CN111210079A - Operation optimization method and system for distributed energy virtual power plant - Google Patents

Operation optimization method and system for distributed energy virtual power plant Download PDF

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CN111210079A
CN111210079A CN202010021407.8A CN202010021407A CN111210079A CN 111210079 A CN111210079 A CN 111210079A CN 202010021407 A CN202010021407 A CN 202010021407A CN 111210079 A CN111210079 A CN 111210079A
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CN111210079B (en
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刘念
吴陈硕
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North China Electric Power University
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Abstract

The invention discloses a distributed energy virtual power plant operation optimization method and system. The method comprises the following steps: determining the power generation power of the virtual power plant according to the power generation power of the gas turbine set, the interruptible load interruption electric quantity, the power of the energy storage device and the wind-solar power generation power in the virtual power plant; determining the operation cost of the virtual power plant according to the generated power of the virtual power plant; determining the operation cost of the traditional power plant according to the generating power of the coal-fired unit in the traditional power plant; determining the pollution emission of the gas turbine set according to the power generation power of the gas turbine set; determining the pollution discharge amount of the coal-fired unit according to the power generation capacity of the coal-fired unit; and performing operation optimization treatment on the distributed energy virtual power plant by taking the minimum sum of the operation cost of the virtual power plant, the operation cost of the traditional power plant, the pollution emission of the gas turbine unit and the pollution emission of the coal-fired unit as a target. By adopting the method and the system, the influence of the uncertainty of the distributed power supply on the power system can be reduced, and the pollutant discharge amount and the total operation cost can be effectively reduced.

Description

Operation optimization method and system for distributed energy virtual power plant
Technical Field
The invention relates to the technical field of power systems, in particular to a distributed energy virtual power plant operation optimization method and system.
Background
The Virtual Power Plant (VPP) is different from a traditional Power Plant in physical structure, and the Virtual Power Plant gathers distributed energy such as a distributed Power supply, an energy storage device, demand response load and the like together by means of a communication technology and a software system, performs unified coordination control, participates in operation scheduling of a Power system, coordinates contradictions existing between a Power grid and the distributed energy, and fully embodies benefits of the distributed energy brought to the Power grid and users. Although the virtual power plant can provide an effective smart grid operation framework for distributed energy resources and can effectively solve the problem of new energy grid-connected consumption theoretically, the problems that power coordination management of internal power units of the virtual power plant and the participation of the power units in the virtual power plant as a whole in power system scheduling still have difficulty in solving, such as power uncertainty of each distributed power supply in the VPP and whether the VPP can provide service for a power system like a traditional power plant, and the like. The demand response is used as an important support technology of the virtual power plant, and coordination interaction between a source side and a load side and bidirectional flow of energy can be realized, so that the virtual power plant can reduce the influence of uncertainty of a distributed power supply on a power system by using demand response resources, and better participate in power grid dispatching.
The existing optimization operation aiming at the participation of a virtual power plant in the power system scheduling mainly converts uncertainty of a plurality of random variables such as distributed power output, load and the like into a deterministic problem so as to solve an optimization scheduling model, but in the participation of an actual virtual power plant in the power grid scheduling operation, the influence of the uncertainty of the distributed power supply on the power system cannot be reduced by the processing method. In addition, no pollutant gas is discharged in the operation process of distributed renewable energy power generation, demand response load and energy storage equipment in a power component aggregated by the distributed energy virtual power plant, the minimum total operation cost of a power system is taken as a target function in the prior art, the economy is singly emphasized, and the environmental protection embodied by the distributed energy virtual power plant in comparison with the traditional thermal power plant participating in power system scheduling is not considered.
Disclosure of Invention
The invention aims to provide a distributed energy virtual power plant operation optimization method and system, which can reduce the influence of distributed power source uncertainty on a power system and can effectively reduce the system pollutant emission and the total system operation cost.
In order to achieve the purpose, the invention provides the following scheme:
a distributed energy virtual power plant operation optimization method comprises the following steps:
acquiring the generating power of a gas turbine set, the interruptible load interruption electric quantity, the power of an energy storage device and the wind and light generating power in a virtual power plant at each time interval;
determining the power generation power of the virtual power plant according to the power generation power of the gas turbine set, the interruptible load interruption electric quantity, the power of the energy storage device and the wind and light power generation power in the virtual power plant;
determining the operation cost of the virtual power plant according to the generated power of the virtual power plant; the virtual power plant operation cost takes the power generation cost of the gas turbine set, the interruptible load outage compensation amount, the energy storage operation cost and the wind and light power generation operation maintenance cost into consideration;
acquiring the power generation power of a coal-fired unit in a traditional power plant at each time interval;
determining the operation cost of the traditional power plant according to the generating power of the coal-fired unit in the traditional power plant; the traditional power plant running cost takes the power generation cost of the coal-fired unit and the starting and stopping cost of the coal-fired unit into consideration;
determining the pollution emission of the gas turbine set according to the power generation power of the gas turbine set;
determining the pollution discharge amount of the coal-fired unit according to the generated power of the coal-fired unit;
and carrying out optimization processing on the target of the minimum of the virtual power plant operation cost, the traditional power plant operation cost, the pollution emission of the gas turbine unit and the pollution emission of the coal-fired unit to obtain the start-stop plan of each unit of each period of the virtual power plant and the traditional power plant and the generated power of each unit of each period.
Optionally, determining the power generation power of the virtual power plant according to the power generation power of the gas turbine set in the virtual power plant, the interruptible load interruption electric quantity, the power of the energy storage device, and the wind and light power generation power specifically includes:
determining the generated power of the virtual power plant according to the following formula:
Figure BDA0002360881000000021
in the formula (I), the compound is shown in the specification,
Figure BDA0002360881000000022
representing the generated power of the jth virtual power plant VPP during the time period t,
Figure BDA0002360881000000023
represents the generated power of the kth gas turbine, PIL,t(M) represents the M-th level interruptible load interruption power amount for the t period, M represents the interruptible load level, M represents the total number of interruptible load levels,
Figure BDA0002360881000000024
representing the nth energy storage device charging power during the t period,
Figure BDA0002360881000000025
representing the discharge power of the nth energy storage device, P, during the period tt wRepresenting the distributed wind generator power, P, over a period of tt pvRepresenting the t-period distributed photovoltaic generator power.
Optionally, determining the operation cost of the virtual power plant according to the generated power of the virtual power plant specifically includes:
determining a virtual plant operating cost according to the following formula:
Figure BDA0002360881000000031
wherein the content of the first and second substances,
Figure BDA0002360881000000032
Figure BDA0002360881000000033
Cen＝kenQ
Figure BDA0002360881000000034
in the formula, CVPPRepresenting the VPP running cost, NvppRepresents the total number of virtual power plants, T represents the total time period, T represents the time period,
Figure BDA0002360881000000035
expressed in power of
Figure BDA0002360881000000036
The cost of the operation of the jth VPP,
Figure BDA0002360881000000037
indicating the on-off state of the jth VPP during time t,
Figure BDA0002360881000000038
indicating that the jth VPP is in a shutdown state during the period t,
Figure BDA0002360881000000039
indicates that the jth VPP is in a starting state in the period of t, delta CenRepresenting an amount of environmental benefit awarded to the virtual power plant,
Figure BDA00023608810000000310
represents the jth VPP running cost function,
Figure BDA00023608810000000311
expressed in power of
Figure BDA00023608810000000312
The cost of the gas turbine to generate electricity,
Figure BDA00023608810000000313
indicating the start-up and shut-down status of the kth gas turbine during the period t,
Figure BDA00023608810000000314
indicating that the kth gas turbine is in a shutdown state during the period t,
Figure BDA00023608810000000315
indicating that the kth gas turbine is on during the period t,
Figure BDA00023608810000000316
representing distributed wind generator power P during a period tt wCost of maintenance of the operation of the machine, Cpv(Pt pv) Representing the power P of the distributed photovoltaic generator in the period tt pvThe cost of the operation and maintenance of the device,
Figure BDA00023608810000000320
indicating that the time period t can interrupt the load power-off compensation amount,
Figure BDA00023608810000000321
indicating an interruptible load interrupt state and,
Figure BDA00023608810000000322
indicating that the load interrupt can be interrupted,
Figure BDA00023608810000000323
indicating that the interruptible load is not interrupted,
Figure BDA00023608810000000324
indicating that the nth energy storage device charging power in the t period is
Figure BDA00023608810000000325
The cost of the charging at the time of day,
Figure BDA00023608810000000326
indicating that the nth energy storage device has discharge power of
Figure BDA00023608810000000327
Put in timeThe cost of the electricity is such that,
Figure BDA00023608810000000328
indicating the state of charge of the energy storage device,
Figure BDA00023608810000000329
indicating the discharge state of the energy storage device, pIL(m) represents the m-th order interruptible load compensation electricity price, omega represents the environmental benefit coefficient, Cen(Pcpp) The power of the coal-fired unit of the traditional power plant is PcppCost of environmental pollution, Cen(PGT) Representing the turbine power P of the combustion engine at a virtual power plantGTCost of environmental pollution, ScppRepresents the installed capacity of the conventional power plant, SvppRepresenting the rated power of the virtual power plant, CenRepresenting the cost function, k, of environmental pollutionenAnd Q is the pollutant discharge amount of the gas turbine unit or the coal-fired unit.
Optionally, determining the operation cost of the conventional power plant according to the generated power of the coal-fired unit in the conventional power plant specifically includes:
determining the traditional power plant operating cost according to the following formula:
Figure BDA0002360881000000041
in the formula, CCPPRepresents the CPP running cost of the traditional power plant, NCPPThe total number of the traditional power plants is shown,
Figure BDA0002360881000000042
represents the ith CPP power generation cost function,
Figure BDA0002360881000000043
represents the ith CPP generated power in the t period,
Figure BDA0002360881000000044
represents the ith CPP on-off state in the t period,
Figure BDA0002360881000000045
indicating that the ith CPP is in a shutdown state for a period t,
Figure BDA0002360881000000046
indicates that the ith CPP is in a startup state and SU in the t periodi,tRepresents the i-th CPP boot cost, SD in the t periodi,tRepresenting the ith CPP outage cost for period t.
Optionally, determining the pollution emission of the gas turbine set according to the power generation power of the gas turbine set specifically includes:
determining the pollution emission of the gas turbine set according to the following formula:
QGT(PGT)＝γGTPGT+βGTPGT+αGTPGT+ζGTPGT
in the formula, QGT(PGT) Indicating that the gas turbine unit is at power PGTTime gas turbine unit pollution emission PGTRepresenting the active power, gamma, of the gas turbine unitGTRepresenting the carbon dioxide gas emission coefficient of the gas turbine plant, βGTRepresenting the emission coefficient of nitric oxide gas from a gas turbine plant, αGTRepresents the sulfur dioxide gas emission coefficient, ζ, of the gas turbine unit emissionsGTWhich represents the co gas emission coefficient emitted by the gas turbine group.
Optionally, the determining of the pollution emission of the coal-fired unit according to the power generation capacity of the coal-fired unit specifically includes:
determining the pollution emission of the coal-fired unit according to the following formula:
QCU(PCU)＝A×(γCU+βCUPCU+αCU(PCU)2)+ζCU×exp(λCUPCU)
in the formula, QCU(PCU) Indicating that the coal-fired unit is at power PCUTime coal-fired unit pollution discharge PCURepresenting the active power of the coal-fired unit, A representing a coefficient quantifying the severity of pollutant emissions, gammaCUIndicating combustionCarbon dioxide gas emission coefficient of coal units, βCURepresenting the nitrogen oxide gas emission coefficient of the coal-fired unit, αCURepresents the sulfur dioxide gas emission coefficient, zeta, of the coal-fired unit emissionCUDenotes the coefficient of emission of carbon monoxide gas, lambda, from the coal-fired unitCUAnd the smoke emission coefficient of the coal-fired unit is shown.
Optionally, with virtual power plant running cost traditional power plant running cost gas turbine unit pollution emission and coal-fired unit pollution emission sum minimum as the target and carry out optimization, obtain each period of virtual power plant and each period of traditional power plant and open and stop plan and each period of each unit generated power, specifically include:
the objective function is determined according to the following formula:
Min f(X)＝CCPP+CVPP+QCPP+QVPP
Figure BDA0002360881000000051
Figure BDA0002360881000000052
Figure BDA0002360881000000053
wherein f (X) represents an objective function,
Figure BDA0002360881000000054
representing the power generated by the gas turbine in the jth VPP during the t period;
determining a constraint condition; the constraint conditions comprise power system active balance constraint, power system load standby constraint, traditional power plant active output upper and lower limit constraint, virtual power plant active output uncertain constraint, CPP minimum on-off time constraint, VPP balance constraint, gas turbine set output and climbing constraint, energy storage device charge-discharge constraint and interruptible load calling constraint;
and determining the start-stop plans of each unit of the virtual power plant and the traditional power plant at each time period and the generating power of each unit at each time period by adopting an NSGA-II multi-target genetic algorithm according to the objective function and the constraint condition.
The invention also provides a distributed energy virtual power plant operation optimization system, which comprises:
the virtual power plant data acquisition module is used for acquiring the power generation power of the gas turbine set, the interruptible load interruption electric quantity, the power of the energy storage device and the wind and light power generation power in the virtual power plant at each time interval;
the virtual power plant generating power determining module is used for determining the generating power of the virtual power plant according to the generating power of the gas turbine set, the interruptible load interruption electric quantity, the power of the energy storage device and the wind and light generating power in the virtual power plant;
the virtual power plant operation cost determining module is used for determining the virtual power plant operation cost according to the virtual power plant generating power; the virtual power plant operation cost takes the power generation cost of the gas turbine set, the interruptible load outage compensation amount, the energy storage operation cost and the wind and light power generation operation maintenance cost into consideration;
the traditional power plant data acquisition module is used for acquiring the power generation power of the coal-fired unit in the traditional power plant at each time interval;
the traditional power plant operation cost determining module is used for determining the traditional power plant operation cost according to the generated power of the coal-fired unit in the traditional power plant; the traditional power plant running cost takes the power generation cost of the coal-fired unit and the starting and stopping cost of the coal-fired unit into consideration;
the gas turbine set pollution emission determining module is used for determining the gas turbine set pollution emission according to the power generation power of the gas turbine set;
the coal-fired unit pollution emission determining module is used for determining the coal-fired unit pollution emission according to the coal-fired unit generating power;
and the operation optimization module is used for performing optimization treatment by taking the operation cost of the virtual power plant, the operation cost of the traditional power plant, the pollution emission of the gas turbine unit and the pollution emission of the coal-fired unit as a minimum target to obtain start-stop plans of all units of the virtual power plant and the traditional power plant at all periods and the generated power of all units at all periods.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a method and a system for optimizing operation of a distributed energy virtual power plant, wherein the virtual power plant is used for reducing the influence of uncertainty of power of a distributed power supply on a virtual power plant system architecture by coordinating energy interaction among a distributed power supply (a gas turbine unit and a wind power generation unit), an interruptible load and an energy storage device. The system total operation cost and the pollutant emission level of the virtual power plant environmental benefits are optimized, the distributed energy virtual power plant is optimized to participate in power grid dispatching, advantages of the distributed energy virtual power plant are complementary with those of a traditional power plant, and meanwhile, the pollutant emission amount of a power system and the total operation cost of the power system are effectively reduced.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a distributed energy virtual power plant operation optimization method in an embodiment of the invention;
FIG. 2 is a diagram of a distributed energy virtual power plant operation optimization system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a demand response based distributed energy virtual power plant architecture according to an embodiment of the present invention;
FIG. 4 is a block diagram of a NSGA-II multi-target genetic algorithm solving process in the embodiment of the present 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 invention aims to provide a distributed energy virtual power plant operation optimization method and system, which can reduce the influence of distributed power source uncertainty on a power system and can effectively reduce the system pollutant emission and the total system operation cost.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Examples
Fig. 1 is a flowchart of a method for optimizing operation of a distributed energy virtual power plant in an embodiment of the present invention, and as shown in fig. 1, the embodiment provides a method for optimizing operation of a distributed energy virtual power plant, including:
step 101: and acquiring the generating power of the gas turbine set, the interruptible load interruption electric quantity, the power of the energy storage device and the wind and light generating power in the virtual power plant at each time interval.
Step 102: and determining the generating power of the virtual power plant according to the generating power of the gas turbine set, the interruptible load interruption electric quantity, the power of the energy storage device and the wind and light generating power in the virtual power plant.
Step 103: determining the operation cost of the virtual power plant according to the generated power of the virtual power plant; the operation cost of the virtual power plant takes the power generation cost of the gas turbine set, the interruptible load outage compensation amount, the energy storage operation cost and the wind and light power generation operation maintenance cost into consideration.
Step 104: and acquiring the power generation power of the coal-fired unit in the traditional power plant at each time interval.
Step 105: determining the operation cost of the traditional power plant according to the generating power of the coal-fired unit in the traditional power plant; the operating cost of a traditional power plant takes the power generation cost of a coal-fired unit and the starting and stopping cost of the coal-fired unit into consideration.
Step 106: and determining the pollution emission of the gas turbine set according to the power generation power of the gas turbine set.
Step 107: and determining the pollution discharge amount of the coal-fired unit according to the power generation capacity of the coal-fired unit.
Step 108: and performing optimization treatment by taking the minimum sum of the operation cost of the virtual power plant, the operation cost of the traditional power plant, the pollution emission of the gas turbine unit and the pollution emission of the coal-fired unit as a target to obtain start and stop plans of each unit of the virtual power plant and the traditional power plant at each time period and the generated power of each unit at each time period.
Fig. 2 is a structural diagram of an operation optimization system of a distributed energy virtual power plant in an embodiment of the present invention, and as shown in fig. 2, the embodiment provides an operation optimization system of a distributed energy virtual power plant, including:
the virtual power plant data acquisition module 201 is configured to acquire the power generation power of the gas turbine set, the interruptable load interruption electric quantity, the power of the energy storage device, and the wind and light power generation power in the virtual power plant at each time interval.
The virtual power plant power generation power determining module 202 is configured to determine the virtual power plant power generation power according to the gas turbine set power generation power, the interruptible load interruption electric quantity, the power of the energy storage device, and the wind and light power generation power in the virtual power plant.
The virtual power plant operation cost determining module 203 is used for determining the virtual power plant operation cost according to the virtual power plant generating power; the operation cost of the virtual power plant takes the power generation cost of the gas turbine set, the interruptible load outage compensation amount, the energy storage operation cost and the wind and light power generation operation maintenance cost into consideration.
And the traditional power plant data acquisition module 204 is used for acquiring the power generation power of the coal-fired unit in the traditional power plant in each period.
A conventional power plant operating cost determining module 205, configured to determine a conventional power plant operating cost according to the generated power of the coal-fired unit in the conventional power plant; the operating cost of a traditional power plant takes the power generation cost of a coal-fired unit and the starting and stopping cost of the coal-fired unit into consideration.
And the gas turbine set pollution emission amount determining module 206 is configured to determine the gas turbine set pollution emission amount according to the power generation power of the gas turbine set.
And the coal-fired unit pollution emission amount determining module 207 is used for determining the coal-fired unit pollution emission amount according to the coal-fired unit generating power.
And the operation optimization module 208 is configured to perform optimization processing with the minimum sum of the operation cost of the virtual power plant, the operation cost of the conventional power plant, the pollution emission of the gas turbine unit and the pollution emission of the coal-fired unit as a target, and obtain a start-stop plan of each unit of the virtual power plant and the conventional power plant at each time period and a power generation power of each unit at each time period.
In particular, the method comprises the following steps of,
1. distributed energy virtual power plant system architecture based on demand response
Compared with a micro-grid, the concept of the virtual power plant is wider, distributed energy in the virtual power plant is not limited to geographical positions, ownership of distributed power supplies and demand side resources is not considered, and users can participate in scheduling operation of the power system after a user terminal is provided with certain software control equipment as long as the users like. The invention provides a demand response-based distributed energy virtual power plant system architecture by taking a virtual power plant as an energy management mode of distributed energy, and as shown in fig. 3, fig. 3 is a schematic diagram of the demand response-based distributed energy virtual power plant system architecture in the embodiment of the invention.
In fig. 3 arrow 2 represents the energy flow, i.e. the electric power, arrow 1 represents the currency flow, i.e. the electricity purchase expenditure or the electricity sales income, the dotted line represents the demand response service and arrow 3 represents the revenue obtained by the participation of the user in the demand response. The distributed energy virtual power plant system architecture comprises distributed power supplies, an energy storage system and users (comprising controllable loads and fixed loads), which are collectively called virtual power plant participants. The distributed power supply sells electric energy to the virtual power plant, and since the energy storage system can both release and store the electric energy, the arrows of energy flow and currency flow for the energy storage system are both bidirectional, which means that the energy storage system can both sell and purchase the electric energy to the virtual power plant; the method comprises the steps that users on demand sides purchase electric energy from a virtual power plant to meet own power consumption demands, loads of the users can be divided into two parts, namely fixed loads and interruptible loads, and the virtual power plant gives corresponding economic subsidies to the users according to contribution of the users participating in demand response.
As an aggregator of these participants, the virtual power plant needs to purchase power to an internal distributed power supply, an energy storage system, or an external power grid company to ensure the power supply stability of the user load on the demand side, and meanwhile, the remaining power in the virtual power plant can be sold to the main grid power company to realize "surplus internet surfing". Thus, within the virtual power plant, these participants form a system framework with the virtual power plant such that all participants can participate in the electric energy transaction. The system framework utilizes a virtual power plant to coordinate energy interaction optimization among the distributed power supply, the stored energy and the demand response load, reduces the influence of the output uncertainty of the distributed power supply on the power system, and further improves the overall benefit of the system.
2. Establishing a model of each power component inside a virtual power plant
(1) Hierarchical interruptible load demand response model
The traditional interruptible load is a fixed price compensation mechanism established between a power consumer and a dispatching operation manager, the power consumer obtains economic compensation by providing load interruption service, and meanwhile, a power grid operation manager also achieves the purpose of safe operation of a power system by cutting off specified load. However, it is not reasonable to implement such a fixed price compensation mechanism regardless of the difference in the importance of the interrupted load: from the perspective of users, the interruption load has larger influence when the importance degree is larger, and the interruption load is expected to be compensated more; from the perspective of a power grid operation manager, when the importance degree of the interrupted load is low, the compensation price paid by the power grid operation manager is high, and the economy is poor. The more practical mechanism should be: the price compensation of electricity available to the user for interruptible load service should be gradually increased according to the degree of importance from low to high.
The Interruptible Load ideal compensation electrovalence curve is a nonlinear continuous function, the compensation electrovalence integrates the Interruptible Load according to the importance degree to obtain accurate compensation amount, as shown in formula 1, but the integral calculation brings difficulty to subsequent model solution, so the Interruptible Load is subjected to refined grading treatment, as shown in formula 2, a more practical Grading Interruptible Load (GIL) demand response model is established, and the mathematical expression of the cost is as follows:
Figure BDA0002360881000000101
Figure BDA0002360881000000102
wherein M is the number of interruptible load levels; p is a radical ofIL(m) compensating the electricity prices for the m-th order interruptible load; pIL,t(m) an m-th level interruptible load interruption power amount for a period t.
(2) Controllable distributed power supply unit model
Taking Gas Turbines (GT) as an example, their output is fully controllable, and the start and ramp speeds are fast, playing a role in tracking the net load and backup in the VPP.
The generating cost of the controllable distributed generator set can be described by a quadratic function:
CGT(PGT)＝aGT(PGT)2+bPGT+cGT(3)
in the formula, CGTThe power generation cost of the controllable distributed generator set is reduced; pGTThe active power is taken out; a isGT、bGTAnd cGTThe power generation cost function coefficient thereof.
The emissions from gas turbines are primarily some gases (e.g., CO)2、NOX、SO2CO, etc.), the present invention classifies greenhouse gases, harmful gases, and respirable particulates as environmental pollutants. For a gas turbine, the amount of pollutant emissions can be expressed as a function of:
QGT(PGT)＝γGTPGT+βGTPGT+αGTPGT+ζGTPGT(4)
in the formula, QGTThe pollutant discharge amount of the gas turbine set is determined; gamma rayGT、βGT、αGTAnd ζGTRespectively, the pollutant gas CO discharged by the gas turbine set2、NOX、SO2CO emission coefficient.
The emission of Coal-fired Unit (CU) in traditional thermal power plant is mainly some gases (such as CO)2、NOX、SO2CO, etc.) and smoke, etc. For a coal-fired unit, the pollutant emissions can be expressed as a function of:
QCU(PCU)＝A×(γCU+βCUPCU+αCU(PCU)2)+ζCU×exp(λCUPCU) (5)
in the formula, QCUThe pollutant discharge amount of the coal-fired unit is reduced; pCUThe active power is taken out; a is a coefficient for quantifying the severity of pollutant emission; gamma rayCU、βCU、αCU、ζCUAnd λCURespectively CO emissions from coal-fired units2、NOX、SO2CO and soot emission coefficients.
The environmental cost and the pollutant discharge amount are positively correlated, so the invention adopts a direct proportional functional relationship to describe the relationship between the environmental cost and the pollutant discharge amount.
Cen＝kenQ (6)
In the formula, CenCost for environmental pollution; k is a radical ofenThe cost conversion coefficient of pollutant discharge and environmental pollution is shown, and Q is the pollutant discharge of the unit.
(3) Random distributed power supply model
The internal uncertainty of the virtual power plant mainly comes from prediction errors and random fluctuation of uncontrollable distributed power sources such as wind power and photovoltaic. Therefore, only distributed wind power generation and distributed photovoltaic power generation are considered in the model of the random distributed power supply in the virtual power plant.
1) Distributed wind power generation model
Wind speed uncertainty directly results in uncertainty in wind power output, while two-parameter Weibull distribution models are widely used to fit natural wind:
Figure BDA0002360881000000111
wherein v is the wind speed; (v) is the probability density of the wind speed distribution; k and c are the shape parameter and the scale parameter, respectively.
Wind power generation power PwThe relationship with the wind speed can be expressed by the following relationship:
Figure BDA0002360881000000112
in the formula, vci、vr、vcoRespectively the cut-in wind speed, the rated wind speed and the cut-out wind speed; a and b are fitting parameters determined from the power curve; pw,nRated power for wind power generation.
2) Distributed photovoltaic power generation model
Solar radiation intensity is often described by a Beta distribution:
Figure BDA0002360881000000113
in the formula, I is the intensity of solar radiation; i ismaxα and β are two parameters of Beta distribution, and Γ (-) is a gamma function.
Power P generated by photovoltaicPVThe power is approximately in direct proportion to the illumination intensity I, so that the photovoltaic power generation power also obeys Beta distribution. Its probability density function:
Figure BDA0002360881000000121
in the formula, PPV,nIs the rated power of the photovoltaic power generation unit.
(4) Energy storage device model
Figure BDA0002360881000000122
In the formula (I), the compound is shown in the specification,
Figure BDA0002360881000000123
storing electric quantity for the energy storage device in a time period t; sigma is energy storageSelf energy loss rate of the device ηescAnd ηesdRespectively charging and discharging efficiency of the energy storage equipment; pt escAnd Pt esdAnd the charging power and the discharging power of the energy storage device are respectively set in the time period t.
Energy storage device operating cost function:
Cesc(Pesc)＝aesc(Pesc)2+bescPesc+cesc(12)
Cesd(Pesd)＝aesd(Pesd)2+besdPesd+cesd(13)
in the formula, CescAnd CesdThe costs of charging and discharging the energy storage device are respectively; a isesc、bescAnd cescCharging the energy storage device with a cost function coefficient; a isesd、besdAnd cesdA discharge cost function coefficient for the energy storage device.
3. Virtual plant environmental benefits
The distributed renewable energy sources in the power assembly of the virtual power plant have no emission of pollution gas in the running process of power generation, demand response and energy storage equipment, and the only emission of pollutants comes from light-emission schedulable fuel units such as gas turbines.
The pollutant emission cost of the traditional thermal power generating unit with the same capacity as that of the virtual power plant is increased by the environmental benefit of the emission cost of the gas turbine unit in the virtual power plant, and the pollutant emission cost is all or partially awarded to the virtual power plant operator in proportion. Therefore, the virtual power plant environmental benefit model is constructed as follows:
Figure BDA0002360881000000126
in the formula,. DELTA.CenAn amount of environmental benefit awarded to the virtual power plant; pcppAnd ScppRespectively the active power and installed capacity of the traditional coal-fired thermal power plant; pGTAnd SvppRespectively the active power of a distributed fuel unit in a virtual power plant and the rated power of the virtual power plant; omega is more than or equal to 0 and less than or equal to 1, which is the environmental benefit coefficient.
4. Multi-objective optimization model for constructing distributed energy virtual power plant facing power grid dispatching
The virtual power plant, as a "virtual Unit", can accept system scheduling as in conventional power plants and participate in the Unit Commission (UC) program of the day ahead. And inspired by the traditional power system unit combination concept, the 'on-off arrangement' of the power components in the virtual power plant is defined as a unit combination optimization problem of the VPP power components. The method specifically comprises the steps of arranging whether each established stage of the grade interruptible load is cut off or not, arranging the start-stop and output of the controllable distributed power unit and arranging the charging and discharging strategy of the energy storage power unit.
(1) Objective function
On the basis of the virtual power plant environmental benefit model provided in the step 3, according to the actual conditions of wind power, photovoltaic resources and demand response load in the distributed energy virtual power plant and economic indexes such as investment, operation and maintenance cost and the like related to each device, the environmental protection index is comprehensively considered, and the invention surrounds 2 indexes: and (4) considering the total system operation cost and pollutant emission level of the environmental benefits of the virtual power plant, and constructing a virtual power plant multi-objective optimization model facing power grid dispatching.
The target function expression is:
Min fi(X),i＝1,2 (15)
1) total system operating cost accounting for virtual power plant environmental benefits
The operation cost of the virtual power plant mainly comprises the fuel cost of the controllable distributed power supply, the electric energy consumption cost of the energy storage equipment, the operation maintenance cost of the wind power photovoltaic power generation and the graded interruptible load demand response cost, and specific parameters can be obtained by fitting actual system operation historical data. The VPP part consists of a fuel unit with quick adjustment capacity and an energy storage power unit, the output flexibility of the VPP part is much faster than that of the conventional unit, and the climbing rate is sufficiently large on a small-scale time scale; meanwhile, the wind power, photovoltaic and other random distributed generator sets in the VPP are used for generating power according to the predicted output, and the actual prediction deviation is made up by additional purchase of the VPP or the power market for standby, so that the method is not deeply researched. And wind power and photovoltaic primary energy are renewable energy and should be consumed preferentially, so the wind power and photovoltaic primary energy is defaulted to be normally started in the invention.
Therefore, a power system unit combination optimization model considering the CPP and the VPP is established, the virtual power plant environmental benefits are considered, the CPP and the VPP total operation cost are the minimum target, and the mathematical expression of the target function is defined as follows:
min f1(X)＝CCPP+CVPP(16)
Figure BDA0002360881000000141
Figure BDA0002360881000000142
Figure BDA0002360881000000143
Figure BDA0002360881000000144
in formulae 15 and 16, CCPPAnd CVPPThe total running cost of the traditional power plant and the virtual power plant respectively; n is a radical ofCPPAnd NVPPCPP and VPP numbers respectively;
Figure BDA0002360881000000145
for the ith CPP power generation cost function,
Figure BDA0002360881000000146
generating power for the ith CPP in the t period; SUi,tAnd SDi,tThe startup and shutdown costs of the ith CPP in the period t are respectively;
Figure BDA0002360881000000147
and
Figure BDA0002360881000000148
respectively an operation cost function of the jth VPP and active output of the jth VPP in the t period;
Figure BDA0002360881000000149
and
Figure BDA00023608810000001410
the variables 0-1 represent the CPP and VPP on-off states, respectively.
CwAnd CpvThe distributed wind power generation total operation maintenance cost and the distributed photovoltaic power generation total operation maintenance cost are respectively in a linear relation with the generated energy;
Figure BDA00023608810000001411
and
Figure BDA00023608810000001412
all the state variables are 0-1 state variables which respectively represent the stop state, the graded interruptible load interruption state, the energy storage charging state and the energy storage discharging state of the distributed fuel unit.
2) Pollutant discharge
The traditional power plant mainly uses a coal-fired thermal power unit for generating power, and the emission of the traditional coal-fired thermal power unit is mainly some gases (such as CO)2、NOX、SO2CO, etc.) and inhalable particles. And the power components in the virtual power plant are more in type: for a random distributed power generating unit, distributed wind power generation and a distributed photovoltaic generating unit are mainly considered, primary energy is utilized by the random distributed power generating unit, air flow and illumination radiation from the nature are realized, no pollutant is discharged in the operation process, and the environmental protection performance is better; for the energy storage device, no pollutant is discharged when the energy storage device operates; the demand response load also has no pollutant emission; for the controllable distributed power supply, the virtual power plant for power grid dispatching constructed by the invention mainly adopts the controllable gas turbine to generate power, the natural gas is used as fuel, and the emission is mainly some gases, such as CO2、NOX、SO2CO, etc. Thus, toThe minimum system pollutant emission is taken as a target, and a mathematical expression of an objective function is defined as follows:
min f2(X)＝QCPP+QVPP(21)
Figure BDA00023608810000001413
Figure BDA0002360881000000151
in the formula, QCPPAnd QVPPThe total pollutant emission of the traditional power plant and the virtual power plant respectively;
Figure BDA0002360881000000152
for the power generated by the gas turbine in the jth VPP during time t,
Figure BDA0002360881000000153
(1) constraint conditions
1) Considering the active power balance of a power system, the system standby and the running technical conditions of the traditional power plant and the virtual power plant as constraints:
a) active power balance constraint of the power system:
Figure BDA0002360881000000154
in the formula, Pt DIs the t-period load of the power system.
b) System standby requirement constraints:
Figure BDA0002360881000000156
in the formula, Pt RFor the load standby of the power system at t time, the invention takes the total load Pt D10% of the total.
c) The active output upper and lower limits of the traditional power plant are restricted:
Figure BDA0002360881000000158
d) the active output of the virtual power plant generated by the random distributed power supply is uncertain and is restrained by the two-sided interval:
Figure BDA0002360881000000159
in the formula (I), the compound is shown in the specification,
Figure BDA00023608810000001510
the lower interval is a range in which,
Figure BDA00023608810000001511
for a certain lower limit of the risk operation,
Figure BDA00023608810000001512
a deterministic lower limit of zero operational risk;
Figure BDA00023608810000001513
in order to be the upper section,
Figure BDA00023608810000001514
for a deterministic upper limit of the risk run,
Figure BDA00023608810000001515
a deterministic upper limit of zero operational risk;
the VPP coordinates the economy and the risk of operation according to the probability degree, and the VPP energy management system calculates and provides an upper and a lower output limit values for the system:
Figure BDA00023608810000001516
in the formula (I), the compound is shown in the specification,
Figure BDA00023608810000001517
a 0-1 variable representing a VPP on-off state;
Figure BDA00023608810000001518
and
Figure BDA00023608810000001519
lower and upper limits of output provided to the system are calculated for the VPP energy management system, respectively.
CPP climbing upper and lower limit restraint:
Figure BDA00023608810000001520
in the formula, Pi RDAnd Pi RUThe lower limit and the upper limit of the ith CPP climbing rate are respectively.
e) CPP minimum on-off time constraint:
Figure BDA0002360881000000161
Figure BDA0002360881000000162
in the formula (I), the compound is shown in the specification,
Figure BDA0002360881000000163
and
Figure BDA0002360881000000164
the duration time from the i-th CPP to the starting-up and the shutdown of the position of the scheduling time interval t-1 are respectively; MUTiAnd MDTiThe minimum start-stop time of the ith CPP is respectively obtained.
It is emphasized that the power unit in the VPP model constructed by the present invention belongs to a multi-energy source flexible mobile unit with coordination capability, and has no start-stop time constraint.
2) Considering the power exchange between the virtual power plant and the system and the operation requirement of the inner assembly of the VPP as constraints:
f) VPP balance constraint:
Figure BDA0002360881000000165
in the formula (I), the compound is shown in the specification,
Figure BDA0002360881000000166
predicting total load for the VPP internally;
Figure BDA0002360881000000167
power is exchanged between the VPP and the system in anticipation.
g) Output and climbing restraint of the distributed gas turbine unit:
Figure BDA0002360881000000168
Figure BDA0002360881000000169
in the formula (I), the compound is shown in the specification,
Figure BDA00023608810000001610
and
Figure BDA00023608810000001611
respectively representing the upper limit and the lower limit of the output of the distributed gas turbine unit;
Figure BDA00023608810000001612
and
Figure BDA00023608810000001613
the upward and downward ramp rates of the distributed gas turbine unit are respectively.
h) And (3) charge and discharge restraint of the energy storage unit:
Figure BDA00023608810000001614
Figure BDA00023608810000001615
Figure BDA00023608810000001616
Figure BDA00023608810000001617
in the formula, Ses0Initially storing energy for the energy storage unit;
Figure BDA00023608810000001618
and
Figure BDA00023608810000001619
respectively representing the lower limit and the upper limit of the capacity of the energy storage power unit;
Figure BDA00023608810000001620
and
Figure BDA00023608810000001621
respectively the maximum charging power and the maximum discharging power; equations (3-38) constrain the energy storage unit to have equal capacities in the initial state and the end state during one operation period.
i) Interruptible load invocation constraint:
the output of the power components in the virtual power plant including the classified interruptible loads is optimized, the interruption amount and the interruption times of the power components are still required to be restrained, and the great influence on the life and production of users is avoided. While the order of the hierarchical interruptible load interruption is strictly from low to high interruption level.
And (3) constraint of interrupt quantity:
Figure BDA0002360881000000171
in the formula (I), the compound is shown in the specification,
Figure BDA0002360881000000172
a 0-1 variable representing a status of whether the hierarchical interruptible load is interrupted or not; pIL,minAnd PIL,maxRespectively, the lower limit and the upper limit of the m-th level interruptible load interruption electric quantity in the t period.
And (3) constraint of interruption times:
Figure BDA0002360881000000173
in the formula (I), the compound is shown in the specification,
Figure BDA0002360881000000174
upper limit of interrupt times representing a hierarchical interruptible load interrupt
5. Solving the model by adopting a multi-target genetic algorithm based on NSGA-II
When the multi-target evolutionary algorithm is applied, the difference between the quality of each target does not exist, and the trouble of how to select different target weights when the multi-target problem is converted into the single-target problem is avoided. Therefore, the multi-objective optimization model constructed in the step 4 is solved by using the NSGA-II-based multi-objective genetic algorithm, a solving flow diagram is shown in FIG. 4, and the specific implementation method is as follows:
1) and (5) initializing the system. And reading parameters of equipment and genetic algorithms of a fuel unit, a gas turbine, a fan, a photovoltaic unit, an energy storage power unit and the like in the system.
2) Initializing a population P, generating an optimized variable of a first generation population through a random function, and calculating an initial population individual fitness function value, a Pareto sequence and an aggregation distance through quasi-steady state simulation.
3) And obtaining a child population from the parent population P through selection, crossing and mutation operations. And selecting individuals with high Pareto ranking by the selection operator, and selecting individuals with large aggregation distance if the Pareto sequences are the same.
4) And calling a quasi-steady-state simulation strategy for the filial generation population, and calculating indexes such as output and running time of each device in each time step, unsatisfied load capacity of the system, power waste and the like.
5) The objective function and the constraint value of each individual are respectively calculated and used as the evaluation indexes of the individual fitness function, namely
Figure BDA0002360881000000175
In the formula (f)1,max(X) is allThe maximum of the 1 st objective function values of the volume; f. of2,max(X) is the maximum of the 2 nd objective function values for all individuals; Δ is the sum of the absolute values of the correlation constraints of individuals who do not satisfy the constraint condition.
6) And calculating the dominance relation of each individual according to the obtained fitness function value, carrying out Pareto sequencing on the individuals and calculating the aggregation distance.
7) And according to the sequencing result, selecting the optimal N individuals from the parent population and the child population to generate a new parent population P.
8) And judging a termination condition, if so, calling a quasi-steady-state simulation strategy for the last generation of population, and outputting a final optimized unit combination result, namely a start-stop plan of each unit in each time period, optimal output of each unit in each time period, included direct current flow in each time period and the like in the traditional power plant and the virtual power plant, otherwise, returning to the step 3).
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In summary, this summary should not be construed to limit the present invention.

Claims (8)

1. A distributed energy virtual power plant operation optimization method is characterized by comprising the following steps:
acquiring the generating power of a gas turbine set, the interruptible load interruption electric quantity, the power of an energy storage device and the wind and light generating power in a virtual power plant at each time interval;
determining the power generation power of the virtual power plant according to the power generation power of the gas turbine set, the interruptible load interruption electric quantity, the power of the energy storage device and the wind and light power generation power in the virtual power plant;
determining the operation cost of the virtual power plant according to the generated power of the virtual power plant; the virtual power plant operation cost takes the power generation cost of the gas turbine set, the interruptible load outage compensation amount, the energy storage operation cost and the wind and light power generation operation maintenance cost into consideration;
acquiring the power generation power of a coal-fired unit in a traditional power plant at each time interval;
determining the operation cost of the traditional power plant according to the generating power of the coal-fired unit in the traditional power plant; the traditional power plant running cost takes the power generation cost of the coal-fired unit and the starting and stopping cost of the coal-fired unit into consideration;
determining the pollution emission of the gas turbine set according to the power generation power of the gas turbine set;
determining the pollution discharge amount of the coal-fired unit according to the generated power of the coal-fired unit;
and carrying out optimization processing on the target of the minimum of the virtual power plant operation cost, the traditional power plant operation cost, the pollution emission of the gas turbine unit and the pollution emission of the coal-fired unit to obtain the start-stop plan of each unit of each period of the virtual power plant and the traditional power plant and the generated power of each unit of each period.
2. The operation optimization method of the distributed energy virtual power plant according to claim 1, wherein the determining the generated power of the virtual power plant according to the generated power of the gas turbine set, the interruptible load interruption power, the power of the energy storage device and the wind-solar power generation power in the virtual power plant specifically comprises:
determining the generated power of the virtual power plant according to the following formula:
Figure FDA0002360880990000011
in the formula (I), the compound is shown in the specification,
Figure FDA0002360880990000012
representing the generated power of the jth virtual power plant VPP during the time period t,
Figure FDA0002360880990000013
represents the generated power of the kth gas turbine, PIL,t(M) represents the M-th level interruptible load interruption power in the t period, M represents the interruptible load level, and M represents the total number of interruptible load levels，
Figure FDA0002360880990000014
Representing the nth energy storage device charging power during the t period,
Figure FDA0002360880990000015
representing the discharge power of the nth energy storage device, P, during the period tt wRepresenting the distributed wind generator power, P, over a period of tt pvRepresenting the t-period distributed photovoltaic generator power.
3. The operation optimization method of the distributed energy virtual power plant according to claim 2, wherein the determining the operation cost of the virtual power plant according to the generated power of the virtual power plant specifically comprises:
determining a virtual plant operating cost according to the following formula:
Figure FDA0002360880990000021
wherein the content of the first and second substances,
Figure FDA0002360880990000022
Figure FDA0002360880990000023
Cen＝kenQ
Figure FDA0002360880990000024
in the formula, CVPPRepresenting the VPP running cost, NvppRepresents the total number of virtual power plants, T represents the total time period, T represents the time period,
Figure FDA0002360880990000025
expressed in power of
Figure FDA0002360880990000026
The cost of the operation of the jth VPP,
Figure FDA0002360880990000027
indicating the on-off state of the jth VPP during time t,
Figure FDA0002360880990000028
indicating that the jth VPP is in a shutdown state during the period t,
Figure FDA0002360880990000029
indicates that the jth VPP is in a starting state in the period of t, delta CenRepresenting an amount of environmental benefit awarded to the virtual power plant,
Figure FDA00023608809900000210
represents the jth VPP running cost function,
Figure FDA00023608809900000211
expressed in power of
Figure FDA00023608809900000212
The cost of the gas turbine to generate electricity,
Figure FDA00023608809900000213
indicating the start-up and shut-down status of the kth gas turbine during the period t,
Figure FDA00023608809900000214
indicating that the kth gas turbine is in a shutdown state during the period t,
Figure FDA00023608809900000215
indicating that the kth gas turbine is in the on state during the period t, Cw(Pt w) Representing distributed wind generator power P during a period tt wCost of maintenance of the operation of the machine, Cpv(Pt pv) Representing the power P of the distributed photovoltaic generator in the period tt pvThe cost of the operation and maintenance of the device,
Figure FDA00023608809900000216
indicating that the time period t can interrupt the load power-off compensation amount,
Figure FDA00023608809900000217
indicating an interruptible load interrupt state and,
Figure FDA00023608809900000218
indicating that the load interrupt can be interrupted,
Figure FDA00023608809900000219
indicating that the interruptible load is not interrupted,
Figure FDA00023608809900000220
indicating that the nth energy storage device charging power in the t period is
Figure FDA00023608809900000221
The cost of the charging at the time of day,
Figure FDA00023608809900000222
indicating that the nth energy storage device has discharge power of
Figure FDA00023608809900000223
The cost of the discharge at the time of discharge,
Figure FDA00023608809900000224
indicating the state of charge of the energy storage device,
Figure FDA00023608809900000225
indicating the discharge state of the energy storage device, pIL(m) represents the m-th order interruptible load compensation electricity price, omega represents the environmental benefit coefficient, Cen(Pcpp) The power of the coal-fired unit of the traditional power plant is PcppCost of environmental pollution, Cen(PGT) Representing the turbine power P of the combustion engine at a virtual power plantGTCost of environmental pollution, ScppRepresents the installed capacity of the conventional power plant, SvppRepresenting the rated power of the virtual power plant, CenRepresenting the cost function, k, of environmental pollutionenAnd Q is the pollutant discharge amount of the gas turbine unit or the coal-fired unit.
4. The operation optimization method for the distributed energy virtual power plant according to claim 3, wherein the determining the operation cost of the conventional power plant according to the generated power of the coal-fired unit in the conventional power plant specifically comprises:
determining the traditional power plant operating cost according to the following formula:
Figure FDA0002360880990000031
in the formula, CCPPRepresents the CPP running cost of the traditional power plant, NCPPThe total number of the traditional power plants is shown,
Figure FDA0002360880990000032
represents the ith CPP power generation cost function,
Figure FDA0002360880990000033
represents the ith CPP generated power in the t period,
Figure FDA0002360880990000034
represents the ith CPP on-off state in the t period,
Figure FDA0002360880990000035
indicating that the ith CPP is in a shutdown state for a period t,
Figure FDA0002360880990000036
when represents tSegment i CPP is in start-up state, SUi,tRepresents the i-th CPP boot cost, SD in the t periodi,tRepresenting the ith CPP outage cost for period t.
5. The operation optimization method for the distributed energy virtual power plant according to claim 4, wherein the determining of the pollution emission amount of the gas turbine set according to the power generation power of the gas turbine set specifically comprises:
determining the pollution emission of the gas turbine set according to the following formula:
QGT(PGT)＝γGTPGT+βGTPGT+αGTPGT+ζGTPGT
in the formula, QGT(PGT) Indicating that the gas turbine unit is at power PGTTime gas turbine unit pollution emission PGTRepresenting the active power, gamma, of the gas turbine unitGTRepresenting the carbon dioxide gas emission coefficient of the gas turbine plant, βGTRepresenting the emission coefficient of nitric oxide gas from a gas turbine plant, αGTRepresents the sulfur dioxide gas emission coefficient, ζ, of the gas turbine unit emissionsGTWhich represents the co gas emission coefficient emitted by the gas turbine group.
6. The operation optimization method of the distributed energy virtual power plant according to claim 5, wherein the determining of the pollution emission amount of the coal-fired unit according to the power generation capacity of the coal-fired unit specifically comprises:
determining the pollution emission of the coal-fired unit according to the following formula:
QCU(PCU)＝A×(γCU+βCUPCU+αCU(PCU)2)+ζCU×exp(λCUPCU)
in the formula, QCU(PCU) Indicating that the coal-fired unit is at power PCUTime coal-fired unit pollution discharge PCURepresenting the active power of the coal-fired unit, A representing a coefficient quantifying the severity of pollutant emissions, gammaCURepresenting the carbon dioxide gas emission coefficient of the coal-fired unit, βCURepresenting the nitrogen oxide gas emission coefficient of the coal-fired unit, αCURepresents the sulfur dioxide gas emission coefficient, zeta, of the coal-fired unit emissionCUDenotes the coefficient of emission of carbon monoxide gas, lambda, from the coal-fired unitCUAnd the smoke emission coefficient of the coal-fired unit is shown.
7. The operation optimization method for the distributed energy virtual power plant according to claim 6, wherein the optimization processing is performed with a goal that the sum of the operation cost of the virtual power plant, the operation cost of the traditional power plant, the pollution emission of the gas turbine unit and the pollution emission of the coal-fired unit is minimum, so as to obtain a start-stop plan of each unit of the virtual power plant and the traditional power plant at each time period and a power generation power of each unit at each time period, and specifically comprises:
the objective function is determined according to the following formula:
Min f(X)＝CCPP+CVPP+QCPP+QVPP
Figure FDA0002360880990000041
Figure FDA0002360880990000042
Figure FDA0002360880990000043
wherein f (X) represents an objective function,
Figure FDA0002360880990000044
representing the power generated by the gas turbine in the jth VPP during the t period;
determining a constraint condition; the constraint conditions comprise power system active balance constraint, power system load standby constraint, traditional power plant active output upper and lower limit constraint, virtual power plant active output uncertain constraint, CPP minimum on-off time constraint, VPP balance constraint, gas turbine set output and climbing constraint, energy storage device charge-discharge constraint and interruptible load calling constraint;
and determining the start-stop plans of each unit of the virtual power plant and the traditional power plant at each time period and the generating power of each unit at each time period by adopting an NSGA-II multi-target genetic algorithm according to the objective function and the constraint condition.
8. The utility model provides a virtual power plant operation optimization system of distributed energy which characterized in that includes:
the virtual power plant data acquisition module is used for acquiring the power generation power of the gas turbine set, the interruptible load interruption electric quantity, the power of the energy storage device and the wind and light power generation power in the virtual power plant at each time interval;
the virtual power plant generating power determining module is used for determining the generating power of the virtual power plant according to the generating power of the gas turbine set, the interruptible load interruption electric quantity, the power of the energy storage device and the wind and light generating power in the virtual power plant;
the virtual power plant operation cost determining module is used for determining the virtual power plant operation cost according to the virtual power plant generating power; the virtual power plant operation cost takes the power generation cost of the gas turbine set, the interruptible load outage compensation amount, the energy storage operation cost and the wind and light power generation operation maintenance cost into consideration;
the traditional power plant data acquisition module is used for acquiring the power generation power of the coal-fired unit in the traditional power plant at each time interval;
the traditional power plant operation cost determining module is used for determining the traditional power plant operation cost according to the generated power of the coal-fired unit in the traditional power plant; the traditional power plant running cost takes the power generation cost of the coal-fired unit and the starting and stopping cost of the coal-fired unit into consideration;
the gas turbine set pollution emission determining module is used for determining the gas turbine set pollution emission according to the power generation power of the gas turbine set;
the coal-fired unit pollution emission determining module is used for determining the coal-fired unit pollution emission according to the coal-fired unit generating power;
and the operation optimization module is used for performing optimization treatment by taking the operation cost of the virtual power plant, the operation cost of the traditional power plant, the pollution emission of the gas turbine unit and the pollution emission of the coal-fired unit as a minimum target to obtain start-stop plans of all units of the virtual power plant and the traditional power plant at all periods and the generated power of all units at all periods.
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