CN112465208B - Virtual power plant random self-adaptive robust optimization scheduling method considering block chain technology - Google Patents

Virtual power plant random self-adaptive robust optimization scheduling method considering block chain technology Download PDF

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CN112465208B
CN112465208B CN202011314030.1A CN202011314030A CN112465208B CN 112465208 B CN112465208 B CN 112465208B CN 202011314030 A CN202011314030 A CN 202011314030A CN 112465208 B CN112465208 B CN 112465208B
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vpp
gas turbine
power plant
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周洪益
马汝祥
侍红兵
柏晶晶
胥峥
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Yancheng Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
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    • G—PHYSICS
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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Abstract

The invention discloses a random self-adaptive robust optimization scheduling method for a virtual power plant, which takes block chain technology into consideration, and comprises the following steps: constructing a virtual power plant deterministic model taking the optimal virtual power plant overall profit as a target according to the original data; further considering uncertainty in the operation process of the virtual power plant, processing uncertainty of market electricity price by adopting a stochastic programming method, processing uncertainty of photovoltaic output by adopting a self-adaptive robust method, establishing a stochastic self-adaptive robust scheduling model of the virtual power plant, and solving by optimizing a modeling software GAMS; and finally, the virtual power plant aggregator issues the optimized day-ahead planned output result to an Ethernet workshop platform through an intelligent contract editor Remix and sends the optimized day-ahead planned output result to each distributed energy node.

Description

Virtual power plant random self-adaptive robust optimization scheduling method considering block chain technology
Technical Field
The invention belongs to the field of power supply scheduling of power systems, and particularly relates to a random adaptive robust optimization scheduling method for a virtual power plant, which takes block chain technology into account.
Background
The energy structure of China is forward and clean, the low carbon direction is changed, and renewable energy has the characteristics of dispersed geographic positions, strong randomness, large volatility, weaker controllability and the like, and provides new challenges for safe, reliable and economic operation of a power grid along with continuous expansion of the total scale of accessing the power grid. A Virtual Power Plant (VPP) aggregates various distributed energy sources such as renewable energy sources, energy storage and Demand Response (DR) through advanced communication, metering and control technologies, and participates in the operation of a power grid as a whole, so that the impact of independent grid connection of the distributed energy sources on the public grid can be reduced, and the market competitiveness of the virtual power plant is improved.
The VPP is influenced by uncertainty factors such as renewable energy output and market electricity price in the process of optimal scheduling. Aiming at the high uncertainty degree of the photovoltaic output, the photovoltaic output under the worst condition is considered by adopting a self-adaptive robust method; aiming at the lower uncertainty degree of the market electricity price, the uncertainty of the market electricity price is processed by adopting a random planning method, and a VPP random adaptive robust scheduling model is established by combining adaptive robust with random planning.
Further, the existing centralized virtual power plant management mode has the problems of unsafe information data, unreasonable profit allocation, complex management and the like. The block chain technology provides a new approach for solving some problems existing in the virtual power plant management mode by the characteristics of decentralization, openness and transparency, no tampering and the like. The method is combined with each other to establish a random self-adaptive robust optimization scheduling method for the virtual power plant, which takes the block chain technology into account.
Disclosure of Invention
Aiming at the problems, the invention provides a random self-adaptive robust optimization scheduling method for a virtual power plant, which takes block chain technology into consideration, and can effectively treat uncertainty in the operation process and ensure the openness and transparency of optimization scheduling.
The invention relates to a random self-adaptive robust optimization scheduling method for a virtual power plant, which takes a block chain technology into consideration, and specifically comprises the following steps:
step (1), constructing a VPP deterministic model aiming at maximizing virtual power plant VPP profit according to original data, and constructing a model constraint condition; the original data comprises DAM of a day-ahead energy market, RTM data of a real-time energy market and parameters of each polymerization unit of VPP; the constraints include gas turbine constraints, ESS constraints, interruptible load constraints, DAM/RTM transaction amount constraints, power balance constraints;
step (2), on the basis of a VPP deterministic model, adopting a stochastic programming method to process uncertainty of market electricity price, and adopting a self-adaptive robust method to process uncertainty of photovoltaic output, thereby establishing the VPP stochastic self-adaptive robust model; solving by adopting optimization modeling software GAMS;
and (3) the VPP aggregator issues the optimized day-ahead planned output result to an Ethernet workshop platform through an intelligent contract editor Remix and sends the optimized day-ahead planned output result to each distributed energy node.
Further, the establishing of the VPP deterministic model in step (1) specifically includes the following steps:
step 1.1: the optimization objective of the VPP owner is to maximize the cumulative profit, including revenue gained by participating DAMs and RTMs, operating costs of the gas turbine, and outage load costs, with an objective function expressed as:
Figure GDA0003710094820000021
wherein T is the total time period number of one day;
Figure GDA0003710094820000022
DAM and RTM electricity prices, respectively;
Figure GDA0003710094820000023
the purchase and sale electricity quantity of the VPP in the DAM is obtained;
Figure GDA0003710094820000024
the electricity quantity purchased and sold by the VPP at RTM; k is a radical of formula p The electricity purchasing coefficient represents the ratio of electricity purchasing price to electricity selling price;
Figure GDA0003710094820000025
the operating cost of the gas turbine;
Figure GDA0003710094820000026
to interrupt load costs;
the operating cost of the gas turbine is described by a piecewise linear function:
Figure GDA0003710094820000027
Figure GDA0003710094820000028
wherein a is the fixed cost of the gas turbine; boolean variables
Figure GDA0003710094820000029
Indicating whether the gas turbine is operating; k l Generating cost slope for the first section of the gas turbine; g l,t Outputting the output for the first section of the gas turbine; n is a radical of l Is the number of linear segments of the gas turbine cost curve; lambda [ alpha ] su 、λ sd Respectively the start-stop cost of the gas turbine; boolean variables
Figure GDA00037100948200000210
Respectively indicating whether the gas turbine is started or stopped;
Figure GDA00037100948200000211
total output for the gas turbine;
the interruptible load cost is the compensation fee of the interruptible load paid by the VPP to the user, the user is compensated according to the level of the interruptible load, and the interruptible load cost is expressed as follows:
Figure GDA00037100948200000212
wherein n is m Is the number of interrupt levels;
Figure GDA00037100948200000213
a compensation price for the m-th order interrupt load;
Figure GDA00037100948200000214
the m level load interruption amount is the t period;
step 1.2: constructing constraints of a VPP deterministic model, wherein the constraints comprise:
(1) gas turbine constraints:
Figure GDA00037100948200000215
Figure GDA00037100948200000216
Figure GDA0003710094820000031
Figure GDA0003710094820000032
Figure GDA0003710094820000033
Figure GDA0003710094820000034
Figure GDA0003710094820000035
Figure GDA0003710094820000036
Figure GDA0003710094820000037
wherein, g GT,max 、g GT,min Maximum and minimum output power of the gas turbine, respectively; r is a radical of hydrogen U 、r D The upward and downward ramp rates of the gas turbine;
Figure GDA0003710094820000038
the upper limit of the output of the first section of the gas turbine is; t is t su 、t sd Respectively gas turbineMinimum on-off time of; t is t su，0 、t sd，0 Respectively the initial startup and shutdown time of the gas turbine;
Figure GDA0003710094820000039
the total output of the gas turbine is respectively in the t period and the t-1 period; boolean variables
Figure GDA00037100948200000310
Indicating whether the gas turbine is operated during the t period and the t-1 period;
(2) electrical Energy Storage System (ESS) constraints:
Figure GDA00037100948200000311
Figure GDA00037100948200000312
Figure GDA00037100948200000313
Figure GDA00037100948200000314
wherein, the first and the second end of the pipe are connected with each other,
Figure GDA00037100948200000315
the electric Energy Storage System (ESS) t time period and t-1 time period of the electric energy storage system; eta c 、η d Respectively the charge-discharge efficiency of the ESS;
Figure GDA00037100948200000316
the charge and discharge capacity of the ESS respectively; s es,max 、S es,min Respectively the upper limit and the lower limit of the electric capacity of the ESS; g esc,max 、g esd,max Respectively the maximum charge and discharge power of the ESS;
(3) interruptible load constraints:
Figure GDA00037100948200000317
Figure GDA00037100948200000318
Figure GDA00037100948200000319
wherein the content of the first and second substances,
Figure GDA0003710094820000041
the load interruption coefficient of the mth level;
Figure GDA0003710094820000042
the m level load interruption amount is the t period;
Figure GDA0003710094820000043
an electrical load for a period of t;
Figure GDA0003710094820000044
load interruption amounts of a t period and a t-1 period, respectively; l is curt,max The maximum load interruption amount in continuous time;
(4) DAM/RTM transaction amount constraints:
Figure GDA0003710094820000045
Figure GDA0003710094820000046
Figure GDA0003710094820000047
Figure GDA0003710094820000048
wherein the content of the first and second substances,
Figure GDA0003710094820000049
the power consumption of VPP in DAM in t period is respectively;
Figure GDA00037100948200000410
respectively the purchasing and selling electric quantity of VPP in RTM at t time period; p DA,max 、S DA,max Maximum purchase and sale electricity quantity of VPP in DAM; p is RT,max 、S RT,max The maximum electricity purchasing and selling amount of the VPP at RTM is obtained;
(5) power balance constraint conditions:
Figure GDA00037100948200000411
wherein the content of the first and second substances,
Figure GDA00037100948200000412
and outputting power for the photovoltaic power station.
Further, in the step (2), a stochastic programming method is adopted to process uncertainty of market electricity price, a self-adaptive robust method is adopted to process uncertainty of photovoltaic output, and a stochastic self-adaptive robust model of the virtual power plant is established, and the method comprises the following steps:
step 2.1: consider the case where the VPP participates in both the DAM and RTM; in the DAM stage, VPP makes a decision before the photovoltaic uncertain parameters are realized; in the RTM stage, VPP makes a decision after photovoltaic uncertain parameters and day-ahead market decisions are realized; the objective function of the stochastic adaptive robust model of the virtual power plant is represented as follows:
Figure GDA00037100948200000413
wherein n is p The number of power price scenes; pi (p) is the electricity price scene probability; subscripts p and s denote the p-th group of electric valence fieldsScene and the s group photovoltaic output scene; omega is an original photovoltaic scene set;
step 2.2: the random adaptive robust model considers the electricity price scene in the DAM stage, the decision variables in the day ahead are characterized by comprising subscripts p and t, the subscript p represents the p-th group of electricity price scenes, and the subscript t represents the t time period; in the RTM stage, an electricity price scene and a photovoltaic scene are considered, real-time decision variable characteristics are that subscripts p, t and s are contained, and the subscript s represents the photovoltaic output scene of the s group; the constraint conditions of the random self-adaptive robust model are as follows:
(1) day-ahead operation constraints:
Figure GDA00037100948200000414
Figure GDA0003710094820000051
Figure GDA0003710094820000052
Figure GDA0003710094820000053
Figure GDA0003710094820000054
Figure GDA0003710094820000055
Figure GDA0003710094820000056
Figure GDA0003710094820000057
(2) real-time operation constraint conditions:
Figure GDA0003710094820000058
Figure GDA0003710094820000059
Figure GDA00037100948200000510
Figure GDA00037100948200000511
Figure GDA00037100948200000512
Figure GDA00037100948200000513
Figure GDA00037100948200000514
Figure GDA00037100948200000515
Figure GDA00037100948200000516
Figure GDA00037100948200000517
Figure GDA00037100948200000518
Figure GDA0003710094820000061
Figure GDA0003710094820000062
Figure GDA0003710094820000063
Figure GDA0003710094820000064
Figure GDA0003710094820000065
further, a VPP management framework based on a block chain technology is constructed in the step (3), and comprises three main elements, namely a distributed energy node, a VPP aggregator and an intelligent electric meter; on the basis, the VPP aggregator issues the day-ahead planned output result obtained by optimization to an Ethernet platform through an intelligent contract editor Remix and sends the day-ahead planned output result to each distributed energy node; the method comprises the following steps:
step 3.1: the method comprises the following steps of constructing a VPP management framework based on a block chain technology, wherein the VPP management framework comprises three main elements, namely a distributed energy node, a VPP aggregation provider and an intelligent electric meter, and specifically comprises the following steps:
(1) distributed energy node
The virtual power plant is formed by aggregating distributed energy resources such as a photovoltaic power station, a gas turbine, an electric automobile, an energy storage system, a commercial building and the like through an advanced information technology and a software system, and all the distributed energy resources and loads have the characteristic of dispersion autonomy and can be regarded as energy nodes;
(2) virtual power plant aggregator
The virtual power plant aggregator, namely a control center of a virtual power plant operator, can provide information consultation, transaction management and wireless communication service for each distributed energy node in the virtual power plant;
(3) intelligent electric meter
In order to realize information transmission between the distributed energy nodes and the virtual power plant aggregator, each distributed energy node needs to be provided with an intelligent electric meter, and the distributed energy nodes have the functions of calculating and recording the consumption in real time;
step 3.2: the VPP aggregator issues the day-ahead planned output result obtained by optimization to an Ethernet platform through an intelligent contract editor Remix and sends the day-ahead planned output result to each distributed energy node;
because the blockchain system has the characteristics of public transparency and non-falsification, when the actual output of each distributed energy node is deviated from the planned output and needs to be punished, the required penalty can be calculated according to the planned output record in the day and the formulas (51) and (52):
Figure GDA0003710094820000066
Figure GDA0003710094820000067
compared with the prior art, the technical scheme of the invention has the following beneficial technical effects: the invention relates to a random self-adaptive robust optimal scheduling method for a virtual power plant based on a block chain technology, which can effectively process uncertainty in the operation process of the virtual power plant and ensure the openness and transparency of optimal scheduling.
Drawings
FIG. 1 is a flow chart of a virtual power plant stochastic adaptive robust optimization scheduling method in consideration of a block chain technology according to the present invention;
FIG. 2 is a graph of load demand by a VPP over a day;
FIG. 3 is a graph of photovoltaic unit output data;
FIG. 4 is a DAM power rate and RTM power rate scenario diagram;
FIG. 5 is a graph of the detailed optimization results for each aggregation unit and the power purchased and sold by VPPs at the DAM and RTM;
FIG. 6 is a day-ahead planned contribution result on the Etherhouse platform (taking the planned contribution result at time 9:00 as an example).
Detailed Description
The following describes in detail a specific implementation of the virtual power plant stochastic adaptive robust optimization scheduling method in consideration of the block chain technology with reference to the accompanying drawings.
The invention relates to a random self-adaptive robust optimization scheduling method for a virtual power plant, which takes block chain technology into account, and as shown in figure 1, the random self-adaptive robust optimization scheduling method comprises the following steps:
step 1, constructing a Virtual Power Plant (VPP) certainty model aiming at maximizing profit of a VPP according to original data; constructing a model constraint condition; the raw data includes: day-ahead energy market (DAM), real-time energy market (RTM) data, and parameters of each VPP aggregation unit; the constraint conditions include: gas turbine constraints, ESS constraints, interruptible load constraints, DAM/RTM transaction volume constraints, power balance constraints;
step 2, due to the uncertainty of the market electricity price and the photovoltaic output and the existence of the imbalance punishment, the VPP target profit and the actual profit have deviation, and in order to improve the overall profit and reduce the imbalance punishment, the influence of the uncertainty needs to be considered in the VPP optimization scheduling; therefore, on the basis of a VPP deterministic model, uncertainty of market electricity price is processed by adopting a stochastic programming method, and uncertainty of photovoltaic output is processed by adopting a self-adaptive robust method, so that the VPP stochastic self-adaptive robust model is established;
and 3, the VPP aggregator issues the optimized day-ahead planned output result to an Ethernet workshop platform through an intelligent contract editor Remix and sends the optimized day-ahead planned output result to each distributed energy node.
The optimization modeling software GAMS24.4 is adopted to program and solve the VPP random self-adaptive robust model considering the block chain technology, and the result shows that: VPP carries out nimble dispatch according to market price of electricity, can effectively improve VPP profit to can alleviate the power consumption peak problem, play the effect of peak clipping and valley filling. Uncertainty in the operation process of the virtual power plant can be effectively processed through a random self-adaptive robust method, and the open transparency of optimized scheduling can be guaranteed by utilizing a block chain technology.
The step 1 establishes a VPP deterministic model, and comprises the following steps:
step 1.1: the optimization goal of the VPP owner is to maximize the cumulative profitability, including revenue obtained from participation in the DAM and RTM, operating costs of the gas turbine, outage load costs, with an objective function expressed as:
Figure GDA0003710094820000081
wherein T is the total time period number of one day and is 24;
Figure GDA0003710094820000082
DAM and RTM electricity prices, respectively;
Figure GDA0003710094820000083
the purchase and sale electricity quantity of the VPP in the DAM is obtained;
Figure GDA0003710094820000084
the electricity quantity purchased and sold by the VPP at RTM; k is a radical of p The electricity purchasing coefficient represents the ratio of electricity purchasing price to electricity selling price;
Figure GDA0003710094820000085
the operating cost of the gas turbine;
Figure GDA0003710094820000086
to interrupt load costs;
the operating cost of the gas turbine is described by a piecewise linear function:
Figure GDA0003710094820000087
Figure GDA0003710094820000088
wherein a is the fixed cost of the gas turbine; boolean variable
Figure GDA0003710094820000089
Indicating whether the gas turbine is operating; k l Generating cost slope for the first section of the gas turbine; g l,t Outputting the output for the first section of the gas turbine; n is a radical of l Is the number of linear segments of the gas turbine cost curve; lambda su 、λ sd The gas turbine start-stop costs are respectively; boolean variable
Figure GDA00037100948200000810
Respectively indicating whether the gas turbine is started or stopped;
Figure GDA00037100948200000811
total output for the gas turbine;
the interruptible load cost is the compensation cost of the interruptible load paid by the VPP to the user, and because the influence degrees of different interrupt degrees on the user are different, the user is compensated according to the level of the interruptible load, and the interruptible load cost is expressed as:
Figure GDA00037100948200000812
wherein n is m Is the number of interrupt levels;
Figure GDA00037100948200000813
a compensation price for the m-th order interrupt load;
Figure GDA00037100948200000814
the m level load interruption amount is the t period;
step 1.2: constructing constraints of a VPP deterministic model, wherein the constraints comprise:
(1) gas turbine constraints:
Figure GDA00037100948200000815
Figure GDA00037100948200000816
Figure GDA00037100948200000817
Figure GDA00037100948200000818
Figure GDA00037100948200000819
Figure GDA0003710094820000091
Figure GDA0003710094820000092
Figure GDA0003710094820000093
Figure GDA0003710094820000094
wherein, g GT,max 、g GT,min Maximum and minimum output power of the gas turbine, respectively; r is U 、r D The upward and downward ramp rates of the gas turbine;
Figure GDA0003710094820000095
the upper limit of the output of the first section of the gas turbine is; t is t su 、t sd Minimum on-off time of the gas turbine; t is t su，0 、t sd，0 Initial startup and shutdown times of the gas turbine are respectively;
Figure GDA0003710094820000096
the total output of the gas turbine is respectively in the t period and the t-1 period; boolean variable
Figure GDA0003710094820000097
Indicating whether the gas turbine is operated during the period t and the period t-1;
(2) electrical Energy Storage System (ESS) constraints:
Figure GDA0003710094820000098
Figure GDA0003710094820000099
Figure GDA00037100948200000910
Figure GDA00037100948200000911
wherein the content of the first and second substances,
Figure GDA00037100948200000912
the electric Energy Storage System (ESS) t time period and t-1 time period of the electric energy storage system; eta c 、η d Respectively the charge-discharge efficiency of the ESS;
Figure GDA00037100948200000913
respectively the charge and discharge capacity of the ESS; s es,max 、S es,min Respectively the upper limit and the lower limit of the electric capacity of the ESS; g esc,max 、g esd,max The maximum charge and discharge power of the ESS respectively;
(3) interruptible load constraints:
Figure GDA00037100948200000914
Figure GDA00037100948200000915
Figure GDA00037100948200000916
wherein, the first and the second end of the pipe are connected with each other,
Figure GDA00037100948200000917
the load interruption coefficient of the mth level;
Figure GDA00037100948200000918
the m level load interruption amount is the t period;
Figure GDA00037100948200000919
an electrical load for a period of t;
Figure GDA00037100948200000920
load interruption amounts of a t period and a t-1 period, respectively; l is curt,max The maximum load interruption amount in the continuous time is adopted, so that the problem of reduction of user satisfaction caused by overlarge load interruption amount in the continuous time is solved;
(4) DAM/RTM transaction amount constraints:
Figure GDA0003710094820000101
Figure GDA0003710094820000102
Figure GDA0003710094820000103
Figure GDA0003710094820000104
wherein the content of the first and second substances,
Figure GDA0003710094820000105
the power consumption of VPP in DAM in t period is respectively;
Figure GDA0003710094820000106
respectively the purchasing and selling electric quantity of VPP in RTM at t time period; p DA,max 、S DA,max The maximum purchasing and selling electric quantity of the VPP in the DAM is obtained; p is RT,max 、S RT,max The maximum electricity purchasing and selling amount of the VPP at RTM is obtained;
(5) power balance constraint conditions:
Figure GDA0003710094820000107
wherein, the first and the second end of the pipe are connected with each other,
Figure GDA0003710094820000108
and outputting power for the photovoltaic power station.
Step 2, a stochastic programming method is adopted to process the uncertainty of the market electricity price, a self-adaptive robust method is adopted to process the uncertainty of the photovoltaic output, and a stochastic self-adaptive robust model of the virtual power plant is established, wherein the stochastic self-adaptive robust model comprises the following steps:
step 2.1: consider the case where a VPP participates in both a DAM and an RTM. In the DAM stage, VPP makes a decision before the implementation of the photovoltaic uncertain parameters; in the RTM stage, the VPP makes a decision after photovoltaic uncertain parameters and day-ahead market decisions are implemented. Therefore, the random adaptive robust model of the virtual power plant can adopt a three-layer structure max-min-max form and consists of two stages, and the target function of the model is expressed as follows:
Figure GDA0003710094820000109
wherein n is p The number of electricity price scenes; pi (p) is the electricity price scene probability; subscripts p and s respectively represent a p-th group of electricity price scenes and an s-th group of photovoltaic output scenes; omega is an original photovoltaic scene set;
step 2.2: compared with a deterministic model, the random adaptive robust model considers the electricity price scene in the DAM stage, the day-ahead decision variables are characterized by comprising subscripts p and t, the subscript p represents the p-th group of electricity price scenes, and the subscript t represents the t period; in the RTM stage, an electricity price scene and a photovoltaic scene are considered, real-time decision variable characteristics are that subscripts p, t and s are contained, and the subscript s represents the photovoltaic output scene of the s group; the constraint conditions of the random self-adaptive robust model are as follows:
(1) day-ahead operation constraints:
Figure GDA00037100948200001010
Figure GDA00037100948200001011
Figure GDA00037100948200001012
Figure GDA0003710094820000111
Figure GDA0003710094820000112
Figure GDA0003710094820000113
Figure GDA0003710094820000114
Figure GDA0003710094820000115
(2) real-time operation constraint conditions:
Figure GDA0003710094820000116
Figure GDA0003710094820000117
Figure GDA0003710094820000118
Figure GDA0003710094820000119
Figure GDA00037100948200001110
Figure GDA00037100948200001111
Figure GDA00037100948200001112
Figure GDA00037100948200001113
Figure GDA00037100948200001114
Figure GDA00037100948200001115
Figure GDA00037100948200001116
Figure GDA00037100948200001117
Figure GDA00037100948200001118
Figure GDA0003710094820000121
Figure GDA0003710094820000122
Figure GDA0003710094820000123
and 3, constructing a VPP management framework based on a block chain technology, wherein the VPP management framework comprises three main elements, namely a distributed energy node, a VPP aggregator and an intelligent electric meter. On the basis, the VPP aggregator issues the day-ahead planned output result obtained by optimization to an Ethernet platform through an intelligent contract editor Remix and sends the day-ahead planned output result to each distributed energy node; the method comprises the following steps:
step 3.1: the method comprises the following steps of constructing a VPP management framework based on a block chain technology, wherein the VPP management framework comprises three main elements, namely a distributed energy node, a VPP aggregation provider and an intelligent electric meter, and specifically comprises the following steps:
(1) distributed energy node
The virtual power plant is formed by aggregating distributed energy resources such as a photovoltaic power station, a gas turbine, an electric automobile, an energy storage system and a commercial building through an advanced information technology and a software system, and all the distributed energy resources and loads have the characteristic of dispersion and autonomy and can be regarded as energy nodes.
(2) Virtual power plant aggregator
The virtual power plant aggregator, namely the control center of the virtual power plant operator, can provide information consultation, transaction management and wireless communication service for each distributed energy node in the virtual power plant.
(3) Intelligent electric meter
In order to realize information transmission between the distributed energy nodes and the virtual power plant aggregator, each distributed energy node needs to be provided with an intelligent electric meter, and the distributed energy nodes have the functions of calculating and recording the consumption in real time.
Step 3.2: and the VPP aggregator issues the optimized day-ahead planned output result to an Ethernet platform through an intelligent contract editor Remix and sends the optimized day-ahead planned output result to each distributed energy node.
Due to the fact that the blockchain system has the characteristics of being transparent and incapable of being tampered, when the actual output of each distributed energy node is deviated from the planned output and needs to be punished, the needed punishment can be calculated according to the planned output record in the day and the formulas (51) and (52).
Figure GDA0003710094820000124
Figure GDA0003710094820000125
The present embodiment constitutes a VPP with a gas turbine plant, a photovoltaic plant, an ESS, and an interruptible load. Considering the case of VPPs participating in DAM, RTM, the scheduling period is set to 1 day, divided into 24 periods.
The gas turbine adopts a TAU5670 model, the specific parameters are shown in a table 1, the specific parameters of an electric energy storage system are shown in a table 2, the load demand of a VPP in one day is shown in a table 2, the interruptible load is divided into 3 stages which are all set to be 10 percent of the total load, and the compensation price of each stage is respectively 40-/MWh, 45-/MWh and 50-/MWh. The output historical data of the photovoltaic unit is shown in figure 3, and 50 groups of photovoltaic scenes are randomly generated by adopting a Monte Carlo method; the DAM power rates and RTM power rates are shown in FIG. 4.
TABLE 1TAU5670 gas turbine parameters
Figure GDA0003710094820000131
Solving the VPP random self-adaptive robust scheduling model by adopting an optimized modeling software GAMS24.4, wherein the VPP obtains profits
Figure GDA0003710094820000132
The specific optimization results of each polymerization unit in the VPP and the purchase and sale electric quantity of the VPP in the DAM and RTM are shown in FIG. 5.
In FIG. 5(a), the gas turbine is started when the market price of electricity is higher than the cost of electricity generation, otherwise stopped; and the ESS is charged in a low electricity price period and discharged in a high electricity price period so as to obtain profits and realize the functions of peak clipping and valley filling. In fig. 5(b), the VPP selects a suitable electricity market according to the current price and the real-time price for electricity to purchase and sell electricity.
As shown in fig. 5(c), during a high electricity price period, the VPP interrupts the load partially without affecting the comfort of the user, and preferentially interrupts the first stage load. Through the combined action of interruptible loads, the VPP can sell more electric quantity in high price of electricity period to obtain bigger profit, and can alleviate the power consumption peak problem, play the effect of load is filled out in the peak clipping.
The VPP aggregator issues the day-ahead planned output result obtained by optimization to the Ethernet workshop platform, and sends the day-ahead planned output result to each distributed energy node, as shown in FIG. 6 (taking the planned output result at the time of 9:00 as an example).
Due to the fact that the blockchain system has the characteristics of being transparent and non-falsifiable, when the actual output of each distributed energy node deviates from the planned output and needs to be punished, the required penalty can be calculated according to the day-ahead planned output record shown in fig. 6 and the formulas (51) and (52).
The effectiveness and the practicability of the invention are verified by the simulation result. The invention enables the VPP to be flexibly scheduled according to the market electricity price, can effectively improve the VPP profit, can relieve the problem of power utilization peak and play a role in peak clipping and valley filling. Uncertainty in the operation process of the virtual power plant can be effectively processed through a random self-adaptive robust method, and the open transparency of optimized scheduling can be guaranteed by utilizing a block chain technology.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the same. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (3)

1. The block chain technology-based random adaptive robust optimization scheduling method for the virtual power plant is characterized by comprising the following steps of: the method specifically comprises the following steps:
step (1), constructing a VPP deterministic model aiming at maximizing virtual power plant VPP profit according to original data, and constructing a model constraint condition; the original data comprise DAM of a day-ahead energy market, RTM data of a real-time energy market and parameters of each polymerization unit of VPP; the constraint conditions comprise gas turbine constraint, electric energy storage system ESS constraint, interruptible load constraint, DAM/RTM transaction amount constraint and power balance constraint;
step (2), on the basis of a VPP deterministic model, adopting a stochastic programming method to process uncertainty of market electricity price, and adopting a self-adaptive robust method to process uncertainty of photovoltaic output, thereby establishing the VPP stochastic self-adaptive robust model; solving by adopting optimization modeling software GAMS; when the market electricity price is higher than the electricity generation cost, starting the gas turbine, otherwise, stopping the gas turbine; the ESS is charged in a low-price time period and discharged in a high-price time period; the VPP selects a proper power market to purchase and sell power according to the current power price and the real-time power price; in a high electricity price period, the VPP can interrupt the load partially and interrupt the first-level load preferentially on the premise of not influencing the comfort level of a user;
step (3), the VPP aggregator issues the optimized day-ahead planned output result to an Ethernet workshop platform through an intelligent contract editor Remix and sends the optimized day-ahead planned output result to each distributed energy node;
a VPP management framework based on a block chain technology is constructed in the step (3), and comprises three main elements, namely a distributed energy node, a VPP aggregation quotient and an intelligent electric meter; on the basis, the VPP aggregator issues the optimized day-ahead planned output result to an Ethernet workshop platform through an intelligent contract editor Remix and sends the result to each distributed energy node; the method comprises the following steps:
step 3.1: the method comprises the following steps of constructing a VPP management framework based on a block chain technology, wherein the VPP management framework comprises three main elements, namely a distributed energy node, a VPP aggregation provider and an intelligent electric meter, and specifically comprises the following steps:
(1) distributed energy node
The virtual power plant is formed by aggregating a photovoltaic power station, a gas turbine, an electric automobile, an energy storage system and commercial buildings, and all distributed energy sources and loads have the characteristic of dispersion and autonomy and can be regarded as energy nodes;
(2) virtual power plant aggregator
The virtual power plant aggregator, namely a control center of a virtual power plant operator, can provide information consultation, transaction management and wireless communication service for each distributed energy node in the virtual power plant;
(3) intelligent electric meter
In order to realize information transmission between the distributed energy nodes and the virtual power plant aggregator, each distributed energy node needs to be provided with an intelligent electric meter, and the distributed energy nodes have the functions of calculating and recording the sending amount in real time;
step 3.2: the VPP aggregator issues the day-ahead planned output result obtained by optimization to an Ethernet platform through an intelligent contract editor Remix and sends the day-ahead planned output result to each distributed energy node;
when deviation is generated between the actual output and the planned output of each distributed energy node and punishment is needed, calculating the needed punishment according to the planned output record before the day and the following formula:
Figure FDA0003710094810000021
Figure FDA0003710094810000022
2. the virtual power plant stochastic adaptive robust optimization scheduling method considering the block chain technology as claimed in claim 1, wherein the step (1) of establishing a VPP deterministic model specifically comprises the following steps:
step 1.1: the optimization objective of the VPP owner is to maximize the cumulative profit, including revenue gained by participating DAMs and RTMs, operating costs of the gas turbine, and outage load costs, with an objective function expressed as:
Figure FDA0003710094810000023
wherein T is the total time period number of one day;
Figure FDA0003710094810000024
DAM and RTM electricity prices, respectively; p t DA 、
Figure FDA0003710094810000025
The purchase and sale electricity quantity of the VPP in the DAM is obtained; p t RT 、
Figure FDA0003710094810000026
The electric quantity is bought and sold by the VPP in the RTM; k is a radical of p The electricity purchasing coefficient represents the ratio of electricity purchasing price to electricity selling price;
Figure FDA0003710094810000027
the operating cost of the gas turbine;
Figure FDA0003710094810000028
to interrupt load costs;
the operating cost of the gas turbine is described by a piecewise linear function:
Figure FDA0003710094810000029
Figure FDA00037100948100000210
wherein a is the fixed cost of the gas turbine; boolean variables
Figure FDA00037100948100000211
Indicating whether the gas turbine is operating; k l Generating cost slope for the first section of the gas turbine; g is a radical of formula l,t Outputting the output for the first section of the gas turbine; n is a radical of l Is the number of linear segments of the gas turbine cost curve; lambda [ alpha ] su 、λ sd Respectively the start-stop cost of the gas turbine; boolean variables
Figure FDA00037100948100000212
Respectively indicating whether the gas turbine is started or stopped;
Figure FDA00037100948100000213
is the total gas turbine output;
the interruptible load cost is the compensation fee of the interruptible load paid by the VPP to the user, the user is compensated according to the level of the interruptible load, and the interruptible load cost is expressed as follows:
Figure FDA00037100948100000214
wherein n is m Is the number of interrupt levels;
Figure FDA00037100948100000215
a compensation price for the m-th order interrupt load;
Figure FDA00037100948100000216
the m level load interruption amount is the t period;
step 1.2: constructing constraints of a VPP deterministic model, wherein the constraints comprise:
(1) gas turbine constraints:
Figure FDA00037100948100000217
Figure FDA00037100948100000218
Figure FDA0003710094810000031
Figure FDA0003710094810000032
Figure FDA0003710094810000033
Figure FDA0003710094810000034
Figure FDA0003710094810000035
Figure FDA0003710094810000036
Figure FDA0003710094810000037
wherein, g GT,max 、g GT,min Maximum and minimum output power of the gas turbine, respectively; r is a radical of hydrogen U 、r D The upward and downward ramp rates of the gas turbine;
Figure FDA0003710094810000038
the upper limit of the output of the first section of the gas turbine is; t is t su 、t sd Minimum on-off time of the gas turbine; t is t su ，0 、t sd，0 Initial startup and shutdown times of the gas turbine are respectively;
Figure FDA0003710094810000039
the total output of the gas turbine is respectively in the t period and the t-1 period; boolean variable
Figure FDA00037100948100000310
Indicating whether the gas turbine is operated during the period t and the period t-1;
(2) electrical energy storage system ESS constraints:
Figure FDA00037100948100000311
Figure FDA00037100948100000312
Figure FDA00037100948100000313
Figure FDA00037100948100000314
wherein the content of the first and second substances,
Figure FDA00037100948100000315
the electric storage capacity of the electric energy storage system ESS in the t time period and the t-1 time period respectively; eta c 、η d Respectively the charge-discharge efficiency of the ESS;
Figure FDA00037100948100000316
respectively the charge and discharge capacity of the ESS; s es,max 、S es,min Respectively the upper limit and the lower limit of the electric capacity of the ESS; g esc ,max 、g esd,max The maximum charge and discharge power of the ESS respectively;
(3) interruptible load constraints:
Figure FDA00037100948100000317
Figure FDA00037100948100000318
Figure FDA00037100948100000319
wherein the content of the first and second substances,
Figure FDA0003710094810000041
the load interruption coefficient of the mth level;
Figure FDA0003710094810000042
for the m-th level of load interruption of t period；
Figure FDA0003710094810000043
An electrical load for a period of t;
Figure FDA0003710094810000044
load interruption amounts of a t period and a t-1 period, respectively; l is curt,max The maximum load interruption amount in continuous time;
(4) DAM/RTM transaction amount constraints:
Figure FDA0003710094810000045
0≤P t DA ≤P DA,max (22)
Figure FDA0003710094810000046
0≤P t RT ≤P RT,max (24)
wherein, P t DA 、
Figure FDA0003710094810000047
The power consumption of VPP in DAM in t period is respectively; p is t RT 、
Figure FDA0003710094810000048
Respectively the purchasing and selling electric quantity of VPP in RTM at t time period; p is DA,max 、S DA,max Maximum purchase and sale electricity quantity of VPP in DAM; p RT,max 、S RT,max The maximum electricity purchasing and selling amount of the VPP at RTM is obtained;
(5) power balance constraint conditions:
Figure FDA0003710094810000049
wherein, P t RES And outputting power for the photovoltaic power station.
3. The virtual power plant stochastic adaptive robust optimization scheduling method considering the block chain technology according to claim 2, wherein in the step (2), a stochastic programming method is adopted to process market electricity price uncertainty, an adaptive robust method is adopted to process photovoltaic output uncertainty, and a virtual power plant stochastic adaptive robust model is established, and the method comprises the following steps:
step 2.1: consider the case where the VPP participates in both the DAM and RTM; in the DAM stage, VPP makes a decision before the implementation of the photovoltaic uncertain parameters; in the RTM stage, VPP makes a decision after photovoltaic uncertain parameters and day-ahead market decisions are realized; the objective function of the stochastic adaptive robust model of the virtual power plant is represented as follows:
Figure FDA00037100948100000410
wherein n is p The number of power price scenes; pi (p) is the electricity price scene probability; subscripts p and s respectively represent a p-th group of electricity price scenes and an s-th group of photovoltaic output scenes; omega is an original photovoltaic scene set;
step 2.2: the random adaptive robust model considers the electricity price scene in the DAM stage, the decision variables in the day ahead are characterized by comprising subscripts p and t, the subscript p represents the p-th group of electricity price scenes, and the subscript t represents the t time period; in the RTM stage, an electricity price scene and a photovoltaic scene are considered, real-time decision variable characteristics are that subscripts p, t and s are contained, and the subscript s represents the photovoltaic output scene of the s group; the constraint conditions of the random self-adaptive robust model are as follows:
(1) day-ahead operation constraints:
Figure FDA0003710094810000051
Figure FDA0003710094810000052
Figure FDA0003710094810000053
Figure FDA0003710094810000054
Figure FDA0003710094810000055
Figure FDA0003710094810000056
Figure FDA0003710094810000057
Figure FDA0003710094810000058
(2) real-time operation constraint conditions:
Figure FDA0003710094810000059
Figure FDA00037100948100000510
Figure FDA00037100948100000511
Figure FDA00037100948100000512
Figure FDA00037100948100000513
Figure FDA00037100948100000514
Figure FDA00037100948100000515
Figure FDA00037100948100000516
Figure FDA00037100948100000517
Figure FDA00037100948100000518
Figure FDA0003710094810000061
Figure FDA0003710094810000062
Figure FDA0003710094810000063
Figure FDA0003710094810000064
Figure FDA0003710094810000065
Figure FDA0003710094810000066
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