thermal power generating unit i in the period t, P i,t-1 For the active output of the thermal power generating unit i in the period t-1, delta i For the maximum value of the climbing rate of the thermal power unit i in each period, u i,t Is the start-stop state of the thermal power generating unit i in the period t, y i,τ A sign for changing the state of the thermal power unit i from shutdown to startup in a period tau, z i,τ Is a mark for changing the state of the thermal power unit i from start-up to stop in the period tau, UT i And DT (DT) i Respectively the minimum start-up time and the minimum stop time of the thermal power unit i, E i,min And E is i,max The upper limit and the lower limit of the generating capacity of the unit i are respectively;
the energy storage unit constraint specifically comprises: the power accumulation constraint, the charging power constraint, the discharging power constraint, the electric quantity accumulation constraint and the electric quantity limit value constraint are calculated according to the following formula:
E s,min ≤E s,t ≤E s,max
wherein P is s,t For the active output of the energy storage unit s in the period t,and->Respectively discharging power and charging power of the energy storage unit s in t period, P s d,max And P s d,min Respectively the upper limit and the lower limit of the discharge power of the energy storage unit s, P s c,max And P s c,min Respectively the upper limit and the lower limit of the charging power of the energy storage unit s, alpha s Is the charging efficiency coefficient beta of the energy storage unit s s For the discharge efficiency coefficient of the energy storage unit s, E s,t For accumulating electric quantity of the energy storage unit s in t period E s,t-1 For accumulating electric quantity of the energy storage unit s in t-1 period, E s,max And E is s,min Respectively an upper limit and a lower limit of accumulated electric quantity of the energy storage unit s;
the new energy unit constraint specifically comprises an output range constraint, and the calculation formula is as follows:
wherein P is w,t The electric power is discarded by the new energy unit w in the period t,the predicted power of the new energy unit w in the t period is calculated;
the flexible load constraint specifically includes: the load adjusting range and the load availability constraint are calculated according to the following formula:
wherein P is m,t For the regulated power of the flexible load m during period t,and->The upper limit and the lower limit of the flexible load m in the t period are respectively adjusted, D m,t Baseline load for flexible load m in period t, < >>And->The upper limit and the lower limit of the usable amount of the flexible load m in the t period are respectively set;
the system operation constraints specifically include: the power balance constraint, the system standby constraint and the power grid safety constraint are calculated according to the following formula:
wherein L is t R is the total rigid load demand of the system in the period t t For the standby demand of the system in period t, l p、Respectively the lower limit and the upper limit of the tide of the first power transmission section, N is a power grid computing node set, P n,t Calculating the power generation power of a node n for a period t power grid, l n,t Calculating the load power of a node n for a period t power grid, S n,l,t And calculating the sensitivity of the injection power of the node n to the first transmission section for the period t power grid.
5. The multi-resource co-scheduling method of claim 4, wherein: the Euclidean distance has the following calculation formula:
wherein: d (D) q The euclidean distance to the power grid real-time prediction and power balance decision scenario q,predicting the load demand for the grid in real time during period t +.>Load demand for power balance decision scenario q in period t, < >>Predicting the sum of new energy output in time t for the power grid in real time, +.>And the new energy output sum of the power balance decision scene q in the period t is obtained.
6. The utility model provides a many resources cooperation dispatch device which characterized in that: the method comprises the following modules:
a power supply side data acquisition module: the power supply side data is used for acquiring power balance decisions;
load side data acquisition module: load side data for acquiring power balance decisions;
the power balance decision scene construction module: the method comprises the steps of predicting rigid load data in new energy unit data in power supply side data and load side data to obtain prediction data, and constructing a power balance decision scene according to the prediction data;
The optimal scheduling model building module: the power source side data and the load side data are used for establishing a multi-resource collaborative optimal scheduling model according to the power balance decision;
and (3) an optimal scheduling module: the method comprises the steps of solving a multi-resource collaborative optimization scheduling model under different power balance decision scenes, obtaining optimal output results of thermal power units, new energy units, energy storage units and flexible loads of the power balance decision scenes, calculating Euclidean distances according to real-time prediction of a power grid and loads and new energy output of the different power balance decision scenes, selecting a power balance decision scene with the minimum Euclidean distance, and taking the solving result of the power balance decision scene as a formal scheduling plan.
7. The multi-resource co-scheduling apparatus of claim 6, wherein: the construction of the power balance decision scene according to the prediction data specifically comprises the following steps:
generating a plurality of power balance decision scenarios according to the new energy unit prediction data, including: the new energy prediction standard scene, the new energy prediction high-rise scene with 10 percent of floating up, and the new energy prediction low-rise scene with 10 percent of floating down;
generating a plurality of power balance decision scenarios from the rigid load prediction data, comprising: a reference scene of system rigid load prediction, a high load scene of 10% floating up of system rigid load prediction, and a low load scene of 10% floating down of system rigid load prediction.
8. The multi-resource co-scheduling apparatus of claim 7, wherein: the multi-resource collaborative optimization scheduling model has the following calculation formula:
wherein T is the number of optimal scheduling time periods, I is the number of thermal power units comprising coal-fired units and gas-fired units, and C i Is the running cost of the thermal power unit i, P i,t For the active output of the thermal power generating unit i in the period t, S i Is the starting cost, y of the thermal power generating unit i i,t A sign for changing the state from shutdown to startup of the thermal power unit i; s is the number of energy storage units, C s For the scheduling cost of the energy storage unit s, P s,t Active output of the energy storage unit s in a period t; w is the number of new energy units comprising wind power and photovoltaic, C w The electricity discarding cost of the new energy unit w is P w,t The electric power is discarded for the new energy unit w in the period t; m is the number of flexible loads, C m For the adjustment cost of the flexible load m, P m,t The power is adjusted for the flexible load m during the period t.
9. The multi-resource co-scheduling apparatus of claim 8, wherein: the constraint condition of the multi-resource collaborative optimization scheduling model comprises the following steps: thermal power unit constraint, energy storage unit constraint, new energy unit constraint, flexible load constraint and system operation constraint;
The thermal power generating unit constraint specifically comprises an output range constraint, a climbing rate constraint, a minimum starting and stopping time constraint and an electric energy generation capacity constraint, and the calculation formula is as follows:
P i,min ≤P i,t ≤P i,max
-Δ i ≤P i,t -P i,t-1 ≤Δ i
wherein P is i,max And P i,min Respectively the upper limit and the lower limit of the output power of the thermal power unit i, P i,t For the active output of the thermal power generating unit i in the period t, P i,t-1 For the active output of the thermal power generating unit i in the period t-1, delta i For the maximum value of the climbing rate of the thermal power unit i in each period, u i,t Is the start-stop state of the thermal power generating unit i in the period t, y i,τ A sign for changing the state of the thermal power unit i from shutdown to startup in a period tau, z i,τ Is a mark for changing the state of the thermal power unit i from start-up to stop in the period tau, UT i And DT (DT) i Respectively the minimum start-up time and the minimum stop time of the thermal power unit i, E i,min And E is i,max The upper limit and the lower limit of the generating capacity of the unit i are respectively;
the energy storage unit constraint specifically comprises: the power accumulation constraint, the charging power constraint, the discharging power constraint, the electric quantity accumulation constraint and the electric quantity limit value constraint are calculated according to the following formula:
E s,min ≤E s,t ≤E s,max
wherein P is s,t For the active output of the energy storage unit s in the period t,and->Respectively discharging power and charging power of the energy storage unit s in t period, P s d,max And P s d,min Respectively the upper limit and the lower limit of the discharge power of the energy storage unit s, P s c,max And P s c,min Respectively the upper limit and the lower limit of the charging power of the energy storage unit s, alpha s Is the charging efficiency coefficient beta of the energy storage unit s s For the discharge efficiency coefficient of the energy storage unit s, E s,t For accumulating electric quantity of the energy storage unit s in t period E s,t-1 For accumulating electric quantity of the energy storage unit s in t-1 period, E s,max And E is s,min Respectively an upper limit and a lower limit of accumulated electric quantity of the energy storage unit s;
the new energy unit constraint specifically comprises an output range constraint, and the calculation formula is as follows:
wherein P is w,t The electric power is discarded by the new energy unit w in the period t,the predicted power of the new energy unit w in the t period is calculated;
the flexible load constraint specifically includes: the load adjusting range and the load availability constraint are calculated according to the following formula:
wherein P is m,t For the regulated power of the flexible load m during period t,and->The upper limit and the lower limit of the flexible load m in the t period are respectively adjusted, D m,t Baseline load for flexible load m in period t, < >>And->The upper limit and the lower limit of the usable amount of the flexible load m in the t period are respectively set;
the system operation constraints specifically include: the power balance constraint, the system standby constraint and the power grid safety constraint are calculated according to the following formula:
wherein L is t R is the total rigid load demand of the system in the period t t For the standby demand of the system in period t, l p、Respectively the lower limit and the upper limit of the tide of the first power transmission section, N is a power grid computing node set, P n,t Calculating the power generation power of a node n for a period t power grid, l n,t Calculating the load power of a node n for a period t power grid, S n,l,t And calculating the sensitivity of the injection power of the node n to the first transmission section for the period t power grid.
10. The multi-resource co-scheduling apparatus of claim 9, wherein: the Euclidean distance has the following calculation formula:
wherein: d (D) q The euclidean distance to the power grid real-time prediction and power balance decision scenario q,predicting the load demand for the grid in real time during period t +.>Load demand for power balance decision scenario q in period t, < >>Predicting the sum of new energy output in time t for the power grid in real time, +.>And the new energy output sum of the power balance decision scene q in the period t is obtained.
11. A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a multi-resource co-scheduling method according to any of claims 1-5.
12. A computer device, comprising:
a memory for storing instructions;
a processor configured to execute the instructions to cause the computer device to perform the operations of a multi-resource co-scheduling method according to any one of claims 1-5.
CN202310627007.5A 2023-05-30 2023-05-30 Multi-resource cooperative scheduling method, device, equipment and storage medium Pending CN116646987A (en)

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Cited By (1)

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CN117134366A (en) * 2023-10-27 2023-11-28 南方电网数字电网研究院有限公司 Load control method, device, equipment and storage medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117134366A (en) * 2023-10-27 2023-11-28 南方电网数字电网研究院有限公司 Load control method, device, equipment and storage medium
CN117134366B (en) * 2023-10-27 2024-02-23 南方电网数字电网研究院有限公司 Load control method, device, equipment and storage medium

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