CN115906456A - Hydrogen-containing energy IES scheduling optimization model considering response uncertainty of demand side - Google Patents
Hydrogen-containing energy IES scheduling optimization model considering response uncertainty of demand side Download PDFInfo
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Abstract
The invention discloses an IES (energy storage and equipment) scheduling optimization model for hydrogen-containing energy storage considering response uncertainty of a demand side, which specifically comprises the steps of constructing an energy hub mathematical model and a demand response uncertainty model, and constructing a mathematical model of main equipment of a comprehensive energy system coupled with hydrogen energy storage and a scheduling optimization model thereof; the model describes the uncertainty of comprehensive demand response based on a price incentive utilization interval method, establishes an optimized operation model, can obviously reduce the cost of system operation, improves a load curve, and realizes win-win of energy companies and users.
Description
Technical Field
The invention belongs to the technical field of power systems, and relates to an IES (energy storage equipment) scheduling optimization model for hydrogen-containing energy, which considers response uncertainty of a demand side.
Background
In the aspect of utilization of renewable energy, due to the fact that synchronization of power generation characteristics and load characteristics of energy such as wind power and light energy is poor, the problem of consumption of new energy needs to be solved urgently, the problems of power generation and disjunction of power utilization centers exist at the same time, hydrogen energy storage is used as a technology for converting electric power into hydrogen, and has important significance for promoting large-scale and low-carbon development of renewable energy power generation; with the emphasis of China on the problem of power consumption and the adjustment of the price of power on the power grid for power generation of renewable energy sources, hydrogen energy storage becomes a key technology for consuming, abandoning wind and abandoning light, and the hydrogen is produced by electrolyzing water by utilizing the electric energy of the abandoned wind and abandoned light, so that the pollutant emission can be effectively reduced, and the comprehensive benefit of the renewable energy utilization can be improved; based on the method, the application of the hydrogen energy storage technology in the power industry is researched, the future development potential is explored, the large-scale investment construction and scientific development of renewable energy sources are facilitated to be promoted, and the method has great significance in the aspects of improving the wind and light absorption rate of China, reducing the energy environmental pollution, relieving the energy crisis, promoting the change of economic growth modes and the like; meanwhile, the method is beneficial to building a conservation-oriented society and accelerating the transformation of an energy structure.
Disclosure of Invention
The invention aims to provide a hydrogen-containing energy storage IES dispatching optimization model considering the response uncertainty of a demand side, study and analyze a traditional energy hub model and construct an energy coupling matrix of the response of the demand side, construct a mathematical model of main equipment in a comprehensive energy system considering the response uncertainty of the demand side, and finally establish a coupled hydrogen energy storage comprehensive energy system optimizing dispatching model considering the response uncertainty of the demand side.
The technical scheme adopted by the invention is that a hydrogen-containing energy storage IES scheduling optimization model considering the response uncertainty of the demand side specifically comprises the steps of constructing an energy hub mathematical model and a demand response uncertainty model, and constructing a comprehensive energy system main equipment mathematical model coupling hydrogen energy storage and a scheduling optimization model thereof;
the invention is also characterized in that:
the method for constructing the energy junction mathematical model and the demand response uncertainty model specifically comprises the following steps: a basic mathematical model of an energy hub, an uncertainty model considering a demand response expansion mathematical model and a demand response;
the basic mathematical model of the energy hub is specifically as follows: the energy hub model with the basic construction units of the energy conversion equipment, the energy distribution equipment and the energy storage equipment can be integrated and abstracted into an input-output port model, and the coupling relation of the energy storage equipment is transformed and corrected and then is regarded as the energy hub model to be placed at an input port of the EH model for model analysis; let the energy supply input variable of EH be vector P, the energy storage input variable be vector E, the load output variable be vector Z, the energy supply-load coupling matrix be C, and the energy storage-load coupling matrix be the basic mathematical model of EH, which can be expressed as:
in the formula, P represents the multi-form energy input IES of the energy supply equipment, E represents the multi-form energy input of the energy storage equipment, L represents the multi-form load output after coupling conversion, and C and S are connected with the input and output ports of the EH model and are abstract mathematical expressions of the multi-source coupling conversion relation; each C in the matrix C ij The energy supply coupling factor represents the conversion coefficient of the energy supply input of the ith form and the load output of the jth form, and is specifically determined by the characteristics of the energy conversion unit and energy scheduling distribution parameters;
the demand response consideration expanding mathematical model specifically comprises the following steps:
demand side response, namely, a user makes a benign electricity utilization response behavior under the excitation of market price or policy mechanism so as to achieve the purpose of transferring or reducing the total amount of electricity utilization load, the action effect of DR is generally regarded as being equivalent to an EH output port, and the load change amount caused under the action of DR can be described according to the formula:
ΔL＝DH (4)
the EH extension model, which thus accounts for DR, is:
wherein Δ L = [ Δ L = 1 ,ΔL 2 ,...,ΔL n ] T Δ H = [ H ] for load variation due to DR 1 ,H 2 ,...,H n ] T For the load adjustment of the user response, D is the DR coupling matrix, where D ij The DR coupling factor represents the influence on the output of the energy source in the first form when the energy source in the ith form participates in the demand response;
the uncertainty model of demand response specifically includes:
because the types of the load users are excessive, the total demand response amount of the users has high uncertainty, and an interval method is adopted to describe the uncertainty of the demand response based on price incentive;
demand response uncertainty model demand response coefficient lambda based on price incentive DR The incentive price is x, and when the incentive price is 0, the user has a certain response space
But with strong uncertainties; with the increase of the incentive price, the demand response coefficient is increased, the user load is reduced, the randomness of the load is reduced, and the fluctuation amplitude is reduced; when the incentive price reaches a critical point N, a user can ensure that the energy output is not increased, the balance of supply and demand is achieved through load reduction, transfer and substitution, when the incentive price reaches a saturation point Q, the response coefficient of the user demand is maximum, the fluctuation amplitude is approximately negligible, and the maximum incentive intensity of the model is obtained at the moment; when the incentive price is x, the upper and lower bounds of the demand response coefficient are ≥>
And &>
The expression is as follows:
due to the differences in electrical, cold, hot, and air load characteristics, users have different curtailment, diversion, and alternative load response potentials; the uniform distribution describes the uncertainty behavior of the user response under different incentive prices, and the fluctuation of the incentive price time load demand response is recorded as R (x):
assuming that each load in the integrated energy system participating in demand response is N j Incentive price of x j Then the total capacity S participating in the different kinds of user demand responses may be expressed as follows:
the total incentive price of the integrated energy system is C s ：
Wherein
Is the demand response coefficient of the load. S. the j Is the total load of load j;
the method comprises the following steps of constructing a mathematical model of main equipment of a coupling hydrogen energy storage comprehensive energy system, wherein the mathematical model comprises a wind turbine generator set mathematical model, a photovoltaic cell mathematical model, a hydrogen energy storage system mathematical model, a CHP (hydrogen-gas boiler) set mathematical model and a gas boiler mathematical model;
the hydrogen energy storage system mainly comprises the following three parts, namely an electrolytic cell mathematical model, a fuel cell mathematical model and a hydrogen storage tank mathematical model, wherein the mathematical models are respectively as follows:
mathematical model of the electrolyzer:
using an alkaline solution as an electrolytic bath model solution, wherein the voltage equation is as follows:
in the formula, r is ohmic resistance, and A is electrode area;
the hydrogen generated by the electrolytic cell in the delta t period is n, mol; the hydrogen production per unit time of each small electric room is n s ，mol；
Power in the hydrogen production process is P elec ；
P elec ＝N el I el V el (15)
In the formula, N el Indicates the number of cells, V el Indicating the efficiency of hydrogen generation;
fuel cell mathematical model:
the fuel cell can convert hydrogen into electric energy, and the output electric power equation is as follows:
P HDC (t)＝η HDC P H2,HDC (t) (16)
in the formula: p H2,HDC (t) inputting hydrogen energy of the hydrogen fuel cell at time t; p HDC (t) the electrical energy output by the hydrogen fuel cell at time t; eta HDC Is the hydrogen-to-electricity conversion efficiency of the hydrogen fuel cell;
the mathematical model of the hydrogen storage tank is as follows:
the equation of state of the gas is expressed by adopting an R-K equation, and the equation can accurately describe the relationship among the temperature, the pressure and the volume in the hydrogen storage tank:
in the formula, a and b are called physical property constants and are related to the temperature and the pressure when a substance is in a critical state; r represents a gas constant;
in the formula, T c And P c The critical temperature and the critical pressure are sequentially called;
the photovoltaic power generation system mainly comprises a photovoltaic cell, a PN junction in the photovoltaic cell is illuminated to generate electromotive force, solar energy is converted into electric energy, and an equivalent model of the electric energy is as follows:
in the formula, P STC Represents the maximum output power of the photovoltaic cell under standard test conditions; p pv (t) represents the actual output power of the photovoltaic cell at time t; g C (t) represents the actual illumination intensity of the photovoltaic cell at time t; k is a radical of p Represents the power temperature coefficient; t is C (t) represents the actual temperature of the surface of the photovoltaic panel battery at the time t;
the wind power generation set output of the wind power mathematical model is related to the wind speed, and the function expressions of the wind power generation set output and the wind speed can be expressed as follows:
in the formula, P wtN Rated power of the fan, kW; v. of C 、v R 、v F The wind speed, the rated wind speed and the cut-off wind speed are cut off for the fan, and the cut-off wind speed is m/s;
the CHP unit mathematical model is core energy flow coupling equipment in the comprehensive energy system, and the mathematical model is as follows:
in the formula, P CHPh And P CHPe The CHP unit outputs thermal power and electric power in a period t; c oph And η P The heating coefficient and the flue gas recovery rate, eta of the bromine refrigerator respectively W And η S Respectively the generating efficiency and the heat dissipation loss of the micro-gas turbineLoss rate, P CHPg Is the gas consumption power H of the CHP unit in the period of t L Is the heat value of natural gas;
the mathematical model of the gas boiler is as follows: the gas boiler can convert natural gas into heat energy to supply heat load, and the mathematical model is as follows:
P GBh ＝η GB P GBg (23)
in the formula, P GBh And P GBg Respectively outputting thermal power and gas consumption power of the gas boiler in the time period t; eta GB The heat production efficiency of the gas boiler is obtained;
the optimization scheduling model of the comprehensive energy system specifically comprises the following steps: the IES considers the uncertainty of demand response during optimized scheduling, and establishes a hydrogen energy storage coupled comprehensive energy system optimized scheduling model considering the uncertainty of demand side response by taking the minimum total scheduling cost of the system as a target, wherein the optimization scheduling model comprises the following specific steps;
the operation target of the integrated energy system for coupling hydrogen energy storage considering response uncertainty of a demand side is to reasonably schedule controllable load to enable the economic cost of daily operation of the system to be the lowest according to a distributed output prediction curve of the integrated energy system in one day in the future under the condition of meeting the constraint condition, namely the target function is as follows:
min C IES ＝C DG +C grid +C CHP +C HST +C GB +C H2 +C load (24)
in the formula, C IES The total operation cost of the comprehensive energy system is obtained; c DG Cost for distributed power supply operation; c grid The cost of purchasing electricity from the power grid; c CHP The operating cost of the gas turbine; c GB The operating cost of the gas boiler; c HST The cost of the heat storage tank is low; c H2 The operating cost of the hydrogen energy storage system; c load Total compensation cost for controllable load;
operating cost of the distributed power supply:
in the formula, T is an operation period; k is a radical of formula w 、k pv Respectively representing the running cost coefficients of a fan and a photovoltaic; p W (t)、P PV (t) output powers of the fan and the photovoltaic in a period t respectively;
the electricity purchasing cost of the power grid is as follows:
in the formula, k buy The time-of-use electricity price of the power grid is obtained; p grid (t) the interactive power with the power grid in a period t;
the operating cost of the gas boiler and the heat storage tank is as follows:
in the formula, k GB 、k HST The running cost coefficients of the gas boiler and the heat storage tank are respectively; p is GB (t)、P HST (t) the thermal power and the heat storage tank power of the gas boiler at the time period t are respectively;
compensation cost of controllable load:
C load ＝C shift +C tran +C cut (28)
the operating cost of the hydrogen energy storage system is as follows:
in the formula, k buy The time-of-use electricity price of the power grid at the moment t;
CHP unit running cost:
the CHP unit operation cost has a relation with its heat supply load and generated energy, and its cost is:
in the formula, a 0 ,a 1 ,a 2 ,a 3 ,a 4 ,a 5 Respectively, the cost coefficients; p is chp 、H chp The CHP unit is used for generating electricity and heat respectively;
the constraints of the objective function are as follows:
the power supply side output end in the power supply side power balance constraint comprises: wind-solar power generation, power grid side electricity purchasing and selling, heat generation of a heat source and gas generation of a gas source, and therefore, a power balance equality constraint matrix at a power supply side is expressed as follows:
the load side power balance constraint is that because a user participates in demand response, the terminal load not only considers the electric cold and hot basic load, but also considers adjustable loads including reducible, transferable and interruptible electric loads and heat loads; thus, the terminal load matrix is represented as:
in the formula (I), the compound is shown in the specification,
and &>
Cold load, heat load, and gas load, respectively, of the IES over time period t;
and &>
The comprehensive energy system can reduce the load, can transfer the load and can interrupt the response quantity of the load demand side;
and (3) constraint of unit equipment:
in the formula (I), the compound is shown in the specification,
and &>
The upper limit and the lower limit of the electrical output and the thermal output of the CHP unit are respectively set; />
The upper and lower limits of the input power of the gas boiler; />
Respectively inputs upper and lower limits of power for the electrolyzer reactor, and in addition, the pressure is greater than or equal to>
The upper limit and the lower limit of the climbing slope of the CHP unit, the gas boiler and the electrolytic cell are respectively set;
and (3) restraining a hydrogen energy storage system:
the volumes of the electrolytic cell, the fuel cell and the hydrogen storage tank respectively determine the charging power, the discharging power and the capacity of the hydrogen storage energy;
in the formula (I), the compound is shown in the specification,
respectively the minimum and maximum capacities of the hydrogen storage tank; p Emax 、P FCmax The upper limits of the electrolyzer power and the fuel cell discharge power respectively; />
Is a binary variable, which means that the electrolyzer and the fuel cell are operated at the same time by at most one item, P E (t) is the output power of the electrolytic cell at time t; v (t) and V (t-1) are the hydrogen storage volumes of the hydrogen storage tank at the time t and the time t-1 respectively; eta h 、η fc The efficiency of the electrolyzer and the fuel cell, respectively; />
Is the heating value constant of hydrogen.
The invention has the following benefits:
the optimization scheduling model of the coupling hydrogen energy storage comprehensive energy system considering the response uncertainty of the demand side is established, the uncertainty of the comprehensive demand response is described based on a price incentive utilization interval method, the optimization operation model is established, the system operation cost can be obviously reduced, the load curve is improved, and the win-win situation of energy companies and users is realized.
Drawings
FIG. 1 is a diagram of an energy hub architecture for an integrated energy system coupled with hydrogen energy storage according to the present invention;
FIG. 2 is a model diagram of the scheduling optimization of the integrated energy system of coupling hydrogen energy storage with uncertainty of demand side response constructed by the present invention;
FIG. 3 is a graph of cold-heat-electricity load and temperature in data validation according to the present invention;
FIG. 4 is a graph showing the output of typical solar photovoltaic and wind power in the data verification of the present invention;
FIG. 5 is a graph of the system throughput and load conditions in the data validation of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention provides a hydrogen-containing energy IES scheduling optimization model considering response uncertainty of a demand side, as shown in fig. 1 and 2, specifically as follows:
1. the construction of the energy hub mathematical model and the demand response expansion and uncertainty model means that the energy hub has the functions of transfer, conversion and storage among multi-energy flow carriers such as gas, heat, electricity and the like, and is an important embodiment of the flexibility of the comprehensive energy system. With the common adoption of the electrified heating technology, a regional power distribution network and a regional heating network are closely coupled through an energy hub with combined heat and power supply, so that the complementation of electric energy and heat energy is realized, and the heat and power multi-energy flow synergistic consumption of renewable energy power is promoted; traditional demand responses include price-type and incentive-type response strategies; with the proposal of the energy Internet, various types of energy users can participate in demand response as flexible loads, the alternative demand response characterized by coupling complementation between energy sources increases the adjustable range of user load, and with excessive types of load users, the demand response of the users has high uncertainty, if the problem is ignored, the model precision of system optimization scheduling is inevitably caused, and the system scheduling result has deviation; therefore, uncertainty factors of the flexible load are considered and a model is established on the basis of traditional demand side response, the problem that scheduling deviation occurs due to large-scale flexible load access can be solved, a load curve can be stabilized, and economic operation of an energy system is promoted;
the basic mathematical model of the energy junction is specifically as follows: energy Hub (EH) models with Energy conversion equipment, energy distribution equipment and Energy storage equipment as basic constituent units can be integrated and abstracted into input-output port models, and it is noted that Energy storage equipment can be usually placed at an input port of a model and used as an Energy supply device, and can also be placed at an output port of the model and used as an Energy storage load; according to the method, the coupling relation of the energy storage equipment is converted and corrected to a certain extent and then is regarded as the energy storage equipment to be placed at an input port of an EH (electric-hydraulic) model for model analysis; let the energy supply input variable of EH be vector P, the energy storage input variable be vector E, the load output variable be vector Z, the energy supply-load coupling matrix be C, and the energy storage-load coupling matrix be the basic mathematical model of EH, which can be expressed as:
in the formula, P represents the multi-form energy input IES of the energy supply equipment, E represents the multi-form energy input of the energy storage equipment, L represents the multi-form load output after coupling conversion, and C and S are connected with the input and output ports of the EH model and are abstract mathematical expressions of the multi-source coupling conversion relation; each C in the matrix C ij The energy supply coupling factor represents the conversion coefficient of the energy supply input of the ith form and the load output of the jth form, and is specifically determined by the characteristics of the energy conversion unit and energy scheduling distribution parameters;
the expanding mathematical model considering the demand response specifically comprises the following steps:
the basic model of the energy hub only refers to energy conversion, distribution and storage units of the comprehensive energy system, and obviously has model limitation of practical application; considering that demand side response and new energy grid connection in the multi-energy flow coupling comprehensive energy system are popularized and highly coupled to participate in system operation, the units have important practical significance in the expansion of the EH model; the EH model demand response expansion analysis is as follows: demand side response, namely, a user makes a benign electricity utilization response behavior under the excitation of market price or policy mechanism so as to achieve the purpose of transferring or reducing the total amount of electricity utilization load, the action effect of DR is generally regarded as being equivalent to an EH output port, and the load change amount caused under the action of DR can be described according to the formula:
ΔL＝DH (4)
the EH extension model to account for DR is thus:
wherein Δ L = [ Δ L = 1 ,ΔL 2 ,...,ΔL n ] T For the load change amount due to DR,. DELTA.H = [ H ] 1 ,H 2 ,...,H n ] T For the load adjustment of the user response, D is the DR coupling matrix, where D ij The DR coupling factor represents the influence on the output of the energy source in the first form when the energy source in the ith form participates in the demand response;
the uncertainty model of demand response specifically includes:
the total amount of demand response by the consumers has a high degree of uncertainty due to the overload of consumer types. However, in certain cases, the randomness of the user's participation in the response decreases as the incentive price increases and the ability of the user to participate in the response increases as the incentive price increases, and vice versa. It can be seen that the incentive price controls two characteristic quantities, namely the intensity and uncertainty of the user demand response, and therefore determines the level of the user demand response. At present, the main methods for solving the problem of uncertainty of demand response include a probabilistic method, a fuzzy method, an interval method and a robust method. At present, the research on the demand response of the comprehensive energy system is still in a starting stage, and accurate and large amount of statistical data cannot be obtained. Compared with the first three methods, the interval method needs less data, does not need an accurate probability distribution model, can optimize the result of the objective function interval by only solving the upper boundary and the lower boundary, and accords with the actual situation. The influence of uncertainty parameters on the system can be highlighted, so that the uncertainty of demand response based on price incentive is described by using an interval method;
demand response uncertainty model demand response coefficient lambda based on price incentive DR The incentive price is x, and when the incentive price is 0, the user has a certain response space
But with strong uncertainties; with the increase of the incentive price, the demand response coefficient is increased, the user load is reduced, the randomness of the load is reduced, and the fluctuation amplitude is reduced; when the incentive price reaches the critical point N, the userThe energy output can be ensured not to be increased, the balance of supply and demand can be achieved through load reduction, transfer and substitution, when the incentive price reaches a saturation point Q, the response coefficient of the user demand is maximum, the fluctuation amplitude is approximately negligible, and the maximum incentive intensity of the model is obtained at the moment; when the incentive price is x, the upper and lower bounds of the demand response coefficient are ÷ in each case>
And &>
The expression is as follows:
due to the differences in electrical, cold, hot, and air load characteristics, users have different curtailment, diversion, and alternative load response potentials; the uniform distribution describes the uncertainty behavior of the user response under different incentive prices, and the fluctuation of the incentive price as the time load demand response is recorded as R (x):
assuming that each load in the integrated energy system participating in demand response is N j Incentive price of x j Then the total capacity S participating in the different kinds of user demand responses may be expressed as follows:
the total incentive price of the integrated energy system is C s ：
Wherein
Is the demand response coefficient of the load. S j Is the total load of load j;
2. establishing a mathematical model of main equipment of a coupling hydrogen energy storage comprehensive energy system, wherein the mathematical model comprises a wind turbine generator set mathematical model, a photovoltaic cell mathematical model, a hydrogen energy storage system mathematical model, a CHP (hydrogen storage process) set mathematical model and a gas boiler mathematical model;
the hydrogen energy storage system mainly comprises the following three parts, namely an electrolytic cell mathematical model, a fuel cell mathematical model and a hydrogen storage tank mathematical model, wherein the mathematical models are respectively as follows:
mathematical model of the cell:
using an alkaline solution as an electrolytic bath model solution, wherein the voltage equation is as follows:
in the formula, r is ohmic resistance, and A is the electrode area;
the hydrogen generated by the electrolytic cell in the delta t period is n, mol; the hydrogen production per unit time of each small electric room is n s ，mol；
The power in the process of generating hydrogen is P elec ；
P elec ＝N el I el V el (15)
In the formula, N el Indicating small electricityNumber of chambers, V el Indicating the efficiency of hydrogen generation;
fuel cell mathematical model:
the fuel cell can convert hydrogen into electric energy, and the output electric power equation is as follows:
P HDC (t)＝η HDC P H2,HDC (t) (16)
in the formula: p H2,HDC (t) inputting hydrogen energy of the hydrogen fuel cell at time t; p HDC (t) the electrical energy output by the hydrogen fuel cell at time t; eta HDC Is the hydrogen-to-electricity conversion efficiency of the hydrogen fuel cell;
the mathematical model of the hydrogen storage tank is as follows:
the state equation of the gas is expressed by adopting an R-K equation, and the equation can accurately describe the relationship among the temperature, the pressure and the volume in the hydrogen storage tank:
in the formula, a and b are called physical property constants and are related to the temperature and the pressure when a substance is in a critical state; r represents the gas constant, with a value of: 8.314J/mol.K;
in the formula, T c And P c In turn called critical temperature, critical pressure, when this equation is used to describe the state of hydrogen, T c ＝33.3K，P c ＝12.80atm；
The photovoltaic power generation system is mainly composed of photovoltaic cells, PN junctions in the photovoltaic cells are illuminated to generate electromotive force, solar energy is converted into electric energy, and an equivalent model of the electric energy is as follows:
in the formula, P STC Shows the standard test conditions (the solar radiation intensity is 1000W/m) 2 25 ℃ maximum output power of the photovoltaic cell; p pv (t) represents the actual output power of the photovoltaic cell at time t; g C (t) represents the actual illumination intensity of the photovoltaic cell at time t; k is a radical of p Representing the power temperature coefficient, and taking 0.00485/DEG C; t is C (t) represents the actual temperature of the surface of the photovoltaic panel battery at the moment t;
the wind power generation set output of the wind power mathematical model is related to the wind speed, and the functional expressions of the wind power generation set output and the wind speed can be expressed as follows:
in the formula, P wtN Rated power of the fan, kW; v. of C 、v R 、v F The wind speed, the rated wind speed and the cut-off wind speed are cut off for the fan, and the cut-off wind speed is m/s;
the CHP unit mathematical model is core energy flow coupling equipment in a comprehensive energy system, and the mathematical model is as follows:
in the formula, P CHPh And P CHPe The CHP unit outputs thermal power and electric power in a period t; c oph And η P The heating coefficient and the flue gas recovery rate, eta of the bromine refrigerator respectively W And η S Respectively, the generating efficiency and the heat dissipation loss rate P of the micro-combustion engine CHPg Is the gas consumption power of the CHP unit in the period of t, H L Taking 9.7 kW.h/m for the heat value of the natural gas 3 ；
The mathematical model of the gas boiler is specifically as follows: the gas boiler can convert natural gas into heat energy to supply heat load, and the mathematical model is as follows:
P GBh ＝η GB P GBg (23)
in the formula, P GBh And P GBg The output thermal power and the gas consumption power of the gas boiler at the time period t are respectively; eta GB The heat production efficiency of the gas boiler;
3. the construction of the integrated energy system scheduling optimization model for coupling hydrogen energy storage is to perform hydrogen production and storage on redundant wind and light resources through water electrolysis by taking the lowest system operation cost as a target, so that energy flow among new energy, electric energy and hydrogen energy is realized, the system economy is improved, and the utilization efficiency of the wind and light resources is improved; the comprehensive energy system optimization scheduling model specifically comprises the following steps: the IES considers the uncertainty of demand response when carrying out optimized scheduling and takes the minimum total scheduling cost of the system as a target; meanwhile, considering power balance constraint at the power supply side of the system, power balance constraint at the load side, upper and lower constraints on generated output, constraint of a hydrogen energy storage system and the like, and establishing a comprehensive energy system optimization scheduling model for coupling hydrogen energy storage considering response uncertainty at the demand side, wherein the method comprises the following specific steps;
an objective function:
the operational objectives of the integrated energy system for coupling hydrogen energy storage taking into account the uncertainty of the demand side response are: according to output prediction curves of a distributed power supply, an electric load, a heat load and the like in one day of the future of the comprehensive energy system, the controllable load is reasonably scheduled to enable the economic cost of daily operation of the system to be the lowest under the condition that the constraint conditions are met; i.e. the objective function is:
min C IES ＝C DG +C grid +C CHP +C HST +C GB +C H2 +C load (24)
in the formula, C IES The total operation cost of the comprehensive energy system is obtained; c DG Cost for distributed power operation; c grid The cost of purchasing electricity from the power grid; c CHP The operating cost of the gas turbine; c GB The operating cost of the gas boiler; c HST The cost of the heat storage tank is low; c H2 The operating cost of the hydrogen energy storage system; c load Total compensation cost for controllable load;
operating cost of the distributed power supply:
in the formula, T is an operation period; k is a radical of w 、k pv Respectively representing the running cost coefficients of a fan and a photovoltaic; p W (t)、P PV (t) the output power of the fan and the photovoltaic are respectively in the period of t;
the electricity purchasing cost of the power grid:
in the formula, k buy The time-of-use electricity price of the power grid is obtained; p grid (t) the interactive power with the power grid in a period t;
the operating cost of the gas boiler and the heat storage tank is as follows:
in the formula, k GB 、k HST The running cost coefficients of the gas boiler and the heat storage tank are respectively; p GB (t)、P HST (t) the thermal power and the heat storage tank power of the gas boiler at the time period t are respectively;
compensation cost of controllable load:
C load ＝C shift +C tran +C cut (28)
operating cost of the hydrogen energy storage system:
in the formula, k buy The time-of-use electricity price of the power grid at the moment t;
CHP unit running cost:
the CHP unit operation cost has a relation with its heat supply load and generated energy, and its cost is:
in the formula, a 0 ,a 1 ,a 2 ,a 3 ,a 4 ,a 5 Respectively are cost coefficients; p is chp 、H chp The CHP unit is used for generating electricity and heat respectively;
the constraints of the objective function are as follows:
the power side output end in the power side power balance constraint comprises: wind-solar power generation, power grid side electricity purchasing and selling, heat source heat production and gas source gas production, and therefore, a power source side power balance equality constraint matrix is expressed as follows:
the load side power balance constraint is that because a user participates in demand response, the terminal load not only considers the electric cold and hot basic load, but also considers adjustable loads including reducible, transferable and interruptible electric loads and heat loads; thus, the terminal load matrix is represented as:
in the formula (I), the compound is shown in the specification,
and &>
Cold load, heat load, and gas load, respectively, of the IES over time period t;
and &>
The comprehensive energy system can reduce the load, can transfer the load and can interrupt the response quantity of the load demand side;
constraint of unit equipment:
in the formula (I), the compound is shown in the specification,
and &>
The upper limit and the lower limit of the electrical output and the thermal output of the CHP unit are respectively set;
the upper and lower limits of the input power of the gas boiler; />
Respectively inputting upper and lower limits of power for the electrolyzer reactor, in addition, combining>
The upper limit and the lower limit of the climbing slope of the CHP unit, the gas boiler and the electrolytic cell are respectively set;
and (3) restraining a hydrogen energy storage system:
the volumes of the electrolytic cell, the fuel cell and the hydrogen storage tank respectively determine the charging power, the discharging power and the capacity of the hydrogen storage energy;
in the formula (I), the compound is shown in the specification,
respectively the minimum and maximum capacities of the hydrogen storage tank; p is Emax 、P FCmax Respectively electrolysis cell power and fuelAn upper limit of battery discharge power; />
Is a binary variable representing that the electrolyzer and the fuel cell are operated at the same time by at most one item, P E (t) is the output power of the electrolytic cell at time t; v (t) and V (t-1) are the hydrogen storage volumes of the hydrogen storage tank at the time t and the time t-1 respectively; eta h 、η fc The efficiency of the electrolyzer and the fuel cell, respectively; />
Is the heating value constant of hydrogen.
And (3) verifying model data:
TABLE 1 demand response coefficient and price subsidy for each time segment
TABLE 2 Hydrogen energy storage configurations
| Device | Electrolytic cell | Hydrogen storage tank | Hydrogen fuel cell |
| Capacity allocation | 122.5kW | 97.45kg | 390kW |
The cold, heat and electricity loads and the temperature of the garden are shown in a graph 3, a small fan is arranged for 500kW, the price of natural gas is 2.76 yuan/cubic meter, and the output of a typical solar photovoltaic and wind power is shown in a graph 4; the internal yield and the load condition of the system are shown in fig. 5, the established hydrogen-containing energy storage park IES can fully utilize the peak clipping and valley filling functions of energy storage equipment, effectively meet the energy demand of users in real time, ensure the reliability of an energy supply network, and reduce the total load demand within a certain range by considering the demand response, reduce the peak-valley difference and make the load curve smooth.
Claims (10)
1. The hydrogen-containing energy storage IES scheduling optimization model considering the response uncertainty of the demand side is characterized by specifically comprising the steps of constructing an energy hub mathematical model and a demand response uncertainty model, and constructing a hydrogen energy storage-coupled comprehensive energy system main equipment mathematical model and a scheduling optimization model thereof.
2. The hydrogen-containing energy storage IES scheduling optimization model considering demand-side response uncertainty according to claim 1, wherein the constructing an energy hub mathematical model and a demand response uncertainty model specifically comprises: the method comprises a basic mathematical model of the energy hub, an expansion mathematical model considering the demand response and an uncertainty model considering the demand response.
3. The hydrogen-bearing IES scheduling optimization model considering uncertainty of demand-side response of claim 2, wherein the basic mathematical model of the energy hub is specifically: the energy hub model with the basic construction units of the energy conversion equipment, the energy distribution equipment and the energy storage equipment can be integrated and abstracted into an input-output port model, and the coupling relation of the energy storage equipment is transformed and corrected and then is regarded as the energy hub model to be placed at an input port of the EH model for model analysis; let the energy supply input variable of EH be vector P, the energy storage input variable be vector E, the load output variable be vector Z, the energy supply-load coupling matrix be C, and the energy storage-load coupling matrix be the basic mathematical model of EH, which can be expressed as:
in the formula, P represents the multi-form energy input IES of the energy supply equipment, E represents the multi-form energy input of the energy storage equipment, L represents the multi-form load output after coupling conversion, and C and S are connected with the input and output ports of the EH model and are abstract mathematical expressions of the multi-source coupling conversion relation; each C in the matrix C ij The energy supply coupling factor represents the conversion coefficient of the energy supply input of the ith form and the load output of the jth form, and is specifically determined by the characteristics of the energy conversion unit and energy scheduling distribution parameters;
the demand response considered expanding mathematical model specifically comprises the following steps:
demand side response, namely, a user makes a benign electricity utilization response behavior under the excitation of market price or policy mechanism so as to achieve the purpose of transferring or reducing the total amount of electricity utilization load, the action effect of DR is generally regarded as being equivalent to an EH output port, and the load change amount caused under the action of DR can be described according to the formula:
ΔL＝DH (4)
the EH extension model to account for DR is thus:
wherein, Δ L =[ΔL 1 ,ΔL 2 ,...,ΔL n ] T For the load change amount due to DR,. DELTA.H = [ H ] 1 ,H 2 ,...,H n ] T For the load adjustment of the user response, D is the DR coupling matrix, where D ij The DR coupling factor represents the influence on the output of the energy source in the first form when the energy source in the ith form participates in the demand response;
the uncertainty model of demand response specifically includes:
because the types of the load users are excessive, the total demand response amount of the users has high uncertainty, and an interval method is adopted to describe the uncertainty of the demand response based on price incentive;
demand response uncertainty model demand response coefficient lambda based on price incentive DR The incentive price is x, and when the incentive price is 0, the user has a certain response space
But with strong uncertainties; with the increase of the incentive price, the demand response coefficient is increased, the user load is reduced, the randomness of the load is reduced, and the fluctuation amplitude is reduced; when the incentive price reaches a critical point N, a user can ensure that the energy output is not increased, the balance of supply and demand is achieved through load reduction, transfer and substitution, when the incentive price reaches a saturation point Q, the response coefficient of the user demand is maximum, the fluctuation amplitude is approximately negligible, and the maximum incentive intensity of the model is obtained at the moment; when the incentive price is x, the upper and lower bounds of the demand response coefficient are ÷ in each case>
And &>
The expression is as follows:
due to the differences in electrical, cold, hot, and air load characteristics, users have different curtailment, diversion, and alternative load response potentials; the uniform distribution describes the uncertainty behavior of the user response under different incentive prices, and the fluctuation of the incentive price time load demand response is recorded as R (x):
assuming that each load in the integrated energy system participating in demand response is N j Incentive price of x j Then the total capacity S participating in the different kinds of user demand responses may be expressed as follows:
the total incentive price of the integrated energy system is C s ：
Wherein
Is the demand response coefficient of the load; s j Is the total load of load j.
4. The IES scheduling optimization model for hydrogen-containing energy storage considering response uncertainty on demand side according to claim 1, wherein the mathematical models of main equipment of the integrated energy system coupled with hydrogen energy storage are constructed and comprise a wind turbine generator set mathematical model, a photovoltaic cell mathematical model, a hydrogen energy storage system mathematical model, a CHP set mathematical model and a gas boiler mathematical model.
5. The hydrogen-containing energy storage IES scheduling optimization model considering demand-side response uncertainty according to claim 4, wherein the hydrogen storage system consists essentially of three parts, namely an electrolyzer mathematical model, a fuel cell mathematical model and a hydrogen storage tank mathematical model, as follows:
mathematical model of the cell:
using an alkaline solution as an electrolytic bath model solution, wherein the voltage equation is as follows:
in the formula, r is ohmic resistance, and A is the electrode area;
the hydrogen generated by the electrolytic cell in the delta t period is n, mol; the hydrogen production per unit time of each small electric room is n s ，mol；
The power in the process of generating hydrogen is P elec ；
P elec ＝N el I el V el (15)
In the formula, N el Indicates the number of cells, V el Indicating the efficiency of hydrogen generation;
fuel cell mathematical model:
the fuel cell can convert hydrogen into electric energy, and the output electric power equation is as follows:
in the formula:
inputting hydrogen energy of the hydrogen fuel cell for t moment; p is HDC (t) the electrical energy output by the hydrogen fuel cell at time t; eta HDC Is the hydrogen-to-electricity conversion efficiency of the hydrogen fuel cell;
the mathematical model of the hydrogen storage tank is as follows:
the state equation of the gas is expressed by adopting an R-K equation, and the equation can accurately describe the relationship among the temperature, the pressure and the volume in the hydrogen storage tank:
in the formula, a and b are called physical property constants and are related to the temperature and the pressure when a substance is in a critical state; r represents a gas constant;
in the formula, T c And P c In turn, the critical temperature and critical pressure.
6. The IES scheduling optimization model for hydrogen-containing energy storage taking account of uncertainty of response at demand side according to claim 4, wherein the photovoltaic power generation system is mainly composed of photovoltaic cells, PN junctions inside the photovoltaic cells are illuminated to generate electromotive force, and an equivalent model for converting solar energy into electric energy is as follows:
in the formula, P STC Represents the maximum output power of the photovoltaic cell under standard test conditions; p pv (t) represents the actual output power of the photovoltaic cell at time t; g C (t) represents the actual illumination intensity of the photovoltaic cell at time t; k is a radical of p Represents the power temperature coefficient; t is C And (t) represents the actual temperature of the surface of the photovoltaic panel battery at the moment t.
7. The hydrogen-containing energy storage IES scheduling optimization model considering the uncertainty of the demand side response of claim 4, wherein the wind turbine generator set output is related to the wind speed in the wind power mathematical model, and the functional expressions of the wind turbine generator set output and the wind speed are as follows:
in the formula, P wtN Rated power of the fan, kW; v. of C 、v R 、v F The cut-off wind speed, the rated wind speed and the cut-off wind speed are m/s for the fan.
8. The hydrogen-bearing IES scheduling optimization model considering the uncertainty of the demand side response according to claim 4, wherein the CHP unit mathematical model is a core power flow coupling device in an integrated energy system, and the mathematical model is as follows:
in the formula, P CHPh And P CHPe The CHP unit outputs thermal power and electric power in a period t; c oph And η P The heating coefficient and the flue gas recovery rate of the bromine refrigerator are respectively eta W And η S Respectively, the generating efficiency and the heat dissipation loss rate P of the micro-combustion engine CHPg Is the gas consumption power H of the CHP unit in the period of t L Is the heating value of natural gas.
9. The hydrogen-containing energy storage IES scheduling optimization model considering uncertainty of demand side response of claim 4, wherein the gas boiler mathematical model is specifically: a gas boiler can convert natural gas into heat energy to supply a heat load, and its mathematical model is as follows:
P GBh ＝η GB P GBg (23)
in the formula, P GBh And P GBg Respectively outputting thermal power and gas consumption power of the gas boiler in the time period t; eta GB The heat production efficiency of the gas boiler.
10. The hydrogen-containing energy storage IES scheduling optimization model considering demand-side response uncertainty according to claim 1, wherein the integrated energy system optimization scheduling model is specifically: the IES considers the uncertainty of demand response during optimized scheduling, and establishes a hydrogen energy storage coupled comprehensive energy system optimized scheduling model considering the response uncertainty of a demand side with the aim of minimizing the total scheduling cost of the system, specifically as follows;
the operation target of the integrated energy system for coupling hydrogen energy storage considering response uncertainty of a demand side is to reasonably schedule controllable load to enable the economic cost of daily operation of the system to be the lowest according to a distributed output prediction curve of the integrated energy system in one day in the future under the condition of meeting the constraint condition, namely the target function is as follows:
in the formula, C IES The total operation cost of the comprehensive energy system is obtained; c DG Cost for distributed power supply operation; c grid The cost of purchasing electricity from the power grid; c CHP The operating cost of the gas turbine; c GB The operating cost of the gas boiler; c HST Cost of heat storage tank; c H2 The operating cost of the hydrogen energy storage system; c load The total compensation cost for the controllable load;
operating cost of the distributed power supply:
in the formula, T is an operation period; k is a radical of w 、k pv Respectively representing the running cost coefficients of a fan and a photovoltaic; p is W (t)、P PV (t) the output power of the fan and the photovoltaic are respectively in the period of t;
the electricity purchasing cost of the power grid:
in the formula, k buy The time-of-use electricity price of the power grid is obtained; p grid (t) the interactive power with the power grid in a period t;
operating costs of the gas boiler and the heat storage tank:
in the formula, k GB 、k HST The running cost coefficients of the gas boiler and the heat storage tank are respectively; p GB (t)、P HST (t) the thermal power and the heat storage tank power of the gas boiler at the time period t are respectively;
compensation cost of controllable load:
C load ＝C shift +C tran +C cut (28)
the operating cost of the hydrogen energy storage system is as follows:
in the formula, k buy The time-of-use electricity price of the power grid at the moment t;
CHP unit running cost:
the CHP unit operation cost has a relation with its heat supply load and generated energy, and its cost is:
in the formula, a 0 ,a 1 ,a 2 ,a 3 ,a 4 ,a 5 Respectively are cost coefficients; p is chp 、H chp The CHP unit is used for generating power and heat output respectively;
the constraints of the objective function are as follows:
the power side output end in the power side power balance constraint comprises: wind-solar power generation, power grid side electricity purchasing and selling, heat generation of a heat source and gas generation of a gas source, and therefore, a power balance equality constraint matrix at a power supply side is expressed as follows:
the load side power balance constraint is that because a user participates in demand response, the terminal load not only considers the electric cold and hot basic load, but also considers adjustable loads including reducible, transferable and interruptible electric loads and heat loads; thus, the terminal load matrix is represented as:
in the formula, P t c,load 、P t h,load And P t g,load Cold load, heat load, and gas load, respectively, of the IES over time period t; p t cut ,P t mov And P t trans The comprehensive energy system can reduce the load, can transfer the load and can interrupt the response quantity of the load demand side;
and (3) constraint of unit equipment:
in the formula (I), the compound is shown in the specification,
and &>
The upper limit and the lower limit of the electrical output and the thermal output of the CHP unit are respectively set; />
The upper and lower limits of the input power of the gas boiler; />
Respectively the upper and lower limits of the input power of the reactor of the electrolytic bath, in addition,
the upper limit and the lower limit of the climbing slope of the CHP unit, the gas boiler and the electrolytic cell are respectively set;
and (3) restraining a hydrogen energy storage system:
the volumes of the electrolytic cell, the fuel cell and the hydrogen storage tank respectively determine the charging power, the discharging power and the capacity of the hydrogen energy storage;
in the formula (I), the compound is shown in the specification,
respectively the minimum and maximum capacities of the hydrogen storage tank; p is Emax 、P FCmax The upper limits of the electrolyzer power and the fuel cell discharge power respectively;/>
is a binary variable, which means that the electrolyzer and the fuel cell are operated at the same time by at most one item, P E (t) is the output power of the electrolytic cell at time t; v (t) and V (t-1) are the hydrogen storage volumes of the hydrogen storage tank at the time t and the time t-1 respectively; eta h 、η fc The efficiency of the electrolyzer and the fuel cell, respectively; />
Is the heating value constant of hydrogen. />
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