CN116545025A - Optimal scheduling method, device, equipment and storage medium for power distribution network - Google Patents
Optimal scheduling method, device, equipment and storage medium for power distribution network Download PDFInfo
- Publication number
- CN116545025A CN116545025A CN202310359789.9A CN202310359789A CN116545025A CN 116545025 A CN116545025 A CN 116545025A CN 202310359789 A CN202310359789 A CN 202310359789A CN 116545025 A CN116545025 A CN 116545025A
- Authority
- CN
- China
- Prior art keywords
- power
- scheduling
- distribution network
- power distribution
- model
- 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
- 238000009826 distribution Methods 0.000 title claims abstract description 105
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000003860 storage Methods 0.000 title claims abstract description 30
- 230000004044 response Effects 0.000 claims abstract description 131
- 238000005457 optimization Methods 0.000 claims abstract description 68
- 238000004146 energy storage Methods 0.000 claims abstract description 56
- 230000005611 electricity Effects 0.000 claims description 49
- 230000006870 function Effects 0.000 claims description 29
- 238000004590 computer program Methods 0.000 claims description 27
- 230000003993 interaction Effects 0.000 claims description 25
- 230000008859 change Effects 0.000 claims description 13
- 230000005284 excitation Effects 0.000 claims description 13
- 238000005096 rolling process Methods 0.000 claims description 11
- 238000012423 maintenance Methods 0.000 claims description 7
- 230000003247 decreasing effect Effects 0.000 claims description 4
- 238000007599 discharging Methods 0.000 claims description 4
- 238000010248 power generation Methods 0.000 abstract description 8
- 230000008569 process Effects 0.000 description 5
- 230000002452 interceptive effect Effects 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000000087 stabilizing effect Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000009133 cooperative interaction Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000002699 waste material Substances 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
- H02J3/38—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
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/15—Correlation function computation including computation of convolution operations
-
- 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
-
- 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
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
-
- 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
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
- H02J3/144—Demand-response operation of the power transmission or distribution network
-
- 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
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
-
- 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
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- 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
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
-
- 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
- H02J2300/28—The renewable source being wind 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
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
- H02J2310/62—The condition being non-electrical, e.g. temperature
- H02J2310/64—The condition being economic, e.g. tariff based load management
-
- 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)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Computing Systems (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Algebra (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a power distribution network optimal scheduling method, a device, equipment and a storage medium, which comprise the following steps: establishing a demand response model according to the data information responded by the user demand side; based on the demand response model, constructing a power distribution network day-ahead optimization scheduling model, and forming a preliminary scheduling strategy for obtaining power distribution network day-ahead optimization through a preset algorithm; and obtaining prediction data of the preliminary scheduling strategy through a preset prediction model, and optimizing the preliminary scheduling strategy according to the prediction data to obtain a final scheduling strategy, thereby completing multi-time-scale optimized scheduling of the power distribution network. The invention solves the technical problems that the dispatching optimization of the distribution network cannot be carried out according to actual demands, the power generation pressure is high during peak load and the frequent output of a generator set is required in the prior art, reduces the peak-valley difference of load power, simultaneously stabilizes the fluctuation and uncertainty of wind and light loads, comprehensively considers the coordination optimization of new energy, load and energy storage system, and strengthens the connection of source and load storage.
Description
Technical Field
The invention relates to the technical field of power grid power dispatching, in particular to a power distribution network optimal dispatching method, device, equipment and storage medium.
Background
With the access of various new energy sources and flexible loads, the power distribution network has a more flexible scheduling mode, and the connection between the load side and the power supply side is tighter. With the progress of energy storage technology, energy storage modes such as pumped storage, electric energy storage and compressed air energy storage are widely existing in a power distribution network, and an energy storage system can be rapidly charged and discharged, so that the energy storage system has an important role in stabilizing the fluctuation of new energy output.
At present, in the scheme of power distribution network optimization scheduling, the demand side and the power grid side cannot be flexibly mobilized according to actual demands, so that the power generation pressure is high in peak load, frequent power output of a generator set is required, users cannot be guided to reasonably use electricity, and the electricity utilization satisfaction degree of the users cannot be considered.
Therefore, a method for optimizing power distribution network dispatching and reducing frequent output and power generation pressure of a generator set during peak load according to actual requirements is needed.
Disclosure of Invention
The invention provides a power distribution network optimal scheduling method, device, equipment and storage medium, which are used for solving the technical problems that power distribution network optimal scheduling cannot be carried out according to actual demands, the power generation pressure is high during peak load and frequent power output of a generator set is required in the prior art.
In order to solve the above technical problems, an embodiment of the present invention provides an optimized scheduling method for a power distribution network, including:
establishing a demand response model according to the data information responded by the user demand side;
based on the demand response model, constructing a power distribution network day-ahead optimization scheduling model, and forming a preliminary scheduling strategy for obtaining power distribution network day-ahead optimization through a preset algorithm;
and obtaining prediction data of the preliminary scheduling strategy through a preset prediction model, and optimizing the preliminary scheduling strategy according to the prediction data to obtain a final scheduling strategy, thereby completing multi-time-scale optimized scheduling of the power distribution network.
As a preferred solution, the establishing a demand response model according to the data information responded by the user demand side specifically includes:
respectively constructing a price type demand response model and an incentive type demand response model according to price data and incentive data responded by a user demand side;
wherein the demand response model comprises: a price type demand response model and an incentive type demand response model; the price type demand response model is used for describing the influence of the adjusted electricity price on the electricity consumption of the user, and the excitation type demand response model is used for describing the compensation cost generated after the user carries out load interruption.
Preferably, the price data comprises electricity consumption before demand response, electricity consumption after demand response, electricity price before response and electricity price change value;
the excitation data comprises interrupt load compensation cost, interrupt load set, response power of interrupt load in each period in the scheduling period, compensation cost of unit interruptible load and time step in the scheduling period.
As a preferred scheme, the construction of the power distribution network day-ahead optimization scheduling model based on the demand response model is specifically as follows:
based on the price type demand response model and the excitation type demand response model, constructing an objective function of power distribution network optimization scheduling by taking the lowest running cost of the power distribution network as a scheduling target, and taking the objective function as a power distribution network day-ahead optimization scheduling model;
the objective function of the optimal scheduling of the power distribution network is as follows:
wherein S is the total cost in the scheduling period; s is S DG (t) is the cost of the traditional unit in the period t; s is S E (t) is the energy storage operation and maintenance cost in the t period; s is S G (t) is the power interaction cost of the period t and the upper grid; s is S WP (t) wind-discarding and light-discarding penalty costs for the t period; s is S T (t) demand response costs based on time-of-use electricity prices for the period t; s is S IL (t) an interruptible load interruption cost for a period t; t is the total number of time periods within one scheduling period.
Preferably, the method further comprises:
constraint is carried out on the condition of the objective function of the power distribution network optimization scheduling;
wherein the condition constraints include:
P DG (t)+P G (t)+P w (t)+P v (t)＝P E (t)+P R (t)+P L (t)-P IL (t)
wherein P is G (t) is the interaction power of the moment t and the upper power grid, P DG (t) is the output force of the traditional machine set in the period t, P E (t) is the charge and discharge power of the energy storage battery at the moment t, P w (t) is the consumed power of the wind generating set before the day of the t period, P v (t) is the consumed power of the photovoltaic generator set before the day of the t period, P R (t) is the load power after demand response, P L (t) is the load power before demand response, P IL (t) is the response power of the interrupt load in the t period,the upper limit and the lower limit of the output power of the traditional unit are set; />The power of the traditional unit is increased and decreased; Δt is the scheduled time difference;charging and discharging power for the energy storage battery at the moment t; />Maximum charge and discharge power of the energy storage battery; e (E) S (t)、E S (t-1) is the remaining capacity of the storage battery at the time t and the time t-1 respectively, < + >>Is the upper and lower limits of the residual capacity, and eta is the charge-discharge efficiency; />To interact power with large power gridsAn upper limit.
As a preferred solution, the obtaining the prediction data of the preliminary scheduling policy through a preset prediction model, and optimizing the preliminary scheduling policy according to the prediction data to obtain a final scheduling policy, which specifically includes:
Obtaining state data corresponding to the current moment according to the preliminary scheduling strategy;
carrying out prediction iteration for preset times on the predicted data through a preset prediction model, so as to obtain predicted data corresponding to each subsequent moment;
according to the predicted data corresponding to each subsequent moment, obtaining a predicted output value of the power of the upper power grid in a predicted time period, the residual capacity of the energy storage battery and a planned daily power interaction value of the power of the upper power grid;
and converting the corresponding daily rolling optimization scheduling into a quadratic programming problem function by taking the residual electric quantity of the energy storage battery and the minimum error between the estimated output value and the daily planned value as targets, so as to solve the quadratic programming problem function and obtain a final scheduling strategy.
As a preferred solution, the predicting data is repeatedly iterated for a preset number of times through a preset predicting model, so as to obtain predicted data corresponding to each subsequent moment, which specifically includes:
in each prediction iteration, according to the current time and the prediction data of the current time, a control instruction sequence of each future time is obtained or updated based on a preset prediction model, and a first value of the control instruction sequence is applied to a control system, so that state data corresponding to the next time is updated and obtained until the state data corresponding to all the times are updated once, and the prediction data corresponding to each time is output; wherein the control instruction sequence includes a value corresponding to each future time instant.
Correspondingly, the invention also provides an optimal scheduling device of the power distribution network, which comprises the following steps: the system comprises a model building module, a preliminary scheduling strategy module and a final scheduling strategy module;
the model building module is used for building a demand response model according to the data information responded by the user demand side;
the preliminary scheduling strategy module is used for constructing a power distribution network day-ahead optimization scheduling model based on the demand response model, and forming a preliminary scheduling strategy for obtaining the power distribution network day-ahead optimization through a preset algorithm;
the final scheduling strategy module is used for obtaining the prediction data of the preliminary scheduling strategy through a preset prediction model, optimizing the preliminary scheduling strategy according to the prediction data to obtain a final scheduling strategy, and thus completing multi-time-scale optimized scheduling of the power distribution network.
Correspondingly, the invention further provides a terminal device, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the power distribution network optimization scheduling method is realized when the processor executes the computer program.
Correspondingly, the invention further provides a computer readable storage medium, the computer readable storage medium comprises a stored computer program, and when the computer program runs, equipment where the computer readable storage medium is located is controlled to execute the power distribution network optimal scheduling method according to any one of the above.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
according to the technical scheme, a demand response model is established through data information of user demand side response, and then a power distribution network day-ahead optimization scheduling model is established, so that the power distribution network day-ahead optimization scheduling can consider the user demand side response, a preliminary scheduling plan is formed based on a preset algorithm, the prediction data of the preliminary scheduling strategy is obtained through the preset prediction model, the preliminary scheduling strategy is optimized, a final scheduling strategy is obtained, fluctuation and uncertainty of wind and solar loads are stabilized, and the problems that the power generation pressure is high during peak load and frequent power output of a generator set is required are avoided.
Drawings
Fig. 1: the method for optimizing and scheduling the power distribution network comprises the following steps of a flow chart;
fig. 2: the wind power, photovoltaic and load prediction curve graph provided by the embodiment of the invention;
fig. 3: the simulation result diagram of the regulation and control process of each device in the power distribution network after the source load storage is participated is provided for the embodiment of the invention;
fig. 4: the load change curves before and after the demand response provided by the embodiment of the invention;
fig. 5: the power intra-day scheduling result graph for interaction with the upper power grid provided by the embodiment of the invention;
Fig. 6: the structure diagram of the power distribution network optimization scheduling device is provided for the embodiment of the invention;
wherein, the reference numerals of the specification drawings are as follows:
a model building module 201, a preliminary scheduling policy module 202 and a final scheduling policy module 203.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, the power distribution network optimization scheduling method provided by the embodiment of the invention includes the following steps S101-S103:
step S101: and establishing a demand response model according to the data information responded by the user demand side.
As a preferred solution of this embodiment, the establishing a demand response model according to the data information responded by the user demand side specifically includes:
respectively constructing a price type demand response model and an incentive type demand response model according to price data and incentive data responded by a user demand side; wherein the demand response model comprises: a price type demand response model and an incentive type demand response model; the price type demand response model is used for describing the influence of the adjusted electricity price on the electricity consumption of the user, and the excitation type demand response model is used for describing the compensation cost generated after the user carries out load interruption.
As a preferable mode of the present embodiment, the price data includes a demand-before-response electricity consumption amount, a demand-response electricity amount, a pre-response electricity price, and an electricity price change value; the excitation data comprises interrupt load compensation cost, interrupt load set, response power of interrupt load in each period in the scheduling period, compensation cost of unit interruptible load and time step in the scheduling period.
In this embodiment, the price type demand response model influences the electricity consumption of the user by adjusting the electricity price, and the influence of the electricity price change rate on the load change rate of the user is generally described by using a price demand elastic coefficient. In practice, a multi-period power rate response is generally adopted, and the power of a user at a certain moment is affected by the power rate change at that moment and also by the power rate change at other moments. The elastic coefficient is divided into self-elastic coefficient b ii The mutual elasticity coefficient is calculated as:
wherein: b ii And b ij The self-elasticity coefficient and the cross-elasticity coefficient respectively represent the response of the user to the declared capacity at the current moment and other moments after the incentive price is changed, and the self-elasticity coefficient b ii The value should be positive, the cross-over spring coefficient b ij The value should be negative; p (P) i 、ΔP i The power consumption before the demand response and the power consumption after the demand response are respectively; epsilon i 、Δε i The values of the change in the electricity prices before the response and the electricity price, respectively.
Further, the response of the price type demand response declaration capacity to the incentive price of the electricity price of the user can be obtained as follows:
wherein:price elasticity matrices are motivated for demand response capacity.
The electricity consumption requirement of each period after price response can be obtained by the electricity quantity change matrix, and the electricity consumption change quantity of the user in each period can be obtained according to the formulas (1) - (3) as follows:
wherein: t is a scheduling period;
further, the incentive type demand response model adjusts the load size by contracting with the user for economic compensation or rewards, wherein the interruptible load is most widely used. The load can be interrupted, and the purpose of adjusting the load is achieved by contracting with the user. The incentive-based demand response compensation cost is as follows:
wherein: s is S IL Compensating the cost for the interrupt load in the scheduling period; omega shape IL For interrupting a load set;response power for the ith interrupt load in t period; epsilon IL Compensation cost for unit interruptible load; Δt is the time step.
Step S102: and constructing a power distribution network day-ahead optimization scheduling model based on the demand response model, and forming a preliminary scheduling strategy for obtaining the power distribution network day-ahead optimization through a preset algorithm.
As a preferred solution of this embodiment, the construction of the power distribution network day-ahead optimization scheduling model based on the demand response model specifically includes:
based on the price type demand response model and the excitation type demand response model, constructing an objective function of power distribution network optimization scheduling by taking the lowest running cost of the power distribution network as a scheduling target, and taking the objective function as a power distribution network day-ahead optimization scheduling model; the objective function of the optimal scheduling of the power distribution network is as follows:wherein S is the total cost in the scheduling period; s is S DG (t) is the cost of the traditional unit in the period t; s is S E (t) is the energy storage operation and maintenance cost in the t period; s is S G (t) is the power interaction cost of the period t and the upper grid; s is S WP (t) wind-discarding and light-discarding penalty costs for the t period; s is S T (t) demand response costs based on time-of-use electricity prices for the period t; s is S IL (t) an interruptible load interruption cost for a period t; t is the total number of time periods within one scheduling period.
In the embodiment, on the basis of electricity price response, source, storage and load coordination optimization scheduling of an active power distribution network is performed, wherein the scheduling target is the lowest running cost of the power distribution network, and the lowest running cost comprises the power interaction cost with an upper power network, the power generation cost, the running maintenance cost of energy storage, the wind and light discarding cost and the demand response cost, so that an objective function of power distribution network optimization scheduling is constructed.
It should be noted that, the costs include the traditional unit cost, the energy storage battery cost, the power interaction cost with the upper grid, the wind-discarding and light-discarding penalty cost, the demand response cost based on the time-of-use electricity price and the incentive type demand response cost.
The specific mathematical model of each cost is as follows:
(1) The traditional unit cost is:
wherein: a. b and c are operation and maintenance cost coefficients of the traditional unit; p (P) DG And (t) the output of the traditional unit in the t period.
(2) The cost of the energy storage battery is as follows:
wherein:the maintenance cost and the depreciation cost of the unit power of the energy storage battery are respectively; p (P) E And (t) is the charge and discharge power of the energy storage battery at the time t, and the charge is positive and the discharge is negative.
(3) The power interaction cost with the upper power grid is as follows: s is S G (t)＝μ G (t)P G (t) (9)
Wherein: mu (mu) G (t) trade electricity price with the upper power grid at the moment t; p (P) G And (t) is the interaction power of the upper power grid at the moment t, the electricity purchasing is positive, and the electricity selling is negative.
(4) The wind and light discarding punishment cost is as follows:
wherein: delta is punishment cost of unit air discarding quantity;and P w (t) respectively predicting output power value and consumption of the wind generating set before the day of the period t; gamma is unit waste quantity penalty cost; />And P v And (t) respectively predicting the output power value and the consumption of the photovoltaic generator set before the day of the period t.
(5) The demand response cost based on the time-of-use electricity price is price type demand response, and the following formula is shown: s is S T ＝ε 0 (t)P R0 (t)-ε(t)P R (t) (11)
Wherein: epsilon 0 (t) and epsilon (t) are respectively the electricity prices before and after the demand response in the period of t; p (P) R0 (t)、P R And (t) is the power before and after the demand response, respectively.
The incentive type demand response cost is as shown in formula (5):
as a preferable mode of the present embodiment, further comprising:
constraint is carried out on the condition of the objective function of the power distribution network optimization scheduling; wherein the condition constraints include: p (P) DG (t)+P G (t)+P w (t)+P v (t)＝P E (t)+P R (t)+P L (t)-P IL (t)；
Wherein P is G (t) is the interaction power of the moment t and the upper power grid, P DG (t) is the output force of the traditional machine set in the period t, P E (t) is the charge and discharge power of the energy storage battery at the moment t, P w (t) is the consumed power of the wind generating set before the day of the t period, P v (t) is the consumed power of the photovoltaic generator set before the day of the t period, P R (t) is the load power after demand response, P L (t) is the load power before demand response, -P IL (t) is the response power of the interrupt load in the t period,the upper limit and the lower limit of the output power of the traditional unit are set; />The power of the traditional unit is increased and decreased; Δt is the scheduled time difference;charging and discharging power for the energy storage battery at the moment t; />Maximum charge and discharge power of the energy storage battery; e (E) S (t)、E S (t-1) is the remaining capacity of the storage battery at the time t and the time t-1 respectively, < + > >Is the upper and lower limits of the residual capacity, and eta is the charge-discharge efficiency; />Is the upper limit of the interaction power with the large power grid.
In this embodiment, with the minimum scheduling cost as a goal, a preset algorithm, preferably including but not limited to a genetic algorithm and other particle swarm algorithms or cplex toolbox solution in matlab, is adopted in consideration of constraint conditions, so that day-ahead optimal scheduling is completed, and a preliminary scheduling strategy of the output among the traditional unit, wind power, photovoltaic and energy storage batteries is obtained.
Step S103: and obtaining prediction data of the preliminary scheduling strategy through a preset prediction model, and optimizing the preliminary scheduling strategy according to the prediction data to obtain a final scheduling strategy, thereby completing multi-time-scale optimized scheduling of the power distribution network.
As a preferred solution of this embodiment, the obtaining, by a preset prediction model, prediction data of the preliminary scheduling policy, and optimizing the preliminary scheduling policy according to the prediction data, to obtain a final scheduling policy, specifically includes:
obtaining state data corresponding to the current moment according to the preliminary scheduling strategy; carrying out prediction iteration for preset times on the predicted data through a preset prediction model, so as to obtain predicted data corresponding to each subsequent moment; according to the predicted data corresponding to each subsequent moment, obtaining a predicted output value of the power of the upper power grid in a predicted time period, the residual capacity of the energy storage battery and a planned daily power interaction value of the power of the upper power grid; and converting the corresponding daily rolling optimization scheduling into a quadratic programming problem function by taking the residual electric quantity of the energy storage battery and the minimum error between the estimated output value and the daily planned value as targets, so as to solve the quadratic programming problem function and obtain a final scheduling strategy.
In this embodiment, the power distribution network includes a conventional unit, an energy storage battery energy storage device, wind power, photovoltaic units, loads, and the like, and then the demand response loads do not participate in scheduling in a daily period, and according to a power balance equation and an energy storage remaining power iterative equation of each period of the power distribution network, the output, the energy storage charge and discharge power, the energy storage remaining power of the conventional unit are selected, and the output, the energy storage charge and discharge power, the energy storage remaining power and the upper level are selectedVector x (k) = [ P ] of power exchanged by connecting lines of power grid DG (k),P E (k),E S (k),P G (k)]As a state variable of the current moment k, acquiring acquired data when the preliminary scheduling strategy is executed; vector u (k) = [ delta P ] formed by output increment of traditional unit and energy storage battery DG (k),ΔP E (k)]Is a control variable; vector r (k) = [ delta P ] formed by ultra-short-term predicted power increment of load, wind power and photovoltaic unit L (k),ΔP w (k),ΔP v (k)]Is a disturbance input; vector y (k) = [ P ] formed by exchanging power with upper grid G (k)]For the output variable, where k is the current time, the following multiple-input, multiple-output state space model can be built:
wherein the method comprises the steps ofD＝(0001)。
Based on ultra-short-term power prediction data of wind power and load, the vector Y formed by the estimated output value of the power of the upper-level power grid within the prediction duration mDeltat can be obtained by repeatedly iterating the state space prediction model until m steps are predicted forwards, and the expression is shown as follows:
Y＝[P G (k+Δt),…,P G (k+mΔt)] (17)
Taking a vector G formed by the residual electric quantity of the energy storage battery and a daily planned value of the interaction power with the upper power grid in a period of m delta t forward at the current moment as a tracking control target, wherein G can be described as follows:
and then, taking the minimum error between the residual capacity of the energy storage battery and the estimated output value of the interactive power with the upper power grid and the planned value before the day as a target, and simultaneously ensuring that the control and adjustment increment of each unit is as small as possible, so that the corresponding rolling optimization scheduling in the day can be converted into the following quadratic programming problem:
J＝(G-Y) T H(G-Y)+u T Qu (19)
wherein H is a weight coefficient matrix of the interactive power tracking error with the upper power grid; q is a weight coefficient matrix of the control quantity, and u is a control variable.
As a preferred solution of this embodiment, the predicting data is repeatedly iterated for a preset number of predictions through a preset prediction model, so as to obtain predicted data corresponding to each subsequent moment, which specifically includes:
in each prediction iteration, according to the current time and the prediction data of the current time, a control instruction sequence of each future time is obtained or updated based on a preset prediction model, and a first value of the control instruction sequence is applied to a control system, so that state data corresponding to the next time is updated and obtained until the state data corresponding to all the times are updated once, and the prediction data corresponding to each time is output; wherein the control instruction sequence includes a value corresponding to each future time instant.
In the embodiment, uncertainty caused by wind-light load prediction errors is considered, and daily optimization scheduling is designed based on a model prediction control algorithm, so that the aim of stabilizing interaction power with a superior power grid is fulfilled. The model predictive control is a closed-loop optimization control method based on a model, and the core idea of the algorithm is a rolling time domain strategy. The strategy mainly comprises the following steps:
(1) By the current time k And the current state x (k) predicts the state of the system at the future moment based on a preset prediction model, and simultaneously considers the current constraint condition and the future constraint condition to obtain a control instruction sequence at the future moment k+1, k+2, … and k+M by solving the optimization problem.
(2) A first value of the control instruction sequence is applied to the control system.
(3) At time k+1, the update state is x (k+1), and the above steps are repeated.
Model predictive control (Model Predictive Control, MPC) is an alternating process of continuous rolling local optimization and continuous rolling control action, namely MPC rolling optimization algorithm.
It should be noted that, the predicted data includes state variables at future times, that is, the predicted state variables at future times k+1, k+2, …, k+m are obtained by inputting the current time k and the current state variable x (k) into a preset prediction model, and are further used as a control instruction sequence. And applying the predicted state variable at the time k+1 in the control instruction sequence to a control system to obtain an actual state variable at the time k+1, thereby taking the actual state variable at the time k+1 as a current state, taking the time k+1 as a current time to update the actual state variable at the time k+2, and the like until all the time k+1, k+2, … and k+M in the future obtain the corresponding actual state variable as predicted data corresponding to each time in the future.
In order to further understand the invention, the accuracy and the effectiveness of the optimized scheduling of the provided power distribution network are verified, and the example simulation is carried out. The parameters were set as follows: the power transmission system comprises a power transmission system, a power distribution network, a photovoltaic installation system, a traditional unit, an energy storage output upper limit, an energy storage capacity and a power interaction lower limit, wherein the power transmission system is 10MW, the photovoltaic installation system is 5MW, the output of the traditional unit is 1.5-15MW, the energy storage output upper limit and the energy storage output lower limit are-10-15 MW, the energy storage capacity is 20MWh, and the power interaction upper limit and the power interaction lower limit with an upper power grid are-15-15 MW. In the optimal scheduling of the power distribution network, in the day-ahead scheduling, the time interval is 1h, and the scheduling duration is 24h; the intra-day scheduling is based on the day-ahead scheduling, the scheduling time is 1h, and the time interval is 15min. Wind power, photovoltaic and load prediction curves are shown in figure 2.
In order to explain the regulation and control process of each device in the power distribution network after the participation of the source load storage. As shown in fig. 3, the simulation result is shown in fig. 3, in the optimization scheduling strategy of the power distribution network obtained in the day before, the electricity purchasing cost of the power distribution network from the large power grid is low in valley period (23:00-08:00), at the moment, the power distribution network stores energy and charges, and the traditional unit maintains the lowest output to keep the starting state, at the moment, the wind and light energy is full, and the power distribution network sells electricity to the large power grid to reduce the whole day cost; the electricity purchasing cost of the power distribution network in the flat period (08:00-17:00) is increased from a large power grid, and the power of the power distribution network is balanced mainly through a traditional unit and the large power grid at the moment, so that load supply is met; peak time (17:00-23:00) distribution network continues to rise from the large power grid electricity purchasing cost, and at the moment, the distribution network mainly meets load supply by a traditional unit and energy storage capacity; at the end of the optimization cycle (23:00-00:00), the stored energy is charged, returning the SOC to the initial value to be ready for the next optimization.
Fig. 4 is a graph showing load change curves before and after demand response, and it can be seen that after a user participates in demand response, load power in a peak period is obviously reduced, and part of users shift electricity consumption to a valley period, so that the load curve is obviously improved. Therefore, the traditional unit, energy storage and demand side response coordination scheduling are comprehensively considered, the electricity purchasing power can be reduced, and the economic benefit is improved.
Fig. 5 is a daily scheduling result of the interactive power with the upper power grid, and when the daily rolling optimization is not applied, the interactive power fluctuates severely near the planned value, so that stable and controllable scheduling of the power distribution network accessing the upper power grid is difficult to realize. After MPC rolling optimization scheduling is applied, the joint interaction power is basically consistent with a daily plan value, so that the requirements of online application can be completely met, and the effectiveness of the coordination optimization scheduling scheme of the embodiment is fully illustrated.
It can be understood that by implementing the demand response, a part of the load can be translated to other scheduling periods without affecting the user electricity satisfaction degree when the load is in peak value, or a part of the load is reduced according to the contract, so that the peak-valley difference of the load power is reduced, and the frequent adjustment of the output of the generator is avoided, so that the demand response has important practical significance for optimizing the scheduling of the power system. In the optimal scheduling of the power distribution network, the coordination and optimization of the new energy, the load and the energy storage system are comprehensively considered, so that the source-load storage connection can be enhanced, and the active power distribution network optimal scheduling research considering the coordination of the source-load storage is beneficial to the power grid side and the demand side, and has very strong practical significance. Through price type and excitation type demand response models, a power distribution network day-ahead optimal scheduling model comprising wind-solar energy storage, a traditional unit and demand response loads thereof is provided; an intra-day optimal scheduling model is designed based on a model predictive control algorithm to stabilize the fluctuation and uncertainty of wind and light loads.
The implementation of the above embodiment has the following effects:
according to the technical scheme, a demand response model is established through data information of user demand side response, and then a power distribution network day-ahead optimization scheduling model is established, so that the power distribution network day-ahead optimization scheduling can consider the user demand side response, a preliminary scheduling plan is formed based on a preset algorithm, the prediction data of the preliminary scheduling strategy is obtained through the preset prediction model, the preliminary scheduling strategy is optimized, a final scheduling strategy is obtained, fluctuation and uncertainty of wind and solar loads are stabilized, and the problems that the power generation pressure is high during peak load and frequent power output of a generator set is required are avoided.
Example two
Referring to fig. 6, the power distribution network optimizing and scheduling device provided by the present invention includes: a model building module 201, a preliminary scheduling policy module 202 and a final scheduling policy module 203.
The model building module 201 is configured to build a demand response model according to data information of a user demand side response.
The preliminary scheduling policy module 202 is configured to construct a daily optimization scheduling model of the power distribution network based on the demand response model, and form a preliminary scheduling policy for daily optimization of the power distribution network through a preset algorithm.
The final scheduling policy module 203 is configured to obtain prediction data of the preliminary scheduling policy through a preset prediction model, and optimize the preliminary scheduling policy according to the prediction data to obtain a final scheduling policy, thereby completing multi-time-scale optimized scheduling of the power distribution network.
As a preferred solution, the establishing a demand response model according to the data information responded by the user demand side specifically includes:
respectively constructing a price type demand response model and an incentive type demand response model according to price data and incentive data responded by a user demand side; wherein the demand response model comprises: a price type demand response model and an incentive type demand response model; the price type demand response model is used for describing the influence of the adjusted electricity price on the electricity consumption of the user, and the excitation type demand response model is used for describing the compensation cost generated after the user carries out load interruption.
Preferably, the price data comprises electricity consumption before demand response, electricity consumption after demand response, electricity price before response and electricity price change value;
the excitation data comprises interrupt load compensation cost, interrupt load set, response power of interrupt load in each period in the scheduling period, compensation cost of unit interruptible load and time step in the scheduling period.
As a preferred scheme, the construction of the power distribution network day-ahead optimization scheduling model based on the demand response model is specifically as follows:
based on the price type demand response model and the excitation type demand response model, constructing an objective function of power distribution network optimization scheduling by taking the lowest running cost of the power distribution network as a scheduling target, and taking the objective function as a power distribution network day-ahead optimization scheduling model; the objective function of the optimal scheduling of the power distribution network is as follows:wherein S is the total cost in the scheduling period; s is S DG (t) is the cost of the traditional unit in the period t; s is S E (t) is the energy storage operation and maintenance cost in the t period; s is S G (t) is the power interaction cost of the period t and the upper grid; s is S WP (t) wind-discarding and light-discarding penalty costs for the t period; s is S T (t) demand response costs based on time-of-use electricity prices for the period t; s is S IL (t) an interruptible load interruption cost for a period t; t is the total number of time periods within one scheduling period.
Preferably, the method further comprises:
constraint is carried out on the condition of the objective function of the power distribution network optimization scheduling; wherein the condition constraints include: p (P) DG (t)+P G (t)+P w (t)+P v (t)＝P E (t)+P R (t)+P L (t)-P IL (t)；
Wherein P is G (t) is the interaction power of the moment t and the upper power grid, P DG (t) is the output of the traditional machine set in the period t，P E (t) is the charge and discharge power of the energy storage battery at the moment t, P w (t) is the consumed power of the wind generating set before the day of the t period, P v (t) is the consumed power of the photovoltaic generator set before the day of the t period, P R (t) is the load power after demand response, P L (t) is the load power before demand response, P IL (t) is the response power of the interrupt load in the t period,the upper limit and the lower limit of the output power of the traditional unit are set; />The power of the traditional unit is increased and decreased; Δt is the scheduled time difference;charging and discharging power for the energy storage battery at the moment t; />Maximum charge and discharge power of the energy storage battery; e (E) S (t)、E S (t-1) is the remaining capacity of the storage battery at the time t and the time t-1 respectively, < + >>Is the upper and lower limits of the residual capacity, and eta is the charge-discharge efficiency; />Is the upper limit of the interaction power with the large power grid.
As a preferred solution, the obtaining the prediction data of the preliminary scheduling policy through a preset prediction model, and optimizing the preliminary scheduling policy according to the prediction data to obtain a final scheduling policy, which specifically includes:
obtaining state data corresponding to the current moment according to the preliminary scheduling strategy; carrying out prediction iteration for preset times on the predicted data through a preset prediction model, so as to obtain predicted data corresponding to each subsequent moment; according to the predicted data corresponding to each subsequent moment, obtaining a predicted output value of the power of the upper power grid in a predicted time period, the residual capacity of the energy storage battery and a planned daily power interaction value of the power of the upper power grid; and converting the corresponding daily rolling optimization scheduling into a quadratic programming problem function by taking the residual electric quantity of the energy storage battery and the minimum error between the estimated output value and the daily planned value as targets, so as to solve the quadratic programming problem function and obtain a final scheduling strategy.
As a preferred solution, the predicting data is repeatedly iterated for a preset number of times through a preset predicting model, so as to obtain predicted data corresponding to each subsequent moment, which specifically includes:
in each prediction iteration, according to the current time and the prediction data of the current time, a control instruction sequence of each future time is obtained or updated based on a preset prediction model, and a first value of the control instruction sequence is applied to a control system, so that state data corresponding to the next time is updated and obtained until the state data corresponding to all the times are updated once, and the prediction data corresponding to each time is output; wherein the control instruction sequence includes a value corresponding to each future time instant.
It will be clear to those skilled in the art that, for convenience and brevity of description, reference may be made to the corresponding process in the foregoing method embodiment for the specific working process of the above-described apparatus, which is not described herein again.
The implementation of the above embodiment has the following effects:
according to the technical scheme, a demand response model is established through data information of user demand side response, and then a power distribution network day-ahead optimization scheduling model is established, so that the power distribution network day-ahead optimization scheduling can consider the user demand side response, a preliminary scheduling plan is formed based on a preset algorithm, the prediction data of the preliminary scheduling strategy is obtained through the preset prediction model, the preliminary scheduling strategy is optimized, a final scheduling strategy is obtained, fluctuation and uncertainty of wind and solar loads are stabilized, and the problems that the power generation pressure is high during peak load and frequent power output of a generator set is required are avoided.
Example III
Correspondingly, the invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the power distribution network optimization scheduling method according to any one of the embodiments above when executing the computer program.
The terminal device of this embodiment includes: a processor, a memory, a computer program stored in the memory and executable on the processor, and computer instructions. The processor, when executing the computer program, implements the steps of the first embodiment described above, such as steps S101 to S103 shown in fig. 1. Alternatively, the processor, when executing the computer program, implements the functions of the modules/units in the above apparatus embodiments, for example, the final scheduling policy module 203.
The computer program may be divided into one or more modules/units, which are stored in the memory and executed by the processor to accomplish the present invention, for example. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used for describing the execution of the computer program in the terminal device. For example, the final scheduling policy module 203 is configured to obtain prediction data of the preliminary scheduling policy through a preset prediction model, and optimize the preliminary scheduling policy according to the prediction data to obtain a final scheduling policy, thereby completing multi-time-scale optimized scheduling of the power distribution network.
The terminal equipment can be computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The terminal device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that the schematic diagram is merely an example of a terminal device and does not constitute a limitation of the terminal device, and may include more or less components than illustrated, or may combine some components, or different components, e.g., the terminal device may further include an input-output device, a network access device, a bus, etc.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center of the terminal device, and which connects various parts of the entire terminal device using various interfaces and lines.
The memory may be used to store the computer program and/or the module, and the processor may implement various functions of the terminal device by running or executing the computer program and/or the module stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the mobile terminal, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Wherein the terminal device integrated modules/units may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as stand alone products. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
Example IV
Correspondingly, the invention further provides a computer readable storage medium, which comprises a stored computer program, wherein when the computer program runs, equipment where the computer readable storage medium is located is controlled to execute the power distribution network optimal scheduling method according to any one of the embodiments.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present invention, and are not to be construed as limiting the scope of the invention. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the spirit and principles of the present invention are intended to be included in the scope of the present invention.
Claims (10)
1. The power distribution network optimal scheduling method is characterized by comprising the following steps of:
establishing a demand response model according to the data information responded by the user demand side;
based on the demand response model, constructing a power distribution network day-ahead optimization scheduling model, and forming a preliminary scheduling strategy for obtaining power distribution network day-ahead optimization through a preset algorithm;
And obtaining prediction data of the preliminary scheduling strategy through a preset prediction model, and optimizing the preliminary scheduling strategy according to the prediction data to obtain a final scheduling strategy, thereby completing multi-time-scale optimized scheduling of the power distribution network.
2. The power distribution network optimization scheduling method according to claim 1, wherein the establishing a demand response model according to the data information of the user demand side response specifically comprises:
respectively constructing a price type demand response model and an incentive type demand response model according to price data and incentive data responded by a user demand side;
wherein the demand response model comprises: a price type demand response model and an incentive type demand response model; the price type demand response model is used for describing the influence of the adjusted electricity price on the electricity consumption of the user, and the excitation type demand response model is used for describing the compensation cost generated after the user carries out load interruption.
3. A power distribution network optimal scheduling method according to claim 2, wherein the price data includes a power consumption before demand response, a power price before response, and a power price change value;
the excitation data comprises interrupt load compensation cost, interrupt load set, response power of interrupt load in each period in the scheduling period, compensation cost of unit interruptible load and time step in the scheduling period.
4. A power distribution network optimization scheduling method according to claim 2 or 3, wherein the power distribution network day-ahead optimization scheduling model is constructed based on the demand response model, specifically:
based on the price type demand response model and the excitation type demand response model, constructing an objective function of power distribution network optimization scheduling by taking the lowest running cost of the power distribution network as a scheduling target, and taking the objective function as a power distribution network day-ahead optimization scheduling model;
the objective function of the optimal scheduling of the power distribution network is as follows:
wherein S is the total cost in the scheduling period; s is S DG (t) is the cost of the traditional unit in the period t; s is S E (t) is the energy storage operation and maintenance cost in the t period; s is S G (t) is the power interaction cost of the period t and the upper grid; s is S WP (t) wind-discarding and light-discarding penalty costs for the t period; s is S T (t) demand response costs based on time-of-use electricity prices for the period t; s is S IL (t) an interruptible load interruption cost for a period t; t is the total number of time periods within one scheduling period.
5. The power distribution network optimal scheduling method as set forth in claim 4, further comprising:
constraint is carried out on the condition of the objective function of the power distribution network optimization scheduling;
wherein the condition constraints include:
P DG (t)+P G (t)+P w (t)+P v (t)＝P E (t)+P R (t)+P L (t)-P IL (t)
wherein P is G (t) is the interaction power of the moment t and the upper power grid, P DG (t) is the output force of the traditional machine set in the period t, P E (t) is the charge and discharge power of the energy storage battery at the moment t, P w (t) is the consumed power of the wind generating set before the day of the t period, P v (t) is the consumption of the photovoltaic generator set before the day of the period tPower, P R (t) is the load power after demand response, P L (t) is the load power before demand response, -P IL (t) is the response power of the interrupt load in the t period,the upper limit and the lower limit of the output power of the traditional unit are set; />The power of the traditional unit is increased and decreased; Δt is the scheduled time difference; />Charging and discharging power for the energy storage battery at the moment t; />Maximum charge and discharge power of the energy storage battery; e (E) S (t)、E S (t-1) is the remaining capacity of the storage battery at the time t and the time t-1 respectively, < + >>Is the upper and lower limits of the residual capacity, and eta is the charge-discharge efficiency; />Is the upper limit of the interaction power with the large power grid.
6. The power distribution network optimization scheduling method according to claim 1, wherein the obtaining the prediction data of the preliminary scheduling policy through a preset prediction model, and optimizing the preliminary scheduling policy according to the prediction data, obtains a final scheduling policy, specifically includes:
obtaining state data corresponding to the current moment according to the preliminary scheduling strategy;
Carrying out prediction iteration for preset times on the predicted data through a preset prediction model, so as to obtain predicted data corresponding to each subsequent moment;
according to the predicted data corresponding to each subsequent moment, obtaining a predicted output value of the power of the upper power grid in a predicted time period, the residual capacity of the energy storage battery and a planned daily power interaction value of the power of the upper power grid;
and converting the corresponding daily rolling optimization scheduling into a quadratic programming problem function by taking the residual electric quantity of the energy storage battery and the minimum error between the estimated output value and the daily planned value as targets, so as to solve the quadratic programming problem function and obtain a final scheduling strategy.
7. The power distribution network optimization scheduling method according to claim 6, wherein the predicting data is repeatedly iterated for a preset number of times through a preset predicting model, so as to obtain predicted data corresponding to each subsequent moment, specifically:
in each prediction iteration, according to the current time and the prediction data of the current time, a control instruction sequence of each future time is obtained or updated based on a preset prediction model, and a first value of the control instruction sequence is applied to a control system, so that state data corresponding to the next time is updated and obtained until the state data corresponding to all the times are updated once, and the prediction data corresponding to each time is output; wherein the control instruction sequence includes a value corresponding to each future time instant.
8. An optimized scheduling device for a power distribution network, comprising: the system comprises a model building module, a preliminary scheduling strategy module and a final scheduling strategy module;
the model building module is used for building a demand response model according to the data information responded by the user demand side;
the preliminary scheduling strategy module is used for constructing a power distribution network day-ahead optimization scheduling model based on the demand response model, and forming a preliminary scheduling strategy for obtaining the power distribution network day-ahead optimization through a preset algorithm;
the final scheduling strategy module is used for obtaining the prediction data of the preliminary scheduling strategy through a preset prediction model, optimizing the preliminary scheduling strategy according to the prediction data to obtain a final scheduling strategy, and thus completing multi-time-scale optimized scheduling of the power distribution network.
9. A terminal device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the power distribution network optimization scheduling method according to any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored computer program, wherein the computer program when run controls a device in which the computer readable storage medium is located to perform the power distribution network optimization scheduling method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310359789.9A CN116545025A (en) | 2023-04-04 | 2023-04-04 | Optimal scheduling method, device, equipment and storage medium for power distribution network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310359789.9A CN116545025A (en) | 2023-04-04 | 2023-04-04 | Optimal scheduling method, device, equipment and storage medium for power distribution network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116545025A true CN116545025A (en) | 2023-08-04 |
Family
ID=87444336
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310359789.9A Pending CN116545025A (en) | 2023-04-04 | 2023-04-04 | Optimal scheduling method, device, equipment and storage medium for power distribution network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116545025A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117293923A (en) * | 2023-09-25 | 2023-12-26 | 南栖仙策(南京)高新技术有限公司 | Method, device, equipment and storage medium for generating day-ahead scheduling plan of power grid |
| CN117811019A (en) * | 2023-12-29 | 2024-04-02 | 广东汇丰综合能源有限公司 | Collaborative stabilizing method and system for optimizing power distribution network state and readable storage medium |
-
2023
- 2023-04-04 CN CN202310359789.9A patent/CN116545025A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117293923A (en) * | 2023-09-25 | 2023-12-26 | 南栖仙策(南京)高新技术有限公司 | Method, device, equipment and storage medium for generating day-ahead scheduling plan of power grid |
| CN117811019A (en) * | 2023-12-29 | 2024-04-02 | 广东汇丰综合能源有限公司 | Collaborative stabilizing method and system for optimizing power distribution network state and readable storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Foruzan et al. | Reinforcement learning approach for optimal distributed energy management in a microgrid | |
| Athari et al. | Operational performance of energy storage as function of electricity prices for on-grid hybrid renewable energy system by optimized fuzzy logic controller | |
| Huang et al. | A self-learning scheme for residential energy system control and management | |
| Samende et al. | Multi-agent deep deterministic policy gradient algorithm for peer-to-peer energy trading considering distribution network constraints | |
| CN116545025A (en) | Optimal scheduling method, device, equipment and storage medium for power distribution network | |
| Dong et al. | A strategic day-ahead bidding strategy and operation for battery energy storage system by reinforcement learning | |
| Mohammadjafari et al. | Optimal energy management of a microgrid incorporating a novel efficient demand response and battery storage system | |
| Lu et al. | A hybrid deep learning-based online energy management scheme for industrial microgrid | |
| CN107453407B (en) | Intelligent micro-grid distributed energy scheduling method | |
| Jeddi et al. | Distributed load scheduling in residential neighborhoods for coordinated operation of multiple home energy management systems | |
| CN112671022A (en) | Optical storage charging station capacity optimal configuration method, system, terminal and storage medium | |
| Chen et al. | Profit-seeking energy-intensive enterprises participating in power system scheduling: Model and mechanism | |
| CN117039959A (en) | Shared energy storage system operation control method based on hybrid game | |
| CN116432862A (en) | Multi-main-body game optimization method and device for renewable energy micro-grid | |
| CN116054270A (en) | Power grid dispatching optimization method and device and nonvolatile storage medium | |
| Yang et al. | Deep reinforcement learning for economic energy scheduling in data center microgrids | |
| Lee et al. | Non-intrusive load management under forecast uncertainty in energy constrained microgrids | |
| CN118157143A (en) | GA-MADRL-PPO combination-based distributed photovoltaic optimal scheduling strategy method, device and system | |
| Hassaballah et al. | A novel strategy for real-time optimal scheduling of grid-tied microgrid considering load management and uncertainties | |
| CN117791591A (en) | Method, system, device and medium for optimizing storage and transportation of source network charges based on differentiated rewards | |
| Liu et al. | Improved reinforcement learning-based real-time energy scheduling for prosumer with elastic loads in smart grid | |
| CN115663868A (en) | Energy storage operation optimization method considering local intermittent power supply income | |
| CN115864364A (en) | Operation strategy planning method and device for user to participate in demand response | |
| CN113555887A (en) | Power grid energy control method and device, electronic equipment and storage medium | |
| CN113554219A (en) | Renewable energy power station shared energy storage capacity planning method and device |
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 |