CN112508604A - Optimization method, system, equipment and storage medium of integrated energy system - Google Patents
Optimization method, system, equipment and storage medium of integrated energy system Download PDFInfo
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Abstract
The invention provides an optimization method, a system, equipment and a storage medium of a comprehensive energy system, which are suitable for the technical field of energy systems, and the method comprises the following steps: the management equipment acquires the electricity purchasing quantity and the electricity selling quantity which are respectively reported by the multiple integrated energy systems, and sets the transaction electricity prices of the management equipment and the multiple integrated energy systems according to the electricity purchasing quantity, the electricity selling quantity and the electricity price information of the electricity market and a dynamic pricing game model; the multiple comprehensive energy systems set the electric quantity purchased and the electric quantity sold which are reported respectively according to the transaction electricity price and the energy management game model; the dynamic pricing game model aims at maximizing net profit, and the energy management game model aims at minimizing running cost. The invention can reduce the operation cost of the comprehensive energy system.
Description
Technical Field
The invention belongs to the technical field of energy systems, and particularly relates to an optimization method, system, equipment and storage medium of an integrated energy system.
Background
In order to improve the energy utilization efficiency, a comprehensive energy system is produced. Compared with the traditional single-stage utilization form of energy, the comprehensive energy system can be used for multi-stage utilization of energy such as electricity, heat, gas and the like. With the development of the integrated energy system and the gradual reform of the electric power market, the integrated energy system has become a development trend.
However, the operation cost of the existing integrated energy system is often high, and an optimization method capable of reducing the operation cost of the integrated energy system is urgently needed.
Disclosure of Invention
In view of this, embodiments of the present invention provide an optimization method, system, device and storage medium for an integrated energy system, so as to solve the problem in the prior art that the operation cost of the integrated energy system is often high. In order to achieve the purpose, the invention adopts the technical scheme that:
a first aspect of an embodiment of the present invention provides a method for optimizing an integrated energy system, where the method is applied to a multi-agent integrated energy system, the multi-agent integrated energy system includes a plurality of integrated energy systems and a management device that manages the plurality of integrated energy systems, and the method includes:
the management equipment acquires the electricity purchasing quantity and the electricity selling quantity which are respectively reported by the multiple integrated energy systems, and sets the transaction electricity prices of the management equipment and the multiple integrated energy systems according to the electricity purchasing quantity, the electricity selling quantity and the electricity price information of the electricity market and a dynamic pricing game model;
the multiple comprehensive energy systems set the electric quantity purchased and the electric quantity sold which are reported respectively according to the transaction electricity price and the energy management game model;
the dynamic pricing game model aims at maximizing net profit, and the energy management game model aims at minimizing running cost.
A second aspect of an embodiment of the present invention provides an optimization system of an integrated energy system, including a plurality of integrated energy systems and a management apparatus that manages the plurality of integrated energy systems;
the management equipment is used for acquiring the electricity purchasing quantity and the electricity selling quantity which are respectively reported by the multiple integrated energy systems, and setting the transaction electricity prices of the management equipment and the multiple integrated energy systems according to the electricity purchasing quantity, the electricity selling quantity and the electricity price information of the electricity market and a dynamic pricing game model;
the comprehensive energy systems are respectively used for setting the electric quantity purchased and the electric quantity sold which are reported respectively according to the transaction electricity price and the energy management game model;
the dynamic pricing game model aims at maximizing net profit, and the energy management game model aims at minimizing running cost.
A third aspect of an embodiment of the present invention provides a management apparatus, including: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, performs the steps of the method according to the first aspect.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, performs the steps of the method according to the first aspect.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
compared with the prior art, the management device in the optimization method of the integrated energy system provided by the embodiment of the invention can acquire the electric quantity purchased and the electric quantity sold by the plurality of integrated energy systems, and sets the transaction electricity prices of the management device and the plurality of integrated energy systems according to the electric quantity purchased, the electric quantity sold and the electricity price information of the electric power market and the dynamic pricing game model. In addition, the comprehensive energy systems set the electric quantity purchased and the electric quantity sold which are reported respectively according to the transaction electricity price and the energy management game model. The dynamic pricing game model aims at maximizing net profits, and the energy management game model aims at minimizing the running cost, so the running cost of the comprehensive energy system can be reduced.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a flowchart illustrating steps of a method for optimizing an integrated energy system according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating the calculation of an external module according to an embodiment of the present invention;
FIG. 3 is a flow chart of a calculation of an inner module according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an optimization system of an integrated energy system according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a management device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
In order to solve the problems in the prior art, embodiments of the present invention provide an optimization method, system, device, and storage medium for an integrated energy system. First, the method for optimizing the integrated energy system according to the embodiment of the present invention will be described.
The main body of the optimization method of the Integrated Energy System may be a multi-body Integrated Energy System, and the multi-body Integrated Energy System may include a plurality of Integrated Energy Systems (IES), and an Integrated Energy System Operator (IESO) that manages the plurality of Integrated Energy systems, wherein the Integrated Energy System Operator may make trade electricity prices with the plurality of Integrated Energy systems, including a purchase electricity price at which the Integrated Energy System purchases electric quantity from the management device, and a sale electricity price at which the Integrated Energy System sells electric quantity to the management device.
Due to the fact that equipment components and operation conditions are inconsistent, electricity utilization costs of different comprehensive energy systems are different at different moments, the situation that one comprehensive energy system is abundant in electricity and low in electricity utilization cost, and the other comprehensive energy system needs to purchase electricity from a power grid to meet electricity utilization requirements can exist. Under the condition, the internal electricity trading of the comprehensive energy system can reduce the electricity utilization cost, and the multiple comprehensive energy systems run cooperatively, so that the pressure caused by deviation electricity examination can be reduced, and the operation risk is reduced.
Embodiments of the present invention provide a trading mechanism of an integrated energy system, wherein a plurality of integrated energy systems can be uniformly managed by an integrated energy system operator, the integrated energy system operator can participate in an electric power spot market, and the integrated energy system operator can declare electric quantity and electricity price in the electric power spot market on the basis of evaluating electric quantity that can be provided by each integrated energy system. After the declared electric quantity and the electricity price are agreed, the comprehensive energy system operator can issue the formed electric quantity and electricity price information to each comprehensive energy system, then each comprehensive energy system carries out internal optimization according to the electric quantity and electricity price information, electric quantity support is provided for the comprehensive energy system operator, and the comprehensive energy system operator distributes the electric quantity.
For convenience of description, the IES is used to refer to the integrated energy system, and the IESO is used to refer to the integrated energy system operator.
It should be noted that, in the established trading mechanism, based on the difference between the statuses and the roles of the IESO and the IES, an IESO and IES master-slave game model in the electric power spot market can be constructed, wherein the IESO is an upper leader, and the trading electricity prices of the IES and the IESO are formed by summarizing the purchased and sold electricity quantities reported by the IES in the composition main body and combining the electricity quantity and electricity price information formed in the spot market with the goal of maximizing the self benefit. Each IES is used as a lower-layer follower, the transaction electricity price established by the IESO is established, and the electricity amount transacted with the IESO is established by internal operation optimization with the aim of lowest operation cost. The leader of the upper layer and the follower of the lower layer play games in sequence to achieve balance, and the operation optimization of the comprehensive energy system under the master-slave game is realized.
The IESO and the participating IES master-slave gaming model comprise the following 3 parts:
1) and (4) participants. The IESO and IES as participants constitute a master-slave game.
2) And (4) strategy. When the IESO and the IES play games, the strategy of the IESO is based on electricity purchasing price, the strategy of the IES is based on electricity purchasing quantity, and the strategies of the IESO and the IES take values in respective strategy spaces respectively.
3) A utility function. IESO and IES in gaming differ in their goals, with IESO targeting the maximization of the net profit on its own, and IES targeting the minimization of the operating costs.
As shown in fig. 1, the method for optimizing an integrated energy system according to an embodiment of the present invention includes the following steps:
s110, the management device obtains the electricity purchasing quantity and the electricity selling quantity which are respectively reported by the multiple integrated energy systems, and sets the transaction electricity prices of the management device and the multiple integrated energy systems according to the electricity purchasing quantity, the electricity selling quantity and the electricity price information of the electricity market and the dynamic pricing game model.
In some embodiments, the management device may be the above mentioned integrated energy system operator IESO.
In some embodiments, the dynamic pricing gambling model may include:
wherein, FDSOAn objective function of the dynamic pricing game model;
and
respectively the power price of the power market at the moment i, the power price of the power grid, Pi DSO，sAnd Pi DSO，bRespectively the electricity selling quantity and the electricity purchasing quantity of the management equipment DSO (IESO for short) to the electricity market;
and
the electricity purchasing price and the electricity selling price of the plurality of integrated energy systems are respectively set at the moment i by the management equipment,
and
respectively representing integrated energy systems IESaReporting the electricity selling quantity and the electricity purchasing quantity to the management equipment; n is the total number of the comprehensive energy system, N, I and I are positive integers, N is more than or equal to 2, I belongs to [1, I ∈ [ ]]，I≥2。
To ensure a balance of supply and demand between the various integrated energy systems, Pi DSo，sAnd Pi DSO，bThe following relationship can be satisfied:
wherein the content of the first and second substances,
after the power consumption of each comprehensive energy system is gathered for the operator, the total electric energy traded with the electric power market is represented to the electric power market when the value is larger than zeroAnd when the electricity is purchased and is less than zero, electricity is sold to the electricity market.
In some embodiments of the present invention, the,
and
the following constraint conditions are satisfied:
therefore, when the electricity purchasing price established by the IESO is not more than the electricity price of the power grid and the electricity selling price is not less than the electricity price of the internet, each comprehensive energy system can select to trade with an operator to ensure the maximization of the benefit of the comprehensive energy system.
S120, setting the electric quantity purchased and sold respectively reported by the multiple comprehensive energy systems according to the transaction electricity price and the energy management game model;
in some embodiments, the energy management gaming model may include:
wherein the content of the first and second substances,
for the objective function of the energy management gaming model,
is the output power of the micro gas turbine,
is the charging and discharging power of the stored energy,
a value greater than zero indicates discharge, and less than zero indicates charge;
in order to be able to interrupt the interrupt power of the load,
m belongs to N for the output power of the wind turbine generatora，NaFor integrated energy system IESaA set of all devices involved;
in order to reduce the cost of the micro gas turbine,
in order to save the energy cost,
to interruptible load cost, xm、ym、zmIn order to be a cost factor for the micro gas turbine,
in order to be a cost factor for the stored energy,
the electricity prices are compensated for the interruption.
In some embodiments, the energy management gaming model may further include the following constraints:
wherein the content of the first and second substances,
for integrated energy system IESaPredicted value of power at time i, thetaa，iIs a Boolean variable when thetaa，iRepresenting the IES when the value is 1aSelling power to the management device at time i, when thetaa，iWhen the value is 0, the IES is expressedaAcquiring the electric quantity from the management equipment at the moment i;
representing integrated energy systems IESaA maximum value of the amount of electricity transacted with the management device,
represents the maximum value of the micro gas turbine output power,
respectively representing the downward and upward ramp rates of the micro gas turbine,
to store the state of charge at time i,
is the upper limit value of the state of charge,
is the lower limit value of the state of charge,
is the upper limit value of the energy storage charging and discharging power,
for charging and discharging energy storageLower limit value of rate, Em，maxIs the maximum value of the energy storage capacity,
is the maximum value of the interruptible load interruption amount,
and the maximum value of the output power of the wind turbine generator at the moment i.
In the embodiment of the invention, the management device can acquire the electricity purchasing quantity and the electricity selling quantity respectively reported by the multiple integrated energy systems, and sets the transaction electricity prices of the management device and the multiple integrated energy systems according to the electricity purchasing quantity, the electricity selling quantity and the electricity price information of the electricity market and the dynamic pricing game model. In addition, the comprehensive energy systems set the electric quantity purchased and the electric quantity sold which are reported respectively according to the transaction electricity price and the energy management game model. The dynamic pricing game model aims at maximizing net profits, and the energy management game model aims at minimizing the running cost, so the running cost of the comprehensive energy system can be reduced.
In order to better understand the optimization method of the comprehensive energy system provided by the embodiment of the invention, a master-slave game model is established as follows:
p＝(p1,p2,…,pN)T。
wherein the yield maximization and the operation cost minimization are respectively used as respective targets of the IESO and the IES to make respective adaptive strategies. The income of the IESO is closely related to the established purchase and sale electricity price and the transaction electricity quantity of the IES: the greater the difference between the electricity purchase price and the electricity sale price, the greater the amount of electricity shared by the IES, thereby increasing the revenue of the IESO.
In addition, the response behavior of the IES to electricity prices also affects the yield of the IESO: the higher the electricity purchase price given by the IESO, the less the electricity purchased by the comprehensive energy system; similarly, the lower the price of electricity sold by the IESO, the less electricity sold by the integrated energy system, resulting in less and less electricity shared between the various integrated energy systems. It can be seen that there is really a interest game between the operator and the integrated energy system. In order to maximize the self income, operators need to consider the response behavior of the comprehensive energy system to the price, and use the determined Nash equilibrium solution as the optimal electricity price strategy.
Specifically, the integrated energy system aims at the minimum operation cost and the minimum pollutant emission, wherein the operation cost mainly comprises fuel cost consumed by equipment, equipment maintenance cost, purchased energy cost, equipment depreciation cost and subsidy income brought by adopting renewable energy power generation, and pollutant emission is converted into pollutant treatment cost, and the method comprises the following steps:
wherein the content of the first and second substances,
is the amount of total fuel consumption of plant k;
is the price of fuel consumed by device k; cm_kIs the maintenance cost of the equipment k;
is the price of the purchased energy i; pi ENIs the purchase amount of energy i;
is the depreciation cost of device k;
is the subsidy price of the nth energy;
is the power generation of the nth energy source; piIs the total consumption of the ith energy source; omegai,jIs the emission coefficient of the pollutant j generated by the consumed energy i;
is the cost of treating the pollutant j.
Furthermore, the constraints of the integrated energy system may include power balance constraints, equipment operation constraints, and customer demand side constraints, wherein the equipment operation constraints may include ramp rate constraints, energy storage battery constraints, power network constraints, and customer demand side constraints.
And power balance constraint:
wherein the content of the first and second substances,
is the power generation of the device k over time period t; pt eThe electric energy purchase amount in the time period t;
is the electrical load demand of the user for time period t;
is the amount of power that device k consumes for energy supply;
is the heat supply of the device k in the time period t;
is the total heat load demand within the optimization zone for time period t;
is the cooling capacity of the device k during the period t;
is the total cooling load demand in the optimized region for time period t.
And (3) equipment output constraint:
where γ (t) is the operating state of the device k for time period t; pmax_k(t)、Pmin_k(t) maximum minimum output power of the device for time period t, respectively; pΔmin_k(t)、PΔmax_k(t) is the ramp rate at which device k decreases or increases the force, respectively, for time period t.
And (3) slope climbing rate constraint:
Pm,dn≤Pm,i-Pm,i-1≤Pm,up；
wherein, Pm,dn、Pm,upRespectively representing the downward and upward climbing rates of the equipment
And (4) energy storage battery restraint:
network transmission constraints:
wherein the content of the first and second substances,
respectively, the maximum and minimum power allowed to be transmitted by the grid node n;
is the voltage value of the grid node n during the period t;
respectively the maximum and the maximum allowed when the grid node n transmits electric energyA small voltage;
is the traffic of the heat network node n for the time period t;
the maximum flow and the minimum flow allowed by the node n when the heat supply network transmits heat energy are respectively;
is the flow of the cooling network at node n during the period t;
the maximum flow and the minimum flow allowed by the node n when the cold network supplies cold are respectively set;
is the flow of the natural gas network at the node n at the time period t;
the maximum and minimum flow rates allowed by the node n when the natural gas network transmits natural gas are respectively.
User side interruptible load constraint:
wherein the content of the first and second substances,
is the maximum value of the interruptible load interruption amount.
After the master-slave game model is obtained, the following solving algorithm can be adopted for solving.
The chaotic particle swarm algorithm is adopted for solving, the ordinary particle swarm algorithm is low in solving speed and poor in convergence effect and is easy to fall into a local optimum point, and the performance of the algorithm can be greatly improved by introducing the particle swarm algorithm into a chaotic searching mode.
The solving process comprises two modules, wherein the optimization outer module is a net profit maximization optimization module and corresponds to the main body net profit of the master-slave game model; the optimized inner module is a minimum optimized scheduling module of the operation cost of the comprehensive energy system and corresponds to the slave operation cost of the master-slave game model.
The outer module process of the master-slave game model solving based on the chaotic particle swarm optimization is as follows:
(1) and predicting the load value of each comprehensive energy system on the next day.
(2) And initializing the population of the chaotic particle swarm algorithm of the external module (namely selling the electricity price and purchasing the electricity price) and conforming to the boundary constraint of the chaotic particle swarm of the external module. And (4) formulating parameters of the optimized external module chaotic particle swarm algorithm.
(3) And providing the predicted load value for the optimization inner module, and calling the optimization inner module to obtain 'low electricity' and 'multi-electricity' quantities when the operation cost of each comprehensive energy system is minimum. Because the internal power generation amount and the power consumption amount of the integrated energy system are not necessarily balanced, if the power surplus is presented to the outside, the integrated energy system is called as 'multi-power', and the power shortage is called as 'low power', namely, the integrated energy system can sell power to the part of an operator or the part needing power purchase from the operator.
(4) And calculating the fitness function value of each particle in the population of the outer module by combining the objective function of the optimized outer module and necessary constraint conditions according to the 'low electricity' and 'high electricity' quantities returned by the optimized inner module.
(5) And recording the optimal particles in the population and the historical optimal condition of each particle.
(6) Each particle is optimized by changing positions according to the global optimal particle and the historical optimal particle. And chaotic search is carried out on each particle subjected to position change in a local area by using an attraction chaotic algorithm.
(7) And judging whether the optimized inner module is converged according to the convergence condition of the chaotic particle swarm algorithm, and if so, obtaining a global optimal solution and a corresponding operation optimization result thereof. If not, return to (3).
For the optimized outer module, the optimized inner module is a function which can be called by the optimized outer module and is mainly used for solving the 'less electricity' and 'more electricity' quantities required by the optimized outer module. For optimizing the inner module, the comprehensive energy system load value provided by the outer module is known and is used as a basis for making a comprehensive energy system operation optimization scheme, and the optimized 'low-electricity' and 'multi-electricity' quantities are fed back to the optimizing outer module.
The inner module process of the master-slave game model solution comprises the following steps:
(1) random initialization and equipment effort. And formulating and optimizing parameters of the internal module chaotic particle swarm algorithm.
(2) And calculating the operation cost of the equipment output scheduling scheme corresponding to all the particles.
(3) And calculating the fitness function value of each particle by combining the constraint conditions of each optimization inner module according to the running cost corresponding to each particle.
(4) And recording the optimal particles in the population and the historical optimal condition of each particle.
(5) Each particle is optimized by changing positions according to the global optimal particle and the historical optimal particle. And introducing each particle with the changed position into a chaos algorithm to perform chaos search in a local area.
(6) And judging whether the optimized inner module is converged according to the convergence condition of the chaotic particle swarm algorithm, if so, obtaining a global optimal solution and corresponding 'low power' or 'multi-power' quantity, and returning the global optimal solution to the optimized outer module. If not, return to (2).
In the embodiment of the invention, the final output of the master-slave game model is a 24-hour combined operation plan of the energy supply equipment, which comprises the output conditions of different equipment at different moments, a master-slave game combination scheme formed by operation main bodies of different resources, the obtained operation benefit distribution and the like. This is actually a dynamic cell combination problem with complex constraints and objectives, a mixed quadratic integer programming process. And in consideration of the requirements of the accuracy and the optimizing speed of the model solution, constructing a multi-main-body comprehensive energy model of the comprehensive energy system by adopting a genetic algorithm based on a chain type circulating structure.
Specifically, the Genetic Algorithm (GA) has good characteristics of learning, organization and adaptability, is a method for simulating natural evolution, and can find an optimal solution by simulating the process of heredity and variation in nature. The GA can find information and determine a search direction by utilizing the evolutionary characteristics of population according to a constantly changing rule, and has wide application in the fields of planning and operation of power systems. However, general genetic algorithms have a problem of easily falling into local optima. Aiming at the phenomenon that the basic genetic algorithm is optimal in progress due to premature convergence, the improved genetic algorithm based on the circular chain structure is applied to improve the convergence effect of the optimization algorithm and improve the optimization precision of the operation of the multi-subject comprehensive energy system.
The specific operation steps of the genetic algorithm based on the chain circulation structure are as follows:
(1) and (5) encoding. The GA requires encoding the control variables in the problem to be optimized into a finite length set of numbers, either in real or binary form. The encoding string of solutions with the optimization problem is called a "chromosome", and a set of such solutions (chromosomes) is called a population.
(2) And initializing the population. The population is initialized in order to determine the scale of the number of factors participating in optimization iteration in the genetic algorithm, and the convergence speed and the calculation time of the algorithm are influenced by the size of the population. In general, genetic algorithms produce an initial population primarily through two forms: one is to generate population samples with limited range through basic data or constraint conditions; the other is generated by a completely random method such as a random number generation tool.
(3) Individuals in the population are evaluated using a fitness function. Calculating the fitness function value can be regarded as an interface between a genetic algorithm and an optimization problem, namely, the fitness of different individuals to an optimization environment is evaluated through setting of an optimization objective function, so that the evolution direction of the individuals is determined.
(4) And (4) selecting. The population individuals perform a directional self-replication process based on their fitness value, which is called a selection process of a genetic algorithm. In the process of self-replication of individuals, the individuals with high optimized environmental fitness can survive more easily, and the excellent characteristics of the individuals are inherited to next generation individuals. And (3) carrying out fitness comparison on the individuals participating in the selection process in a neighborhood range specified by the individuals based on the genetic algorithm of the circular chain structure, and if the fitness of the current individual is smaller than the maximum fitness value of the neighborhood, stopping the replication of the individual and exiting the survival competition of the individual. Assuming that the current individual is located at the (1, i) grid point, and there are n competing individuals in the domain, the competition selection formula among these individuals is as follows:
(5) and (4) crossing. Crossover is the process of exchanging the genetic characteristics of individuals with good fitness and further optimizing the generation of more optimal crossover progeny individuals. Compared with a general random crossover operator, the typical individuals uniformly distributed in the search space can be identified by using orthogonal crossover. The process of the orthogonal crossover operator is as follows: firstly, determining 2 parent individuals required by children; secondly, performing a multi-factor orthogonal test on 2 individuals; and finally, selecting the individual with the highest adaptation degree from the three individuals obtained by the orthogonal result as an optimizing offspring.
(6) And (5) carrying out mutation. The variation is a process of implementing variation within a limited range on crossed optimization individuals to solve the improvement state of the individuals, and implementing variation within a small range by adding superior Gaussian disturbance to the individuals in a relative definition domain. The process of compiling new variant individual Xe, i ═ (ne1, ne2, …, nen) is shown in formula X:
wherein, in the formula, P1/nRepresenting the variation probability, the value of which is equal to 1/n; ggsThe random number generated for the Gaussian distribution takes a value between 0 and 1/t. t is the number of loop iterations at the present time of the variation.
(7) And (4) terminating the conditions. And when the fitness function value of the optimal individual in the population meets the set limit, or the fitness function value of the optimal individual and the whole population does not change any more, or the iteration times reach any of the 3 conditions of the set upper limit, stopping the operation, and outputting the final optimization result.
In consideration of the accuracy and the optimization speed requirements of the comprehensive energy system multi-main-body operation optimization solution, a genetic algorithm based on a chain circulation structure is adopted to construct an operation optimization model of the comprehensive energy system multi-main-body, and the calculation flow of the model is shown in fig. 2 and fig. 3, wherein fig. 2 is a calculation flow chart of the outer module, and fig. 3 is a calculation flow chart of the inner module, and the specific steps are as follows:
the input parameters mainly comprise 24-hour load prediction results of cold, heat and electricity, prediction power of the uncontrollable distributed energy, initial SOC (state of charge) state and charge-discharge power range of stored energy, working power range of CCHP (combined cycle power) and exchange power limit between a system and a power grid, power price information, natural gas price information and the like.
Basic parameters of case analysis are input, including optimization targets, initial population number, maximum iteration number, equipment parameters in the system and the like.
And (4) operation simulation, namely simulating the coordinated operation optimization process of the simulated comprehensive energy system according to the original comprehensive energy load data (24 hours) and the basic parameters.
And calculating an objective function, namely continuously changing the fitness of population individuals through selection, intersection and variation on the premise of meeting energy load and taking energy price response as an optimization condition, and optimizing and calculating the optimal value of the objective function under the constraint of a system.
And outputting the result to obtain a simulation result of the coordinated operation optimization, wherein the simulation result comprises a power output plan of each distributed energy unit, an optimization value of an objective function, a power purchase plan of a power grid, natural gas consumption and the like.
In addition, the embodiment of the invention also provides a system for optimizing the operation of the comprehensive energy system based on the master-slave game, which comprises the following steps: the system comprises a scene setting module, a short-term load prediction module and an optimized output module:
a scene setting module: the existing scenes and various boundary conditions of the current stage of the park are selected, the existing scenes and various boundary conditions enter a selection scheme library, and a proper scheme is selected from the scheme library according to equipment of each link of the source network load storage.
A short-term load prediction module: a short-term load prediction model considering the demand response is established, the demand response signal is received in real time, and the load demand in the future 24 hours is subjected to rolling prediction. The system mainly considers electric load prediction, gas load prediction, heat load prediction and cold load prediction, and sets multiple prediction methods, and an optimal prediction method can be selected according to the calculation errors of the prediction methods.
An optimization result output module: and finally, outputting a 24-hour source side equipment combined operation plan, wherein the 24-hour source side equipment combined operation plan comprises the output conditions of different equipment at different moments, and finally, a master-slave game combination scheme formed by operation subjects of different resources and the obtained operation benefit distribution.
Based on the optimization method of the comprehensive energy system provided by the embodiment, correspondingly, the invention also provides a specific implementation mode of the optimization system of the comprehensive energy system applying the optimization method of the comprehensive energy system. Please see the examples below.
As shown in fig. 4, there is provided an optimization system of an integrated energy system, including a plurality of integrated energy systems 410 and a management apparatus 420 for managing the plurality of integrated energy systems;
the management device 420 is configured to obtain the electricity purchasing amount and the electricity selling amount respectively reported by the multiple integrated energy systems 410, and set the transaction electricity prices of the management device 420 and the multiple integrated energy systems 410 according to the electricity purchasing amount, the electricity selling amount and the electricity price information of the electricity market and according to a dynamic pricing game model;
the multiple comprehensive energy systems 410 are respectively used for setting the electric quantity purchased and the electric quantity sold which are reported respectively according to the transaction electricity price and the energy management game model;
the dynamic pricing game model aims at maximizing net profit, and the energy management game model aims at minimizing running cost.
Optionally, the dynamic pricing gaming model includes:
wherein, FDSOAn objective function of the dynamic pricing game model;
and
respectively the power price of the power market at the moment i, the power price of the power grid, Pi DSO，sAnd Pi DSO，bRespectively managing the electricity selling quantity and the electricity purchasing quantity of the DSO to the electricity market;
and
the electricity purchasing price and the electricity selling price of the plurality of integrated energy systems are respectively set at the moment i by the management equipment,
and
respectively representing integrated energy systems IESaReporting the electricity selling quantity and the electricity purchasing quantity to the management equipment; n is the total number of the comprehensive energy system, N, I and I are positive integers, N is more than or equal to 2, I belongs to [1, I ∈ [ ]]，I≥2。
Alternatively to this, the first and second parts may,
and
the following constraint conditions are satisfied:
optionally, the energy management gaming model includes:
wherein the content of the first and second substances,
for the objective function of the energy management gaming model,
is the output power of the micro gas turbine,
is the charging and discharging power of the stored energy,
in order to be able to interrupt the interrupt power of the load,
m belongs to N for the output power of the wind turbine generatora，NaFor integrated energy system IESaA set of all devices involved;
in order to reduce the cost of the micro gas turbine,
in order to save the energy cost,
to interruptible load cost, xm、ym、zmIn order to be a cost factor for the micro gas turbine,
in order to be a cost factor for the stored energy,
the electricity prices are compensated for the interruption.
Optionally, the energy management gaming model further includes the following constraints:
wherein the content of the first and second substances,
for integrated energy system IESaPredicted value of power at time i, thetaa，iIs a Boolean variable when thetaa，iRepresenting the IES when the value is 1aSelling power to the management device at time i, when thetaa，iWhen the value is 0, the IES is expressedaAcquiring the electric quantity from the management equipment at the moment i;
representing integrated energy systems IESaA maximum value of the amount of electricity transacted with the management device,
represents the maximum value of the micro gas turbine output power,
respectively represents the downward slope climbing rate and the upward slope climbing rate of the micro gas turbine,
to store the state of charge at time i,
is the upper limit value of the state of charge,
is the lower limit value of the state of charge,
is the upper limit value of the energy storage charging and discharging power,
for storing lower limit values of charging and discharging power, Em，maxIs the maximum value of the energy storage capacity,
is the maximum value of the interruptible load interruption amount,
and the maximum value of the output power of the wind turbine generator at the moment i.
In the embodiment of the invention, the management device can acquire the electricity purchasing quantity and the electricity selling quantity respectively reported by the multiple integrated energy systems, and sets the transaction electricity prices of the management device and the multiple integrated energy systems according to the electricity purchasing quantity, the electricity selling quantity and the electricity price information of the electricity market and the dynamic pricing game model. In addition, the comprehensive energy systems set the electric quantity purchased and the electric quantity sold which are reported respectively according to the transaction electricity price and the energy management game model. The dynamic pricing game model aims at maximizing net profits, and the energy management game model aims at minimizing the running cost, so the running cost of the comprehensive energy system can be reduced.
Fig. 5 is a schematic diagram of a hardware structure of a management device for implementing various embodiments of the present invention.
The management device may comprise a processor 501 and a memory 502 storing computer program instructions.
Specifically, the processor 501 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured as one or more Integrated circuits implementing embodiments of the present invention.
The processor 501 reads and executes the computer program instructions stored in the memory 502 to implement any one of the above-described embodiments of the optimization method of the integrated energy system.
In one example, the management device may also include a communication interface 503 and a bus 310. As shown in fig. 3, the processor 501, the memory 502, and the communication interface 503 are connected via the bus 310 to complete communication therebetween.
The communication interface 503 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the embodiments of the present invention.
Bus 310 includes hardware, software, or both to couple the components of the management device to each other. By way of example, and not limitation, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hypertransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus or a combination of two or more of these. Bus 310 may include one or more buses, where appropriate. Although specific buses have been described and shown in the embodiments of the invention, any suitable buses or interconnects are contemplated by the invention.
An embodiment of the present invention further provides a computer-readable storage medium, where the computer storage medium has computer program instructions stored thereon; the computer program instructions, when executed by the processor, implement the processes of the above-described embodiment of the optimization method for an integrated energy system, and achieve the same technical effects, and are not described herein again to avoid repetition.
It is to be understood that the invention is not limited to the specific arrangements and instrumentality described above and shown in the drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present invention are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications and additions or change the order between the steps after comprehending the spirit of the present invention.
The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments mentioned in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
As described above, only the specific embodiments of the present invention are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present invention, and these modifications or substitutions should be covered within the scope of the present invention.
Claims (12)
1. A method for optimizing an integrated energy system, the method being applied to a multi-agent integrated energy system including a plurality of integrated energy systems and a management apparatus for managing the plurality of integrated energy systems, the method comprising:
the management equipment acquires the electricity purchasing quantity and the electricity selling quantity which are respectively reported by the multiple integrated energy systems, and sets the transaction electricity prices of the management equipment and the multiple integrated energy systems according to a dynamic pricing game model according to the electricity purchasing quantity, the electricity selling quantity and electricity price information of an electricity market;
the multiple comprehensive energy systems set the electric quantity purchased and the electric quantity sold which are reported respectively according to the transaction electricity price and the energy management game model;
wherein the dynamic pricing gaming model targets net profit maximization and the energy management gaming model targets operating cost minimization.
2. The method of optimizing an integrated energy system of claim 1, wherein the dynamic pricing gambling model comprises:
wherein, FDSOAn objective function of the dynamic pricing gaming model;
and
respectively the power price of the power market at the moment i, the power price of the power grid, Pi DSO，sAnd Pi DSO，bRespectively managing the electricity selling quantity and the electricity purchasing quantity of the DSO to the electricity market;
and
the electricity purchasing price and the electricity selling price of the plurality of integrated energy systems are respectively set by the management equipment at the moment i,
and
respectively representing integrated energy systems IESaThe electricity selling amount and the electricity purchasing amount reported to the management equipment are reported; n is the total number of the comprehensive energy system, N, I and I are positive integers, N is more than or equal to 2, I belongs to [1, I ∈ [ ]]，I≥2。
3. The method of optimizing an integrated energy system of claim 2, wherein said method comprises
And
the following constraint conditions are satisfied:
4. the method for optimizing an integrated energy system of claim 2, wherein the energy management gaming model comprises:
wherein the content of the first and second substances,
is that it isThe objective function of the energy management gaming model,
is the output power of the micro gas turbine,
is the charging and discharging power of the stored energy,
in order to be able to interrupt the interrupt power of the load,
m belongs to N for the output power of the wind turbine generatora，NaFor integrated energy system IESaA set of all devices involved;
in order to reduce the cost of the micro gas turbine,
in order to save the energy cost,
to interruptible load cost, xm、ym、zmIn order to be a cost factor for the micro gas turbine,
in order to be a cost factor for the stored energy,
the electricity prices are compensated for the interruption.
5. The method for optimizing an integrated energy system according to claim 4, wherein the energy management gaming model further comprises the following constraints:
wherein the content of the first and second substances,
for integrated energy system IESaPredicted value of power at time i, thetaa，iIs a Boolean variable when thetaa，iRepresenting the IES when the value is 1aSelling power to the management device at time i, when thetaa，iWhen the value is 0, the IES is expressedaAcquiring the electric quantity from the management equipment at the moment i;
representing integrated energy systems IESaA maximum value of the amount of electricity transacted with the management device,
represents the maximum value of the micro gas turbine output power,
respectively represents the downward slope climbing rate and the upward slope climbing rate of the micro gas turbine,
to store the state of charge at time i,
is the upper limit value of the state of charge,
is the lower limit value of the state of charge,
is the upper limit value of the energy storage charging and discharging power,
for storing lower limit values of charging and discharging power, Em，maxIs the maximum value of the energy storage capacity,
is the maximum value of the interruptible load interruption amount,
and the maximum value of the output power of the wind turbine generator at the moment i.
6. An optimization system of an integrated energy system, comprising a plurality of integrated energy systems and a management apparatus for managing the plurality of integrated energy systems;
the management equipment is used for acquiring the electricity purchasing quantity and the electricity selling quantity which are respectively reported by the multiple integrated energy systems, and setting the transaction electricity prices of the management equipment and the multiple integrated energy systems according to a dynamic pricing game model according to the electricity purchasing quantity, the electricity selling quantity and the electricity price information of the electricity market;
the multiple comprehensive energy systems are respectively used for setting the electric quantity purchased and the electric quantity sold which are reported respectively according to the transaction electricity price and the energy management game model;
wherein the dynamic pricing gaming model targets net profit maximization and the energy management gaming model targets operating cost minimization.
7. The system for optimizing an integrated energy system according to claim 6, wherein the dynamic pricing gambling model comprises:
wherein, FDSOAn objective function of the dynamic pricing gaming model;
and
respectively the power price of the power market at the moment i, the power price of the power grid, Pi DSO，SAnd Pi DSO，bRespectively managing the electricity selling quantity and the electricity purchasing quantity of the DSO to the electricity market;
and
the electricity purchasing price and the electricity selling price of the plurality of integrated energy systems are respectively set by the management equipment at the moment i,
and
respectively representing the selling electricity quantity and the purchasing electricity quantity reported to the management equipment by the integrated energy system IESa; n is the total number of the comprehensive energy system, N, I and I are positive integers, N is more than or equal to 2, I belongs to [1, I ∈ [ ]]，I≥2。
8. The system for optimizing an integrated energy system according to claim 7, wherein the system is characterized by
And
the following constraint conditions are satisfied:
9. the system for optimizing an integrated energy system according to claim 7, wherein the energy management gaming model comprises:
wherein the content of the first and second substances,
for the objective function of the energy management gaming model,
is the output power of the micro gas turbine,
for charging of stored energyThe power of the electric discharge is set to be,
in order to be able to interrupt the interrupt power of the load,
m belongs to N for the output power of the wind turbine generatora，NaFor integrated energy system IESaA set of all devices involved;
in order to reduce the cost of the micro gas turbine,
in order to save the energy cost,
to interruptible load cost, xm、ym、zmIn order to be a cost factor for the micro gas turbine,
in order to be a cost factor for the stored energy,
the electricity prices are compensated for the interruption.
10. The system for optimizing an integrated energy system according to claim 9, wherein the energy management gaming model further comprises the following constraints:
wherein the content of the first and second substances,
for integrated energy system IESaPredicted value of power at time i, thetaa，iIs a Boolean variable when thetaa，iRepresenting the IES when the value is 1aSelling power to the management device at time i, when thetaa，iWhen the value is 0, the IES is expressedaAcquiring the electric quantity from the management equipment at the moment i;
representing integrated energy systems IESaA maximum value of the amount of electricity transacted with the management device,
represents the maximum value of the micro gas turbine output power,
respectively represents the downward slope climbing rate and the upward slope climbing rate of the micro gas turbine,
to store the state of charge at time i,
is the upper limit value of the state of charge,
is the lower limit value of the state of charge,
is the upper limit value of the energy storage charging and discharging power,
for storing lower limit values of charging and discharging power, Em，maxIs the maximum value of the energy storage capacity,
is the maximum value of the interruptible load interruption amount,
and the maximum value of the output power of the wind turbine generator at the moment i.
11. A management device, comprising: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements the steps of the method of any of claims 1 to 5.
12. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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