CN110941799B - Energy hub stochastic programming method considering comprehensive uncertainty factors of system - Google Patents
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Abstract
The invention provides an energy hub stochastic programming method considering comprehensive uncertainty factors of a system, and relates to the technical field of energy Internet. According to the method, the system comprehensive uncertainty factors are determined, a random scene containing the uncertainty factors is generated based on a Monte Carlo method, the system comprehensive uncertainty factors are characterized by utilizing a probability density function, the number and the capacity of components in the energy hub are determined by taking the economical efficiency optimization as a planning target, the initial installation cost, the operation maintenance cost and the load loss penalty cost of production equipment, conversion equipment and energy storage devices in the energy hub are comprehensively considered, a random planning model is established under the condition that various constraints of the energy hub are met, the random planning model is solved to obtain the proper number and the capacity of the energy hub components, the comprehensive cost is minimum, and the accuracy and the reliability of energy hub planning are improved.
Description
Technical Field
The invention relates to the technical field of energy Internet, in particular to an energy hub stochastic programming method considering comprehensive uncertainty factors of a system.
Background
Environmental pollution and energy crisis promote the research on the collaborative planning operation of various energy sources such as electricity, natural gas, heat and the like. The energy hub can represent the coupling relation among energy sources such as electric power, natural gas, heating load and the like, and is an important component in the energy Internet. The energy hub planning has important significance for promoting the consumption of renewable energy sources and improving the energy utilization efficiency. At present, the planning and optimizing of the energy hub in China has a certain research, but in the actual planning process, uncertain factors in a plurality of systems, such as wind speed change, load demand side response, market price, random start and stop of components in the hub and the like, are faced, and the factors influence the accuracy and the reliability of the planning of the energy hub.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an energy hub stochastic programming method considering the comprehensive uncertainty factor of a system, which comprises the following steps:
an energy hub stochastic programming method considering system comprehensive uncertainty factors comprises the following steps:
step 1, determining comprehensive uncertainty factors of a system, including wind speed change, load demand, market price and random start and stop of components in a hub;
the wind speed change, the load demand, the market price and the random start and stop of components in the hub are calculated based on a wind power generation model, a comprehensive demand side response model, a price model and a component availability model respectively;
the wind power generation model is expressed as:
in the abovev r And->Represents cut-in wind speed, rated wind speed and cut-out wind speed, < >>Rated output power of the installed wind turbine generator;
the comprehensive demand side response model includes a translatable class load and a translatable class load, wherein the translatable class load is represented as:
W i,j,t ＝W i N ε i,j,t
w in the above i,j,t Andthe power value and rated power of the user j type i translatable type load at the time t are respectively, wherein the electric power value is the electric power value for the electric load, and the cold/hot power value is the cold/hot power value for the cold/hot load; epsilon i,j,t 0-1 variable, ε, representing the start-up state of the i-th class translatable class load of user j at time t i,j,t =1 and ε i,j,t =0 represents the activation and deactivation of the class i translatable class load of user j at time t, respectively; />And H i The method comprises the steps of respectively starting using time, ending using time and load duration time for the i-th translatable class load habit;
the transferable class load is represented as:
in the aboveAnd S is k，t The charging power state of charge and the state of charge SOC of the kth EV at the time t are respectively; />And E is EV,k Charging efficiency and battery capacity of the kth EV, respectively; />And->Minimum and maximum SOCs allowed for the kth EV battery, respectively; t is t ari.k And t dep.k The time when the kth EV accesses and leaves the power system is respectively; />Maximum charging power for the first EV;
the price model is expressed as:
v(c,f)∈{0,1}
lambda in the above s (c, f) represents the price of user c under price module f under scene s, v (c, f) represents a binary variable, v (c, f) takes 1 when client c chooses to use price module f, otherwise v (c, f) is 0;
the component availability model models component availability using a two-state continuous time Markov model.
Step 2, generating a random scene containing uncertainty factors based on a Monte Carlo method;
step 3, characterizing the comprehensive uncertainty factors of the system by using a probability density function;
step 4, determining the number and capacity of components in the energy hub by taking the optimal economical efficiency as a planning target, comprehensively considering the initial installation cost, the operation maintenance cost and the loss load penalty cost of production equipment, conversion equipment and energy storage devices in the energy hub, and establishing a random planning model under the condition of meeting various constraints of the energy hub;
the optimal objective function of the economical efficiency is as follows:
ZIC, ZOC and ZPC respectively represent total installation cost, total operation and maintenance cost and total punishment cost of components in the energy hub taking uncertainty factors into consideration, NS is the total number of scenes, and ρ s The scene occurrence probability is represented by τ, which is the present value coefficient;
in the above, m, n, i, j and h are respectively the serial numbers of a CHP unit, a wind power unit, a boiler unit, an electric energy storage unit and a thermal energy storage unit,and->The method comprises the following steps of CHP unit installation cost, wind turbine installation cost, boiler unit installation cost, electric energy storage installation cost and thermal energy storage installation cost respectively. CHP, wind, bolier, ES, TS the cogeneration unit, wind turbine, electric boiler, electric energy storage and thermal energy storage respectively. The method comprises the steps of carrying out a first treatment on the surface of the
In the aboveAnd->The operation cost of the CHP unit, the operation cost of the boiler unit and the network operation cost are respectively;
ZPC＝LS s,t ×VOLL
VOLL in the above represents the load loss value, LS s,t Representing the amount of load lost at scene s, time t.
The various constraint conditions of the energy hub comprise:
(1) Energy conservation constraint
In the aboveIndicating transformer efficiency, +.>Indicating the gas-to-electricity efficiency of the CHP unit, < + >>Representing the efficiency of the wind turbine conversion equipment, < >>Representing the input of power from the grid at scene s time t, < >>Representing the natural gas input power of the cogeneration unit in scene s time t, < >>Representing the input work of the wind turbine under the scene s time tThe rate of the product is determined by the ratio,and->Respectively, charging and discharging power of electric energy storage under the scene s time t, < >>And->The load quantity is respectively expressed as the load quantity transferred in and out of the demand response side under the scene s time t;
(2) Technical constraints
In the aboveRepresented as maximum power input from the grid at scene s time t;
in the aboveRepresented as the maximum thermal power provided by the district heating network at the scene s time t;
in the aboveExpressed as the level of electrical energy storage at time t of scene s +.>And->Respectively representing the charge and discharge power of the electric energy storage at the time t of the scene s, < >>The power loss of the electric energy storage at the time t of the scene s;
in the aboveAnd->Representing the minimum and maximum capacity of the electric energy storage, respectively, ">And->Charge and discharge efficiency, respectively denoted as electric energy storage, ">And->Binary variables respectively representing the charge and discharge conditions of the electric energy storage at the time t of the scene s;
the above-mentioned type indicates that the charging and discharging processes of the electric energy storage cannot be performed simultaneously;
(3) Demand response constraints
In the aboveAnd->The load quantity is respectively expressed as the load quantity transferred in and out of the demand response side under the scene s time t;
0≤P up (t,s)≤L upmax P demand (t,s)I up (t,s)
0≤P down (t,s)≤L downmax P demand (t,s)I down (t,s)
l in the above upmax And L downmax Represents the maximum value of the transfer-in and transfer-out demands, P up (t, s) and P down (t, s) represents the demand amount of the transfer-in and transfer-out by the demand-side response, respectively, I up (t, s) and I down (t, s) represent the binary variables of the demand load in-out at the scene s time t, respectively;
the above expression indicates that the transfer-in and transfer-out process of the demand load cannot be performed simultaneously.
And 5, solving the stochastic programming model to obtain the number and capacity of the energy hub components, so that the comprehensive cost of the energy hub components is minimum.
The invention has the beneficial effects that:
according to the method, the number and the capacity of the energy hub components are obtained by solving the random planning model aiming at uncertain factors such as wind speed change, load demand side response, market price and random start-stop of the components in the hub in the actual planning process of the energy hub in China, so that the comprehensive cost is reduced, the accuracy and the reliability of the energy hub planning are improved, and the method has important significance in promoting the consumption of renewable energy sources and improving the energy utilization efficiency.
Drawings
FIG. 1 is a flow chart of a method for randomly planning an energy hub according to the present invention;
FIG. 2 is a diagram of an energy hub structure in accordance with an embodiment of the present invention;
FIG. 3 is a graph of typical solar, thermal, and cold loads according to an embodiment of the present invention.
Detailed Description
The following describes the embodiments of the present invention in detail with reference to the drawings.
An energy hub stochastic programming method considering system comprehensive uncertainty factors, as shown in fig. 1, comprises the following steps:
step 1, determining comprehensive uncertainty factors of a system, wherein the factors comprise wind speed change, load demand, market price and random start and stop of components in a hub as shown in fig. 2;
the wind speed change, the load demand, the market price and the random start and stop of components in the hub are calculated based on a wind power generation model, a comprehensive demand side response model, a price model and a component availability model respectively;
the wind power generation model is expressed as:
in the abovev r And->Represents cut-in wind speed, rated wind speed and cut-out wind speed, < >>Rated output power of the installed wind turbine generator;
the integrated demand side response model includes translatable class loads and translatable class loads, as shown in fig. 3, wherein the translatable class loads are represented as:
W i,j,t ＝W i N ε i,j,t
w in the above i,j,t Andthe power value and rated power of the user j type i translatable type load at the time t are respectively, wherein the electric power value is the electric power value for the electric load, and the cold/hot power value is the cold/hot power value for the cold/hot load; epsilon i,j,t 0-1 variable, ε, representing the start-up state of the i-th class translatable class load of user j at time t i,j,t =1 and ε i,j,t =0 represents the activation and deactivation of the class i translatable class load of user j at time t, respectively; />And H i The method comprises the steps of respectively starting using time, ending using time and load duration time for the i-th translatable class load habit;
the transferable class load is represented as:
in the aboveAnd S is k，t The charging power state of charge and the state of charge SOC of the kth EV at the time t are respectively; />And E is EV,k Charging efficiency and battery capacity of the kth EV, respectively; />And->Minimum and maximum SOCs allowed for the kth EV battery, respectively; t is t ari.k And t dep.k The time when the kth EV accesses and leaves the power system is respectively; />Maximum charging power for the first EV;
the price model is expressed as:
v(c,f)∈{0,1}
lambda in the above s (c, f) represents the price of user c under price module f under scene s, v (c, f) represents a binary variable, v (c, f) takes 1 when client c chooses to use price module f, otherwise v (c, f) is 0;
the component availability model models component availability using a two-state continuous time Markov model.
Step 2, generating a random scene containing uncertainty factors based on a Monte Carlo method;
step 3, characterizing the comprehensive uncertainty factors of the system by using a probability density function;
step 4, determining the number and capacity of components in the energy hub by taking the optimal economical efficiency as a planning target, comprehensively considering the initial installation cost, the operation maintenance cost and the loss load penalty cost of production equipment, conversion equipment and energy storage devices in the energy hub, and establishing a random planning model under the condition of meeting various constraints of the energy hub;
the optimal objective function of the economical efficiency is as follows:
ZIC, ZOC and ZPC respectively represent total installation cost, total operation and maintenance cost and total punishment cost of components in the energy hub taking uncertainty factors into consideration, NS is the total number of scenes, and ρ s The scene occurrence probability is represented by τ, which is the present value coefficient;
in the above, m, n, i, j and h are respectively the serial numbers of a CHP unit, a wind power unit, a boiler unit, an electric energy storage unit and a thermal energy storage unit,and->The method comprises the following steps of CHP unit installation cost, wind turbine installation cost, boiler unit installation cost, electric energy storage installation cost and thermal energy storage installation cost respectively. CHP, wind, bolier, ES, TS the utility model respectively represents a cogeneration unit, a wind turbine unit, an electric boiler, an electric energy storage and a thermal energy storage;
in the aboveAnd->The operation cost of the CHP unit, the operation cost of the boiler unit and the network operation cost are respectively;
ZPC＝LS s,t ×VOLL
VOLL in the above represents the load loss value, LS s,t Representing the amount of load lost at scene s, time t.
The various constraint conditions of the energy hub comprise:
(1) Energy conservation constraint
In the aboveIndicating transformer efficiency, +.>Indicating the gas-to-electricity efficiency of the CHP unit, < + >>Representing the efficiency of the wind turbine conversion equipment, < >>Representing the input of power from the grid at scene s time t, < >>Representing the natural gas input power of the cogeneration unit in scene s time t, < >>Representing the wind turbine input power at scene s time t,and->Respectively, charging and discharging power of electric energy storage under the scene s time t, < >>And->The load quantity is respectively expressed as the load quantity transferred in and out of the demand response side under the scene s time t;
(2) Technical constraints
In the aboveRepresented as maximum power input from the grid at scene s time t;
in the aboveExpressed as maximum thermal power provided by district heating network at scene s time t；
In the aboveExpressed as the level of electrical energy storage at time t of scene s +.>And->Respectively representing the charge and discharge power of the electric energy storage at the time t of the scene s, < >>The power loss of the electric energy storage at the time t of the scene s;
in the aboveAnd->Representing the minimum and maximum capacity of the electric energy storage, respectively, ">And->Charge and discharge efficiency, respectively denoted as electric energy storage, ">And->Binary variables respectively representing the charge and discharge conditions of the electric energy storage at the time t of the scene s;
the above-mentioned type indicates that the charging and discharging processes of the electric energy storage cannot be performed simultaneously;
(3) Demand response constraints
In the aboveAnd->The load quantity is respectively expressed as the load quantity transferred in and out of the demand response side under the scene s time t;
0≤P up (t,s)≤L upmax P demand (t,s)I up (t,s)
0≤P down (t,s)≤L downmax P demand (t,s)I down (t,s)
l in the above upmax And L downmax Represents the maximum value of the transfer-in and transfer-out demands, P up (t, s) and P down (t, s) represents the demand amount of the transfer-in and transfer-out by the demand-side response, respectively, I up (t, s) and I down (t, s) represent the binary variables of the demand load in-out at the scene s time t, respectively;
the above expression indicates that the transfer-in and transfer-out process of the demand load cannot be performed simultaneously.
Step 5, solving the stochastic programming model to obtain proper quantity and capacity of the energy hub components;
because the number of variables to be solved and constraint conditions of the research problem is very large, the embodiment uses a solver which is popular in the current academic world to solve the problem, namely a CPLEX solver. The CPLEX solver is a whole-course ILOG CPLEX, is common software in the current power system problem solving, is also a popular optimizing software package, has the characteristics of high flexibility and good performance, and mainly comprises a CPLEX software interface and a CPLEX software package. The CPLEX interface mainly comprises a component library and an interaction optimization program, wherein the component library can enable a researcher and a developer to have authority to integrate a complete and effective ILOG CPLEX engine into an application; and the interactive software provides different connection modes for development and application deployment. Because the CPLEX interface is very flexible, the possibility is provided for the CPLEX interface to adapt to most development environments and application platforms. CPLEX is not an algorithm, and comprises a series of configurable algorithms for selecting a mode of optimizing an optimization object, wherein the optimization method comprises optimization programs such as single optimization, limit optimization, mixed variable optimization and the like, and a user can pertinently utilize different optimization programs to solve corresponding problems according to the characteristics of actual problems. In the practical application, the Cutting-edge technology in the CPLEX solution mixed variable optimization program is adopted, so that a quick solution can be provided for the complex mixed variable planning problem, and the multi-variable, multi-constraint and multi-variable type planning problem can be solved in a short time.
In this embodiment, a CPLEX solver is used to solve the stochastic programming model to obtain a proper number and capacity of energy hub components, so that the comprehensive cost is the lowest.
In this embodiment, CHP economic and technical parameters, boiler economic and technical parameters, energy storage device economic and technical parameters, and output results of system components are shown in tables 1 to 4, respectively;
TABLE 1 economic and technical parameters of CHP
Table 2 economic and technical parameters of boiler
TABLE 3 energy storage device economic and technical parameters
TABLE 4 output of system components
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions, which are defined by the scope of the appended claims.
Claims (1)
1. An energy hub stochastic programming method considering system comprehensive uncertainty factors is characterized by comprising the following steps of: the method comprises the following steps:
step 1, determining comprehensive uncertainty factors of a system, including wind speed change, load demand, market price and random start and stop of components in a hub;
in the step 1, wind speed change, load demand, market price and random start and stop of components in the hub are calculated based on a wind power generation model, a comprehensive demand side response model, a price model and a component availability model respectively;
the wind power generation model is expressed as:
in the abovev r And->Represents cut-in wind speed, rated wind speed and cut-out wind speed, < >>Rated output power of the installed wind turbine generator;
the comprehensive demand side response model includes a translatable class load and a translatable class load, wherein the translatable class load is represented as:
w in the above i,j,t Andthe power value and rated power of the user j type i translatable type load at the time t are respectively, wherein the electric power value is the electric power value for the electric load, and the cold/hot power value is the cold/hot power value for the cold/hot load; epsilon i,j,t 0-1 variable, ε, representing the start-up state of the i-th class translatable class load of user j at time t i,j,t =1 and ε i,j,t =0 represents the activation and deactivation of the class i translatable class load of user j at time t, respectively; />And H i The method comprises the steps of respectively starting using time, ending using time and load duration time for the i-th translatable class load habit;
the transferable class load is represented as:
in the aboveAnd S is k，t The charging power state of charge and the state of charge SOC of the kth EV at the time t are respectively; />And E is EV,k Charging efficiency and battery capacity of the kth EV, respectively; />And->Minimum and maximum SOCs allowed for the kth EV battery, respectively; t is t ari.k And t dep.k The time when the kth EV accesses and leaves the power system is respectively; />Maximum charging power for the first EV;
the price model is expressed as:
v(c,f)∈{0,1}
lambda in the above s (c, f) represents the price of user c under price module f under scene s, v (c, f) represents a binary variable, v (c, f) takes 1 when client c chooses to use price module f, otherwise v (c, f) is 0;
the component availability model models the component availability by adopting a two-state continuous time Markov model;
step 2, generating a random scene containing uncertainty factors based on a Monte Carlo method;
step 3, characterizing the comprehensive uncertainty factors of the system by using a probability density function;
step 4, determining the number and capacity of components in the energy hub by taking the optimal economical efficiency as a planning target, comprehensively considering the initial installation cost, the operation maintenance cost and the loss load penalty cost of production equipment, conversion equipment and energy storage devices in the energy hub, and establishing a random planning model under the condition of meeting various constraints of the energy hub;
the optimal objective function of the economical efficiency is as follows:
ZIC, ZOC and ZPC respectively represent total installation cost, total operation and maintenance cost and total punishment cost of components in the energy hub taking uncertainty factors into consideration, NS is the total number of scenes, and ρ s The scene occurrence probability is represented by τ, which is the present value coefficient;
in the above, m, n, i, j and h are respectively the serial numbers of a CHP unit, a wind power unit, a boiler unit, an electric energy storage unit and a thermal energy storage unit,and->CHP, wind, bolier, ES, TS represents a cogeneration unit, a wind turbine generator, an electric boiler, electric energy storage and heat energy storage respectively;
in the aboveAnd->The operation cost of the CHP unit, the operation cost of the boiler unit and the network operation cost are respectively;
ZPC＝LS s,t ×VOLL
VOLL in the above represents the load loss value, LS s,t Representing the amount of load lost at scene s, time t;
the various constraint conditions of the energy hub comprise:
(1) Energy conservation constraint
In the aboveIndicating transformer efficiency, +.>Indicating the gas-to-electricity efficiency of the CHP unit, < + >>Representing the efficiency of the wind turbine conversion equipment, < >>Representing the input of power from the grid at scene s time t, < >>Representing the natural gas input power of the cogeneration unit in scene s time t, < >>Representing wind turbine input power, < > in scene s time t>And->Respectively the charging and discharging power of the electric energy storage under the scene s time t,/>and->The load quantity is respectively expressed as the load quantity transferred in and out of the demand response side under the scene s time t;
(2) Technical constraints
In the aboveRepresented as maximum power input from the grid at scene s time t;
in the aboveRepresented as the maximum thermal power provided by the district heating network at the scene s time t;
in the aboveExpressed as the level of electrical energy storage at time t of scene s +.>And->Respectively representing the charge and discharge power of the electric energy storage at the time t of the scene s, < >>The power loss of the electric energy storage at the time t of the scene s;
in the aboveAnd->Representing the minimum and maximum capacity of the electric energy storage, respectively, ">And->Charge and discharge efficiency, respectively denoted as electric energy storage, ">And->Binary variables respectively representing the charge and discharge conditions of the electric energy storage at the time t of the scene s;
the above-mentioned type indicates that the charging and discharging processes of the electric energy storage cannot be performed simultaneously;
(3) Demand response constraints
In the aboveAnd->The load quantity is respectively expressed as the load quantity transferred in and out of the demand response side under the scene s time t;
0≤P up (t,s)≤L upmax P demand (t,s)I up (t,s)
0≤P down (t,s)≤L downmax P demand (t,s)I down (t,s)
l in the above upmax And L downmax Represents the maximum value of the transfer-in and transfer-out demands, P up (t, s) and P down (t, s) represents the demand amount of the transfer-in and transfer-out by the demand-side response, respectively, I up (t, s) and I down (t, s) represent the binary variables of the demand load in-out at the scene s time t, respectively;
the above-mentioned type indicates that the transfer-in and transfer-out processes of the demand load cannot be performed simultaneously;
and 5, solving the stochastic programming model to obtain proper quantity and capacity of the energy hub components, so that the comprehensive cost of the energy hub components is minimum.
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