CN117391445A - Optimized operation method of electric-thermal-hydrogen comprehensive energy system considering conditional risk value - Google Patents
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Abstract
An optimization operation method of an electric-thermal-hydrogen comprehensive energy system considering condition risk value belongs to the field of optimization operation of electric power systems. The invention aims to solve the problem that the scheduling cost of the electric-thermal-hydrogen comprehensive energy system fluctuates due to photovoltaic output and load prediction deviation which are not or less researched at present. Step 1, acquiring economic parameters, technical parameters and output data of a comprehensive energy system; step 2, establishing an objective function of a two-stage optimization operation model of the CVaR comprehensive energy system according to economic parameters, technical parameters and output data of the comprehensive energy system; and 3, restraining an objective function of the two-stage optimization operation model of the comprehensive energy system of the CVaR by using a constraint condition to obtain the minimum total cost of the comprehensive energy system, so that the operation of the comprehensive energy system is optimized. The invention is used for optimizing the operation of the comprehensive energy system.
Description
Technical Field
The invention relates to a photovoltaic output and load prediction deviation evaluation method, and belongs to the field of power system optimization operation.
Background
The energy crisis and environmental pollution can obstruct the development of socioeconomic performance, and is a social problem to be solved in the current stage. The comprehensive energy system is an energy system which integrates multiple energy sources such as electric energy, heat energy, coal, natural gas and the like in a certain area by utilizing advanced physical information technology and innovative management mode, and realizes coordinated operation and complementary interaction among the energy sources. The comprehensive energy system can remarkably improve the utilization efficiency of renewable energy sources and realize energy conservation and emission reduction while meeting the requirement of diversified energy consumption. Therefore, the construction of a multi-energy coupled integrated energy system is a necessary trend of future development.
In recent years, because hydrogen has the advantages of cleanness, environmental protection and long-term energy storage, an electric-thermal-hydrogen comprehensive energy system taking hydrogen as an energy conversion medium is highly focused. Particularly, the electric-thermal-hydrogen system formed by coupling various energy forms such as electric energy, heat energy, hydrogen energy and the like can avoid the installation of a large-capacity and high-price battery for energy storage by means of the long-term energy storage characteristic of hydrogen, and is beneficial to reducing the construction and operation cost of the system. However, inaccurate renewable energy and load prediction can affect a scheduling plan made in advance by an electric-thermal-hydrogen comprehensive energy system, so that scheduling cost fluctuates, and how to prevent the risk of increasing the scheduling cost caused by uncertainty factors to the system is not paid enough attention currently.
At this time, it is a natural idea to perform risk management based on a risk metric method to reduce the fluctuating risk of the system scheduling cost. Risk measurement methods in the financial field include value-at-risk (VaR) and conditional risk value (conditional value-at-risk, CVaR). The former represents the maximum loss threshold obtained at a certain confidence level, while the latter is the average of all cases exceeding this threshold, and thus is more widely used. There have been a great deal of research to assess risk during comprehensive energy system planning and operation using CVaR, but no or less research has focused on the problem of fluctuation in the scheduling cost of the electro-thermo-hydro comprehensive energy system due to photovoltaic output and load prediction bias.
In this respect, it is of research practical significance to integrate CVaR theory with the optimal scheduling process of the electricity-heat-hydrogen integrated energy system to fully consider the scheduling cost fluctuation risk.
Disclosure of Invention
The invention aims to solve the problem that the scheduling cost of an electric-thermal-hydrogen comprehensive energy system is fluctuated due to photovoltaic output and load prediction deviation which is not researched or is less researched and focused at the prior art, and provides an electric-thermal-hydrogen comprehensive energy system optimizing operation method considering condition risk value.
An optimized operation method of an electric-thermal-hydrogen comprehensive energy system considering conditional risk value, which comprises the following steps:
step 1, acquiring economic parameters, technical parameters and output data of a comprehensive energy system;
step 2, establishing an objective function of a two-stage optimization operation model of the CVaR comprehensive energy system according to economic parameters, technical parameters and output data of the comprehensive energy system;
and 3, restraining an objective function of the two-stage optimization operation model of the comprehensive energy system of the CVaR by using a constraint condition to obtain the minimum total cost of the comprehensive energy system, so that the operation of the comprehensive energy system is optimized.
Preferably, the integrated energy system comprises a photovoltaic panel, a battery energy storage, a hydrogen fuel cell, an electrolytic tank and a heat pump;
the economic parameters of the comprehensive energy system comprise the operation and maintenance cost and unit investment cost of a photovoltaic cell panel, the operation and maintenance cost and unit investment cost of battery energy storage, the operation and maintenance cost and unit investment cost of a hydrogen fuel cell, the operation and maintenance cost and unit investment cost of a heat pump, and the operation and maintenance cost and unit investment cost of an electrolytic tank;
technical parameters of the comprehensive energy system comprise the service life of a photovoltaic cell panel, the charge and discharge efficiency of battery energy storage, the heat energy supply efficiency of a hydrogen fuel cell, the heat energy supply efficiency of a heat pump and the service life of an electrolytic tank.
Preferably, the objective function of the two-stage optimization operation model of the integrated energy system of CVaR is expressed as:
min(C day-ahead +C expected-real-time +ξC CVaR ) Equation 1;
wherein C is day-ahead Representing the day-ahead scheduling cost of the system, C expected-real-time Representing a real-time scheduling cost expected value of the system, and xi represents a risk level and C CVaR Representing the risk cost of the system;
C day-ahead expressed as:
wherein t represents a time index; Λ represents a scheduling time range;representing the price of electricity purchasing to an upper power grid; />The power purchasing power of the upper power grid in the day-ahead dispatching stage is represented; c s A marginal power generation cost quotation of the photovoltaic unit is represented; />Representing the photovoltaic power consumed by the energy system;
C expected-real-time expressed as:
wherein w represents indexes of different photovoltaic and load scenes; omega represents a scene set;representing the probability of occurrence of the scene w; />And->Respectively representing the additional purchase of electric power and the sale of redundant electric power to an upper power grid in a real-time dispatching stage; />Andrespectively representing the electricity purchase price and the electricity selling price of the electric power market; />And->Respectively representing the actual power of the photovoltaic and the abandoned light power; />Load reduction amounts respectively representing an electric load, a hydrogen load, and a thermal load; gamma ray P ，γ H ，γ T Penalty costs for unit reduction of electric load, thermal load, and hydrogen load are expressed, respectively; /> Respectively representing the electric power consumed by the electrolytic tank and the heat pump; />Represents the electric power consumed by the hydrogen fuel cell; />Representing the charging power of the energy storage battery; />Indicating the amount of hydrogen gas input into the hydrogen storage device; c elec 、c HP 、c HFC 、c BS And c HS Respectively representing the unit operation maintenance cost of the electrolytic tank, the heat pump, the hydrogen fuel cell and the battery energy storage and hydrogen storage device;
C CVaR expressed as:
in the method, in the process of the invention,represents VaR; η (eta) w Representing higher than +.>And β is the confidence level.
Preferably, the constraint includes a day-ahead schedule constraint, a real-time schedule constraint, and a CVaR constraint.
Preferably, the day-ahead scheduling constraints include power balance constraints, purchase power constraints, equipment operation constraints, and energy storage constraints;
the power balance constraint, expressed as:
in the method, in the process of the invention,represents the discharge power of the hydrogen fuel cell, +.>And->Respectively representing the charging power and the discharging power of the battery energy storage; />Indicating the hydrogen production power consumption of the electrolytic cell; />Representing heat pump power consumption; /> Predicted values representing the electric load, the thermal load, and the hydrogen load, respectively; />And->Representing maximum thermal power output of the hydrogen fuel cell and the heat pump, respectively; />Indicating hydrogen production amount of the electrolytic tank per hour; />Represents the hydrogen consumption per hour of the hydrogen fuel cell;and->Respectively representing the hydrogen output and the hydrogen input of the hydrogen storage device per hour;
purchase power constraint, expressed as:
in the method, in the process of the invention,representing the maximum transmission power of the line;
device operation constraints, expressed as:
wherein H is H Represents the high heating value of hydrogen;representing the maximum input power of the electrolyzer; />Representing a photovoltaic power prediction value; mu (mu) HP Representing the efficiency of the heat pump in converting electrical energy into thermal energy; mu (mu) HFC,P Sum mu HFC,T Respectively representing the efficiency of the hydrogen fuel cell in converting hydrogen energy into electric energy and heat energy; />Represents the maximum electric power that the hydrogen fuel cell can output; epsilon elec Indicating hydrogen production efficiency of the electrolytic cell; />Representing heat pump capacity;
energy storage constraints, expressed as:
in the method, in the process of the invention,representing the energy stored in the energy storage device at time t; />And->Respectively representing the charging power and the discharging power of the energy storage device; sigma (sigma) EES Representing a self-discharge rate of the energy storage device; />And->Respectively representing the charging efficiency and the discharging efficiency of the energy storage device; e (E) EES,max Representing the rated capacity of the energy storage device; />And->Respectively representing the maximum charge/discharge power of the energy storage device; Δt represents a unit time interval.
Preferably, the real-time scheduling constraints include power balance constraints, power purchase constraints, load loss constraints, and equipment operation constraints;
the power balance constraint, expressed as:
in the method, in the process of the invention,representing grid-connected photovoltaic power; />And->Respectively representing the energy storage charging power and the discharging power of the battery; />And->Representing the actual values of the electrical load, the thermal load and the hydrogen load, respectively; />Represents the heat generation power of the hydrogen fuel cell; />Representing heat pump heat generation power; />Indicating hydrogen production amount of the electrolytic tank per hour; />Represents the hydrogen consumption per hour of the hydrogen fuel cell; />And->Respectively representing the hydrogen output and the hydrogen input of the hydrogen storage device per hour;and->The cut-off values of the electric load, the thermal load, and the hydrogen load are respectively represented;
purchase power constraint, expressed as:
load loss constraint, expressed as:
in the method, in the process of the invention,representing the power transmission limits of the transmission line.
Preferably, the CVaR constraint is expressed as:
in the method, in the process of the invention,and->Respectively representing the electricity purchasing price and the electricity selling price of the upper power grid; gamma ray e Indicating the cost reduction of the electrical load.
Preferably, the method further comprises step 4;
and 4, calculating a standard deviation and a weighted average value according to the risk cost, and obtaining the influence of the risk cost on the comprehensive energy system operator according to the standard deviation and the weighted average value.
The beneficial effects of the invention are as follows:
the invention introduces the concept of risk management and proposes an objective function of a two-stage optimization operation model of an electric-thermal-hydrogen integrated energy system related to CVaR. The comprehensive energy system comprises various energy supply and conversion equipment such as photovoltaic, heat storage, hydrogen storage, an electrolytic tank, a heat pump and the like, and the complementary characteristics of three energy sources of electrothermal hydrogen can be fully excavated. According to the objective function of the constructed model, the minimum value of the objective function, namely the minimum total cost of the comprehensive energy system, can be obtained, so that fluctuation of the total cost of the comprehensive energy system is restrained, namely the risk of fluctuation of the system scheduling cost caused by inaccurate photovoltaic and load prediction of the electric-thermal-hydrogen comprehensive energy system is restrained, and the comprehensive energy system is enabled to run safely.
According to the CVaR comprehensive energy system optimization method, fluctuation risk of CVaR measurement comprehensive energy system scheduling cost (minimum value of objective function) is introduced, influence of a quantized risk level on scheduling cost is quantized, and the method has guiding significance on operation of a future high-photovoltaic-permeability power grid.
According to the two-stage optimization operation model of the electric-thermal-hydrogen comprehensive energy system of the CVaR, which is provided by the invention, CVaR indexes are introduced to evaluate fluctuation risks of the dispatching cost of the electric-thermal-hydrogen comprehensive energy system caused by photovoltaic output and load prediction deviation when the electric-thermal-hydrogen comprehensive energy system of the CVaR is considered in real-time prediction error correction of a day-ahead electricity purchasing plan and load, so that the model is beneficial to exploring the comprehensive energy system optimization operation scheme under the condition of high-proportion renewable energy and flexible electric load access in the future.
Drawings
FIG. 1 is a flow chart of a method of optimizing operation of an electro-thermal-hydrogen integrated energy system taking into account conditional risk value;
FIG. 2 is a graph of electricity prices and photovoltaic output predictions;
FIG. 3 is a graph of predicted values of electrical, thermal, and hydrogen loads.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
The invention is further described below with reference to the drawings and specific examples, which are not intended to be limiting.
Examples:
an optimized operation method of an electric-thermal-hydrogen comprehensive energy system considering conditional risk value, which comprises the following steps:
step 1, acquiring economic parameters, technical parameters and output data of a comprehensive energy system;
step 2, establishing an objective function of a two-stage optimization operation model of the CVaR comprehensive energy system according to economic parameters, technical parameters and output data of the comprehensive energy system;
and 3, restraining an objective function of the two-stage optimization operation model of the comprehensive energy system of the CVaR by using a constraint condition to obtain the minimum total cost of the comprehensive energy system, so that the operation of the comprehensive energy system is optimized.
Step 1, except for obtaining economic parameters, technical parameters and output data of different load scenes of the comprehensive energy system, determining a scheduling time range of a model and probabilities of different photovoltaic and load output scenes.
In the embodiment, the comprehensive energy system comprises a photovoltaic cell panel, a battery energy storage, a hydrogen fuel cell, an electrolytic tank and a heat pump;
the economic parameters of the comprehensive energy system comprise the operation and maintenance cost and unit investment cost of a photovoltaic cell panel, the operation and maintenance cost and unit investment cost of battery energy storage, the operation and maintenance cost and unit investment cost of a hydrogen fuel cell, the operation and maintenance cost and unit investment cost of a heat pump, and the operation and maintenance cost and unit investment cost of an electrolytic tank;
technical parameters of the comprehensive energy system comprise the service life of a photovoltaic cell panel, the charge and discharge efficiency of battery energy storage, the heat energy supply efficiency of a hydrogen fuel cell, the heat energy supply efficiency of a heat pump and the service life of an electrolytic tank.
In this embodiment, the objective function of the two-stage optimization operation model of the comprehensive energy system of CVaR is expressed as:
min(C day-ahead +C expected-real-time +ξC CVaR ) Equation 1;
wherein C is day-ahead Representing the day-ahead scheduling cost of the system, C expected-real-time Representing a real-time scheduling cost expected value of the system, and xi represents a risk level and C CVaR Representing the risk cost of the system;
C day-ahead expressed as:
wherein t represents a time index; Λ represents a scheduling time range;representing price of electricity purchase to upper power grid；/>The power purchasing power of the upper power grid in the day-ahead dispatching stage is represented; c s A marginal power generation cost quotation of the photovoltaic unit is represented; />Representing the photovoltaic power consumed by the energy system;
C expected-real-time expressed as:
wherein w represents indexes of different photovoltaic and load scenes; omega represents a scene set;representing the probability of occurrence of the scene w; />And->Respectively representing the additional purchase of electric power and the sale of redundant electric power to an upper power grid in a real-time dispatching stage; />Andrespectively representing the electricity purchase price and the electricity selling price of the electric power market; />And->Respectively representing the actual power of the photovoltaic and the abandoned light power; />Load reduction amounts respectively representing an electric load, a hydrogen load, and a thermal load; gamma ray P ，γ H ，γ T Penalty costs for unit reduction of electric load, thermal load, and hydrogen load are expressed, respectively; /> Respectively representing the electric power consumed by the electrolytic tank and the heat pump; />Represents the electric power consumed by the hydrogen fuel cell; />Representing the charging power of the energy storage battery; />Indicating the amount of hydrogen gas input into the hydrogen storage device; c elec 、c HP 、c HFC 、c BS And c HS Respectively representing the unit operation maintenance cost of the electrolytic tank, the heat pump, the hydrogen fuel cell and the battery energy storage and hydrogen storage device;
C CVaR expressed as:
in the method, in the process of the invention,represents VaR; η (eta) w Representing higher than +.>And β is the confidence level.
The objective function of the model is the sum of three parts of the day-ahead scheduling cost, the real-time scheduling cost expected value and the product of the risk level and the risk cost of the system, and a calculation formula is shown as formula 1.
In this embodiment, the constraint conditions include a day-ahead schedule constraint, a real-time schedule constraint, and a CVaR constraint.
In this embodiment, the day-ahead scheduling constraints include a power balance constraint, a purchase power constraint, a device operation constraint, and an energy storage constraint;
the power balance constraint, expressed as:
in the method, in the process of the invention,represents the discharge power of the hydrogen fuel cell, +.>And->Respectively representing the charging power and the discharging power of the battery energy storage; />Indicating the hydrogen production power consumption of the electrolytic cell; />Representing heat pump power consumption; /> Predicted values representing the electric load, the thermal load, and the hydrogen load, respectively; />And->Representing maximum thermal power output of the hydrogen fuel cell and the heat pump, respectively; />Indicating hydrogen production amount of the electrolytic tank per hour; />Represents the hydrogen consumption per hour of the hydrogen fuel cell;and->Respectively representing the hydrogen output and the hydrogen input of the hydrogen storage device per hour;
purchase power constraint, expressed as:
in the method, in the process of the invention,representing the maximum transmission power of the line;
device operation constraints, expressed as:
wherein H is H Represents the high heating value of hydrogen;representing the maximum input power of the electrolyzer; />Representing a photovoltaic power prediction value; mu (mu) HP Representing the efficiency of the heat pump in converting electrical energy into thermal energy; mu (mu) HFC,P Sum mu HFC,T Respectively representing the efficiency of the hydrogen fuel cell in converting hydrogen energy into electric energy and heat energy; />Represents the maximum electric power that the hydrogen fuel cell can output; epsilon elec Indicating hydrogen production efficiency of the electrolytic cell; />Representing heat pump capacity;
energy storage constraints, expressed as:
in the method, in the process of the invention,representing the energy stored in the energy storage device at time t; />And->Respectively representing the charging power and the discharging power of the energy storage device; sigma (sigma) EES Representing a self-discharge rate of the energy storage device; />And->Respectively representing the charging efficiency and the discharging efficiency of the energy storage device; e (E) EES,max Representing the rated capacity of the energy storage device; />And->Respectively representing the maximum charge/discharge power of the energy storage device; Δt represents a unit time interval, set herein as one hour.
And when the scheduling time range lambda is 0-24. Real-time scheduling cost expectation value C of system in formula 1 expected-real-time The penalty cost of the purchase plan change in each scene, the compensation cost of the light rejection and the load loss and the operation and maintenance cost of each device are related.
CVaR represents the risk cost of the system, the risk cost can measure the fluctuation level of the dispatching cost of the electric-thermal-hydrogen comprehensive energy system caused by inaccurate photovoltaic and load prediction, and the system risk cost can be calculated by a linear model under the confidence level beta, and a calculation formula is shown as formula 4.
The constraint condition first part is a day-ahead scheduling constraint. In order to reduce the day-ahead dispatching cost as much as possible, in the actual operation of the power system, the current purchase plan of the upper power grid is determined in the day-ahead dispatching stage, and the photovoltaic output condition is estimated preliminarily. The day-ahead scheduling constraint in the model mainly explains the coupling relation among electric energy, heat energy and hydrogen energy in the comprehensive energy system, and simultaneously constrains each device to stably operate in a safety range, and mainly comprises power balance constraint, electricity purchasing power constraint and device operation constraint. Each constraint formula is shown in formula 5 to formula 8, wherein formula 8 represents the hydrogen storage device operation constraint and the battery energy storage operation constraint as an energy storage constraint.
In the embodiment, the real-time scheduling constraint comprises a power balance constraint, a power purchase constraint, a load loss constraint and a device operation constraint;
the power balance constraint, expressed as:
in the method, in the process of the invention,representing grid-connected photovoltaic power; />And->Respectively representing the energy storage charging power and the discharging power of the battery; />And->Representing the actual values of the electrical load, the thermal load and the hydrogen load, respectively; />Represents the heat generation power of the hydrogen fuel cell; />Representing heat pump heat generation power; />Indicating hydrogen production amount of the electrolytic tank per hour; />Represents the hydrogen consumption per hour of the hydrogen fuel cell; />And->Respectively representing the hydrogen output and the hydrogen input of the hydrogen storage device per hour; />And->The cut-off values of the electric load, the thermal load, and the hydrogen load are respectively represented;
purchase power constraint, expressed as:
load loss constraint, expressed as:
in the method, in the process of the invention,representing the power transmission limits of the transmission line.
The constraint condition second part is a real-time scheduling constraint. The real-time balance of power supply and demand is a necessary condition for guaranteeing the stable operation of a power system, and the power balance constraint, the power purchase power constraint, the load loss constraint and the equipment operation constraint are mainly considered in the part, wherein the equipment constraint is the same as the equipment constraint of the formulas 7 to 8 in the scheduling stage before date, and the rest constraint formulas are shown in the formulas 9 to 11.
In this embodiment, the CVaR constraint is expressed as:
in the method, in the process of the invention,and->Respectively representing the electricity purchasing price and the electricity selling price of the upper power grid; gamma ray e Indicating the cost reduction of the electrical load.
In this embodiment, the method further includes step 4;
and 4, calculating a standard deviation and a weighted average value according to the risk cost, and obtaining the influence of the risk cost on the comprehensive energy system operator according to the standard deviation and the weighted average value.
The optimization model constructed by the invention is a mixed integer linear programming model, is solved by adopting a Gurobi solver based on a Spyder platform, and analyzes the variation trend of the extremely bad, standard deviation, weighted average and risk cost of the system scheduling cost under different risk levels and different light load output scenes.
And (3) experimental verification:
the scheduling time range adopted by the embodiment is 24 hours, the time interval is 1 hour, the predicted values of electricity price and photovoltaic output are shown in fig. 2, reference numeral 1 represents the predicted value of photovoltaic output, and reference numeral 2 represents the predicted value of electricity price; the predicted value of the load is shown in fig. 3, where reference numeral 3 in fig. 3 represents an electric load, reference numeral 4 represents a thermal load, and reference numeral 5 represents a hydrogen load. Regarding actual values of the photovoltaic and the load, the actual photovoltaic output is set to be 1.2, 1.1, 1.0, 0.9 and 0.8 times of the predicted output, the corresponding probabilities are respectively 0.1, 0.2, 0.4, 0.2 and 0.1, the actual load values are respectively 1.05,1.0 and 0.95 times of the predicted load values, and the corresponding probabilities are respectively 0.25, 0.5 and 0.25; with this, 135 different wind load output scenarios can be generated.
Regarding parameters of the device, assuming that the maximum charge and discharge electric power of the battery is 0.95MW, the maximum discharge power is 1.25MW, and the rated capacity of the stored energy of the battery is 5MWh; the hydrogen storage loss is negligible, and the maximum hydrogen inlet/outlet amount is 12.5kg/h. The remaining parameter settings are shown in table 1.
TABLE 1 principal parameters of electric-thermal-Hydrogen comprehensive energy System
And calling a Gurobi solver to solve the modeled model to obtain statistical data of the dispatching cost of the comprehensive energy system and corresponding risk cost under 135 actual wind load output scenes when the risk levels are 0, 1 and 5 respectively, as shown in a table 2.
Table 2 statistics of system scheduling costs for 135 actual photovoltaic and load scenarios, and risk costs for different risk levels
As can be seen from the data in table 2, the total energy system scheduling cost in 135 scenarios is extremely poor and standard deviation decreases with increasing risk level, but the weighted average of the system scheduling costs increases instead. For example, when the risk level is 0, the standard deviation and weighted average of the system scheduling costs are 1.18×10, respectively 3 Yuanhe 10.48×10 3 Element, but when the risk level is 5, 1.08X10 respectively 3 Yuanhe 10.57×10 3 And (5) a meta. This is because the risk level represents a degree of importance to the system operator for the risk of fluctuating system dispatch costs, and a higher value indicates that the system operator is more averted the risk, and thus the system operator may tend to select a dispatch strategy that circumvents the risk as the risk level increases. In other words, as the risk level increases, the fluctuation level of the system scheduling cost in the 135 wind load output scenarios decreases, and the risk cost also decreases, but the weighted average of the system scheduling costs increases, resulting in a decrease in the system economy. This phenomenon strongly demonstrates the effectiveness of the optimized operation method of the electro-thermal-hydrogen integrated energy system taking the conditional risk value into consideration as proposed by the present patent.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the different dependent claims and the features described herein may be combined in ways other than as described in the original claims. It is also to be understood that features described in connection with separate embodiments may be used in other described embodiments.
Claims (8)
1. An optimized operation method of an electric-thermal-hydrogen comprehensive energy system considering conditional risk value is characterized by comprising the following steps:
step 1, acquiring economic parameters, technical parameters and output data of a comprehensive energy system;
step 2, establishing an objective function of a two-stage optimization operation model of the CVaR comprehensive energy system according to economic parameters, technical parameters and output data of the comprehensive energy system;
and 3, restraining an objective function of the two-stage optimization operation model of the comprehensive energy system of the CVaR by using a constraint condition to obtain the minimum total cost of the comprehensive energy system, so that the operation of the comprehensive energy system is optimized.
2. The method for optimizing operation of an electro-thermal-hydrogen integrated energy system taking into account conditional risk value of claim 1, wherein the integrated energy system comprises a photovoltaic panel, a battery energy storage, a hydrogen fuel cell, an electrolyzer, and a heat pump;
the economic parameters of the comprehensive energy system comprise the operation and maintenance cost and unit investment cost of a photovoltaic cell panel, the operation and maintenance cost and unit investment cost of battery energy storage, the operation and maintenance cost and unit investment cost of a hydrogen fuel cell, the operation and maintenance cost and unit investment cost of a heat pump, and the operation and maintenance cost and unit investment cost of an electrolytic tank;
technical parameters of the comprehensive energy system comprise the service life of a photovoltaic cell panel, the charge and discharge efficiency of battery energy storage, the heat energy supply efficiency of a hydrogen fuel cell, the heat energy supply efficiency of a heat pump and the service life of an electrolytic tank.
3. The method for optimizing operation of an electric-thermal-hydrogen integrated energy system taking into account conditional risk value according to claim 2, wherein the objective function of the two-stage optimizing operation model of the integrated energy system of CVaR is expressed as:
min(C day-ahead +C expected-real-time +ξC CVaR ) Equation 1;
wherein C is day-ahead Representing the day-ahead scheduling cost of the system, C expected-real-time Representing a real-time scheduling cost expected value of the system, and xi represents a risk level and C CVaR Representation ofRisk cost of the system;
C day-ahead expressed as:
wherein t represents a time index; Λ represents a scheduling time range;representing the price of electricity purchasing to an upper power grid; />The power purchasing power of the upper power grid in the day-ahead dispatching stage is represented; c s A marginal power generation cost quotation of the photovoltaic unit is represented; />Representing the photovoltaic power consumed by the energy system;
C expected-real-time expressed as:
wherein w represents indexes of different photovoltaic and load scenes; omega represents a scene set;representing the probability of occurrence of the scene w;and->Respectively representing the additional purchase of electric power and the sale of redundant electric power to an upper power grid in a real-time dispatching stage; />And->Respectively representing the electricity purchase price and the electricity selling price of the electric power market; />And->Respectively representing the actual power of the photovoltaic and the abandoned light power; />Load reduction amounts respectively representing an electric load, a hydrogen load, and a thermal load; gamma ray P ，γ H ，γ T Penalty costs for unit reduction of electric load, thermal load, and hydrogen load are expressed, respectively; />Respectively representing the electric power consumed by the electrolytic tank and the heat pump; />Represents the electric power consumed by the hydrogen fuel cell; />Representing the charging power of the energy storage battery; />Indicating the amount of hydrogen gas input into the hydrogen storage device; c elec 、c HP 、c HFC 、c BS And c HS Respectively representing the unit operation maintenance cost of the electrolytic tank, the heat pump, the hydrogen fuel cell and the battery energy storage and hydrogen storage device;
C CVaR expressed as:
in the method, in the process of the invention,represents VaR; η (eta) w Representing higher than +.>And β is the confidence level.
4. The method for optimizing operation of an electro-thermal-hydro integrated energy system taking into account conditional risk values of claim 3, wherein the constraints include a day-ahead schedule constraint, a real-time schedule constraint, and a CVaR constraint.
5. The method for optimizing operation of an electro-thermal-hydro integrated energy system taking into account conditional risk value of claim 4, wherein the day-ahead scheduling constraints include power balance constraints, purchase power constraints, equipment operation constraints, and energy storage constraints;
the power balance constraint, expressed as:
in the method, in the process of the invention,represents the discharge power of the hydrogen fuel cell, +.>And->Respectively represent batteriesCharging power and discharging power of the stored energy; />Indicating the hydrogen production power consumption of the electrolytic cell; />Representing heat pump power consumption; /> Predicted values representing the electric load, the thermal load, and the hydrogen load, respectively; />And->Representing maximum thermal power output of the hydrogen fuel cell and the heat pump, respectively; />Indicating hydrogen production amount of the electrolytic tank per hour; />Represents the hydrogen consumption per hour of the hydrogen fuel cell; />And->Respectively representing the hydrogen output and the hydrogen input of the hydrogen storage device per hour;
purchase power constraint, expressed as:
in the method, in the process of the invention,representing the maximum transmission power of the line;
device operation constraints, expressed as:
wherein H is H Represents the high heating value of hydrogen;representing the maximum input power of the electrolyzer; />Representing a photovoltaic power prediction value; mu (mu) HP Representing the efficiency of the heat pump in converting electrical energy into thermal energy; mu (mu) HFC,P Sum mu HFC,T Respectively representing the efficiency of the hydrogen fuel cell in converting hydrogen energy into electric energy and heat energy; />Represents the maximum electric power that the hydrogen fuel cell can output; epsilon elec Indicating hydrogen production efficiency of the electrolytic cell; />Representing heat pump capacity;
energy storage constraints, expressed as:
in the method, in the process of the invention,representing the energy stored in the energy storage device at time t; />And->Respectively representing the charging power and the discharging power of the energy storage device; sigma (sigma) EES Representing a self-discharge rate of the energy storage device; />And->Respectively representing the charging efficiency and the discharging efficiency of the energy storage device; e (E) EES,max Representing the rated capacity of the energy storage device; />And->Respectively representing the maximum charge/discharge power of the energy storage device; Δt represents a unit time interval.
6. The method for optimizing operation of an electric-thermal-hydrogen integrated energy system taking into account conditional risk value according to claim 5, wherein the real-time scheduling constraints include power balance constraints, purchase electric power constraints, off-load constraints, and equipment operation constraints;
the power balance constraint, expressed as:
in the method, in the process of the invention,representing grid-connected photovoltaic power; />And->Respectively representing the energy storage charging power and the discharging power of the battery;and->Representing the actual values of the electrical load, the thermal load and the hydrogen load, respectively; />Represents the heat generation power of the hydrogen fuel cell; />Representing heat pump heat generation power; />Indicating hydrogen production amount of the electrolytic tank per hour; />Represents the hydrogen consumption per hour of the hydrogen fuel cell; />And->Respectively representing the hydrogen output and the hydrogen input of the hydrogen storage device per hour; />Andthe cut-off values of the electric load, the thermal load, and the hydrogen load are respectively represented;
purchase power constraint, expressed as:
load loss constraint, expressed as:
in the method, in the process of the invention,representing the power transmission limits of the transmission line.
7. The method for optimized operation of an electro-thermal-hydro integrated energy system taking into account conditional risk value as defined in claim 6, wherein CVaR constraints are expressed as:
in the method, in the process of the invention,and->Respectively representing the electricity purchasing price and the electricity selling price of the upper power grid; gamma ray e Indicating the cost reduction of the electrical load.
8. The method for optimized operation of an electro-thermal-hydro integrated energy system taking into account conditional risk value as defined in claim 7, said method further comprising step 4;
and 4, calculating a standard deviation and a weighted average value according to the risk cost, and obtaining the influence of the risk cost on the comprehensive energy system operator according to the standard deviation and the weighted average value.
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