CN115293457A - Seasonal hydrogen storage optimization configuration method of comprehensive energy system based on distributed collaborative optimization strategy - Google Patents
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Abstract
The comprehensive energy system seasonal hydrogen storage optimal configuration method based on the distributed collaborative optimization strategy comprises the following steps: step1: establishing a seasonal hydrogen energy storage model; step 2: establishing a master-slave multi-slave game model which takes an energy system operator containing a hydrogen storage system as a leader and takes an energy producer, a load aggregator and an energy storage service provider as followers; and step 3: and providing a distributed equilibrium solving algorithm based on a competition search algorithm and combined mixed integer programming/quadratic programming phase, solving the master-slave game, obtaining a pricing strategy of a system operator, a hydrogen storage system configuration strategy, a user demand strategy and an optimal output strategy of each main body device, and realizing the maximization of the operation benefit of each main body. According to the invention, the hydrogen storage system is configured in the energy system operator, so that the initiative of the energy storage system in the game process is improved, and the effect on reducing the risk of energy supply and demand imbalance is obvious.
Description
Technical Field
The invention relates to the technical field of comprehensive energy system optimization configuration, in particular to a seasonal hydrogen storage optimization configuration method of a comprehensive energy system based on a distributed collaborative optimization strategy.
Background
In recent years, due to the global energy crisis and the increasing problem of climate warming, low-carbon and high-efficiency utilization of renewable energy represented by wind and light is the mainstream of future energy development. The high-proportion grid connection of renewable energy sources is a basic characteristic of a future power system, but the renewable energy sources have the characteristics of randomness, intermittence, seasonal electric quantity imbalance and the like, so that huge challenges are brought to the power balance of the power system. The comprehensive energy system can exert the complementary characteristics of different energy sources, realize the cascade utilization of energy and is beneficial to the consumption of renewable energy power generation. An integrated energy system is generally composed of an energy system operator, an energy producer, an energy storage service provider, and a load aggregator. In the operation process, because the external heat supply network can not purchase a large amount of heat energy, if an energy system operator lacks the regulation and control capability for the balance of the multiple energy supply and demand of the system, the energy source is seriously unbalanced, and certain economic risk exists. In addition, under the background of future large-scale new energy grid connection, the power system also needs to deal with the difficult problem of seasonal mismatching between new energy output and load demand, so how to improve seasonal supply and demand balance of energy and improve the income level of system operators becomes a problem which needs to be solved urgently at present.
The seasonal energy storage technology can complement energy across seasons, so that long-time scale energy transfer is realized, and the consumption of renewable energy is effectively promoted. At present, seasonal energy storage technologies mainly include seasonal heat storage and seasonal hydrogen storage. As hydrogen energy is an important clean energy source, the hydrogen energy has the characteristics of high efficiency, purity and the like, has great application potential in the direction of sustainable transformation of energy sources, and is very suitable for participating in regional comprehensive energy system optimization so as to improve the seasonal imbalance problem of the energy sources and promote the local consumption of renewable energy sources. Therefore, in order to improve seasonal supply and demand balance of energy and income of operators, seasonal hydrogen storage is configured in energy system operators, and the seasonal hydrogen storage is solved under a master-slave game framework, so that renewable energy consumption and system energy balance can be effectively promoted.
Disclosure of Invention
The method is used for improving seasonal supply and demand balance of energy and income of an operator and improving the initiative of the energy system operator in the operation process. The invention provides a comprehensive energy system seasonal hydrogen storage optimization configuration method based on a distributed collaborative optimization strategy, which comprises the steps of firstly providing a seasonal hydrogen storage model to realize long-term storage optimization utilization of electric energy in a cross-season and cross-energy form; then, an energy system operator model containing a seasonal hydrogen storage system is provided, and a master-slave-master game model which takes the model as a leader and takes an energy producer, a load aggregator and an energy storage service provider as followers is provided; then, a distributed equilibrium solving algorithm based on a competitive search algorithm and combined mixed integer programming/quadratic programming phase is provided to solve the master-slave game; and by solving the game equilibrium solution, a pricing strategy of a system operator, a hydrogen storage system configuration strategy, a user demand strategy and an optimal output strategy of each main body device are obtained, and the operation benefit maximization of each main body is realized.
The technical scheme adopted by the invention is as follows:
the comprehensive energy system seasonal hydrogen storage optimal configuration method based on the distributed collaborative optimization strategy comprises the following steps:
step1: establishing a seasonal hydrogen energy storage model, realizing long-term storage and optimized utilization of electric energy in a cross-season and cross-energy form, promoting local consumption of renewable energy and improving the income of an energy system operator;
step 2: establishing a master-slave multi-slave game model which takes an energy system operator containing a hydrogen storage system as a leader and takes an energy producer, a load aggregator and an energy storage service provider as followers,
and 3, step 3: and providing a distributed equilibrium solving algorithm based on a competition search algorithm and combined mixed integer programming/quadratic programming phase, solving the master-slave game, obtaining a pricing strategy of a system operator, a hydrogen storage system configuration strategy, a user demand strategy and an optimal output strategy of each main body device, and realizing the maximization of the operation benefit of each main body.
In the step1, the seasonal hydrogen storage refers to a hydrogen energy storage device which can realize long-time scale hydrogen energy storage and participate in the monthly, seasonal, annual and even trans-annual hydrogen energy regulation process.
In the step1, the long-term storage of electric energy in a cross-season and cross-energy form refers to the conversion of electric energy into a hydrogen energy form to realize long-time scale storage.
In the step1, the energy system operator refers to a main body which exists at the network side of the comprehensive energy system, makes energy prices for main bodies of the source side, the load side and the storage side, and earns income from an energy price difference; the comprehensive energy system is an integrated energy system which realizes the gradient utilization of energy by exerting the complementary characteristics of different energy sources.
In the step1, the seasonal hydrogen storage can only be charged or discharged within a typical day, so that the seasonal hydrogen storage works in different typical days, and daytime hydrogen energy interaction is realized. In addition, the seasonal hydrogen storage should also consider the hydrogen charge and discharge accumulation of the last typical day scene at the initial time of the next typical day scene, and the seasonal hydrogen storage mathematical model is described as follows:
equations (1) -5 describe operating conditions for the seasonal hydrogen store, and equation (1) describes capacity operation for the seasonal hydrogen store at the first typical day and maximum charge-discharge hydrogen constraints for the seasonal hydrogen store.
In formula (1):
the stored energy value of seasonal hydrogen storage at the w typical day t;
respectively storing hydrogen storage power and hydrogen discharge power for the w typical day at the t moment;
hydrogen storage efficiency and hydrogen discharge efficiency, respectively; beta is a LTHS Seasonal hydrogen energy storage rate; q LTHS Capacity values for installation of seasonal hydrogen stores;
respectively are a hydrogen storage state variable and a hydrogen discharge state variable of the w typical day t seasonal hydrogen storage, which are Boolean variables;
is the stored energy value of seasonal hydrogen storage at time t-1 on the 1 st typical day.
Formula (2) illustrates the operating conditions of seasonal hydrogen storage on other typical days: the initial capacity for the next typical day is the accumulation of hydrogen charge and discharge during the season of the last typical day.
In the formula (2): p (w) is the probability of occurrence of a typical day w; w is the maximum typical number of days;
the stored energy value of seasonal hydrogen storage at time 1 on the w-th typical day;
the stored energy value of seasonal hydrogen storage at the 1 st time of the w-1 typical day;
is the 24 th season of the w-1 typical dayA stored energy value of sexual hydrogen;
the storage energy value of seasonal hydrogen storage at the t-1 th time of the w typical day; p (w-1) is the probability of occurrence on the w-1 th typical day.
Equation (3) is plotted to show that the initial capacity of the seasonal hydrogen storage on the first typical day is equal to the hydrogen charge and discharge accumulation of the seasonal hydrogen storage in the last season.
In formula (3):
the stored energy value of seasonal hydrogen storage at time 1 on the 1 st typical day;
the stored energy value of seasonal hydrogen storage at the 1 st time of the W-th typical day;
is the stored value of seasonal hydrogen storage at time 24 on the W-th typical day.
In formulae (4) to (5):
and
the hydrogen storage and release variable of the w typical day seasonal hydrogen storage is a Boolean variable which is used for restricting the seasonal hydrogen storage to be only charged with hydrogen or only discharged with hydrogen on each typical day.
In the step 2, the energy system operator of the hydrogen storage system is configured in the energy system operator, and the hydrogen storage system comprises seasonal hydrogen storage, short-term hydrogen storage, an electrolyzer device and a fuel cell. The short-term hydrogen storage is used for hydrogen energy complementation in one day, the electrolytic cell device is used for electrolyzing water to produce hydrogen, and the fuel cell is used for burning hydrogen to produce electricity.
In the step 2, an energy producer serves as the source side of the system and comprises a wind turbine, a photovoltaic cogeneration unit, a cogeneration unit and gas boiler equipment, the output of each equipment in the main body is optimized according to the electricity purchasing price and the heat purchasing price set by an energy system operator, and electricity and heat energy are sold to the energy system operator;
the load aggregator is used as the load side of the system, comprises three energy loads of electricity, heat and hydrogen, adjusts the user energy consumption condition according to an energy price signal set by an energy system operator, and realizes the minimum overall energy purchasing benefit cost;
the energy storage service provider is composed of electric energy storage and heat energy storage, serves as an energy storage side of the system, and obtains profits by buying/selling electricity and heat energy from the energy system operator according to prices set by the energy system operator.
In the step 2, the primary-secondary game is a game type in which one party acts first and the other party acts later, and the primary-secondary game and the multi-secondary game are primary-secondary game types composed of a leader and a plurality of followers, wherein the leader is a front acting party and the followers are rear acting parties. Firstly, an energy system operator serving as a leader publishes established prices of electricity and heat for purchase and sale to followers, then an energy producer, an energy storage service provider and a load aggregator serve as followers to respectively respond according to the energy prices established by the leader, response results are fed back to the energy producer, and the energy producer optimizes and configures the hydrogen storage system according to response conditions of the followers.
In the step 2, the hydrogen storage system comprises seasonal hydrogen storage, short-term hydrogen storage, an electrolyzer device and a fuel cell. Wherein, the seasonal hydrogen storage model is shown as a formula (1) to a formula (5);
the short-term hydrogen storage model is shown as formula (6).
In formula (6):
the stored energy value of short-term hydrogen storage at the time t of the w-th typical day;
the hydrogen storage power and the hydrogen discharge power of short-term hydrogen storage at the time t of the w typical day are respectively;
respectively representing the hydrogen storage efficiency and the hydrogen release efficiency of short-term hydrogen storage;
self-release rate for short term hydrogen storage; beta is a beta STHS Short-term hydrogen energy storage rate; q STHS Capacity value for installing short-term hydrogen storage; t is the total scheduled period in each typical day.
In the process of electrolyzing water to produce hydrogen in the electrolytic tank and burning hydrogen to produce electricity in the fuel cell, the heat energy produced may be supplied to the heat load with water as the working medium. The electrolyzer and fuel cell operation models were as follows:
in the formulas (7) to (13), the output power ranges of the electrolyzer and the fuel cell are restricted by the formula (7), wherein,
electric power consumed by the electrolytic cell and electric power output by the fuel cell at the time t of the w-th typical day; q EL 、Q FC The configuration capacities of the electrolytic cell and the fuel cell are respectively;
respectively are state variables of an electrolytic cell and a fuel cell, and are Boolean variables; gamma ray EL 、γ FC The minimum load factor of the electrolyzer and the fuel cell.
Equation (8) imposes constraints on the operating conditions of the electrolyzer and the fuel cell, i.e. the electrolyzer and the fuel cell cannot be operated simultaneously.
Equation (9) describes the relationship of the electric heat power output of the fuel cell, wherein,
respectively outputting thermal power of the electrolysis cell and the fuel cell at the w typical day t;
the hydrogen production power of the electrolytic cell and the hydrogen power consumed by the fuel cell are respectively;
respectively the waste heat utilization efficiency and the electricity-hydrogen conversion efficiency of the electrolytic cell;
respectively the waste heat utilization efficiency and the hydrogen-electricity conversion efficiency of the fuel cell.
The formula (10) is the climbing power constraint of the electrolytic cell, wherein,
the power fluctuation of the electrolytic cell at the time t of the w typical day; m is a constant;
the electric power consumed by the electrolyzer at the t-1 moment of the w-th typical day.
Equations (11) to (12) constrain the minimum start/stop interval of the electrolytic cell, wherein,
the minimum start and stop periods of the electrolytic cell are respectively;
respectively are starting state variables and stopping state variables of the electrolytic cell, and are Boolean variables; s is an index representing time and is set as s E [1,22 ]]。
In the formula (13), the reaction mixture is,
the state variable of the electrolytic cell at the t-1 th time point on the w-th typical day is a Boolean variable.
In the step 2, the energy system operator revenue model of the hydrogen storage system is described as formula (14).
In formula (14), p (w) is the probability of occurrence of typical day w; w is the maximum typical number of days; u shape ESO Earnings for energy system operators;
respectively selling energy income and purchasing energy cost for the w typical daily energy system operator to other main bodies in the system;
to the outside for the w typical day energy system operatorThe electricity purchase cost;
the operation and maintenance costs of w typical daily energy system operators;
a heat loss penalty cost for the w typical daily energy system operator;
annual investment costs for configuring hydrogen energy storage systems for energy system operators.
The above items can be represented as:
in the formulae (15) to (20),
respectively w typical day-time and t-time energy system operatorsElectricity and heat sales unit price; gamma ray sell,H Price per unit energy of hydrogen;
the electricity selling quantity, the heat selling quantity and the hydrogen selling quantity of an energy system operator at t moment in w typical days are respectively;
ζ loss,h penalizing cost for heat loss;
is the heat loss at time t;
The configuration of the hydrogen storage system enables the energy system operator to regulate the electricity, heat and hydrogen balance in the system by adjusting the output of the electrolyzer and the fuel cell, as shown in the formula (21) to the formula (23):
in the formulae (21) to (23),
respectively selling the electric quantity sold to a power distribution network at the time t of the w typical day by an energy system operator;
the electric consumption of the electrolytic cell at the w typical day t;
the electricity generation quantity of the fuel cell at the time t of the w typical day;
for the energy system operator on the w typical daythe electricity purchasing quantity at the time t;
purchasing power from the power distribution network for the energy system operator at the w typical day t;
In the step 2, the energy producer serves as the source side of the system and comprises a wind turbine, a photovoltaic and cogeneration unit and gas boiler equipment, wherein the cogeneration unit has the running characteristic of 'fixing power by heat', and the thermal power output by the cogeneration unit at the w typical day t moment
Can be described as shown in formula (24).
In the formula (24), the reaction mixture is,
respectively the maximum and minimum electric energy values which can be output by the combined heat and power generation unit; h is a total of 1 Outputting the corresponding electric heat conversion coefficient of the combined heat and power generator set for the minimum power of the combined heat and power generator; h is 2 Outputting a corresponding thermoelectric conversion coefficient for the maximum power of the cogeneration unit; h is m Providing a linear supply slope for the thermoelectric power of the cogeneration unit;
electric power output for the cogeneration unit;
correspondingly outputting heat energy when the minimum electric power is output for the cogeneration unit;
the heat energy output by the cogeneration unit at the w-th typical day t.
In addition, the cogeneration unit and the gas boiler plant should also satisfy the upper and lower limit range constraints of the output power shown in formula (25).
In formula (25), Q CHP 、Q GB Rated capacities of a cogeneration unit and gas boiler equipment are respectively set;
electric power output for the cogeneration unit;
the heat production power of the gas boiler equipment at the moment t.
The output model of the wind turbine and the photovoltaic is shown as a formula (26).
In the formula (26), the reaction mixture is,
output power of w typical days at time t PV and WT respectively;
the predicted contribution of PV and WT at time t, respectively.
In the step 2, in the game process, the energy producer optimizes the best output of the equipment according to the energy price established by the energy system operator, the goal is to maximize income, and the model can be described as shown in formula (27).
In the formula (27), p (w) is the probability of occurrence of typical day w; w is the maximum typical number of days; u shape EP The income of energy manufacturers;
respectively energy sales revenue, fuel costs, and operation and maintenance costs for the w typical daily energy producer. Each of the above may be represented by formula (28) to formula (30):
in the formulae (28) to (30),
the electric power and the thermal power sold to an energy system operator by an energy producer at t moment in w typical days are respectively provided;
the electricity purchase price and the heat purchase price of the energy system operator at t moment under w typical days are respectively; a is a e 、b e 、c e Are all gas cost coefficients of cogeneration units, wherein a e Is the secondary coefficient of the gas cost of the cogeneration unit, b e Is a first-order coefficient of the gas cost of the cogeneration unit, c e The method comprises the following steps of (1) obtaining a gas cost constant term of a cogeneration unit; a is h 、b h 、c h Are all gas cost factors of gas boiler plants, wherein a h Is a secondary coefficient of the gas cost of the gas boiler equipment, b h Is a first order coefficient of the gas cost of the gas boiler equipment, c h Is the constant term of the gas cost of the gas boiler equipment.
Electric power output by combined heat and power generating unitRate;
the heat production power of the gas boiler equipment at the time t;
the operation and maintenance costs of the photovoltaic power generation equipment, the wind turbine equipment, the gas boiler equipment and the cogeneration equipment are respectively calculated;
w output powers at typical times of day t PV, WT, respectively.
Furthermore, energy producers need to meet the corresponding power balance constraints, as shown in equations (31) to (32):
in the formulae (31) to (32),
and respectively selling the total electric power and the heat power to the energy system operator for the energy producer at the w typical day t.
In step 2, mathematical models of the electricity and heat energy storage devices in the energy storage service provider are shown as formulas (33) to (36):
in formulae (33) to (36):
respectively representing the energy storage values of electric energy storage and thermal energy storage at the t moment of the w typical day;
respectively representing the electricity and heat power sold and purchased by an energy storage service provider at the time t from an energy system operator;
the storage efficiency and the discharge efficiency are respectively;
heat storage efficiency and heat release efficiency respectively;
the self-release rates of the electrical energy storage and the thermal energy storage are respectively; beta is a EES 、β TT Energy multiplying power of electric energy storage and thermal energy storage respectively; q EES 、Q TT The maximum storage capacities of electrical energy storage and thermal energy storage are respectively.
In step 2, the energy storage service provider determines the optimal energy purchase and sale of the energy storage device in each time period according to the energy price established by the energy system manufacturer, and the optimization target can be expressed as:
in the formula (37), p (w) is the probability of occurrence of typical day w; w is the maximum typical number of days; u shape ESP Earnings for energy storage service providers;
the sales income and the purchase expenditure of the w typical daily ESP are respectively expressed as formula (38) to formula (39):
in the formulae (38) to (39),
the electricity purchase price and the heat purchase price of the energy system operator at t moment of w typical days are respectively;
the electricity selling price and the heat selling price of the energy system operator at t moment under w typical days are respectively;
and respectively representing the electricity and heat power sold and purchased by an energy storage service provider from an energy system operator at the time t.
In the step 2, the load aggregator has three load models of electricity, heat and hydrogen, and the load aggregator can be expressed as formulas (40) - (42) by considering the transferable characteristics of electricity and heat loads and the reducible characteristics of electricity, heat and hydrogen loads
In the formulae (40) to (42),
the loads are respectively the electricity, heat and hydrogen loads required by the load aggregator at the w typical day t;
respectively basic electric, thermal and hydrogen loads;
respectively the transferred electricity and the heat load at the time t;
respectively the upper limit of electric load transfer and the upper limit of heat load transfer; k is a radical of cut,e 、k cut,h 、k cut,H The coefficients of electrical, thermal, and hydrogen load, respectively, can be reduced.
In the step 2, because the demand response of the load is considered in the load aggregator, there is a baseline load in each time interval to represent the optimal energy use of the time interval, and when the energy use of the user deviates from the load, a corresponding satisfaction loss is caused, and the satisfaction loss U is caused w,SL As shown in equation (43):
in formula (43), ω E 、
Is a satisfactory loss parameter of the energy source E; e 'is a set of load classes, E' = { E, H }; d E,w,t The adjustment amount of the load E at the time t is the actual load
And baseline load
By introducing auxiliary variables
And
and constraints (45), (46) that convert the absolute value term in equation (43) to a linear form represented by equation (44):
the load aggregator adjusts its own load with the objective of minimizing the sum of the energy satisfaction loss and the energy purchase cost on the basis of the energy price established by the energy system operator, and the objective function can be described as formula (47):
in formula (47), p (w) is the probability of occurrence of typical day w; w is the maximum typical number of days; u shape LA Purchasing energy benefit cost for a user; u shape w,SL A loss of user satisfaction for the w-th typical day;
is the cost of energy purchased at a typical day w, as shown in equation (48).
In the formula (I), the compound is shown in the specification,
γ sell,H the prices of electricity, heat and hydrogen sold by the operators of the energy system at t time of w typical days are respectively;
electricity, heat and hydrogen power sold to the load aggregator are respectively for w typical day time t energy system operators.
In the step 2, the electricity and the heat energy sold by the energy system operator are equal to the sum of the electricity and the heat energy purchased by the energy storage service provider and the load aggregation provider; the hydrogen energy sold by the energy system operator is equal to the hydrogen energy purchased by the load aggregator; the electricity and heat energy purchased by the energy system operator are equal to those sold by the energy producer and the energy storage service provider, namely, the constraint of the formula (49) is required to be met.
In the formula (49), the reaction mixture is,
electric power and thermal power purchased respectively for an energy system operator at the time t of the w typical day;
electricity, heat and hydrogen power sold by an energy system operator at the w typical day t respectively;
electric power sold to an energy system operator by an energy producer and an energy storage service provider at the w typical day t moment respectively;
respectively selling thermal power to an energy system operator for an energy producer and an energy storage service provider at the t moment of the w typical day;
electric power and thermal power sold to an energy storage service provider by an energy system operator at the time t of the w typical day are respectively provided;
and respectively selling electricity, heat and hydrogen power to the load aggregation business for the energy system operator at the w typical day t.
In the step 2, the energy producer, the energy storage service provider and the load aggregator optimize the main body of the energy producer, the energy storage service provider and the load aggregator based on the energy price set by the energy system operator, and the optimization results of the energy producer, the energy storage service provider and the load aggregator influence the energy price set by the energy system operator, so that the process accords with the dynamic game situation of a master-slave progressive structure. Therefore, an energy system operator is taken as a leader, an energy storage service provider, an energy producer and a load aggregator are taken as different followers to establish a one-master multi-slave game model, the leader energy system operator firstly makes an energy price, and then the energy producer, the energy storage service provider and the load aggregator are taken as the followers to simultaneously respond according to the energy price and feed an optimization result back to the energy system operator. And then, the energy system operator configures the hydrogen energy storage device according to the output of the energy producer unit, the energy charging and discharging strategy of the energy storage service provider, the load aggregation commercial energy purchasing strategy and the energy price information of the external energy network, and participates in the energy network through the actions of hydrogen production, hydrogen combustion and hydrogen supply to promote the energy supply and demand balance. Through solving the one-master multi-slave game model, the energy optimal pricing strategy, the user optimal energy purchasing strategy and the hydrogen storage system optimal configuration capacity and output condition of an energy producer can be obtained.
In the step 3, the competition search algorithm is a group intelligent optimization algorithm and is commonly used for solving the engineering optimization problem; mixed integer programming refers to an integer programming problem in which part of decision variables in a problem to be solved are integers; quadratic programming refers to a non-linear programming in which the objective function is a quadratic function.
The competition search algorithm is a group intelligent optimization algorithm. In the optimization process, each particle is first evaluated and its target value is calculated, and all particles are classified into two groups, excellent and general, according to the target value ranking.
Subsequently, the position update is performed on the particles in the excellent group according to equations (50) and (51).
In the formulas (50) to (51), g is the current iteration number;
the value of the jth evaluation index of the ith particle in the g iteration is taken as the value of the jth evaluation index;
the value of the jth evaluation index of the ith particle in the g +1 th iteration is taken as the value of the jth evaluation index; a (i) is the learning ability of the ith particle; s 1 And S 2 Respectively serving as search range functions of particles with strong learning ability and ordinary learning ability; ρ is the slave matrix [ -1,0,1]The value of (1) random extraction;
and
respectively representing the upper limit and the lower limit of the particle in the j-dimension search range; l is a radical of an alcohol 1 A threshold value of learning ability strength belonging to the matrix (0, 1); l is a radical of an alcohol B And U B Are all constants; rand (1) represents the selection of a random number from (0, 1).
The particles in the general group are location updated according to equation (52).
In the formula (52), g is the current iteration number;
the value of the jth evaluation index of the ith particle in the g iteration is taken as the value of the jth evaluation index;
the value of the jth evaluation index of the ith particle in the g +1 th iteration is taken as the value of the jth evaluation index; alpha and Q are matrices [ -1, respectively]And [0,2]The random number of (2); d is a 1 × D matrix, all elements are 1; l is 2 Is a 1 x d matrix, all elements are-1 or 1 randomly distributed; p is a standard normal distribution with a mean value of 0 and a variance of 1; o is a random factor, derived from matrices [0.1,0.2,0.3,0.4,0.5 ]]Selecting randomly; a (i) is the learning ability of the ith particle; l is 1 The threshold value of learning ability strength belongs to the matrix (0, 1).
In addition, when the learning ability of each particle is larger than the reference threshold value L 3 Then the reference behavior occurs: the particles learn to the particles with the best fitness according to the learning ability of the particles. This process can be represented by equation (53).
In the formula (53), g is the current iteration number;
the value of the jth evaluation index of the ith particle in the g iteration is taken as the value of the jth evaluation index of the ith particle;
the index value of the optimal particle in the jth dimension in the process of the ith iteration is obtained; a (i) is the learning ability of the ith particle; l is 3 For reference threshold, belong to the interval (0, 1).
After the positions of the particles are updated through the equations (50) - (53), the competition search algorithm eliminates the random number of the particles, randomly generates a corresponding number of particles, and randomly generates the learning capacity and various indexes of the newly generated particles.
In the step 3, in the solving process, the energy system operator strategy is to optimize the system purchase and sale energy price and the capacity allocation and output of each device in the hydrogen storage system; the energy producer strategy is the best output condition of each device; the energy storage service provider strategy is an optimal charge and discharge strategy for electricity and heat energy storage; the load aggregator strategy is the best energy purchase.
In the step 3, a distributed equilibrium solving algorithm based on a competitive search algorithm and combined mixed integer programming/secondary programming refers to a distributed computing method capable of solving a one-master multi-slave game, firstly, an energy system operator is simulated through the competitive search algorithm to formulate an energy price to followers, then, an energy storage service provider performs mixed integer programming according to the energy price to solve electricity and heat energy storage charging and discharging strategies, an energy producer performs secondary programming according to the energy price to solve the optimal output of equipment, a load aggregator performs secondary programming according to the energy price to optimize the optimal energy purchasing strategy of a user, then, each follower feeds the optimized result back to the energy system operator, and the energy system operator performs optimized configuration on a hydrogen storage system according to the follower result to achieve the purpose of maximizing income. The algorithm can effectively avoid information leakage of each main body when the principal and subordinate games are solved, and privacy and safety of each main body are protected.
In the step 3, the distributed equilibrium solving algorithm based on the competition search algorithm and the mixed integer programming/quadratic programming is an algorithm for solving the master-slave game by combining an intelligent optimization algorithm and a mixed integer programming/quadratic programming method, and the concrete solving process of the master-slave game is as follows:
step1: inputting initial parameters of each main body, setting initial iteration times g =0, and setting the number m =20 of particles, wherein the dimensionality of each particle is 4 x 24 dimensionalities and respectively represents the full-day electricity selling price, the full-day heat selling price, the full-day electricity purchasing price and the full-day heat purchasing price of an energy system operator;
step 2: generating m groups of energy price initial values by using a competitive search algorithm, and transmitting the energy price initial values to an energy producer, a load aggregator and an energy storage service provider;
step 3: the energy storage service provider performs mixed integer programming according to the energy price to solve the electricity and heat energy storage charging and discharging strategies;
step 4: performing secondary planning by an energy producer according to the energy price to solve the optimal output of the equipment;
step 5: the load aggregator optimizes the optimal energy purchasing strategy of the user by performing secondary planning according to the energy price;
step 6: an energy system operator performs optimal configuration on a hydrogen storage system according to an energy storage charging and discharging strategy of an energy storage service provider, the optimal output of energy producer equipment and a load aggregation commercial energy purchasing strategy to achieve the purpose of maximizing income;
step 7: and (3) updating the positions of the m groups of particles by using formulas (50) - (53), eliminating the random number of the particles, and randomly generating a corresponding number of new particles.
Step 8: updating the optimal objective functions of an energy system operator, an energy storage service provider, an energy producer and a load aggregator, if the optimal strategy obtained by each main body in two adjacent iterations meets the formula (54), considering the strategy as a game balance point, and stopping the iteration if any participant cannot obtain more profits by independently changing the strategy; if not, let g = g +1 and return to Step 3.
In the formula (54), g is the current iteration number;
vector expression forms of strategies of an energy system operator, an energy producer, an energy storage service provider and a load aggregator obtained by the g-th iteration are respectively obtained;
and respectively representing vector expression forms of strategies of an energy system operator, an energy producer, an energy storage service provider and a load aggregator obtained by the g-1 st iteration.
In the step 3:
the pricing strategy of the operator refers to the electric energy purchase price and the heat energy purchase price which are set by the energy system operator to the energy storage service provider, the energy producer and the load aggregator.
The hydrogen storage system configuration strategy refers to the specific capacity configuration of seasonal hydrogen storage, short-term hydrogen storage, fuel cells and electrolysis cells by the energy system operator.
The user demand strategy refers to the final demand of the user on the electricity, heat and hydrogen energy sources, namely the electricity, heat and hydrogen energy sources finally purchased by the load aggregator to the energy system operator.
The optimal output strategy of each main device specifically refers to the power output of photovoltaic generators, wind turbines, gas boilers and cogeneration units in energy manufacturers and the charging and discharging energy of electric energy storage and heat energy storage in energy storage service providers.
The invention discloses a comprehensive energy system seasonal hydrogen storage optimization configuration method based on a distributed collaborative optimization strategy, which has the following technical effects:
1) The seasonal hydrogen storage model is constructed, the long-term storage and optimized utilization of electric energy in a cross-season and cross-energy form is realized, and seasonal supply and demand balance of energy and income of an energy system operator are improved.
2) The method comprises the steps of taking an energy system operator of the hydrogen storage system as a leader, taking an energy producer, a load aggregator and an energy storage service provider as followers to construct a master-slave multi-slave game model, and solving a game equilibrium solution to obtain a pricing strategy of the system operator, a hydrogen storage system configuration strategy, a user demand strategy and an optimal output strategy of each main body device, so that the operation benefit maximization of each main body is realized.
3) The hydrogen storage system is configured in the energy system operator, so that the initiative of the energy system operator in the game process is improved, and the effect of reducing the risk of energy supply and demand imbalance is obvious. The energy consumption measurement is used for demand response through price signal guidance, the energy consumption pressure of the system is relieved while the energy purchasing cost of a user is reduced, and a win-win effect is achieved.
4) The distributed equilibrium solving algorithm based on the competition search algorithm and combined mixed integer programming/quadratic programming phase is provided for solving the master-slave game, the convergence effect is good, the privacy of all subjects participating in the game is protected, and the model is more in line with the independent decision process of all subjects in the actual competition type market.
Drawings
Fig. 1 is a schematic diagram of the operating mechanism of a seasonal hydrogen store.
Fig. 2 is a schematic diagram of an integrated energy system architecture of a hydrogen-containing storage system.
Fig. 3 is a diagram of a master multiple slave gaming model.
Fig. 4 is a flow chart of the solution of the master-slave game.
FIG. 5 is a graph of the output power of photovoltaic and wind turbines and the electrical, thermal, and hydrogen load demands.
FIG. 6 (a) is a schematic diagram of an iterative solution process of scheme 1;
FIG. 6 (b) is a schematic diagram of the iterative solution process of scheme 2;
FIG. 6 (c) is a schematic diagram of the iterative solution process of scheme 3;
fig. 6 (d) is a schematic diagram of the iterative solution process of scheme 4.
Fig. 7 is a diagram illustrating the total amount of heat lost and removed in an energy system operator.
FIG. 8 is a schematic diagram of wind and light local absorption of various schemes.
Detailed Description
The comprehensive energy system seasonal hydrogen storage optimal configuration method based on the distributed collaborative optimization strategy comprises the following steps:
step1: establishing a seasonal hydrogen energy storage model, realizing long-term storage and optimized utilization of electric energy in a cross-season and cross-energy form, promoting local consumption of renewable energy and improving the income of an energy system operator;
step 2: establishing a master-slave multi-slave game model which takes an energy system operator containing a hydrogen storage system as a leader and takes an energy producer, a load aggregator and an energy storage service provider as followers,
and step 3: and providing a distributed equilibrium solving algorithm based on a competition search algorithm and combined mixed integer programming/quadratic programming phase, solving the master-slave game, obtaining a pricing strategy of a system operator, a hydrogen storage system configuration strategy, a user demand strategy and an optimal output strategy of each main body device, and realizing the maximization of the operation benefit of each main body.
The preferred embodiments are described in detail below with reference to the following drawings:
modeling seasonal hydrogen storage:
the operating mechanism of the seasonal hydrogen storage is shown in fig. 1, and it is assumed that the seasonal hydrogen storage can only be charged or discharged within a typical day, so that the seasonal hydrogen storage works in different typical days, and daytime hydrogen energy interaction is realized. The energy self-dissipation rate of the seasonal hydrogen storage is almost close to 0, and the hydrogen charge and discharge accumulation of the last typical day scene needs to be considered at the initial moment of the next typical day scene, so that a seasonal hydrogen storage mathematical model is established.
(II) modeling each main body in the comprehensive energy system:
the overall energy system architecture for a hydrogen storage system including seasonal hydrogen storage, short-term hydrogen storage, electrolyzer unit, and fuel cell is shown in fig. 2. The seasonal hydrogen storage model is shown as a formula (1) to a formula (5), and the short-term hydrogen storage model is shown as a formula (6):
the architecture of the integrated energy system comprising a hydrogen storage system comprising seasonal hydrogen storage, short-term hydrogen storage, electrolyzer unit and fuel cell is shown in fig. 2. The seasonal hydrogen storage model is shown as a formula (1) to a formula (5), and the short-term hydrogen storage model is shown as a formula (6). In the process of electrolyzing water to produce hydrogen in the electrolytic tank and burning hydrogen to produce electricity in the fuel cell, the heat energy produced may be supplied to the heat load with water as the working medium.
The energy system operator revenue model for a hydrogen storage system can be described as equation (14).
In the formula of U ESO Earnings for energy system operators;
and
respectively selling energy income and purchasing energy cost for the w typical daily energy system operator to other main bodies in the system;
the electricity purchase cost of the w typical daily energy system operator to the external power distribution network;
operation and maintenance costs for w typical daily energy system operators;
heat loss penalty cost for the w-th typical daily energy system operator;
annual investment costs for configuring hydrogen energy storage systems for energy system operators. The above items can be represented as:
in the formula (I), the compound is shown in the specification,
and
the electricity and heat selling unit prices of the energy system operators at t time under w typical days are respectively; gamma ray sell,H Price per unit energy of hydrogen;
and
the electricity selling quantity, the heat selling quantity and the hydrogen selling quantity of an energy system operator at t moment under w typical days are respectively;
and
the electricity and heat purchasing unit prices of w energy system operators at t time in typical days are respectively purchased;
and
respectively purchasing electric quantity and heat quantity for w typical day energy system operators at time t;
respectively purchasing/selling unit prices of the energy system operator to the external distribution network at the time t; gamma ray grid,in,H Is an external hydrogen selling unit price;
purchasing/selling power to an external power distribution network for w energy system operators at t moment in typical days respectively;
purchasing hydrogen power to the outside for an energy system operator; ζ represents a unit loss,h Penalizing costs for heat loss;
is the heat loss at time t;
and
respectively start-up, stop and degradation costs of the electrolytic cell, and the rest lambda is the operation and maintenance cost of corresponding equipment; r and m are interest rate and equipment life respectively; q θ And C θ Respectively, the configured capacity and the unit capacity cost of the equipment theta, wherein theta ESO ＝{STHS,LTHS,FC,EL}。
The configuration of the hydrogen storage system allows the energy system operator to regulate the electricity, heat, and hydrogen balance in the system by adjusting the output of the electrolyzer and the fuel cell, as shown in equations (21) to (23).
In the formula (I), the compound is shown in the specification,
is the waste heat at the time t of the w typical day.
The energy producer serves as the source side of the system and comprises a wind turbine, a photovoltaic power generator, a cogeneration unit and gas boiler equipment, wherein the cogeneration unit has the running characteristic of 'fixing power by heat', and the heat power output by the cogeneration unit at the w-th typical day t moment
Can be described as shown in formula (24).
In the formula (I), the compound is shown in the specification,
and
respectively the maximum and minimum electric energy values which can be output by the cogeneration unit; h is a total of 1 、h 2 And h m All are the electric-heat conversion coefficients of the cogeneration units;
electric power output for the cogeneration unit;
correspondingly outputting heat energy when the minimum electric power is output for the combined heat and power generation unit;
the heat energy output by the cogeneration unit at the w-th typical day t.
In addition, the cogeneration unit and the gas boiler plant should satisfy the formula (the upper and lower limits of the output power shown by 25).
In the formula, Q CHP And Q GB Rated capacities of a cogeneration unit and gas boiler equipment, respectively;
the heat production power of the gas boiler equipment at the moment t.
The output model of the wind turbine and the photovoltaic is shown as a formula (26).
In the formula (I), the compound is shown in the specification,
and
output power at w typical times of day t PV and WT respectively;
and
the predicted contribution of PV and WT at time t, respectively.
In the game process, the energy producer optimizes the best output of the equipment according to the energy price established by the energy system operator, the goal is to maximize the income, and the model can be described as shown in an equation (27).
In the formula of U EP The income of energy manufacturers;
and
energy sales revenue, fuel costs, and maintenance costs for the w typical daily energy producer, respectively. The above may be represented by formula (28) to formula (30), respectively:
in the formula (I), the compound is shown in the specification,
and
the electric power and the thermal power sold to an energy system operator by an energy producer at t moment in w typical days are respectively provided; a is e ,b e ,c e /a h ,b h ,c h A gas cost coefficient for cogeneration unit/gas boiler equipment;
and
the operation and maintenance costs of the photovoltaic power generation equipment, the wind turbine equipment, the gas boiler equipment and the cogeneration equipment are respectively.
Furthermore, energy producers need to meet corresponding power balance constraints, as shown by equations (31) - (32).
In the formula (I), the compound is shown in the specification,
and
total electric and thermal power sold to the energy system operator by the energy producer at the w typical day t, respectively.
Mathematical models of the electric and thermal energy storage devices in the energy storage service provider are shown in formulas (33) - (36).
In the formula:
and
respectively representing the energy storage values of the electricity energy storage and the heat energy storage at the t moment of the w typical day;
and
respectively representing the electricity and heat power sold/purchased by an energy storage service provider from an energy system operator at the time t;
and
respectively, the electricity storage efficiency and the discharge efficiency;
and
heat storage efficiency and heat release efficiency, respectively;
and
the self-release rates of the electrical energy storage and the thermal energy storage are respectively; beta is a EES And beta TT Energy multiplying power of electric energy storage and thermal energy storage respectively; q EES And Q TT The maximum storage capacities of electrical and thermal energy storage, respectively.
The energy storage service provider determines the optimal energy purchasing and selling of the energy storage equipment in each time period according to the energy price established by the energy system manufacturer, and the optimization target can be expressed as:
in the formula of U ESP Earnings for energy storage service providers;
and
the sales income and purchase expenditure of the w-th typical day ESP, respectively, can be expressed as formula (38) to formula (39), respectively:
the load aggregator includes three load models of electricity, heat and hydrogen, and can be expressed as formulas (40) - (42) by considering transferable characteristics of electricity and heat load and reducible characteristics of electricity, heat and hydrogen load
In the formula (I), the compound is shown in the specification,
and
the electric load, the heat load and the hydrogen load required by the load aggregator at the w typical day t moment respectively;
and
is a baseElectrical, thermal, hydrogen load;
and
the transferred electric load and the heat load at the time t;
and
the upper limit of electric load transfer and the upper limit of heat load transfer are respectively; k is a radical of cut,e 、k cut,h And k cut,H The coefficients of electrical, thermal, and hydrogen load, respectively, can be reduced.
Because the demand response of the load is considered in the load aggregator, a baseline load is available in each time interval to represent the optimal energy utilization of the time interval, and when the energy utilization of the user deviates from the load, the corresponding satisfaction loss is caused, and the satisfaction loss U is caused w,SL As shown in equation (43).
In the formula, omega E And
a satisfactory loss parameter for energy E; e 'is a set of load types, E' = { E, H }; d E,w,t The adjustment amount of the load E at the time t is the actual load
And baseline load
By introducing auxiliary variables
And
and constraints (45) and (46) for converting the absolute value term in equation (43) to a linear form as shown in equation (44).
The load aggregator adjusts its own load with the goal of minimizing the sum of energy satisfaction loss and energy purchase cost based on the energy prices established by the energy system operator, and its objective function can be described as equation (47).
In the formula of U LA Purchasing energy benefit cost for a user;
is the cost of energy purchased at a typical day w, as shown in equation (48).
The electricity and the heat energy sold by the energy system operator are equal to the sum of the electricity and the heat energy purchased by the energy storage service provider and the load aggregation provider; the hydrogen energy sold by the energy system operator is equal to the hydrogen energy purchased by the load aggregator; the electricity and heat energy purchased by the energy system operator are equal to those sold by the energy producer and the energy storage service provider, namely, the constraint of the formula (49) is required to be met.
(III): a master-slave game model and a solving algorithm thereof are provided:
the energy producer, the energy storage service provider and the load aggregator optimize the main body of the energy producer, the energy storage service provider and the load aggregator based on the energy price set by the energy system operator, the optimization results of the energy producer, the energy storage service provider and the load aggregator influence the energy price set by the energy system operator, and the process accords with the dynamic game situation of a master-slave progressive structure. Therefore, with the energy system operator as a leader, the energy storage service provider, the energy producer and the load aggregator are used as different followers to establish a one-master multi-slave game model, as shown in fig. 3. As can be seen from fig. 3, the energy system operator of the leader first makes an energy price, and then the energy producer, the energy storage service provider, and the load aggregator simultaneously respond according to the energy price as followers, and feed back the optimization result to the energy system operator. And then, the energy system operator configures the hydrogen energy storage device according to the unit output of the energy producer, the charging and discharging strategy of the energy storage service provider, the load aggregation commercial energy purchasing strategy and the energy price information of the external energy network, and participates in the energy network through hydrogen production, hydrogen combustion and hydrogen supply behaviors to promote energy supply and demand balance. Through solving the one-master multi-slave game model, the energy optimal pricing strategy, the user optimal energy purchasing strategy and the hydrogen storage system optimal configuration capacity and output condition of an energy producer can be obtained.
The competition search algorithm is a group intelligent optimization algorithm and is commonly used for solving the engineering optimization problem; mixed integer programming refers to an integer programming problem in which part of decision variables in a problem to be solved are integers; quadratic programming refers to a non-linear programming in which the objective function is a quadratic function.
A distributed equilibrium solving algorithm combining mixed integer programming/secondary programming based on a competitive search algorithm is a distributed computing method capable of solving a one-master multi-slave game, firstly, an energy system operator is simulated through the competitive search algorithm to formulate an energy price to followers, then an energy storage service provider carries out mixed integer programming to solve electricity and heat storage charging and discharging strategies according to the energy price, an energy producer carries out secondary programming according to the energy price to solve the optimal output of equipment, a load aggregator carries out secondary programming according to the energy price to optimize the optimal energy purchasing strategy of a user, then, each follower feeds the results obtained by optimization back to the energy system operator, and the energy system operator carries out optimal configuration on a self hydrogen storage system according to the follower results to achieve the purpose of maximizing income. The algorithm can effectively avoid information leakage of each main body when the principal and subordinate games are solved, and privacy and safety of each main body are protected. The specific solving flow is shown in fig. 4.
If the optimal strategy obtained by each main body in two adjacent times satisfies the formula (54), the strategy is considered as game equilibrium points, and any participant cannot obtain more benefits by changing the strategy alone.
In the formula (54), g is the current iteration number;
and
respectively, vector expression forms of strategies of an energy system operator, an energy producer, an energy storage service provider and a load aggregator.
(IV): the strategy of the invention is compared and analyzed with other 3 strategy schemes:
the energy system operator sets the upper/lower electric energy price limit to be consistent with the purchase/sale price of electricity to the distribution network, as shown in table 1:
TABLE 1 distribution network purchase and sale price
The energy system operator establishes the upper and lower limits of heat energy as 0.5 yuan/kW.h and 0.15 yuan/kW.h respectively. The energy producer main body comprises 6 cogeneration units with 500kW rated capacity and 5 gas boiler devices with 800kW rated capacity, and the maximum storage capacity of electric energy storage and heat energy storage in the energy storage facilitator main body is 10MW. The cell equipment parameters are shown in table 2:
TABLE 2 cell parameters
The remaining equipment parameters are shown in table 3:
TABLE 3 plant efficiency parameters
Other simulation related parameters are shown in table 4.
TABLE 4 simulation-related parameters
The market price of the hydrogen is 19.2-38.4 yuan/kg, the purity of the hydrogen obtained by electrolytic hydrogen production is considered to be higher, 35 yuan/kg is taken, and the density of the hydrogen is 0.089kg/m 3 Since 37.87 kW.h/kg is obtained, the price of hydrogen sold per unit energy is 0.924 yuan/(kW.h). The photovoltaic and wind turbine output power and the electricity, heat and hydrogen load requirements for four typical days of spring, summer, autumn and winter are shown in figure 5.
Four different hair shafts were set for comparative severity analysis:
1) Scheme 1: simultaneously configuring seasonal hydrogen storage, short-term hydrogen storage, a fuel cell and an electrolytic cell in an energy system operator;
2) Scheme 2: short-term hydrogen storage, fuel cells and electrolysis cells are configured in energy system operators, and seasonal hydrogen storage is not configured;
3) Scheme 3, seasonal hydrogen storage, a fuel cell and an electrolytic cell are configured in an energy system operator, and short-term hydrogen storage is not configured;
4) And 4, no equipment is configured in an energy system operator.
Wherein, scheme 1 adopts the strategy provided by the invention, and the energy system operator configures the electrolytic cell, the short-term hydrogen storage, the seasonal hydrogen storage and the fuel cell. The configurations of seasonal and short-term hydrogen storage are not considered in case 2 and case 3, respectively. And in the scheme 4, the energy of the system is regulated and controlled only by depending on a price strategy, and no equipment is configured.
And solving the scheme by adopting a distributed equilibrium solving algorithm of a competitive search algorithm combined with a mixed integer programming/quadratic programming phase in MatlabR2018 b.
The optimization iterative process of the energy system operator, the energy producer, the energy storage facilitator and the load aggregator under each scheme is shown in fig. 6 (a) to 6 (d).
As can be seen from fig. 6 (a) to 6 (d), as the number of iterations increases, the revenue of the energy system operator in each scenario gradually increases, the revenue of the energy storage service provider and the energy producer gradually decreases, and the utility cost of the load aggregator gradually increases. The game process between the leader and each follower is embodied, and the leading position of the energy system operator as the leader in the master-slave game is also embodied. In addition, as can be seen from fig. 6 (a) to 6 (d), the distributed equilibrium solution algorithm of the competitive search algorithm combined mixed integer programming/quadratic programming phase adopted in the present invention has a good convergence effect, and when the schemes 1 to 4 are respectively iterated 303 times, 528 times, 513 times and 564 times, the result reaches master-slave equilibrium, and at this time, no participant can obtain more benefits by changing the strategy. The final convergence costs of each subject and the optimal capacity of each device in the energy system operator for each project at this time are recorded in tables 5 and 6.
TABLE 5 Final cost of each entity for each protocol
TABLE 6 arrangement of devices
As can be seen from table 6, the fuel cells are not configured in the schemes 1 to 4, because the configuration cost of the fuel cells is high, the energy loss from the hydrogen conversion from electricity to hydrogen to electricity is large, and the profit can be realized only by a large peak-to-valley electricity price difference, so the configuration of the fuel cells by the operator of the energy system is conservative.
The analysis of the table 5 and the table 6 shows that, compared with the scheme 2 and the scheme 3, in the scheme 1, after the seasonal hydrogen storage and the short-term hydrogen storage are configured simultaneously, the energy system operator has more space to store the hydrogen energy generated by the electrolytic cell, and in order to obtain more hydrogen selling benefits, the energy system operator can set a higher electricity purchasing average price to output electric energy from the main body of the incentive energy storage service provider and the main body of the energy producer, and set the higher electricity selling average price to promote the main body of the load aggregation provider to perform electric energy response. Therefore, compared with schemes 2 and 3, the energy-benefit purchase cost of the load aggregation business main body and the profit cost of the energy producer main body in the scheme 1 are the highest, and reach 4327.2 ten thousand yuan and 866 ten thousand yuan respectively, and the profits of the energy system operator main body rise by 123 ten thousand and 153 ten thousand yuan respectively.
In addition, as can be seen from fig. 7, the peak-time electricity selling average prices of the schemes 1 to 3 are all higher than that of the scheme 4, because the electrolysis baths are configured in the schemes 1 to 3, and because the electrolysis baths can produce and consume redundant electric energy, the energy system operator does not need to consider the situation that the user response has energy surplus and the income is reduced due to the rise of the electricity price. In addition, because the cogeneration unit in the energy producer main body has the characteristic of ordering electricity by heat, the average heat purchasing price higher than that of the scheme 4 is set in the schemes 1 to 3, the cogeneration unit is guided to generate more electric energy, and meanwhile, the peak time electricity purchasing price of the system is reduced, so that the benefits of the energy system operator main body are improved.
The configuration of the hydrogen storage system in the energy system operator is also beneficial to promoting the power balance of the system and improving the local consumption condition of renewable energy sources. The total heat loss and heat rejection of the energy system operator is shown in fig. 7, and the wind and light local consumption of each scheme is shown in fig. 8. As can be seen from fig. 8, although the electrolyzer is configured in the energy system operator, there is still a heat loss load phenomenon because the system cannot interact with the external heating network, but the heating effect of the system is improved after the electrolyzer is configured, wherein the improvement effect of the scheme 1 is most obvious, and the heat loss load is reduced by about 31.74%. However, the waste heat in the scheme 1 is the most in the several schemes, because seasonal hydrogen storage and short-term hydrogen storage are configured simultaneously in the scheme 1, the hydrogen production behavior of the electrolytic cell is more active, and the initiative of the heat supply capacity of the energy system operator is increased. In addition, compared with other schemes, the net unbalanced power in the scheme 1 is minimum, and the energy imbalance problem is mainly represented by time imbalance. In addition, as can be seen from fig. 8, the on-site consumption rate of the renewable energy in the scheme 1 reaches 93.86%, while the on-site consumption rate of the clean energy in the scheme 4 is only 73.26, which is greatly improved.
Claims (10)
1. The seasonal hydrogen storage optimization configuration method of the comprehensive energy system based on the distributed collaborative optimization strategy is characterized by comprising the following steps of:
step1: establishing a seasonal hydrogen energy storage model;
step 2: establishing a master-slave multi-slave game model which takes an energy system operator containing a hydrogen storage system as a leader and takes an energy producer, a load aggregator and an energy storage service provider as followers,
and step 3: and providing a distributed equilibrium solving algorithm based on a competition search algorithm and combined mixed integer programming/quadratic programming phase, solving the master-slave game, obtaining a pricing strategy of a system operator, a hydrogen storage system configuration strategy, a user demand strategy and an optimal output strategy of each main body device, and realizing the maximization of the operation benefit of each main body.
2. The integrated energy system seasonal hydrogen storage optimization configuration method based on the distributed collaborative optimization strategy according to claim 1, wherein the method comprises the following steps: in the step1, the seasonal hydrogen energy storage mathematical model is described as follows:
in formula (1):
the stored energy value of seasonal hydrogen storage at the w typical day t;
respectively storing hydrogen storage power and hydrogen discharge power for the w typical day at the t moment;
hydrogen storage efficiency and hydrogen discharge efficiency, respectively; beta is a beta LTHS Seasonal hydrogen energy storage rate; q LTHS Capacity values for installation of seasonal hydrogen stores;
respectively a hydrogen storage state variable and a hydrogen discharge state variable of the seasonal hydrogen storage at the time t of the w typical day;
is the stored energy value of seasonal hydrogen storage at the t-1 time of the 1 st typical day;
in the formula (2): p (w) is the probability of occurrence of typical day w; w is the maximum typical number of days;
the stored energy value of seasonal hydrogen storage at time 1 on the w-th typical day;
the stored energy value of seasonal hydrogen storage at the 1 st time of the w-1 typical day;
the stored energy value of seasonal hydrogen storage at the 24 th time at the w-1 th typical day;
the stored energy value of seasonal hydrogen storage at the t-1 time of the w typical day; p (w-1) is the probability of occurrence on the w-1 th typical day;
in formula (3):
a stored value for seasonal hydrogen storage at time 1 on typical day 1;
a stored value for seasonal hydrogen storage at time 1 on the W-th typical day;
the stored energy value of seasonal hydrogen storage at time 24 on the W-th typical day;
in formulae (4) to (5):
and
the hydrogen storage and release variable for the w-th typical seasonal hydrogen storage.
3. The distributed collaborative optimization strategy-based seasonal hydrogen storage optimization configuration method for the integrated energy system according to claim 1, wherein: in the step 2, the hydrogen storage system comprises seasonal hydrogen storage, short-term hydrogen storage, an electrolyzer device and a fuel cell; wherein, the seasonal hydrogen storage model is shown as a formula (1) to a formula (5);
the short-term hydrogen storage model is shown as the formula (6);
in formula (6):
the stored energy value of the short-term hydrogen storage at the w typical day t;
the hydrogen storage power and the hydrogen discharge power of short-term hydrogen storage at the time t of the w typical day are respectively;
respectively representing the hydrogen storage efficiency and the hydrogen release efficiency of short-term hydrogen storage;
self-release rate for short-term hydrogen storage; beta is a beta STHS Short-term hydrogen energy storage rate; q STHS Capacity value for installing short-term hydrogen storage; t is the total scheduling time interval in each typical day;
the electrolyzer and fuel cell operation models were as follows:
equation (7) constrains the output power ranges of the electrolyzer and the fuel cell, wherein,
electric power consumed by the electrolytic cell and electric power output by the fuel cell at the time t of the w-th typical day; q EL 、Q FC The configuration capacities of the electrolytic cell and the fuel cell are respectively;
respectively are state variables of the electrolytic cell and the fuel cell; gamma ray EL 、γ FC Is the most important of an electrolytic cell and a fuel cellA small load rate;
equation (9) describes the relationship of the electric heat power output of the fuel cell, wherein,
respectively outputting thermal power of the electrolysis bath and the fuel cell at the time t of the w typical day;
hydrogen production power of the electrolytic cell and hydrogen power consumed by the fuel cell are respectively;
respectively the waste heat utilization efficiency and the electricity-hydrogen conversion efficiency of the electrolytic cell;
respectively representing the waste heat utilization efficiency and the hydrogen-electricity conversion efficiency of the fuel cell;
the formula (10) is the climbing power constraint of the electrolytic cell, wherein,
the power fluctuation of the electrolytic cell at the time t of the w typical day; m is a constant;
the electric power consumed by the electrolyzer at the t-1 moment of the w typical day;
equations (11) to (12) constrain the minimum start/stop interval of the electrolytic cell, wherein,
the minimum start and stop periods of the electrolytic cell are respectively;
respectively are starting state variables and stopping state variables of the electrolytic cell, and are Boolean variables; s is an index representing time;
in the formula (13), the reaction mixture is,
is the state variable of the cell at time t-1 on the w-th typical day.
4. The distributed collaborative optimization strategy-based seasonal hydrogen storage optimization configuration method for the integrated energy system according to claim 1, wherein: in the step 2, the energy system operator revenue model of the hydrogen storage system is described as follows:
in formula (14), p (w) is the probability of occurrence of typical day w; w is the maximum typical number of days; u shape ESO Earnings for energy system operators;
respectively selling energy income and purchasing energy cost for the w typical daily energy system operator to other main bodies in the system;
the electricity purchase cost of the w typical daily energy system operator to the external power distribution network;
the operation and maintenance costs of w typical daily energy system operators;
a heat loss penalty cost for the w typical daily energy system operator;
annual investment cost for configuring a hydrogen energy storage system for an energy system operator;
the above items are represented as:
in the formulae (15) to (20),
the electricity and heat selling unit prices of the operators of the energy system at t moment under w typical days are respectively; gamma ray sell,H Price per unit energy of hydrogen;
the electricity selling quantity, the heat selling quantity and the hydrogen selling quantity of an energy system operator at t moment in w typical days are respectively;
ζ loss,h penalizing costs for heat loss;
is the heat loss at time t;
the configuration of the hydrogen storage system enables the energy system operator to regulate the electricity, heat and hydrogen balance in the system by adjusting the output of the electrolyzer and the fuel cell, as shown in equations (21) to (23):
in the formulae (21) to (23),
the electricity selling quantity of the energy system operator at the time of the w typical day t and the electricity sold to the power distribution network are respectively;
the electric consumption of the electrolytic cell at the w typical day t;
the electricity generation quantity of the fuel cell at the time t of the w typical day;
the method comprises the steps of (1) obtaining the electricity purchase quantity of an energy system operator at the w typical day t;
purchasing power from the power distribution network for the energy system operator at the w typical day t;
5. The integrated energy system seasonal hydrogen storage optimization configuration method based on the distributed collaborative optimization strategy according to claim 1, wherein the method comprises the following steps: in the step 2, the energy producer serves as the source side of the system and comprises a wind turbine, a photovoltaic cogeneration unit, a cogeneration unit and gas boiler equipment, and the thermal power output by the cogeneration unit at the w-th typical day t moment
Described by the formula (24):
in the formula (24), the reaction mixture is,
respectively the maximum and minimum electric energy values which can be output by the combined heat and power generation unit; h is a total of 1 Outputting the corresponding thermoelectric conversion coefficient of the cogeneration unit for the minimum power of the cogeneration unit; h is 2 Outputting a corresponding thermoelectric conversion coefficient for the maximum power of the cogeneration unit; h is a total of m Providing a linear supply slope for the thermoelectric power of the cogeneration unit;
electric power output for the cogeneration unit;
correspondingly outputting heat energy when the minimum electric power is output for the cogeneration unit;
the heat energy output by the combined heat and power generation unit at the time t of the w typical day;
in addition, the cogeneration unit and the gas boiler equipment should also satisfy the upper and lower limit range constraints of the output power shown in formula (25);
in formula (25), Q CHP 、Q GB Rated capacities of a cogeneration unit and gas boiler equipment are respectively set;
electric power output for the cogeneration unit;
the heat production power of the gas boiler equipment at the time t;
the wind turbine and photovoltaic output model is shown as a formula (26);
in the formula (26), the reaction mixture is,
output power of PV and WT at t time points of w typical days respectively;
the predicted contribution of PV and WT at time t, respectively.
6. The integrated energy system seasonal hydrogen storage optimization configuration method based on the distributed collaborative optimization strategy according to claim 1, wherein the method comprises the following steps: in the step 2, in the game process, the energy producer optimizes the best output of the equipment according to the energy price established by the energy system operator, the goal is maximum income, and the model description is shown as formula (27):
in formula (27), p (w) is the probability of occurrence of typical day w; w is the maximum typical number of days; u shape EP The income of energy manufacturers;
respectively the energy sale income, the fuel cost and the operation and maintenance cost of the w typical daily energy producer; the above terms are respectively represented by formula (28) to formula (30):
in the formulae (28) to (30),
the electric power and the thermal power sold to an energy system operator by an energy producer at t moment in w typical days are respectively provided;
the electricity purchase price and the heat purchase price of the energy system operator at t moment under w typical days are respectively; a is e 、b e 、c e All are gas cost coefficients of cogeneration units, where a e Is a secondary coefficient of the gas cost of the cogeneration unit, b e A first-order coefficient of gas cost, c, for a cogeneration unit e The method comprises the following steps of (1) obtaining a gas cost constant term of a cogeneration unit; a is h 、b h 、c h Are all the gas cost coefficients of gas boiler plants,wherein a is h Is the secondary coefficient of the gas cost of the gas boiler equipment, b h Is a first order coefficient of the gas cost of the gas boiler equipment, c h Is a gas cost constant term of the gas boiler equipment;
electric power output for the cogeneration unit;
the heat production power of the gas boiler equipment at the time t;
the operation and maintenance costs of the photovoltaic power generation equipment, the wind turbine equipment, the gas boiler equipment and the cogeneration equipment are respectively calculated;
output power of w typical days at time t PV and WT respectively;
furthermore, energy producers need to meet corresponding power balance constraints, as shown by equations (31) - (32):
in the formulae (31) to (32),
and respectively selling the total electric power and the heat power to the energy system operator for the energy producer at the w typical day t.
7. The distributed collaborative optimization strategy-based seasonal hydrogen storage optimization configuration method for the integrated energy system according to claim 1, wherein: in step 2, mathematical models of the electricity and heat energy storage devices in the energy storage service providers are shown as formulas (33) to (36):
in formulae (33) to (36):
respectively representing the energy storage values of electric energy storage and thermal energy storage at the t moment of the w typical day;
respectively representing the electricity and heat power sold and purchased by an energy storage service provider at the time t from an energy system operator;
respectively, the electricity storage efficiency and the discharge efficiency;
heat storage efficiency and heat release efficiency respectively;
the self-release rates of the electrical energy storage and the thermal energy storage are respectively; beta is a EES 、β TT Energy multiplying power of electric energy storage and thermal energy storage respectively; q EES 、Q TT The maximum storage capacities of electrical energy storage and thermal energy storage are respectively.
8. The integrated energy system seasonal hydrogen storage optimization configuration method based on the distributed collaborative optimization strategy according to claim 1, wherein the method comprises the following steps: in the step 2, the energy storage service provider determines the optimal energy purchase and sale of the energy storage device in each time period according to the energy price established by the energy system manufacturer, and the optimization target is expressed as:
in the formula (37), p (w) is the probability of occurrence of typical day w; w is the maximum typical number of days; u shape ESP Earnings for energy storage service providers;
the sales income and the purchase expenditure of the w typical daily ESP are respectively expressed as formula (38) to formula (39):
in the formulae (38) to (39),
w typical days at t hours respectivelyThe electricity purchase price and the heat purchase price of an energy system operator;
the electricity selling price and the heat selling price of the energy system operator at t moment under w typical days are respectively;
and respectively representing the electric power and the thermal power sold and bought by an energy storage service provider from an energy system operator at the moment t.
9. The integrated energy system seasonal hydrogen storage optimization configuration method based on the distributed collaborative optimization strategy according to claim 1, wherein the method comprises the following steps: in the step 2, the load aggregator has three load models of electricity, heat and hydrogen, and the load aggregator is specifically represented by formulas (40) to (42) by considering transferable characteristics of electricity and heat loads and reducible characteristics of electricity, heat and hydrogen loads:
in the formulae (40) to (42),
the electric load, the heat load and the hydrogen load required by the load aggregator at the w typical day t moment respectively;
respectively the basic electric, thermal and hydrogen loads;
respectively the transferred electricity and the heat load at the time t;
the upper limit of electric load transfer and the upper limit of heat load transfer are respectively; k is a radical of formula cut,e 、k cut,h 、k cut,H Curtailable coefficients for electrical, thermal, and hydrogen loads, respectively;
when the user is able to deviate from the load, a corresponding loss of satisfaction, a loss of satisfaction U, results w,SL As shown in equation (43):
in formula (43), ω E 、
Is a satisfactory loss parameter of the energy source E; e 'is a set of load classes, E' = { E, H }; d E,w,t The adjustment amount of the load E at the time t is the actual load
And baseline load
By introducing auxiliary variables
And
and constraints (45), (46) that convert the absolute value term in equation (43) to a linear form represented by equation (44):
the load aggregator adjusts the self load by using the minimization of the sum of the energy satisfaction degree loss and the energy purchase cost as a target on the basis of the energy price established by the energy system operator, and the target function is described as an equation (47):
in formula (47), p (w) is the probability of occurrence of typical day w; w is the maximum typical number of days; u shape LA Purchasing energy benefit cost for a user; u shape w,SL Loss of user satisfaction for the w-th typical day;
the cost of energy purchase under the typical day w is shown as a formula (48);
in the formula (I), the compound is shown in the specification,
γ sell,H the prices of electricity, heat and hydrogen sold by the operators of the energy system at t time of w typical days are respectively;
respectively for w typical day t time energy system operationElectricity, heat and hydrogen power sold by the trader to the load aggregation trader;
the electricity and the heat energy sold by the energy system operator are equal to the sum of the electricity and the heat energy purchased by the energy storage service provider and the load aggregation provider; the hydrogen energy sold by the energy system operator is equal to the hydrogen energy purchased by the load aggregator; the electricity and the heat energy purchased by the energy system operator are equal to those sold by energy manufacturers and energy storage service providers, namely the constraint of the formula (49) is met:
in the formula (49), the reaction mixture is,
electric power and thermal power purchased respectively for an energy system operator at the time t of the w typical day;
electricity, heat and hydrogen power sold by an energy system operator at the w typical day t moment respectively;
electric power sold to an energy system operator by an energy producer and an energy storage service provider at the w typical day t moment respectively;
respectively selling thermal power to an energy system operator for an energy producer and an energy storage service provider at the t moment of the w typical day;
electric power and thermal power sold to an energy storage service provider by an energy system operator at the time t of the w typical day respectively;
are respectively asThe energy system operator sells electricity, heat, and hydrogen power to the load aggregator at the w-th typical time of day, t.
10. The integrated energy system seasonal hydrogen storage optimization configuration method based on the distributed collaborative optimization strategy according to claim 1, wherein the method comprises the following steps: the specific solving process for the master-slave game is as follows:
if the optimal strategy obtained by each main body in two adjacent iterations satisfies the formula (54), the strategy is considered as a game equilibrium point, and at the moment, any participant can not obtain more profits by independently changing the strategy, and the iteration is stopped; if not, enabling g = g +1 and returning;
in the formula (54), g is the current iteration number;
vector expression forms of strategies of an energy system operator, an energy producer, an energy storage service provider and a load aggregator obtained by the g-th iteration are respectively obtained;
and respectively representing vector expression forms of strategies of an energy system operator, an energy producer, an energy storage service provider and a load aggregator obtained by the g-1 st iteration.
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| CN117578533A (en) * | 2024-01-15 | 2024-02-20 | 华北电力大学 | Electro-hydrogen fusion collaborative optimization configuration method oriented to electro-hydrogen supply capability improvement |
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| CN115630753B (en) * | 2022-12-19 | 2023-03-03 | 西南交通大学 | Load baseline prediction method for electrolytic hydrogen production based on new energy multi-space-time scene |
| CN117578533A (en) * | 2024-01-15 | 2024-02-20 | 华北电力大学 | Electro-hydrogen fusion collaborative optimization configuration method oriented to electro-hydrogen supply capability improvement |
| CN117578533B (en) * | 2024-01-15 | 2024-05-10 | 华北电力大学 | Electro-hydrogen fusion collaborative optimization configuration method oriented to electro-hydrogen supply capability improvement |
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