CN109861301B - Production simulation method for source-grid load-storage coordination power system - Google Patents
Production simulation method for source-grid load-storage coordination power system Download PDFInfo
- Publication number
- CN109861301B CN109861301B CN201811424897.5A CN201811424897A CN109861301B CN 109861301 B CN109861301 B CN 109861301B CN 201811424897 A CN201811424897 A CN 201811424897A CN 109861301 B CN109861301 B CN 109861301B
- Authority
- CN
- China
- Prior art keywords
- power
- constraint
- load
- predicted value
- production simulation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention provides a production simulation method of a source network, load storage and coordination power system, which comprises the steps of establishing a generating side unit scheduling cost model, a load demand side resource scheduling cost model and a system carbon emission cost model; according to the models, an optimization model comprising an objective function and constraint conditions is established by taking the minimum total cost of the system in a production simulation period as a target; according to the optimization model, acquiring each area of each time point in the production simulation period: the method comprises the steps of generating power by various power supplies, reducing power by demand response load, converting power by demand response load and energy storage charging and discharging power, and transmitting power by each trans-regional power transmission channel at each time point in a production simulation cycle. According to the method, the optimal operation modes of the power supply, the power grid, the load side resources and the stored energy are obtained by solving through the mathematical model, the coordination of the scheduling modes of various resources is ensured, and the integral optimization of the system can be realized.
Description
Technical Field
The invention relates to the technical field of power supply systems, in particular to a production simulation method of a source network load storage coordination power system.
Background
The power system production simulation is a technical means for verifying the feasibility of a power planning scheme and analyzing the operating characteristics of a power system in different development states. After the power planning is carried out, system operation state simulation is carried out based on a certain planning scheme, main indexes of power system operation under the corresponding planning scheme are obtained, the rationality of the planning scheme is analyzed, and influence factors of key indexes (such as wind curtailment rate, light curtailment rate and the like) are researched. According to a conventional cognitive framework, a power system is composed of a power generation side, a power grid side and a load side. In the conventional power system production simulation, only the development scheme of a power supply and a power grid is usually concerned, because the load requirement is regarded as a fixed constant in the traditional power balance mode, and the load requirement is met by allocating a generator set. With the gradual development of related technologies of the smart power grid and the continuous maturity of power market construction, the power load demand has certain adjustability, and can actively participate in power supply and demand balance in a demand response mode. Therefore, in future power systems, the load-side resources may play a role equivalent to the power-supply-side resources to some extent, and should be taken into consideration in power system production simulation.
In addition, with the aging of energy storage technology in recent years, under the condition of large-scale development of fluctuating new energy power generation such as wind power generation and photovoltaic power generation, the energy storage will play an important role in a future power system. The stored energy can play both the role of a power source (discharging) and the role of a load (charging). At the same time, however, energy storage is neither a power source (without true power production capacity) nor a load (and cannot convert electrical energy into some practical use). Therefore, in future power system architectures, the energy storage should become a fourth block component of the power system, which is parallel to the power source, the grid and the load. Therefore, when the power system production simulation is carried out, it is necessary to consider the energy storage, the power supply, the power grid and the load in a comprehensive manner and develop a source grid load storage coordination production simulation method.
The main defects of the existing power system production simulation technology are as follows: the overall optimization simulation of a power supply, a power grid, load side resources and energy storage is not realized. And part of students coordinately consider power supply and power grid planning, so that the production simulation considering the power supply and power grid planning scheme at the same time is realized. And partial scholars take the load side resources into power supply or power grid planning for consideration, so that the production simulation considering various elements of source and grid loads is realized. However, a production simulation method for coordinating and considering various resources on a power supply side, a power grid side, a load side and an energy storage side of a power system and uniformly solving the resources is not available at present.
Disclosure of Invention
The invention provides a source grid load storage coordinated power system production simulation method which at least partially solves the technical problem.
In a first aspect, the present invention provides a method for simulating production of a source-grid load-storage coordinated power system, including:
establishing a generator side unit scheduling cost model, a load demand side resource scheduling cost model and a system carbon emission cost model; the system carbon emission cost model is a relation model of various power generation powers of various regions at various time points in the production simulation period and system carbon emission cost in the production simulation period;
establishing an optimization model comprising an objective function and constraint conditions according to the power generation side unit scheduling cost model, the load demand side resource scheduling cost model and the system carbon emission cost model and by taking the minimum total system cost in a production simulation period as an objective;
the constraint conditions at least comprise power supply and demand balance constraints, wherein the power supply and demand balance constraints are constraint relations among power generation power of various power supplies in various regions at various time points in a production simulation period, power transmission power of trans-regional power transmission channels at various time points in a production simulation period, power reduction of demand response load of various regions at various time points in the production simulation period, power output of demand response load of various regions at various time points in the production simulation period, power conversion of demand response load of various regions at various time points in the production simulation period and energy storage charging and discharging power of various regions at various time points in the production simulation period;
according to the optimization model, the power generation power of various power supplies in various regions at various time points in the production simulation period, the power transmission power of trans-regional power transmission channels at various time points in the production simulation period, the power reduction of the demand response load of various regions at various time points in the production simulation period, the output power of the demand response load of various regions at various time points in the production simulation period, the input power of the demand response load of various regions at various time points in the production simulation period and the energy storage charge-discharge power of various regions at various time points in the production simulation period are obtained.
Preferably, the model of the scheduling cost of the generator-side unit is
In the formula:C 1 scheduling cost for the generator side unit;tthe number of the hours in the simulation period is shown;Hthe hours in the production simulation period is a predicted value;ris the area serial number;Ris the number of regions, which is a predicted value;iis the power supply serial number;Nis the number of power supplies, is a predetermined value;P r,i t,is thattTime point regionrPower supplyiGenerating power;FC i is a power supplyiThe scheduling cost of (1) is a predicted value.
Preferably, the load demand side resource scheduling cost model is
In the formula:C 2 the cost is scheduled for the resources on the load demand side,DRC r,t is thattTime point regionrDemand response load curtailment power;TCC r,t is thattTime point regionrThe excitation cost of the demand response load is reduced and is a predicted value;DRSo r,t is thattTime point regionrThe demand response load converts power;TCS r,t is thattTime point regionrThe demand response shifts the load incentive cost to a predetermined value.
Preferably, the system carbon emission cost model is
In the formula:C 3 in order to reduce the carbon emission cost of the system,Pr c is the unit carbon emission cost, which is a predicted value;e i as a unitiIs a predetermined value.
Preferably, the objective function is
In the formula:Fthe total cost of the system in the production simulation cycle.
Preferably, the constraint conditions further include system spin-up standby constraint, system spin-down standby constraint, system cold standby constraint, power output range constraint, power climbing rate constraint, wind power output constraint, photovoltaic power generation output constraint, power transmission power range constraint, demand response reduction power upper limit constraint, demand response conversion power upper limit constraint, demand response time-span transfer power balance constraint, energy storage charge-discharge balance constraint, energy storage SOC constraint, system non-rotational inertia power permeability constraint, electric power ratio constraint, clean power ratio constraint, maximum wind curtailment constraint and maximum light curtailment constraint.
Preferably, the power supply and demand balance constraint is
In the formula: g is the serial number of a power transmission channel;Pt g,t is composed oftTransmitting power by the ith power transmission channel;Ω r2x is a regionrA power transmission channel set for transmitting power outwards;Ω x2r is a regionrA set of power transmission channels receiving power from the outside;l g is a channelgThe loss rate of the transmission line is a predicted value;DRSi r,t is thattTime point regionrDemand response load is transferred into power;Cc r,t is composed oftTime point regionrThe energy storage charge and discharge power is that the positive value is discharge and the negative value is charge;Load r,t is thatrRegion(s)tThe moment load demand is a predicted value;
the spinning reserve constraint on the system is
In the formula:P r,i,t,max is a regionrPower supplyiIn thattThe maximum output which can be reached at any moment is a predicted value;I r,i,t is a regionrPower supplyiIn thattThe starting state parameter at the moment is a predicted value;DRC r,t,max is thattTime point regionrThe upper limit of the load power is reduced by the demand response and is a predicted value;DRSo r,t,max is thattTime point regionrThe upper limit of the load power is converted out by the demand response and is a predicted value;Cc r,t,max is composed oftTime point regionrThe net discharge power upper limit of the stored energy is a preset value;a 1 the spin reserve coefficient is determined by the load requirement and is a predicted value;b 1 the standby coefficient of the top spin determined by the wind power output is a predicted value;c 1 the standby coefficient of upward spinning determined by the photovoltaic power generation output is a predicted value;Pwt istWind power output at any moment;Pp t is thattPhotovoltaic power generation output at any moment;
the system underspin standby constraint is
In the formula:P r,i,t,min is a regionrPower supplyiIn thattThe minimum output which can be achieved at any moment is a predicted value;DRSi r,t,max is thattTime point regionrThe demand response is shifted into the upper limit of the load power, which is a predicted value;Cc r,t,max is composed oftTime point regionrThe lower limit of the net discharge power of the stored energy is a preset value;a 2 the spin-down standby coefficient is determined by the load requirement and is a predicted value;b 2 the wind power output determines the spin-down standby coefficient which is a predicted value;c 2 is a down-rotation standby coefficient determined by photovoltaic power generation outputA known value;
the cold standby constraint of the system is
In the formula:a 3 the system cold spare coefficient is determined by the load demand and is a preset value.
Preferably, the power output range is constrained to
In the formula:P i,min andP i,max are power supplies respectivelyiThe maximum and minimum technical output of (2) are both predicted values;
the power supply climbing rate is restricted to
In the formula:Ru i andRd i are power supplies respectivelyiThe climbing rate and the landslide rate are both predicted values;
the wind power output is constrained to
In the formula:W r,t is thattTime of dayrThe adjustable coefficient of regional wind power resources is a predicted value;Cw r is thatrThe total installed capacity of regional wind power is a predicted value;
the photovoltaic power generation output constraint is
In the formula:S r,t is thattTime of dayrThe adjustable coefficient of the regional photovoltaic power generation resources is a predicted value;Cp r is thatrThe total installed capacity of regional photovoltaic power generation is a predicted value;
the transmission power range is constrained to
In the formula:Pt g,min andPt g,max are respectively a power transmission channelgThe maximum and minimum transmission power of (2) are both predicted values.
Preferably, the demand response curtailment upper power limit is constrained to
The demand response is transferred out of the upper limit constraint of electric power
The demand response shifts to an upper power limit constraint of
The demand response transfers the electric quantity balance constraint of
The energy storage charging and discharging balance is constrained to
The energy storage SOC is constrained to
In the formula:SOC r,min andSOC r,max are respectivelyrThe upper limit and the lower limit of the regional energy storage SOC are both predicted values;Sci r is the starting point of the production simulation cyclerThe regional energy storage electric quantity level is a predicted value;Ec r is thatrThe total capacity of the regional energy storage is a predicted value;η c the charging efficiency of stored energy is a predetermined value.
Preferably, the system has no rotational inertia power supply permeability constraint of
In the formula:VP max the power ratio upper limit constraint of the non-rotational inertia power supply is a predicted value;
the ratio of the received power is restricted as
In the formula:IP max the upper limit of the ratio of the electric power received outside the zone is a predicted value;
the clean power ratio constraint is
In the formula:Ω CP is a clean power supply set;Rcp r is thatrA clean power proportion target of the area is a preset value;
the maximum air abandon rate is restricted to
In the formula:Rwc r is thatrThe maximum air abandon rate which can be accepted by the region is a predicted value;
the maximum light rejection rate is constrained to
In the formula:Rpc r is thatrThe maximum light rejection rate which can be accepted by the region is a predicted value.
According to the technical scheme, the problem that the existing power system source network storage coordination production simulation model method is missing is solved, the optimal operation modes of the power supply, the power grid, the load side resource and the stored energy can be synchronously solved by utilizing the mathematical model, the scheduling modes of various resources are mutually coordinated, and the overall optimization of the system can be realized.
Drawings
Fig. 1 is an overall idea diagram of a source-grid load-storage coordination power system production simulation method according to an embodiment of the present invention;
fig. 2 is a flowchart of a source-grid load-storage coordination power system production simulation method according to an embodiment of the present invention;
3 a-3 d are diagrams of various resource scheduling operation states in typical results of the source grid load-storage coordinated power system production simulation of the present invention;
fig. 4 a-4 b are diagrams of wind curtailment and light curtailment of the system in the simulation result of the source grid charge storage power system.
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 overall idea of the invention is to comprehensively consider various adjustable elements in the source, network, load and storage links of the power system, consider the interaction relation of various elements in the system and the influence on the system operation, and synchronously solve the optimal solution of various power output, trans-regional transmission channel transmission power, demand response and energy storage output of various regions at various time points in a typical week by adopting a mathematical optimization modeling means in operation research and aiming at minimizing the total cost of the system. The invention can not only verify and simulate a certain planning scheme in the planning research, but also develop the hot problem research (such as the influence of various factors on new energy consumption) in the operation of the power system. The overall thought of the source network charge storage coordination production simulation method is shown in the attached drawing 1, wherein the source network charge storage coordination planning of the power system comprises a power supply side resource scheduling operation mode, a power grid side resource scheduling operation mode, a load side resource scheduling operation mode and an energy storage side resource scheduling operation mode, and realizes unified optimization.
Fig. 2 is a flowchart of a source-grid load-storage coordination power system production simulation method according to an embodiment of the present invention.
A method for simulating the production of a source-grid-load-storage coordinated power system as shown in fig. 2 includes:
establishing a generator side unit scheduling cost model, a load demand side resource scheduling cost model and a system carbon emission cost model; the system carbon emission cost model is a relation model of various power generation powers of various regions at various time points in the production simulation period and system carbon emission cost in the production simulation period;
s202, establishing an optimization model comprising an objective function and constraint conditions according to the generator side unit scheduling cost model, the load demand side resource scheduling cost model and the system carbon emission cost model and with the aim of minimizing the total system cost in a production simulation period;
s203, according to the optimization model, acquiring the power generation power of various power supplies in various regions at various time points in a production simulation period, the power transmission power of trans-regional power transmission channels at various time points in the production simulation period, the power reduction of the demand response load of various regions at various time points in the production simulation period, the power output of the demand response load of various regions at various time points in the production simulation period and the energy storage charge-discharge.
The embodiment of the invention solves the problem of the loss of the current power system source network charge storage coordination production simulation model method, can synchronously solve and obtain the optimized operation modes of the power supply, the power grid, the load side resource and the stored energy by utilizing the mathematical model, ensures the mutual coordination of the scheduling modes of various resources, and can realize the overall optimization of the system.
It should be noted that, the embodiment of the present invention first performs the following steps:
(1) analyzing power supply scheduling characteristics
The method comprises the steps of analyzing the dispatching cost, the carbon emission coefficient, the maximum output, the minimum output, the climbing rate, the landslide rate, the wind power resource adjustable coefficient, the photovoltaic power generation resource adjustable coefficient and the like of various power sources such as coal power, gas power, nuclear power, hydroelectric power, onshore wind power, photovoltaic power generation, photo-thermal power generation, offshore wind power, biomass power generation, pumped storage and the like in each region, and collecting and arranging relevant technical economic parameters.
(2) Analyzing grid dispatching characteristics
Analyzing the transmission line loss rate, the maximum transmission power and the minimum transmission power of each trans-regional transmission channel, and an upward spinning reserve coefficient and a downward spinning reserve coefficient of each regional power grid determined by load requirements, an upward spinning reserve coefficient and a downward spinning reserve coefficient determined by wind power output, an upward spinning reserve coefficient and a downward spinning reserve coefficient determined by photovoltaic power generation output, a system cold reserve coefficient, an upper limit of power ratio of a non-rotational inertia power supply, an upper limit of power ratio of power received outside a region, an acceptable maximum wind curtailment rate and a maximum light curtailment rate, and collecting and sorting related technical and economic parameters.
(3) Analyzing load side resource scheduling characteristics
Analyzing the load reduction excitation cost, the load transfer-out excitation cost, the load reduction power upper limit, the load transfer-out power upper limit and the load transfer-in power upper limit of each region required response load at each time point, and collecting and sorting the related technical economic parameters.
(4) Analyzing energy storage scheduling characteristics
And analyzing the upper limit and the lower limit of the charge-discharge power of the energy storage in each region at each time point, the upper limit and the lower limit of the SOC, the energy storage electric quantity level at the initial time point of the production simulation period, the total capacity and the charging efficiency, and collecting and arranging the relevant technical economic parameters.
Based on the above, as a preferred embodiment, the model of the scheduling cost of the generator-side unit is
In the formula:C 1 scheduling cost for the generator side unit;tthe number of the hours in the simulation period is shown;Hthe hours in the production simulation period is a predicted value;ris the area serial number;Ris the number of regions, which is a predicted value;iis the power supply serial number;Nis the number of power supplies, is a predetermined value;P r,i t,is thattTime point regionrA power supply i generates power;FC i is a power supplyiThe scheduling cost of (1) is a predicted value.
Based on the above, as a preferred embodiment, the load demand side resource scheduling cost model is
In the formula:C 2 the cost is scheduled for the resources on the load demand side,DRC r,t is thattTime point regionrDemand response load curtailment power;TCC r,t is thattTime point regionrThe excitation cost of the demand response load is reduced and is a predicted value;DRSo r,t is thattTime point regionrThe demand response load converts power;TCS r,t is thattTime point regionrThe demand response shifts the load incentive cost to a predetermined value.
Based on the above, as a preferred embodiment, the system carbon emission cost model is
In the formula:C 3 in order to reduce the carbon emission cost of the system,Pr c is the unit carbon emission cost, which is a predicted value;e i as a unitiIs a predetermined value.
Based on the above, as a preferred embodiment, the objective function is
In the formula: f is the total cost of the system in the production simulation cycle.
It should be noted that, since the distribution of power in the space dimension and the distribution of power in the time dimension by the power grid belong to the transportation of power, and the distribution of power in the space dimension and the distribution of power in the time dimension are different from the distribution of power producers and consumers corresponding to the power generation side and the load side, respectively, no relevant cost is generated, and therefore, the distribution of power in the space dimension and the distribution of energy storage in the time dimension are not reflected in the objective function.
In a specific embodiment, the constraint conditions include a power supply and demand balance constraint, a system spin-up reserve constraint, a system spin-down reserve constraint, a system cold reserve constraint, a power output range constraint, a power ramp rate constraint, a wind power output constraint, a photovoltaic power generation output constraint, a power transmission power range constraint, a demand response reduction power upper limit constraint, a demand response spin-out power upper limit constraint, a demand response transfer power balance constraint across time periods, an energy storage charge-discharge balance constraint, an energy storage SOC constraint, a system non-rotational inertia power permeability constraint, an electric power ratio constraint, a clean power ratio constraint, a maximum wind curtailment rate constraint, and a maximum light curtailment rate constraint.
As a preferred embodiment, the power supply and demand balance constraint is
In the formula: g is the serial number of a power transmission channel;Pt g,t is composed oftTransmitting power by the ith power transmission channel;Ω r2x is a regionrA power transmission channel set for transmitting power outwards;Ω x2r is a regionrA set of power transmission channels receiving power from the outside;l g is a channelgThe loss rate of the transmission line is a predicted value;DRSi r,t is thattTime point regionrDemand response load is transferred into power;Cc r,t is composed oftTime point regionrThe energy storage charge and discharge power is that the positive value is discharge and the negative value is charge;Load r,t is thatrRegion(s)tThe moment load demand is a predicted value;
the constraint aims to ensure that the power supply at each time point of each region can effectively meet the power demand.
The spinning reserve constraint on the system is
In the formula:P r,i,t,max is a regionrPower supplyiIn thattThe maximum output which can be reached at any moment is a predicted value;I r,i,t is a regionrPower supplyiIn thattThe starting state parameter at the moment is a predicted value;DRC r,t,max is thattTime point regionrThe upper limit of the load power is reduced by the demand response and is a predicted value;DRSo r,t,max is thattTime point regionrThe upper limit of the load power is converted out by the demand response and is a predicted value;Cc r,t,max is composed oftTime point regionrThe net discharge power upper limit of the stored energy is a preset value;a 1 the spin reserve coefficient is determined by the load requirement and is a predicted value;b 1 the standby coefficient of the top spin determined by the wind power output is a predicted value;c 1 the standby coefficient of upward spinning determined by the photovoltaic power generation output is a predicted value;Pwt istWind power output at any moment;Pp t is thattPhotovoltaic power generation output at any moment;
the constraint aims to ensure that spinning reserve capacity on each time point system of each region can meet the reserve requirements of load and wind, light and electricity.
The system underspin standby constraint is
In the formula:P r,i,t,min is a regionrPower supplyiIn thattThe minimum output which can be achieved at any moment is a predicted value;DRSi r,t,max is thattTime point regionrThe demand response is shifted into the upper limit of the load power, which is a predicted value;Cc r,t,max is composed oftTime point regionrThe lower limit of the net discharge power of the stored energy is a preset value;a 2 the spin-down standby coefficient is determined by the load requirement and is a predicted value;b 2 the wind power output determines the spin-down standby coefficient which is a predicted value;c 2 the spin-down standby coefficient is determined by the photovoltaic power generation output and is a predicted value;
the constraint aims to ensure that the spinning reserve capacity of each time point system in each region can meet the reserve requirements of load and wind, light and electricity.
The cold standby constraint of the system is
In the formula:a 3 the system cold spare coefficient is determined by the load demand and is a preset value.
The constraint aims to ensure that the cold reserve capacity of the system at each time point in each region can meet the requirement of safe and stable operation of the system.
The power output range is restricted as
In the formula:P i,min andP i,max are power supplies respectivelyiThe maximum and minimum technical output of (2) are both predicted values;
the constraint aims to ensure that the output of various power supplies at various points of time in various regions is within the maximum and minimum technical output ranges.
The power supply climbing rate is restricted to
In the formula:Ru i andRd i are power supplies respectivelyiThe climbing rate and the landslide rate are both predicted values;
the constraint aims to ensure that the change rate of various power output at various points in various regions does not exceed the climbing rate limit.
The wind power output is constrained to
In the formula:W r,t is thattTime of dayrThe adjustable coefficient of regional wind power resources is a predicted value;Cw r is thatrTotal installed capacity of regional wind power, in advanceA known value;
the constraint aims to ensure that the wind power output at each time point of each region is within an adjustable power range allowed by natural resources.
The photovoltaic power generation output constraint is
In the formula:S r,t is thattTime of dayrThe adjustable coefficient of the regional photovoltaic power generation resources is a predicted value;Cp r is thatrAnd the total installed capacity of the regional photovoltaic power generation is a predicted value.
The constraint aims to ensure that the photovoltaic power generation output at each time point of each region is within an adjustable power range allowed by natural resources.
The transmission power range is constrained to
In the formula:Pt g,min andPt g,max are respectively a power transmission channelgThe maximum and minimum transmission power of (2) are both predicted values.
The demand response curtailment power upper limit constraint is
The constraint aims to ensure that the power of the demand response load reduction at each time point of each region does not exceed the upper limit of the response potential.
The demand response is transferred out of the upper limit constraint of electric power
The constraint aims to ensure that the output power of the demand response load at each time point of each region does not exceed the upper limit of the response potential.
The demand response shifts to an upper power limit constraint of
The constraint aims to ensure that the response load of each region at each time point is shifted to the power and does not exceed the response potential upper limit.
The demand response transfers the electric quantity balance constraint of
The constraint aims to ensure that the output accumulated electric quantity of the demand response load in each area is equal to the input accumulated electric quantity of the load.
The energy storage charging and discharging balance is constrained to
The constraint aims to ensure that the accumulated energy storage discharge electric quantity and the accumulated charge electric quantity of each area are equal.
The energy storage SOC is constrained to
In the formula:SOC r,min andSOC r,max are respectivelyrThe upper limit and the lower limit of the regional energy storage SOC are both predicted values;Sci r is the starting point of the production simulation cyclerThe regional energy storage electric quantity level is a predicted value;Ec r is thatrThe total capacity of the regional energy storage is a predicted value;η c the charging efficiency of stored energy is a predetermined value.
The constraint is to ensure that the state of charge (SOC) of each region at each point in time fluctuates within an allowable range.
The system has no rotational inertia power supply permeability constraint of
In the formula:VP max the power ratio upper limit constraint of the non-rotational inertia power supply is a predicted value;
the constraint aims to ensure that the power output ratio of the non-rotational inertia at each time point of each region is within the safety and stability allowable range of the system.
The ratio of the received power is restricted as
In the formula:IP max the upper limit of the ratio of the electric power received outside the zone is a predicted value;
the constraint aims to ensure that the power ratio received from other areas at each time point of each area is within the allowable range of system safety and stability.
The clean power ratio constraint is
In the formula:Ω CP is a clean power supply set;Rcp r is thatrA clean power proportion target of the area is a preset value;
the constraint aims to ensure that the cleaning power proportion of each time point in each area can meet the corresponding green development target.
The maximum air abandon rate is restricted to
In the formula:Rwc r is thatrThe maximum air curtailment rate acceptable by the region isA known value;
the constraint aims to ensure that the wind curtailment rate of each time point of each region is within an allowable range.
The maximum light rejection rate is constrained to
In the formula:Rpc r is thatrThe maximum light rejection rate which can be accepted by the region is a predicted value.
The constraint aims to ensure that the light rejection rate at each time point of each area is within an allowable range.
The invention carries out data arrangement according to the optimized scheduling scheme of various power output, trans-regional power transmission channel transmission power, demand response and energy storage output of each region at each time point in the simulation period (production simulation period) obtained by solving the optimized model. In addition, other simulation results such as air curtailment amount, air curtailment rate, light curtailment amount and light curtailment rate of each area can be further calculated according to actual needs. Taking the source network load-storage coordination production simulation in each area of china as an example, part of typical calculation results are shown in fig. 3a to 4 b.
The load side, the demand side, and the load demand side are defined in the same manner.
The present invention may be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
The above embodiments are only suitable for illustrating the present invention and not limiting the present invention, and those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, so that all equivalent technical solutions also belong to the scope of the present invention, and the scope of the present invention should be defined by the claims.
Claims (5)
1. A source-grid load-storage coordination power system production simulation method is characterized by comprising the following steps:
establishing a generator side unit scheduling cost model, a load demand side resource scheduling cost model and a system carbon emission cost model; the system carbon emission cost model is a relation model of various power generation powers of various regions at various time points in the production simulation period and system carbon emission cost in the production simulation period; wherein:
the power generation side unit scheduling cost model comprises the following steps:
in the formula:C 1 scheduling cost for the generator side unit;tthe number of the hours in the simulation period is shown;Hthe hours in the production simulation period is a predicted value;ris the area serial number;Ris the number of regions, which is a predicted value;iis the power supply serial number;Nis the number of power supplies, is a predetermined value;P r,i t,is thattTime point regionrPower supplyiGenerating power;FC i is a power supplyiThe scheduling cost of (1) is a predicted value;
the load demand side resource scheduling cost model is as follows:
in the formula:C 2 the cost is scheduled for the resources on the load demand side,DRC r,t is thattTime point regionrDemand response load curtailment power;TCC r,t is thattTime point regionrThe excitation cost of the demand response load is reduced and is a predicted value;DRSo r,t is thattTime point regionrThe demand response load converts power;TCS r,t is thattTime point regionrThe demand response load transfer incentive cost is a predicted value;
the system carbon emission cost model is as follows:
in the formula:C 3 in order to reduce the carbon emission cost of the system,Pr c is the unit carbon emission cost, which is a predicted value;e i as a unitiThe carbon emission coefficient of (a), is a predetermined value;
establishing an optimization model comprising an objective function and constraint conditions according to the power generation side unit scheduling cost model, the load demand side resource scheduling cost model and the system carbon emission cost model and by taking the minimum total system cost in a production simulation period as an objective; wherein:
the objective function is:
in the formula:Fthe total cost of the system in the production simulation period;
the constraint conditions at least comprise power supply and demand balance constraints, wherein the power supply and demand balance constraints are constraint relations among power generation power of various power supplies in various regions at various time points in a production simulation period, power transmission power of trans-regional power transmission channels at various time points in a production simulation period, power reduction of demand response load of various regions at various time points in the production simulation period, power output of demand response load of various regions at various time points in the production simulation period, power conversion of demand response load of various regions at various time points in the production simulation period and energy storage charging and discharging power of various regions at various time points in the production simulation period; the constraint conditions further comprise system upward spinning reserve constraint, system downward spinning reserve constraint, system cold reserve constraint, power output range constraint, power climbing rate constraint, wind power output constraint, photovoltaic power generation output constraint, power transmission power range constraint, demand response reduction power upper limit constraint, demand response conversion power upper limit constraint, demand response time-span transfer electric quantity balance constraint, energy storage charging and discharging balance constraint, energy storage SOC constraint, system non-rotational inertia power permeability constraint, electric power ratio constraint, clean power ratio constraint, maximum wind curtailment constraint and maximum light curtailment constraint, wherein:
the power supply and demand balance constraint is as follows:
in the formula: g is the serial number of a power transmission channel;Pt g,t is composed oftTime pointThe power transmission of the g power transmission channel;Ω r2x is a regionrA power transmission channel set for transmitting power outwards;Ω x2r is a regionrA set of power transmission channels receiving power from the outside;l g is a channelgThe loss rate of the transmission line is a predicted value;DRSi r,t is thattTime point regionrDemand response load is transferred into power;Cc r,t is composed oftTime point regionrThe energy storage charge and discharge power is that the positive value is discharge and the negative value is charge;Load r,t is thatrRegion(s)tThe moment load demand is a predicted value;
the transmission power range constraint is as follows:
in the formula:Pt g,min andPt g,max are respectively a power transmission channelgThe maximum and minimum transmission power of the power transmission system are predicted values;
according to the optimization model, the power generation power of various power supplies in various regions at various time points in the production simulation period, the power transmission power of trans-regional power transmission channels at various time points in the production simulation period, the power reduction of the demand response load of various regions at various time points in the production simulation period, the output power of the demand response load of various regions at various time points in the production simulation period, the input power of the demand response load of various regions at various time points in the production simulation period and the energy storage charge-discharge power of various regions at various time points in the production simulation period are obtained.
2. The method of claim 1,
the spinning reserve constraint on the system is
In the formula:P r,i,t,max is a regionrPower supplyiIn thattThe maximum output which can be reached at any moment is a predicted value;I r,i,t is a regionrPower supplyiIn thattThe starting state parameter at the moment is a predicted value;DRC r,t,max is thattTime point regionrThe upper limit of the load power is reduced by the demand response and is a predicted value;DRSo r,t,max is thattTime point regionrThe upper limit of the load power is converted out by the demand response and is a predicted value;Cc r,t,max is composed oftTime point regionrThe net discharge power upper limit of the stored energy is a preset value;a 1 the spin reserve coefficient is determined by the load requirement and is a predicted value;b 1 the standby coefficient of the top spin determined by the wind power output is a predicted value;c 1 the standby coefficient of upward spinning determined by the photovoltaic power generation output is a predicted value;Pwt istWind power output at any moment;Pp t is thattPhotovoltaic power generation output at any moment;
the system underspin standby constraint is
In the formula:P r,i,t,min is a regionrPower supplyiIn thattThe minimum output which can be achieved at any moment is a predicted value;DRSi r,t,max is thattTime point regionrThe demand response is shifted into the upper limit of the load power, which is a predicted value;Cc r,t,max is composed oftTime point regionrThe lower limit of the net discharge power of the stored energy is a preset value;a 2 the spin-down standby coefficient is determined by the load requirement and is a predicted value;b 2 the wind power output determines the spin-down standby coefficient which is a predicted value;c 2 the spin-down standby coefficient is determined by the photovoltaic power generation output and is a predicted value;
the cold standby constraint of the system is
In the formula:a 3 the system cold spare coefficient is determined by the load demand and is a preset value.
3. The method of claim 2, wherein the power supply output range is constrained to be
In the formula:P i,min andP i,max are power supplies respectivelyiThe maximum and minimum technical output of (2) are both predicted values;
the power supply climbing rate is restricted to
In the formula:Ru i andRd i are power supplies respectivelyiThe climbing rate and the landslide rate are both predicted values;
the wind power output is constrained to
In the formula:W r,t is thattTime of dayrThe adjustable coefficient of regional wind power resources is a predicted value;Cw r is thatrThe total installed capacity of regional wind power is a predicted value;
the photovoltaic power generation output constraint is
In the formula:S r,t is thattTime of dayrThe adjustable coefficient of the regional photovoltaic power generation resources is a predicted value;Cp r is thatrAnd the total installed capacity of the regional photovoltaic power generation is a predicted value.
4. The method of claim 3, wherein the demand response curtailment upper power limit constraint is
The demand response is transferred out of the upper limit constraint of electric power
The demand response shifts to an upper power limit constraint of
The demand response transfers the electric quantity balance constraint of
The energy storage charging and discharging balance is constrained to
The energy storage SOC is constrained to
In the formula:SOC r,min andSOC r,max are respectivelyrThe upper limit and the lower limit of the regional energy storage SOC are both predicted values;Sci r is the starting point of the production simulation cyclerThe regional energy storage electric quantity level is a predicted value;Ec r is thatrThe total capacity of the regional energy storage is a predicted value;η c the charging efficiency of stored energy is a predetermined value.
5. The method of claim 4, wherein the system has no rotational inertia power supply permeability constraint of
In the formula:VP max the power ratio upper limit constraint of the non-rotational inertia power supply is a predicted value;
the ratio of the received power is restricted as
In the formula:IP max the upper limit of the ratio of the electric power received outside the zone is a predicted value;
the clean power ratio constraint is
In the formula:Ω CP is a clean power supply set;Rcp r is thatrA clean power proportion target of the area is a preset value;
the maximum air abandon rate is restricted to
In the formula:Rwc r is thatrThe maximum air abandon rate which can be accepted by the region is a predicted value;
the maximum light rejection rate is constrained to
In the formula:Rpc r is thatrThe maximum light rejection rate which can be accepted by the region is a predicted value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811424897.5A CN109861301B (en) | 2018-11-27 | 2018-11-27 | Production simulation method for source-grid load-storage coordination power system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811424897.5A CN109861301B (en) | 2018-11-27 | 2018-11-27 | Production simulation method for source-grid load-storage coordination power system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109861301A CN109861301A (en) | 2019-06-07 |
| CN109861301B true CN109861301B (en) | 2021-06-15 |
Family
ID=66890282
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811424897.5A Active CN109861301B (en) | 2018-11-27 | 2018-11-27 | Production simulation method for source-grid load-storage coordination power system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109861301B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113538164B (en) * | 2020-04-13 | 2023-04-25 | 电力规划总院有限公司 | Modeling method and device for power system production simulation model and electronic equipment |
| CN112163698B (en) * | 2020-09-21 | 2022-08-02 | 国网河北省电力有限公司雄安新区供电公司 | Method for optimizing operation strategy of multi-energy collaborative comprehensive energy system in heating period |
| CN112366706B (en) * | 2020-11-17 | 2022-12-30 | 国家电网公司华北分部 | Load side peak regulation resource scale demand prediction method |
| CN112396239B (en) * | 2020-11-25 | 2024-02-02 | 清华大学 | Electric power system refined operation simulation method considering multi-type load demand response |
| CN112583005A (en) * | 2020-12-04 | 2021-03-30 | 国网湖南省电力有限公司 | Source network load storage coordination optimization method based on power unbalance |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9209625B2 (en) * | 2012-04-20 | 2015-12-08 | General Electric Company | Method and system to co-optimize utilization of demand response and energy storage resources |
| CN105631599A (en) * | 2015-12-30 | 2016-06-01 | 国网甘肃省电力公司电力科学研究院 | Multi-target operation scheduling method of virtual power plant |
| CN107196310B (en) * | 2016-03-15 | 2021-04-06 | 中国电力科学研究院 | Active power distribution network multi-time scale optimization method considering source network load coordination interaction |
| CN105990838B (en) * | 2016-05-17 | 2019-01-18 | 杭州电子科技大学 | A kind of island microgrid Optimization Scheduling of meter and schedulable and translatable load |
| CN107947166B (en) * | 2017-12-01 | 2019-08-30 | 湖南水利水电职业技术学院 | Dispatching method and device when a kind of multipotency microgrid change based on dynamic matrix control |
| CN107862160B (en) * | 2017-12-06 | 2020-08-11 | 清华大学 | Method and device for generating future power grid evolution model of compressed air energy storage system |
| CN108539793B (en) * | 2018-05-15 | 2021-05-04 | 佛山科学技术学院 | Comprehensive optimization configuration method and device for island micro-grid |
-
2018
- 2018-11-27 CN CN201811424897.5A patent/CN109861301B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN109861301A (en) | 2019-06-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109861301B (en) | Production simulation method for source-grid load-storage coordination power system | |
| Ma et al. | Integrated sizing of hybrid PV-wind-battery system for remote island considering the saturation of each renewable energy resource | |
| Mišák et al. | A heuristic approach to active demand side management in off-grid systems operated in a smart-grid environment | |
| CN109325608A (en) | Consider the distributed generation resource Optimal Configuration Method of energy storage and meter and photovoltaic randomness | |
| Verzijlbergh et al. | Does controlled electric vehicle charging substitute cross-border transmission capacity? | |
| JPWO2010103650A1 (en) | AC autonomous decentralized power system | |
| CN109473972A (en) | Whole source lotus is assisted to store up optimal control method based on more power curve | |
| CN111144620A (en) | Electricity-hydrogen comprehensive energy system considering seasonal hydrogen storage and robust planning method thereof | |
| Paliwal et al. | Optimal sizing and operation of battery storage for economic operation of hybrid power system using artificial bee colony algorithm | |
| CN112383086B (en) | Island micro-grid day-ahead energy-standby combined optimization scheduling method | |
| CN114936720A (en) | Low-carbon economic dispatching method for source-load-storage coordinated comprehensive energy system | |
| Lv et al. | Generation expansion planning considering the output and flexibility requirement of renewable energy: the case of Jiangsu Province | |
| CN107359611B (en) | Power distribution network equivalence method considering various random factors | |
| Javadi et al. | Optimal planning and operation of hybrid energy system supplemented by storage devices | |
| Haffaf et al. | Experimental performance analysis of an installed microgrid-based PV/battery/EV grid-connected system | |
| Robyns et al. | Electrical energy storage for buildings in smart grids | |
| Harris et al. | Socioeconomically sustainable rural microgrid engineering design | |
| Zhang et al. | Resilient energy management for residential communities under grid outages | |
| Simpkins et al. | Cost-optimal pathways to 75% fuel reduction in remote Alaskan villages | |
| Sadullaev et al. | Micro-grid based power supply of remote consumers located away from the centralized power grid | |
| Dorahaki et al. | Smart grids and building energy flexibility | |
| Dekeda et al. | The case for integration of renewables in rail freight yards in South Africa | |
| Pruckner | Modeling the impact of electrical energy storage systems on future power systems | |
| CN107766970B (en) | Micro-grid planning method based on enterprise benefits and user experience | |
| Soykan et al. | Evaluation of the effect of weather conditions on optimal sizing of an isolated microgrid |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |