CN115882460A - Two-stage new energy micro-grid optimization scheduling method considering demand side management - Google Patents

Two-stage new energy micro-grid optimization scheduling method considering demand side management Download PDF

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CN115882460A
CN115882460A CN202210407547.8A CN202210407547A CN115882460A CN 115882460 A CN115882460 A CN 115882460A CN 202210407547 A CN202210407547 A CN 202210407547A CN 115882460 A CN115882460 A CN 115882460A
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new energy
microgrid
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generating set
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王书峰
李勇
安彬
许满库
赵军愉
葛硕
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State Grid Corp of China SGCC
Baoding Power Supply Co of State Grid Hebei Electric Power Co Ltd
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State Grid Corp of China SGCC
Baoding Power Supply Co of State Grid Hebei Electric Power Co Ltd
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Abstract

The invention provides a two-stage new energy microgrid optimization scheduling method considering demand side management, which comprises the steps of firstly establishing a microgrid model consisting of a wind generating set, a photovoltaic generating set, a diesel generating set, an energy storage system, a fixed load and a plurality of controllable equipment loads; then, constructing a multi-objective optimization model for demand side management by taking the maximized new energy utilization rate, the minimized user electricity purchasing cost and the minimized load peak-valley difference as targets, and solving the model by adopting a multi-objective particle swarm optimization algorithm to obtain an optimized real load; and finally, establishing a multi-target optimization scheduling model of the economic environment of the grid-connected microgrid and solving the model by adopting a multi-target particle swarm optimization algorithm to obtain output scheduling of the distributed power supply, the energy storage system and the main grid in the microgrid at a plurality of future moments. The optimal scheduling method for the new energy microgrid can effectively reduce the electricity purchasing cost of users, improve the utilization rate of new energy and reduce the environmental and economic cost of microgrid electricity generation.

Description

Two-stage new energy micro-grid optimization scheduling method considering demand side management
Technical Field
The invention relates to the technical field of microgrid optimization scheduling, in particular to a two-stage new energy microgrid optimization scheduling method considering demand side management.
Background
With the large amount of new energy grid connection, the randomness and the uncontrollable property of wind and light output lead to low utilization rate of the new energy and serious phenomena of wind abandon and light abandon, so that the improvement of the utilization rate of the new energy and the reduction of the waste of wind and light resources have important significance for the new energy grid connection; with the development of the intelligent power grid, the power load is gradually participated in the optimized dispatching of the micro-grid as a special power resource, so that the source grid load friendly interaction of the micro-grid is realized; demand side management is an important means for controlling the load in the microgrid, and the load curve is directly changed by scheduling load resources, so that load peak clipping and valley filling can be realized, the power consumption cost of a user side can be reduced, and the consumption capacity of new energy is improved;
the demand side management controls the electricity utilization time of the controllable equipment on the premise of meeting the electricity utilization demand of the user, and the time-sharing electricity utilization condition of the user can be changed; therefore, an optimization model for demand side management is established and solved by taking the electricity purchasing cost and the new energy utilization rate of the user as targets, so that the transferred electricity load curve is closer to the target load curve, the new energy utilization rate of the micro-grid can be improved while the electricity cost of the user is reduced, then the optimized scheduling of the micro-grid is realized on the basis, the power generation pressure of a distributed power supply can be effectively relieved, the running cost and the environmental cost of the micro-grid are reduced, and the non-renewable energy is saved;
disclosure of Invention
The invention designs a two-stage new energy microgrid optimization scheduling method considering demand side management, which is characterized in that an optimization scheduling model of microgrid source-grid-load interaction is established and solved by considering load transfer of microgrid controllable equipment and output of distributed power supplies and stored energy, so that the new energy consumption capability of a microgrid is further improved, the economic and environmental cost of microgrid operation is reduced, and electric power resources are saved;
the invention adopts the following technical scheme:
a two-stage new energy microgrid optimization scheduling method considering demand side management is characterized in that a grid-connected new energy microgrid is established by considering load transfer of microgrid controllable equipment and output of a distributed power supply, a main grid and stored energy, and an optimization scheduling model of microgrid source grid load interaction is established and solved; a first stage adopts a demand side management method of controllable equipment power utilization time transfer, establishes a multi-objective optimization model of demand side management by taking the lowest power purchase cost of a user, the highest utilization rate of new energy and the lowest peak-valley difference of a load curve as targets, and adopts a multi-objective particle swarm optimization algorithm to solve to obtain a power utilization load curve which is reshaped and accords with an expected target; in the second stage, on the basis of the optimized power load in the first stage, a wind-light-diesel-storage model and a model interacted with a main network of the microgrid are considered, a multi-objective optimization scheduling model taking the lowest operation economic cost and pollutant discharge amount of the microgrid as optimization targets is established, the multi-objective particle swarm optimization algorithm is adopted to solve the model, and scheduling results of day-ahead output of the distributed power supply, the main network and the energy storage system of the microgrid are obtained;
the grid-connected microgrid system comprises: the system comprises a wind generating set, a photovoltaic generating set, a diesel generating set, an energy storage system, a fixed load, various controllable equipment loads, a microgrid feeder line and a microgrid main isolator;
the wind generating set, the photovoltaic generating set, the diesel generating set, the energy storage system, the fixed load and various controllable equipment loads are all connected to the microgrid feeder;
the microgrid feeder is connected with a power grid through the microgrid main isolator, so that grid-connected operation of the grid-connected microgrid system is realized;
the invention provides a two-stage new energy microgrid optimization scheduling method considering demand side management, which specifically comprises the following steps:
step 1: introducing wind power generation data of a plurality of historical moments, photovoltaic power generation data of a plurality of historical moments, fixed load data of a plurality of historical moments, a plurality of controllable equipment loads of a plurality of historical moments and time-of-use electricity prices of a plurality of future moments, predicting wind power generation capacity of a plurality of future moments according to the long-short term memory neural network by using the wind power generation data of the plurality of historical moments, predicting photovoltaic power generation capacity of a plurality of future moments according to the long-short term memory neural network by using the photovoltaic power generation data of the plurality of historical moments, and predicting the fixed load data of the plurality of historical moments according to the long-short term memory neural network to obtain the fixed load of the plurality of future moments;
step 2: calculating a new energy utilization rate objective function according to the wind power generation capacity at multiple future moments, the photovoltaic power generation capacity at multiple future moments and the fixed load at multiple future moments, calculating a user electricity purchasing cost objective function according to the time-of-use electricity price at multiple future moments and the fixed load at multiple future moments, which are described in the step 1, calculating a load peak-valley difference objective function according to the fixed load at multiple future moments, which are described in the step 1, constructing a multi-objective optimization model of demand side management by using the maximized new energy utilization rate objective function, the minimized user electricity purchasing cost objective function and the minimized load peak-valley difference objective function, and solving the model by using a multi-objective particle swarm algorithm to obtain an optimized real electricity load by using the loads of various controllable devices at multiple future moments as decision variables;
and step 3: calculating a new energy microgrid operation economic cost objective function and a new energy microgrid pollutant emission objective function, constructing a new energy microgrid multi-objective optimization scheduling model by minimizing the new energy microgrid operation economic cost objective function and the new energy microgrid pollutant emission objective function, respectively constructing microgrid power flow constraints, distributed power supply output constraints, distributed power supply climbing constraints, main grid output constraints, main grid climbing constraints, energy storage system operation constraints and rotation standby constraints, and optimizing to obtain optimized wind power generator set output power at multiple future moments, optimized photovoltaic generator set output power at multiple future moments, optimized diesel generator set output power at multiple future moments, optimized main grid output power at multiple future moments, optimized energy storage system charge and discharge power at multiple future moments by adopting a multi-objective particle swarm optimization algorithm as decision variables;
preferably, the multi-objective optimization model managed on the demand side in step 2 is specifically defined as:
Figure BDA0003602393340000031
in the formula (I), the compound is shown in the specification,
F 1 representing the new energy utilization rate of the microgrid;
F 2 representing the electricity purchase cost of the user;
F 3 a peak-to-valley difference representing the electrical load curve;
τ represents the number of future time instants;
WT (t) represents the predicted amount of wind power generation at the tth future time;
PV (t) represents the predicted photovoltaic power generation at the tth future time;
price (t) represents the time-of-use electricity Price at the tth future moment;
PLoad (t) represents the fixed load at the tth future moment and the loads of various controllable devices, and the specific expression is as follows:
Figure BDA0003602393340000032
in the formula (I), the compound is shown in the specification,
FL (t) represents the fixed load at the tth future instant;
SL (t) represents the various controllable device loads at the tth future instant;
l represents the number of types of controllable equipment loads;
m l represents the maximum transfer time of the controllable device load of the l type;
Num lit the number of the load of the first controllable equipment transferred from the time i to the time t is represented;
Pow l1 the load quantity of the first controllable equipment load at the moment of starting power utilization is represented;
d l representing the time span required by the first controllable device load;
step 2, the constraint conditions are as follows:
Figure BDA0003602393340000041
in the formula (I), the compound is shown in the specification,
ctrd (i) represents the total load of various controllable devices with load demands at the ith future moment; preferably, the new energy micro-grid multi-objective optimization scheduling model in step 3
Figure BDA0003602393340000042
In the formula (I), the compound is shown in the specification,
F 1 ' means new energyEconomic cost of the source microgrid;
F 2 ' represents the environmental cost of the new energy microgrid;
C WT (t) represents the economic cost of the wind park at the tth future moment;
C PV (t) represents the economic cost of the photovoltaic generator set at the tth future moment;
C BAT (t) represents the economic cost of the energy storage system at the tth future time;
C grid (t) represents the cost of interaction of the main grid and the new energy microgrid at the tth future moment;
N e indicating the type of pollutant gas emitted;
e DE,r ，e BAT,r ，e grid,r respectively representing the discharge coefficients of the r-th pollution gas discharged by the diesel generator set, the energy storage system and the main network;
P DE (t) represents the output of the diesel-electric set at the tth future moment;
P BAT (t) represents the charge-discharge power of the energy storage system at the tth future moment, and the charge is negative and the discharge is positive;
P grid (t) represents the contribution of the main network at the tth future moment;
step 3, micro-grid power flow constraint:
Figure BDA0003602393340000051
in the formula (I), the compound is shown in the specification,
P WT (t) representing the output of the wind generating set at the tth future moment;
P PV (t) represents the photovoltaic generator set output at the tth future moment;
and 3, performing output constraint and climbing constraint on the distributed power supply and the main network:
Figure BDA0003602393340000052
in the formula
Figure BDA0003602393340000053
Representing the output upper limit of a jth distributed power supply, wherein the distributed power supply comprises a wind generating set, a photovoltaic generating set and a diesel generating set;
Figure BDA0003602393340000054
representing the output lower limit of a jth distributed power supply, wherein the distributed power supply comprises a wind generating set, a photovoltaic generating set and a diesel generating set;
Figure BDA0003602393340000055
representing the upper output limit of the main network;
Figure BDA0003602393340000056
representing the lower output limit of the main network;
Figure BDA0003602393340000057
representing the output climbing upper limit of a jth distributed power supply, wherein the distributed power supply comprises a wind generating set, a photovoltaic generating set and a diesel generating set;
Figure BDA0003602393340000058
representing the output climbing lower limit of a jth distributed power supply, wherein the distributed power supply comprises a wind generating set, a photovoltaic generating set and a diesel generating set;
Figure BDA0003602393340000059
representing the upper limit of the output climbing of the main network;
Figure BDA0003602393340000061
representing the lower limit of the main network for climbing;
and 3, operation constraint of the energy storage system:
Figure BDA0003602393340000062
in the formula (I), the compound is shown in the specification,
E BAT (t) represents the electric quantity of the energy storage system at the tth future moment;
Figure BDA0003602393340000063
represents an upper limit of the energy storage system capacity;
Figure BDA0003602393340000064
represents a lower limit of the energy storage system capacity;
η ch representing the charging efficiency of the energy storage system;
η dch indicating the discharge efficiency of the energy storage system;
P ch (t) represents the charging power of the energy storage system at the tth future moment;
P dch (t) represents the discharge power of the energy storage system at the tth future moment;
Figure BDA0003602393340000065
representing an upper limit of the energy storage system charging power;
Figure BDA0003602393340000066
representing an upper limit of the energy storage system discharge power;
step 3, rotating standby constraint:
Figure BDA0003602393340000067
Figure BDA0003602393340000068
in the formula (I), the compound is shown in the specification,
Figure BDA0003602393340000069
the forward climbing capacity which can be provided by the jth distributed power supply at the tth future moment is represented, wherein the distributed power supply comprises a wind generating set, a photovoltaic generating set and a diesel generating set;
Figure BDA0003602393340000071
the method comprises the steps that negative climbing capacity which can be provided by a jth distributed power supply at a tth future moment is shown, wherein the distributed power supply comprises a wind generating set, a photovoltaic generating set and a diesel generating set;
Figure BDA0003602393340000072
representing the forward climbing capacity available by the main network at the tth future moment;
Figure BDA0003602393340000073
the negative climbing capacity provided by the main network at the tth future moment is represented;
Figure BDA0003602393340000074
indicating a positive spinning reserve demand of the system at a tth future time; />
Figure BDA0003602393340000075
Representing a negative spinning reserve demand of the system at a tth future time;
has the advantages that: compared with the prior art, the method has the advantages that the demand side management model with multi-objective optimization is considered to change the power utilization load curve, the new energy consumption is improved, and the electricity purchasing cost of users is reduced; on the basis of the remolded power load, a wind-light diesel storage and main network connection model in the new energy microgrid is considered, a new energy microgrid economic environment multi-objective optimization scheduling model is established and solved, a new energy microgrid day-ahead distributed power scheduling scheme is obtained, and friendly interaction of 'source network load' of the new energy microgrid is achieved; the new energy microgrid optimization scheduling scheme under the participation of demand side management can effectively improve the new energy permeability, reduce the phenomena of wind and light abandonment, relieve the power generation pressure of a distributed power supply, reduce the electricity purchasing cost of users and reduce the economic and environmental costs of new energy microgrid operation;
drawings
FIG. 1: is a schematic diagram of a new energy microgrid model;
FIG. 2 is a schematic diagram: is a flow chart of the method of the invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The schematic diagram of the new energy microgrid scheduling model of the present embodiment is shown in fig. 1, and includes: the system comprises a wind generating set, a photovoltaic generating set, a diesel generating set, an energy storage system, a fixed load, various controllable equipment loads, a microgrid feeder line and a microgrid main isolator;
the wind generating set, the photovoltaic generating set, the diesel generating set, the energy storage system, the fixed load and the various controllable equipment loads are all connected to the microgrid feeder;
the micro-grid feeder is connected with a power grid through the micro-grid main isolator, so that grid-connected operation of the grid-connected micro-grid system is realized;
the invention discloses a two-stage new energy microgrid optimization scheduling method considering demand side management, which specifically comprises the following steps:
step 1: introducing wind power generation data of a plurality of historical moments, photovoltaic power generation data of a plurality of historical moments, fixed load data of a plurality of historical moments, a plurality of controllable equipment loads of a plurality of historical moments and time-of-use prices of a plurality of future moments, predicting wind power generation capacity of a plurality of future moments according to the long-short term memory neural network by using the wind power generation data of the plurality of historical moments, predicting photovoltaic power generation capacity of a plurality of future moments according to the long-short term memory neural network by using the photovoltaic power generation data of the plurality of historical moments, and predicting fixed load of a plurality of future moments according to the long-short term memory neural network by using the fixed load data of the plurality of historical moments;
step 2: calculating a new energy utilization rate objective function according to the wind power generation capacity at multiple future moments, the photovoltaic power generation capacity at multiple future moments and the fixed load at multiple future moments, calculating a user electricity purchase cost objective function according to the time-of-use electricity prices at multiple future moments and the fixed load at multiple future moments, which are described in the step 1, calculating a load peak-valley difference objective function according to the fixed load at multiple future moments, which is described in the step 1, constructing a multi-objective optimization model for demand side management by using the maximized new energy utilization rate objective function, the minimized user electricity purchase cost objective function and the minimized load peak-valley difference objective function, and solving the model by using a multi-objective particle swarm algorithm to obtain an optimized real electricity consumption load by using the loads of various controllable devices at multiple moments in the future as decision variables;
step 2, the multi-objective optimization model of the demand side management is specifically defined as:
Figure BDA0003602393340000081
in the formula (I), the compound is shown in the specification,
F 1 representing the new energy utilization rate of the microgrid;
F 2 representing the electricity purchase cost of the user;
F 3 a peak-to-valley difference representing the electrical load curve;
τ =24 represents the number of future time instants;
WT (t) represents the predicted amount of wind power generation at the tth future time;
PV (t) represents the predicted photovoltaic power generation at the tth future time;
price (t) represents the time-of-use electricity Price at the tth future moment;
PLoad (t) represents the fixed load at the tth future moment and the loads of various controllable devices, and the specific expression is as follows:
Figure BDA0003602393340000091
in the formula (I), the compound is shown in the specification,
FL (t) represents the fixed load at the tth future moment;
SL (t) represents the various controllable device loads at the tth future instant;
l =8 represents the number of classes of controllable device load;
m l representing the maximum transfer time of the load of the first controllable device;
Num lit the number of the load of the first controllable equipment transferred from the time i to the time t is represented;
Pow l1 the load quantity of the first type of controllable equipment load at the moment of starting power utilization is represented;
d l representing the time span required by the first controllable device load;
step 2, the constraint conditions are as follows:
Figure BDA0003602393340000092
/>
in the formula (I), the compound is shown in the specification,
ctrd (i) represents the total load of the plurality of controllable devices having a load demand at the ith future time;
and step 3: calculating a new energy microgrid operation economic cost objective function and a new energy microgrid pollutant emission objective function, constructing a new energy microgrid multi-objective optimization scheduling model by minimizing the new energy microgrid operation economic cost objective function and the new energy microgrid pollutant emission objective function, respectively constructing microgrid power flow constraints, distributed power supply output constraints, distributed power supply climbing constraints, main grid output constraints, main grid climbing constraints, energy storage system operation constraints and rotation standby constraints, and optimizing to obtain optimized wind power generator set output power at multiple future moments, optimized photovoltaic generator set output power at multiple future moments, optimized diesel generator set output power at multiple future moments, optimized main grid output power at multiple future moments, optimized energy storage system charge and discharge power at multiple future moments by adopting a multi-objective particle swarm optimization algorithm as decision variables;
step 3, the new energy micro-grid multi-objective optimization scheduling model
Figure BDA0003602393340000101
In the formula (I), the compound is shown in the specification,
F 1 ' represents the economic cost of the new energy microgrid;
F 2 ' represents the environmental cost of the new energy microgrid;
C WT (t) represents the economic cost of the wind power plant at the tth future moment;
C PV (t) represents the economic cost of the photovoltaic generator set at the tth future moment;
C BAT (t) represents the economic cost of the energy storage system at the tth future time;
C grid (t) representing the cost of interaction of the main network and the new energy microgrid at the tth future moment;
N e =3 represents the type of pollution gas emitted;
e DE,r ，e BAT,r ，e grid,r respectively representing diesel generatorsThe discharge coefficients of the r-th pollution gas discharged by the group, the energy storage system and the main network;
P DE (t) represents the output of the diesel-electric set at the tth future moment;
P BAT (t) represents the charge-discharge power of the energy storage system at the tth future moment, and the charge is negative and the discharge is positive;
P grid (t) represents the contribution of the main network at the tth future moment;
step 3, micro-grid power flow constraint:
Figure BDA0003602393340000102
in the formula (I), the compound is shown in the specification,
P WT (t) representing the output of the wind generating set at the tth future moment;
P PV (t) represents the photovoltaic generator set output at the tth future moment;
and 3, performing output constraint and climbing constraint on the distributed power supply and the main network:
Figure BDA0003602393340000111
in the formula
Figure BDA0003602393340000112
Representing the output upper limit of a jth distributed power supply, wherein the distributed power supply comprises a wind generating set, a photovoltaic generating set and a diesel generating set;
Figure BDA0003602393340000113
representing the output lower limit of a jth distributed power supply, wherein the distributed power supply comprises a wind generating set, a photovoltaic generating set and a diesel generating set;
Figure BDA0003602393340000114
representing the upper output limit of the main network;
Figure BDA0003602393340000115
representing the lower output limit of the main network;
Figure BDA0003602393340000116
representing the output climbing upper limit of a jth distributed power supply, wherein the distributed power supply comprises a wind generating set, a photovoltaic generating set and a diesel generating set;
Figure BDA0003602393340000117
representing the output climbing lower limit of a jth distributed power supply, wherein the distributed power supply comprises a wind generating set, a photovoltaic generating set and a diesel generating set;
Figure BDA0003602393340000118
representing the upper limit of the output climbing of the main network;
Figure BDA0003602393340000119
representing the lower limit of the main network for climbing;
and 3, operation constraint of the energy storage system:
Figure BDA00036023933400001110
in the formula (I), the compound is shown in the specification,
E BAT (t) represents the electric quantity of the energy storage system at the tth future moment;
Figure BDA00036023933400001111
represents an upper limit of the energy storage system capacity;
Figure BDA00036023933400001112
represents a lower limit of the energy storage system capacity;
η ch =0.9 represents the charging efficiency of the energy storage system;
η dch =0.9 represents the discharge efficiency of the energy storage system;
P ch (t) represents the charging power of the energy storage system at the tth future moment;
P dch (t) represents the discharge power of the energy storage system at the tth future moment;
Figure BDA0003602393340000121
representing an upper limit of the energy storage system charging power;
Figure BDA0003602393340000122
representing an upper limit of the energy storage system discharge power;
step 3, rotating standby constraint:
Figure BDA0003602393340000123
Figure BDA0003602393340000124
in the formula (I), the compound is shown in the specification,
Figure BDA0003602393340000125
the forward climbing capacity which can be provided by the jth distributed power supply at the tth future moment is represented, wherein the distributed power supply comprises a wind generating set, a photovoltaic generating set and a diesel generating set;
Figure BDA0003602393340000126
indicating the negative climbing capacity that the jth distributed power supply can provide at the tth future moment, wherein the distributed power supply comprises windA power generator set, a photovoltaic generator set and a diesel generator set;
Figure BDA0003602393340000127
representing the forward climbing capacity available by the main network at the tth future moment;
Figure BDA0003602393340000128
the negative climbing capacity provided by the main network at the tth future moment is represented;
Figure BDA0003602393340000129
indicating a positive spinning reserve demand of the system at a tth future time;
Figure BDA00036023933400001210
indicating a negative spinning reserve demand of the system at a tth future time;
it should be understood that parts of the specification not set forth in detail are well within the prior art.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. A two-stage new energy microgrid optimization scheduling method considering demand side management is characterized by comprising the following steps:
step 1: considering wind power and photovoltaic power generation with high permeability in the microgrid, and establishing a new energy microgrid model;
step 2: establishing a demand side management multi-objective optimization model of the new energy microgrid at a first stage by taking transferable loads of the new energy microgrid at a plurality of future moments as decision variables, and solving the model by adopting a multi-objective particle swarm algorithm to obtain an optimized real power utilization load;
and step 3: and taking the output power of the distributed power supplies at a plurality of future moments of the new energy microgrid, the stored charge and discharge power and the interaction power of the microgrid and the main grid as decision variables, establishing a second-stage economic environment optimization scheduling model of the new energy microgrid, and solving the model by adopting a multi-objective particle swarm algorithm to obtain the optimized generated energy of the distributed power supplies at the plurality of future moments, the stored charge and discharge power and the interaction power of the microgrid and the main grid.
2. The two-stage new energy microgrid optimization scheduling method considering demand side management as claimed in claim 1, characterized in that the new energy microgrid model in step 1 is composed of distributed power sources, stored energy, fixed loads and transferable loads, and the new energy microgrid is in a grid-connected operation state; the distributed power supply comprises a wind generating set, a photovoltaic generating set and a diesel generating set, and the transferable load is defined as the load of various controllable devices in the new energy microgrid.
3. The two-stage new energy microgrid optimization scheduling method considering demand side management as claimed in claims 1 and 2, characterized in that building a new energy microgrid model requires introducing wind power generation data at a plurality of historical times, photovoltaic power generation data at a plurality of historical times, fixed load data at a plurality of historical times, a plurality of controllable equipment loads at a plurality of historical times, and time-sharing power rates at a plurality of future times, predicting wind power generation capacity at a plurality of future times from the wind power generation data at a plurality of historical times according to a long-short term memory neural network, predicting photovoltaic power generation capacity at a plurality of future times from the photovoltaic power generation data at a plurality of historical times according to a long-short term memory neural network, and predicting fixed load data at a plurality of historical times according to a long-short term memory neural network to obtain fixed loads at a plurality of future times.
4. The two-stage new energy microgrid optimization scheduling method considering demand side management according to claims 1 and 3, characterized in that the demand side management multi-objective optimization model in step 2 takes loads of multiple controllable devices at multiple future times as decision variables, calculates a new energy utilization rate objective function from wind power generation at multiple future times, photovoltaic power generation at multiple future times and fixed loads at multiple future times, calculates a user electricity purchase cost objective function from time-sharing electricity prices at multiple future times and fixed loads at multiple future times, calculates a load peak-valley difference objective function from fixed loads at multiple future times, and constructs a demand side management multi-objective optimization model by maximizing the new energy utilization rate objective function, minimizing the user electricity purchase cost objective function and minimizing the load peak-valley difference objective function, wherein the objective function is specifically represented as:
Figure FDA0003602393330000021
in the formula (I), the compound is shown in the specification,
F 1 representing the new energy utilization rate of the microgrid;
F 2 representing the electricity purchase cost of the user;
F 3 a peak-to-valley difference representing the electrical load curve;
τ represents the number of future time instants;
WT (t) represents the predicted amount of wind power generation at the tth future time;
PV (t) represents the predicted photovoltaic power generation at the tth future time;
price (t) represents the time-of-use electricity Price at the tth future moment;
PLoad (t) represents the fixed load at the tth future moment and the loads of various controllable devices, and the specific expression is as follows:
Figure FDA0003602393330000022
in the formula (I), the compound is shown in the specification,
FL (t) represents the fixed load at the tth future instant;
SL (t) represents the various controllable device loads at the tth future instant;
l represents the number of types of controllable equipment loads;
m l representing the maximum transfer time of the load of the first controllable device;
Num lit the number of the load of the first controllable equipment transferred from the time i to the time t is represented;
Pow l1 the load quantity of the first type of controllable equipment load at the moment of starting power utilization is represented;
d l representing the time span required for the first controllable device load.
5. The two-stage new energy microgrid optimization scheduling method considering demand side management as claimed in claims 1 and 3, characterized in that the demand side management multiobjective optimization model in step 2 takes the power demand of multiple controllable devices at multiple future times as a constraint condition, and the constraint condition is specifically expressed as:
Figure FDA0003602393330000031
in the formula (I), the compound is shown in the specification,
ctrd (i) represents the total load of the various controllable devices with load demands at the ith future time.
6. The two-stage new energy microgrid optimization scheduling method considering demand side management as claimed in claim 1, characterized in that the multi-objective particle swarm optimization algorithm in step 2 solves the actual power load at a plurality of times in the future obtained by the first-stage demand side management optimization model, and the actual power load is higher in energy utilization rate, lower in user electricity purchase cost and lower in load peak-valley difference than the load before optimization, and the optimized actual power load is applied to the second-stage optimization model in step 3.
7. The two-stage new energy microgrid optimization scheduling method considering demand side management as claimed in claim 1, characterized in that the economic and environmental optimization scheduling model of the second stage new energy microgrid in step 3 is established with the economic cost and the environmental cost minimum as optimization targets, the economic cost includes fuel cost of a distributed power supply, operation and maintenance cost of stored energy, and interaction cost of the microgrid and a main grid; the environment cost comprises pollutant emission control cost of a distributed power supply, energy storage and a main network, and the model objective function is specifically expressed as:
Figure FDA0003602393330000032
in the formula (I), the compound is shown in the specification,
F 1 ' represents the economic cost of the new energy microgrid;
F 2 ' represents the environmental cost of the new energy microgrid;
C WT (t) represents the economic cost of the wind power plant at the tth future moment;
C PV (t) represents the economic cost of the photovoltaic generator set at the tth future moment;
C BAT (t) represents the economic cost of the energy storage system at the tth future time;
C grid (t) represents the cost of interaction of the main grid and the new energy microgrid at the tth future moment;
N e indicating the type of pollutant gas emitted;
e DE,r ，e BAT,r ，e grid,r respectively representing the discharge coefficients of the r-th pollution gas discharged by the diesel generating set, the energy storage system and the main network;
P DE (t) represents the output of the diesel-electric set at the tth future moment;
P BAT (t) represents the charge-discharge power of the energy storage system at the tth future moment, and the charge is negative and the discharge is positive; p grid (t) represents the contribution of the main network at the tth future moment.
8. The two-stage new energy microgrid optimization scheduling method considering demand side management as claimed in claim 1, characterized in that the constraint conditions of the second-stage new energy microgrid economic environment optimization scheduling model in step 3 include microgrid power flow constraint, distributed power supply output constraint, distributed power supply climbing constraint, main grid output constraint, main grid climbing constraint, energy storage system operation constraint and rotation standby constraint, and the constraint conditions are specifically expressed as:
and (3) micro-grid power flow constraint:
Figure FDA0003602393330000041
in the formula (I), the compound is shown in the specification,
P WT (t) representing the output of the wind generating set at the tth future moment;
P PV (t) represents the photovoltaic generator set output at the tth future moment;
distributed generator, main network's output restraint and climbing restraint:
Figure FDA0003602393330000042
in the formula
Figure FDA0003602393330000043
Representing the output upper limit of a jth distributed power supply, wherein the distributed power supply comprises a wind generating set, a photovoltaic generating set and a diesel generating set;
Figure FDA0003602393330000044
representing the output lower limit of a jth distributed power supply, wherein the distributed power supply comprises a wind generating set, a photovoltaic generating set and a diesel generating set;
Figure FDA0003602393330000051
representing the upper output limit of the main network;
Figure FDA0003602393330000052
representing the lower output limit of the main network;
Figure FDA0003602393330000053
representing the output climbing upper limit of a jth distributed power supply, wherein the distributed power supply comprises a wind generating set, a photovoltaic generating set and a diesel generating set;
Figure FDA0003602393330000054
representing the output climbing lower limit of a jth distributed power supply, wherein the distributed power supply comprises a wind generating set, a photovoltaic generating set and a diesel generating set;
Figure FDA0003602393330000055
representing the upper limit of the output climbing of the main network;
Figure FDA0003602393330000056
representing the lower limit of the main network for climbing;
and (4) operation restraint of the energy storage system:
Figure FDA0003602393330000057
in the formula (I), the compound is shown in the specification,
E BAT (t) represents the electric quantity of the energy storage system at the tth future moment;
Figure FDA0003602393330000058
representing an upper limit of energy storage system capacity;
Figure FDA0003602393330000059
represents a lower limit of the energy storage system capacity;
η ch representing the charging efficiency of the energy storage system;
η dch indicating the discharge efficiency of the energy storage system;
P ch (t) represents the charging power of the energy storage system at the tth future moment;
P dch (t) represents the discharge power of the energy storage system at the tth future moment;
Figure FDA00036023933300000510
representing an upper limit of the energy storage system charging power;
Figure FDA00036023933300000511
represents an upper limit of the discharge power of the energy storage system;
rotating standby constraint:
Figure FDA0003602393330000061
/>
Figure FDA0003602393330000062
in the formula (I), the compound is shown in the specification,
Figure FDA0003602393330000063
the forward climbing capacity which can be provided by the jth distributed power supply at the tth future moment is represented, wherein the distributed power supply comprises a wind generating set, a photovoltaic generating set and a diesel generating set;
Figure FDA0003602393330000064
the method comprises the steps that negative climbing capacity which can be provided by a jth distributed power supply at a tth future moment is shown, wherein the distributed power supply comprises a wind generating set, a photovoltaic generating set and a diesel generating set;
Figure FDA0003602393330000065
representing the forward climbing capacity available by the main network at the tth future moment;
Figure FDA0003602393330000066
the negative climbing capacity provided by the main network at the tth future moment is represented;
Figure FDA0003602393330000067
representing a positive spinning reserve demand of the system at a tth future time;
Figure FDA0003602393330000068
indicating a negative spinning reserve demand for the system at the tth future time.
9. The two-stage new energy microgrid optimization scheduling method considering demand side management as claimed in claim 1, characterized in that the second-stage new energy microgrid economic environment optimization scheduling model is solved by using a multi-objective particle swarm algorithm in step 3, the output power of the distributed power source at a plurality of future moments, the energy storage charge-discharge power at a plurality of future moments and the main grid output power at a plurality of future moments obtained after the solution are obtained, namely, the new energy microgrid optimization scheduling result, and the scheduling result enables economic and environmental costs of the new energy microgrid to be lower.
10. The two-stage new energy microgrid optimization scheduling method considering demand side management according to claim 1 is characterized in that Pareto non-dominated solution is obtained by multi-objective particle swarm optimization, and final compromise optimal solution is selected by artificially designating importance degrees of a plurality of objectives.
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Publication number Priority date Publication date Assignee Title
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116742724A (en) * 2023-08-16 2023-09-12 杭州太阁未名科技有限公司 Active power distribution network optimal scheduling method and device, computer equipment and storage medium
CN116742724B (en) * 2023-08-16 2023-11-03 杭州太阁未名科技有限公司 Active power distribution network optimal scheduling method and device, computer equipment and storage medium

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