CN106651628B - Regional cooling, heating and power comprehensive energy optimal allocation method and device based on graph theory - Google Patents
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Abstract
The invention relates to a regional cooling, heating and power comprehensive energy optimal allocation method and a device based on graph theory, which are technically characterized by comprising the following steps of: step 1, constructing an optimization model with the minimum regional cooling, heating and power comprehensive energy cost according to a target function with the minimum regional cooling, heating and power comprehensive energy operation cost and preset constraint conditions; step 2, establishing a regional cooling, heating and power comprehensive energy optimization configuration model based on the graph theory by combining the network topology connection of the cooling, heating and power comprehensive energy system based on the optimization model with the minimum regional cooling, heating and power comprehensive energy cost established in the step 1; and 3, solving a regional cooling, heating and power comprehensive energy optimization configuration model based on the graph theory by adopting an improved genetic algorithm to realize the optimization configuration of the cooling, heating and power comprehensive energy. The invention comprehensively considers the mutual coupling characteristics of the hybrid energy of the cold, heat and electricity and takes the output constraint of the hybrid energy of the cold, heat and electricity into consideration, constructs an optimization model with the minimum cost of the regional comprehensive energy of the cold, heat and electricity, and effectively provides a solution for the optimal configuration of the hybrid energy of the cold, heat and electricity within the regional range.
Description
Technical Field
The invention belongs to the technical field of comprehensive energy optimization of cooling, heating and power, and particularly relates to a regional comprehensive energy optimization configuration method and device of cooling, heating and power based on graph theory.
Background
With the development of economic society, the energy consumption of cold, heat, electricity and the like of users is increased year by year, the total energy consumption is increased every year, and the total social energy consumption is high; under the call of national energy conservation and emission reduction and high-efficiency utilization of new energy, a user performs optimized utilization of the hybrid energy of cold, heat and electricity on energy selection, becomes an important component of energy conservation and cost saving of the user, and is an effective way for improving the energy utilization efficiency and effectively reducing the total social energy consumption; however, currently, an effective optimal configuration method for the optimal configuration of the hybrid energy of cold, heat and electricity is lacked, and a feasible technical means is lacked. The traditional optimal configuration method of the power distribution system generally provides a building level location and volume fixing method for an effective combined cooling, heating and power supply unit from the aspects of economic optimization of a large power grid/power distribution network, consideration of unilateral electric energy and the like.
In short, the traditional energy optimization configuration method only aims at the optimization configuration of electric energy, only considers the objective function and constraint condition of the electric power in a unilateral way, and does not consider the optimization configuration of various energy sources such as cold, heat, electricity and the like.
The comprehensive utilization of the hybrid energy of cold, heat and electricity is the future trend of the intelligent power grid/energy Internet. Aiming at the optimal configuration of electric energy, partial scholars develop research: for example, the literature (Wangjiang river, building-level combined cooling heating and power system optimization and multi-attribute comprehensive evaluation method research [ D ]. North China electric power university, 2010, doctor thesis) starts from the cooling and heating load of a single building, and realizes the comprehensive utilization of energy through a combined cooling, heating and power unit; since the starting point of the research of the literature is only from the electric energy source as its main research object, it is often difficult to achieve its goal when the demand of a plurality of buildings or other energy sources in the actual area is largely changed. Other documents also carry out intensive research on various aspects such as planning design, optimal control and the like of the combined cooling heating and power unit, but still have certain limitations: firstly, only a single element or a system of a triple co-generation unit is researched in the documents, and in the practical application process, the centralized cold supply mode is diversified, such as ice cold accumulation and water cold accumulation; there are also various ways of central heating, such as municipal pipe network, thermal power plant, etc.; and these modes are staggered and fused with each other, if only study triple supply unit single component or system, then have certain limitation, also lack practical application meaning.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a regional combined cooling, heating and power energy optimal allocation method and device based on graph theory, which can comprehensively consider the mutual coupling characteristics of the combined cooling, heating and power energy and also consider the output constraint of the combined cooling, heating and power energy.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
a regional cooling, heating and power comprehensive energy optimal configuration method based on graph theory comprises the following steps:
and 3, solving a regional cooling, heating and power comprehensive energy optimization configuration model based on the graph theory by adopting an improved genetic algorithm to realize the optimization configuration of the cooling, heating and power comprehensive energy.
In addition, the objective function of the optimization model with the minimum regional cooling, heating and power integrated energy cost in step 1 is as follows:
in the above formula, EiThe minimum electricity consumption cost of the ith user of the regional cooling, heating and power comprehensive energy cost; qiThe minimum value of the heat energy cost of the ith user is the comprehensive energy cost of the regional cooling, heating and power; wiThe minimum value of the cooling energy cost of the ith user of the regional cooling, heating and power comprehensive energy cost;n is the total number of regional cold, heat and electricity hybrid energy users;
the constraint conditions comprise:
(1) regional combined cooling, heating and power energy flow constraint
In the above formula, NsIs the total node number set; gij、BijIs the admittance coefficient between the nodes i, j; vi，VjIs the voltage amplitude of node i, j; pGi、QGiActive output and reactive output of the generator of the node i are respectively; pDi、QDiRespectively the active and reactive loads of node i; thetaijIs the power angle between node i and node j.
(2) Output power constraint of regional cooling, heating and power comprehensive energy node
Pimin≤Pi≤Pimax
In the above formula, PiminAnd PimaxRespectively is the lower limit and the upper limit of the output power of the cooling, heating and power integrated energy node;
(3) mutual coupling constraint of regional cooling, heating and power comprehensive energy
Pinmax≤Pc≤Poutmax
In the above formula, PinmaxAnd PoutmaxThe lower limit and the upper limit of the power sold to the large power grid are respectively set;
moreover, the specific method of the step 2 is as follows: considering the actual network topology connection of the cooling, heating and power comprehensive energy, the optimal configuration of the regional cooling, heating and power comprehensive energy is equivalent to a undirected weighted graph G (V, E, W), and the minimum weight W of the undirected weighted graph G is searched, so that the cooling, heating and power comprehensive energy is optimally configured;
wherein, the element in the set V is a fixed point or a node or a point of the undirected authorized graph G, which represents an actual cooling, heating and power supply in the regional scope, and the element is a finite nonempty node set consisting of regional cooling, heating and power supplies, and the nodes are numbered from 1 in sequence according to the quantity sequence of the cooling, heating and power supplies until all the cooling, heating and power supplies are numbered;
wherein, the element of the set E is the edge or line of the undirected authorized graph G, which represents the communication switch or PCC switch set between the cold and heat power sources, and can be used as EijIs represented by eijIs 1 and 0; wherein, 1 represents that the connection exists between the cold and heat power supply i and the cold and heat power supply j, and 0 represents that the connection does not exist; e represents a V middle edge set;
wherein, the element of the set W is the active power exchange value between any two nodes, which is called the weight of the undirected weighted graph G, WijAnd the active power value which is exchanged between the node i and the node j is represented, and the value is positive when the active power flows in, and is negative when the active power flows out.
Further, the specific steps of step 3 include:
(1) and (3) initializing the cold, heat and power network parameters in the region and the output data of each cold, heat and power supply according to the regional cold, heat and power hybrid energy optimal configuration model based on the graph theory constructed in the step (2) and taking the actual data of the project into consideration.
(2) Coding three coding decision variables of a grid-connected PCC switch of each cold and heat power supply, the operation cost and the capacity of a single cold and heat power supply by adopting a binary coding scheme;
(3) judging whether a termination condition is met or not by taking whether the maximum iteration times are reached or not as a basis for termination; if the result is reached, the operation is quitted, and a final result is obtained; otherwise, entering the step (4);
(4) setting the size of a population, and determining the parameters of a genetic algorithm by a selection, crossing and variation operation method;
wherein the adaptive crossover rate function is as follows:
in the above formula, favgIs the average fitness of each generation population; f. ofmaxIs the maximum fitness among the individuals to be crossed; f is the greater fitness of the two individuals to be crossed; pc1The value is 0.9，Pc2The value is 0.6;
wherein the adaptive variability function is as follows:
Pm＝Pm1-Pm1×i/N
in the above formula, Pm1The initial value of the variation rate is 0.08; i is the current iteration number; n is the total number of iterations;
(5) changing the cooling, heating and power configuration of the corresponding node, judging the configuration of the cooling, heating and power supply one by one according to the constraint condition of the objective function with the minimum regional cooling, heating and power comprehensive energy cost in the step 1, and entering the step (6) if the configuration of the cooling, heating and power supply meets the condition; otherwise, adding 1 to the iteration number N, and returning to the step (4);
(6) comprehensively considering an optimization target with minimum comprehensive energy cost of the cold-heat-electricity hybrid energy in the region and constraint conditions thereof, and constructing a fitness function shown as the following;
in the above formula, CmaxIs a given constant, f (x) is the objective function after normalization;
(7) and (4) replacing the optimal value generated by the adaptive function, adding 1 to the iteration number N, returning to the step (4), and performing the maximum iteration number termination judgment.
Moreover, the specific method in the step (2) of the step 3 is as follows: firstly, selecting a chromosome with the total coding length of 13, respectively forming chromosome strings by the three coding decision variables, and then connecting the chromosome strings into a complete chromosome; the first two represent grid-connected PCC switches of the cold and heat power supply, the middle five represent the operating cost of a single cold and heat power supply, and the last six represent the capacity of the single cold and heat power supply.
Regional cold, heat and electricity comprehensive energy optimal configuration device based on graph theory includes:
the optimization model building module is used for building an optimization model with the minimum regional cooling, heating and power comprehensive energy cost according to a target function with the minimum regional cooling, heating and power comprehensive energy operation cost and preset constraint conditions;
the optimization configuration model building module is used for building a regional cooling, heating and power comprehensive energy optimization configuration model based on a graph theory by combining network topology connection of a cooling, heating and power comprehensive energy system based on an optimization model with the minimum regional cooling, heating and power comprehensive energy cost built by the optimization module building module;
and the optimization configuration module is used for solving the regional cooling, heating and power comprehensive energy optimization configuration model based on the graph theory by adopting an improved genetic algorithm to realize the optimization configuration of the cooling, heating and power comprehensive energy.
The invention has the advantages and positive effects that:
1. the invention provides an optimization model with minimum comprehensive energy cost of cooling, heating and power, introduces a thought of a graph theory to process the optimization configuration model of the cooling, heating and power hybrid energy, and solves the optimization configuration model of the cooling, heating and power hybrid energy by adopting an improved genetic algorithm. Compared with the existing unilateral power energy optimization configuration, the method provided by the invention considers the mutual coupling characteristic of the hybrid energy of cooling, heating and power, gives consideration to the output constraint of the hybrid energy of cooling, heating and power, constructs an optimization model with the minimum comprehensive energy cost of regional cooling, heating and power, and effectively provides a solution for the optimization configuration of the hybrid energy of cooling, heating and power within a regional range.
2. The invention provides an optimization model with minimum comprehensive energy cost of cooling, heating and power from the whole area range, thereby overcoming the defect that the target is difficult to realize when the requirements of a plurality of buildings or other energy sources in the actual area are greatly changed by taking the electric energy sources as main research objects at the starting point of partial research.
Drawings
FIG. 1 is a schematic diagram of a real system of the city of Tianjin ecological system in the area cooling, heating and power comprehensive energy optimization configuration model based on the graph theory;
fig. 2 is a flow chart of an improved genetic algorithm of the regional cooling, heating and power comprehensive energy optimization configuration method based on graph theory.
Detailed Description
The embodiments of the invention will be described in further detail below with reference to the accompanying drawings:
the invention comprehensively considers the mutual coupling characteristics of the hybrid energy of the cold, heat and electricity, considers the output constraint of the hybrid energy of the cold, heat and electricity, constructs an optimization model with the minimum cost of the regional hybrid energy of the cold, heat and electricity, provides a cold, heat and electricity hybrid energy optimal configuration method based on graph theory, and effectively provides a solution for the optimal configuration of the cold, heat and electricity hybrid energy within the regional range.
A regional cooling, heating and power comprehensive energy optimization configuration method based on graph theory, as shown in figure 2, comprises the following steps:
the objective function of the optimization model with the minimum comprehensive energy cost of the regional cooling, heating and power is as follows:
in the above formula, EiThe minimum electricity consumption cost of the ith user of the regional cooling, heating and power comprehensive energy cost; qiThe minimum value of the heat energy cost of the ith user is the comprehensive energy cost of the regional cooling, heating and power; wiThe minimum value of the cooling energy cost of the ith user of the regional cooling, heating and power comprehensive energy cost; and n is the total number of regional users of the hybrid energy of cold, heat and electricity.
The constraint conditions comprise:
(1) regional combined cooling, heating and power energy flow constraint
The cold, heat and power flow constraint is the active power and reactive power balance constraint of each node, namely the flow constraint equation of the system is as follows:
in the above formula, NsIs the total node number set; gij、BijIs between nodes i and jAdmittance coefficient of (a); vi，VjIs the voltage amplitude of node i, j; pGi、QGiActive output and reactive output of the generator of the node i are respectively; pDi、QDiRespectively the active and reactive loads of node i; thetaijIs the power angle between node i and node j.
(2) Output power constraint of regional cooling, heating and power comprehensive energy node
Pimin≤Pi≤Pimax
In the above formula, PiminAnd PimaxRespectively is the lower limit and the upper limit of the output power of the cooling, heating and power integrated energy node;
its output power should be between its maximum and minimum output power.
(3) Mutual coupling constraint of regional cooling, heating and power comprehensive energy
The mutual coupling constraint means that the maximum interactive capacity at the common connecting point of the cold, the heat and the electricity must meet the physical transmission capacity limit of the connecting line or the supply and demand agreement achieved by the connecting line, and the capacity constraint is as follows:
Pinmax≤Pc≤Poutmax
in the above formula, PinmaxAnd PoutmaxThe lower limit and the upper limit of the power sold to the large power grid are respectively set;
considering the actual network topological connection of the cooling, heating and power comprehensive energy, enabling any cooling and heating power source to be equivalent to a node according to active power, carrying out corresponding connection between nodes which are directly connected in a physical topological manner and have energy exchange according to the actual topological connection, and enabling the regional cooling, heating and power comprehensive energy optimal configuration to be equivalent to an undirected weighted graph G (V, E, W) as shown in a cooling, heating and power comprehensive energy topological graph of the Zhongxintianjin ecological city in fig. 1, and establishing a regional cooling, heating and power comprehensive energy optimal configuration model based on graph theory.
The specific method of the step 2 comprises the following steps: the optimal configuration of the regional cooling, heating and power hybrid energy is equivalent to an undirected weighted graph G (V, E, W) as shown in fig. 1, and according to the above thought, the process of the optimal configuration of the regional cooling, heating and power integrated energy based on the graph theory is a process of finding the minimum weight W of the graph G.
Therefore, the minimum weight W of the undirected weighted graph G is searched, and the cooling, heating and power comprehensive energy is optimally configured.
Wherein, the element in the set V is a fixed point or a node or a point of the undirected authorized graph G, which represents an actual cooling, heating and power supply in the regional scope, and the element is a finite nonempty node set consisting of regional cooling, heating and power supplies, and the nodes are numbered from 1 in sequence according to the quantity sequence of the cooling, heating and power supplies until all the cooling, heating and power supplies are numbered;
wherein, the element of the set E is the edge or line of the undirected authorized graph G, which represents the communication switch or PCC switch set between the cold and heat power sources, and can be used as EijIs represented by eijIs 1 and 0; wherein, 1 represents that the connection exists between the cold and heat power supply i and the cold and heat power supply j, and 0 represents that the connection does not exist; e represents a V middle edge set;
wherein, the element of the set W is the active power exchange value between any two nodes, which is called as the weight of the graph G, WijAnd the active power value which is exchanged between the node i and the node j is represented, and the value is positive when the active power flows in, and is negative when the active power flows out.
In the energy supply range of regional cooling, heating and power supplies, on one hand, the cooling, heating and power supplies can independently operate in an isolated island manner, and on the other hand, due to the restriction of technical, cost, engineering practice and other factors, the practical physical connection switch PCC between all the cooling, heating and power supplies in a region is difficult to achieve, and after the PCC is connected, whether the interaction of active power can be formed, namely W isijNot equal to zero, and is also limited by various conditions such as energy storage and load connected with the cold-heat-electricity power supply.
And 3, solving a regional cooling, heating and power comprehensive energy optimization configuration model based on the graph theory by adopting an improved genetic algorithm to realize the optimization configuration of the cooling, heating and power comprehensive energy.
Because the regional cooling, heating and power comprehensive energy optimization configuration model based on the graph theory is a solving problem of a nonlinear multi-constraint problem, a heuristic improved genetic algorithm can be adopted for solving, and an optimal solution is obtained.
The specific steps of step 3 are shown in fig. 2, and include steps (1) to (7) described below.
(1) And (3) initializing the cold, heat and power network parameters in the region and the output data of each cold, heat and power supply according to the regional cold, heat and power comprehensive energy optimization configuration model based on the graph theory constructed in the step (2) and taking the actual data of the project into consideration.
(2) Coding three coding decision variables of a grid-connected PCC switch of each cold and heat power supply, the operation cost and the capacity of a single cold and heat power supply by adopting a binary coding scheme;
coding is the most important link in genetic operation, and the quality of a coding method directly influences the calculation speed of an algorithm, the correctness of a calculation result and the like. In the conventional binary coding scheme, each node is represented by a one-bit binary, however, the coding length of the coding scheme is too long as the number of nodes increases, thereby affecting the speed of calculation.
The specific method in the step (2) of the step 3 comprises the following steps: firstly, selecting a chromosome with the total coding length of 13, respectively forming chromosome strings by the three coding decision variables, and then connecting the chromosome strings into a complete chromosome; the first two represent grid-connected PCC switches of the cold and heat power supply, the middle five represent the operating cost of a single cold and heat power supply, and the last six represent the capacity of the single cold and heat power supply.
In this embodiment, a binary coding scheme is adopted, and the coded decision variables are a grid-connected PCC switch of the cooling and heating power supply and the operating cost and capacity of a single cooling and heating power supply. The optimization problem contains three decision variables (a grid-connected PCC switch of the cooling and heating power supply, and the running cost and the capacity of a single cooling and heating power supply) to participate in optimization, and belongs to multi-parameter coding. The idea is that each decision variable forms a chromosome string, and then the substrings are connected into a complete chromosome. The total length of the selected chromosome code is 13. The first two represent grid-connected PCC switches of the cold and heat power supply, the middle five represent the operating cost of a single cold and heat power supply, and the last six represent the capacity of the single cold and heat power supply. The coding method has short coding length, the coding length cannot become overlong along with the increase of the nodes, the calculation speed is high, and the convergence is good.
(3) Judging whether a termination condition is met or not by taking whether the maximum iteration times are reached or not as a basis for termination; if the result is reached, the operation is quitted, and a final result is obtained; otherwise, entering the step (4);
(4) setting a population size n, and determining parameters of a genetic algorithm by a selection, crossing and variation operation method;
the selection of population size in genetic algorithm calculations is important. If the size n of the population is selected to be small, the operation speed of the genetic algorithm can be improved, but the diversity of the population is reduced, and the premature phenomenon of the genetic algorithm can be possibly caused; and too large n will result in a reduction in the efficiency of the algorithm. Typically, n is typically from 20 to 100.
Selecting and operating: the selection operation is established on the basis of evaluating the fitness of the individuals, and the probability that the individuals with higher fitness are inherited to the next generation of population is higher, otherwise, the probability is lower.
And (3) calculating the crossing rate: crossover operations are used to create new individuals that play a central role in genetic manipulation. Different cross probabilities are generally chosen depending on the different fitness of the individual to be evolved. The adaptive crossover function is as follows:
in the above formula, favgIs the average fitness of each generation population; f. ofmaxIs the maximum fitness among the individuals to be crossed; f is the greater fitness of the two individuals to be crossed; pc1Value of 0.9, Pc2The value is 0.6;
and (3) calculating the variation rate: the mutation belongs to auxiliary operation in genetic operation, and the self-adaptive mutation rate is adopted, a higher mutation rate is adopted in the initial stage of evolution, and the mutation rate is continuously reduced along with the increase of generations so as to ensure the diversity and the excellence of a population. The adaptive variability function is as follows:
Pm＝Pm1-Pm1×i/N
in the above formula, Pm1The initial value of the mutation rate is set to 0.08; i is the current iteration number; and N is the total number of iterations.
(5) Changing the cooling, heating and power configuration of the corresponding node, judging the configuration of the cooling, heating and power supply one by one according to the constraint condition of the objective function with the minimum regional cooling, heating and power comprehensive energy cost in the step 1, and entering the step (6) if the configuration of the cooling, heating and power supply meets the condition; otherwise, adding 1 to the iteration number N, and returning to the step (4);
(6) comprehensively considering an optimization target with minimum comprehensive energy cost of the cold-heat-electricity hybrid energy in the region and constraint conditions thereof, and constructing a fitness function shown as the following;
in the above formula, CmaxIs a given constant, and f (x) is the normalized objective function.
(7) And (4) replacing the optimal value generated by the adaptive function, adding 1 to the iteration number N, returning to the step (4), and performing the maximum iteration number termination judgment.
A regional cooling, heating and power comprehensive energy optimal configuration device based on graph theory comprises: the system comprises an optimization model building module, an optimization configuration model building module and an optimization configuration module.
The optimization model building module is used for building an optimization model with the minimum regional cooling, heating and power comprehensive energy cost according to a target function with the minimum regional cooling, heating and power comprehensive energy operation cost and preset constraint conditions;
the objective function of the optimization model with the minimum comprehensive energy cost of the regional cooling, heating and power is as follows:
in the above formula, EiThe minimum electricity consumption cost of the ith user of the regional cooling, heating and power comprehensive energy cost; qiThe minimum value of the heat energy cost of the ith user is the comprehensive energy cost of the regional cooling, heating and power; wiThe minimum value of the cooling energy cost of the ith user of the regional cooling, heating and power comprehensive energy cost; n is the total number of regional cold, heat and electricity hybrid energy users;
the constraint conditions comprise:
(1) regional combined cooling, heating and power energy flow constraint
In the above formula, NsIs the total node number set; gij、BijIs the admittance coefficient between the nodes i, j; vi，VjIs the voltage amplitude of node i, j; pGi、QGiActive output and reactive output of the generator of the node i are respectively; pDi、QDiRespectively the active and reactive loads of node i; thetaijIs the power angle between node i and node j;
(2) output power constraint of regional cooling, heating and power comprehensive energy node
Pimin≤Pi≤Pimax
In the above formula, PiminAnd PimaxRespectively is the lower limit and the upper limit of the output power of the cooling, heating and power integrated energy node;
(3) mutual coupling constraint of regional cooling, heating and power comprehensive energy
Pinmax≤Pc≤Poutmax
In the above formula, PinmaxAnd PoutmaxRespectively, a lower limit and an upper limit of the power sold to the large power grid.
The optimization configuration model building module is used for building a regional cooling, heating and power comprehensive energy optimization configuration model based on a graph theory by combining network topology connection of a cooling, heating and power comprehensive energy system based on an optimization model with the minimum regional cooling, heating and power comprehensive energy cost built by the optimization module building module;
the optimized configuration model construction module is specifically configured to: considering the actual network topology connection of the cooling, heating and power comprehensive energy, the optimal configuration of the regional cooling, heating and power comprehensive energy is equivalent to a undirected weighted graph G (V, E, W), and the minimum weight W of the undirected weighted graph G is searched, so that the cooling, heating and power comprehensive energy is optimally configured;
wherein, the element in the set V is a fixed point or a node or a point of the undirected authorized graph G, which represents an actual cooling, heating and power supply in the regional scope, and the element is a finite nonempty node set consisting of regional cooling, heating and power supplies, and the nodes are numbered from 1 in sequence according to the quantity sequence of the cooling, heating and power supplies until all the cooling, heating and power supplies are numbered;
wherein, the element of the set E is the edge or line of the undirected authorized graph G, which represents the communication switch or PCC switch set between the cold and heat power sources, and can be used as EijIs represented by eijIs 1 and 0; wherein, 1 represents that the connection exists between the cold and heat power supply i and the cold and heat power supply j, and 0 represents that the connection does not exist; e represents a V middle edge set;
wherein, the element of the set W is the active power exchange value between any two nodes, which is called the weight of the undirected weighted graph G, WijAnd the active power value which is exchanged between the node i and the node j is represented, and the value is positive when the active power flows in, and is negative when the active power flows out.
And the optimization configuration module is used for solving the regional cooling, heating and power comprehensive energy optimization configuration model based on the graph theory by adopting an improved genetic algorithm to realize the optimization configuration of the cooling, heating and power comprehensive energy.
The optimized configuration module is specifically configured to perform:
(1) initializing the network parameters of the cooling, heating and power in the region and the output data of each cooling, heating and power supply according to the constructed regional cooling, heating and power comprehensive energy optimization configuration model based on the graph theory and considering the actual data of the project;
(2) coding three coding decision variables of a grid-connected PCC switch of each cold and heat power supply, the operation cost and the capacity of a single cold and heat power supply by adopting a binary coding scheme;
firstly, selecting a chromosome with the total coding length of 13, respectively forming chromosome strings by the three coding decision variables, and then connecting the chromosome strings into a complete chromosome; the first two represent grid-connected PCC switches of the cold and heat power supply, the middle five represent the operating cost of a single cold and heat power supply, and the last six represent the capacity of the single cold and heat power supply;
(3) judging whether a termination condition is met or not by taking whether the maximum iteration times are reached or not as a basis for termination; if the result is reached, the operation is quitted, and a final result is obtained; otherwise, triggering a parameter determination submodule;
(4) setting the size of a population, and determining the parameters of a genetic algorithm by a selection, crossing and variation operation method;
wherein the adaptive crossover rate function is as follows:
in the above formula, favgIs the average fitness of each generation population; f. ofmaxIs the maximum fitness among the individuals to be crossed; f is the greater fitness of the two individuals to be crossed; pc1Value of 0.9, Pc2The value is 0.6;
wherein the adaptive variability function is as follows:
Pm＝Pm1-Pm1×i/N
in the above formula, Pm1The initial value of the variation rate is 0.08; i is the current iteration number; n is the total number of iterations;
(5) changing the cooling, heating and power configuration of the corresponding node, judging the configuration of the cooling, heating and power supply one by one according to the constraint condition of the objective function with the minimum regional cooling, heating and power comprehensive energy cost in the step 1, and entering the step (6) if the configuration of the cooling, heating and power supply meets the condition; otherwise, adding 1 to the iteration number N, and returning to the step (4);
(6) comprehensively considering an optimization target with minimum comprehensive energy cost of the cold-heat-electricity hybrid energy in the region and constraint conditions thereof, and constructing a fitness function shown as the following;
in the above formula, CmaxIs a given constant, f (x) is the objective function after normalization;
(7) and (4) replacing the optimal value generated by the adaptive function, adding 1 to the iteration number N, returning to the step (4), and performing the maximum iteration number termination judgment.
In this embodiment, the regional cooling, heating and power comprehensive energy optimization configuration method based on the graph theory is implemented and applied based on the actual data of the intelligent park of the zhongxingjin ecological city, so as to verify the feasibility and the beneficial effects of the method.
The actual cooling, heating and power load data, the installed electric capacity and the municipal pipe network heating situation of an intelligent park in Tianjin ecological city, and the related economic data are shown in the following table 1.
(Table 1): general overview of the Intelligent park
| Item | Capacity of | Node location | Operating state | Economic data | ||||
| | 100M | 1，3，7 | From 11 months per year to 4 months in the following year | 2.3 yuan/Kwh | ||||
| | 120M | 1，3，8，9 | 5 to 9 months per year | 1.8 yuan/KWh | ||||
| | 300M | 1～9 | 1 to 12 months per year | 0.6 yuan/Kwh | ||||
| Large electric network | 300M | 1～9 | 1 to 12 months per year | 0.6 yuan/Kwh | ||||
| Municipal | 100M | 1，3，7 | From 11 months per year to 4 months in the following year | 2.3 yuan/Kwh |
The actual operation energy cost data of 2015 years are calculated, the total annual cooling, heating and power energy cost reaches 3147 ten thousand yuan, the monthly average cooling, heating and power energy cost reaches 262.25 ten thousand, the power cost is 2821 ten thousand, and the municipal heating cost is 326 ten thousand. Compared with the same type in China, the unit economic data of the cold and heat load is higher, the formed actual network topological connection shown in the figure 1 is combined, and by utilizing the regional cold and heat power comprehensive energy optimization configuration method based on the graph theory, an optimization model with the minimum total energy operation cost of the cold and heat power comprehensive energy is constructed firstly, then the optimization model based on the graph theory is constructed, and finally, the improved genetic algorithm is adopted for solving to perform optimization calculation.
After optimized configuration is carried out, a triple co-generation unit is newly added at a node 3, a distributed power supply is newly added at a node 8, an ice storage unit is newly added at a node 9, and the conditions are shown in table 2:
(Table 2): situation after intelligent park optimization planning configuration
From the comparison of table 2 with table 1 it can be readily found that: after the optimal configuration, the economic data of the cooling, heating and power loads of the intelligent park are all reduced. The economic data of the heat load unit is reduced from 2.3 yuan/Kwh to 1.9 yuan/Kwh, and the economic data of the cold load unit is reduced from 1.8 yuan/Kwh to 1.5 yuan/Kwh; the unit economic number of the electric load is reduced from 0.6 yuan/Kwh to 0.5 yuan/Kwh.
The optimal point of the triple generation unit can be found from the position of the optimal configuration node, wherein the triple generation unit is configured at the node where the cold, the heat and the electricity have intersection.
It should be emphasized that the embodiments described herein are illustrative rather than restrictive, and thus the present invention is not limited to the embodiments described in the detailed description, but also includes other embodiments that can be derived from the technical solutions of the present invention by those skilled in the art.
Claims (6)
1. The regional cooling, heating and power comprehensive energy optimization configuration method based on the graph theory is characterized in that: the method comprises the following steps:
step 1, constructing an optimization model with the minimum regional cooling, heating and power comprehensive energy cost according to a target function with the minimum regional cooling, heating and power comprehensive energy operation cost and preset constraint conditions;
step 2, establishing a regional cooling, heating and power comprehensive energy optimization configuration model based on the graph theory by combining the network topology connection of the cooling, heating and power comprehensive energy system based on the optimization model with the minimum regional cooling, heating and power comprehensive energy cost established in the step 1;
step 3, solving a regional cooling, heating and power comprehensive energy optimization configuration model based on a graph theory by adopting an improved genetic algorithm to realize the optimization configuration of the cooling, heating and power comprehensive energy;
the specific steps of the step 3 comprise:
(1) initializing the cold, heat and power network parameters in the region and the output data of each cold, heat and power supply according to the regional cold, heat and power comprehensive energy optimization configuration model based on the graph theory constructed in the step 2 and taking the actual data of the project into consideration;
(2) coding three coding decision variables of a grid-connected PCC switch of each cold and heat power supply, the operation cost and the capacity of a single cold and heat power supply by adopting a binary coding scheme;
(3) judging whether a termination condition is met or not by taking whether the maximum iteration times are reached or not as a basis for termination; if the result is reached, the operation is quitted, and a final result is obtained; otherwise, entering the step (4);
(4) setting the size of a population, and determining the parameters of a genetic algorithm by a selection, crossing and variation operation method;
wherein the adaptive crossover rate function is as follows:
in the above formula, favgIs the average fitness of each generation population; f. ofmaxIs the maximum fitness among the individuals to be crossed; f is the greater fitness of the two individuals to be crossed; pc1Value of 0.9, Pc2The value is 0.6;
wherein the adaptive variability function is as follows:
Pm＝Pm1-Pm1×i/N
in the above formula, Pm1The initial value of the variation rate is 0.08; i is the current iteration number; n is the total number of iterations;
(5) changing the cooling, heating and power configuration of the corresponding node, judging the configuration of the cooling, heating and power supply one by one according to the constraint condition of the objective function with the minimum regional cooling, heating and power comprehensive energy cost in the step 1, and entering the step (6) if the configuration of the cooling, heating and power supply meets the condition; otherwise, adding 1 to the iteration number N, and returning to the step (4);
(6) comprehensively considering an optimization target with minimum comprehensive energy cost of the cold-heat-electricity hybrid energy in the region and constraint conditions thereof, and constructing a fitness function shown as the following;
in the above formula, CmaxIs a given constant, f (x) is the objective function after normalization;
(7) replacing the optimal value generated by the adaptive function, adding 1 to the iteration number N, returning to the step (4), and performing maximum iteration number termination judgment;
the objective function of the optimization model with the minimum regional cooling, heating and power comprehensive energy cost in the step 1 is as follows:
in the above formula, EiThe minimum electricity consumption cost of the ith user of the regional cooling, heating and power comprehensive energy cost; qiThe minimum value of the heat energy cost of the ith user is the comprehensive energy cost of the regional cooling, heating and power; wiThe minimum value of the cooling energy cost of the ith user of the regional cooling, heating and power comprehensive energy cost; n is the total number of regional cold, heat and electricity hybrid energy users;
the constraint conditions comprise:
(1) regional combined cooling, heating and power energy flow constraint
In the above formula, NsIs the total node number set; gij、BijIs the admittance coefficient between the nodes i, j; vi，VjIs the voltage amplitude of node i, j; pGi、QGiActive output and reactive output of the generator of the node i are respectively; pDi、QDiRespectively the active and reactive loads of node i; thetaijIs the power angle between node i and node j;
(2) output power constraint of regional cooling, heating and power comprehensive energy node
Pimin≤Pi≤Pimax
In the above formula, PiminAnd PimaxRespectively is the lower limit and the upper limit of the output power of the cooling, heating and power integrated energy node;
(3) mutual coupling constraint of regional cooling, heating and power comprehensive energy
Pinmax≤Pc≤Poutmax
In the above formula, PinmaxAnd PoutmaxRespectively, a lower limit and an upper limit of the power sold to the large power grid.
2. The method for optimizing the configuration of regional cooling, heating and power integrated energy based on graph theory as claimed in claim 1, wherein: the specific method of the step 2 comprises the following steps: considering the actual network topology connection of the cooling, heating and power comprehensive energy, the optimal configuration of the regional cooling, heating and power comprehensive energy is equivalent to a undirected weighted graph G (V, E, W), and the minimum weight W of the undirected weighted graph G is searched, so that the cooling, heating and power comprehensive energy is optimally configured;
wherein, the element in the set V is a fixed point or a node or a point of the undirected authorized graph G, which represents an actual cooling, heating and power supply in the regional scope, and the element is a finite nonempty node set consisting of regional cooling, heating and power supplies, and the nodes are numbered from 1 in sequence according to the quantity sequence of the cooling, heating and power supplies until all the cooling, heating and power supplies are numbered;
wherein, the element of the set E is the edge or line of the undirected authorized graph G, which represents the communication switch or PCC switch set between the cold and heat power sources, and can be used as EijIs represented by eijIs 1 and 0; wherein, 1 represents that the connection exists between the cold and heat power supply i and the cold and heat power supply j, and 0 represents that the connection does not exist; e represents a V middle edge set;
wherein, the element of the set W is the active power exchange value between any two nodes, which is called the weight of the undirected weighted graph G, WijAnd the active power value which is exchanged between the node i and the node j is represented, and the value is positive when the active power flows in, and is negative when the active power flows out.
3. The method for optimizing the configuration of regional cooling, heating and power integrated energy based on graph theory as claimed in claim 1, wherein: the specific method in the step (2) of the step 3 comprises the following steps: firstly, selecting a chromosome with the total coding length of 13, respectively forming chromosome strings by the three coding decision variables, and then connecting the chromosome strings into a complete chromosome; the first two represent grid-connected PCC switches of the cold and heat power supply, the middle five represent the operating cost of a single cold and heat power supply, and the last six represent the capacity of the single cold and heat power supply.
4. Regional cold, heat and electricity comprehensive energy optimal configuration device based on graph theory, its characterized in that: the method comprises the following steps:
the optimization model building module is used for building an optimization model with the minimum regional cooling, heating and power comprehensive energy cost according to a target function with the minimum regional cooling, heating and power comprehensive energy operation cost and preset constraint conditions;
the optimization configuration model building module is used for building a regional cooling, heating and power comprehensive energy optimization configuration model based on a graph theory by combining network topology connection of a cooling, heating and power comprehensive energy system based on an optimization model with the minimum regional cooling, heating and power comprehensive energy cost built by the optimization model building module;
the optimization configuration module is used for solving a regional cooling, heating and power comprehensive energy optimization configuration model based on graph theory by adopting an improved genetic algorithm to realize the optimization configuration of the cooling, heating and power comprehensive energy;
the optimization configuration module is specifically configured to perform:
(1) initializing the network parameters of the cooling, heating and power in the region and the output data of each cooling, heating and power supply according to the constructed regional cooling, heating and power comprehensive energy optimization configuration model based on the graph theory and considering the actual data of the project;
(2) coding three coding decision variables of a grid-connected PCC switch of each cold and heat power supply, the operation cost and the capacity of a single cold and heat power supply by adopting a binary coding scheme;
(3) judging whether a termination condition is met or not by taking whether the maximum iteration times are reached or not as a basis for termination; if the result is reached, the operation is quitted, and a final result is obtained; otherwise, triggering a parameter determination submodule;
(4) setting the size of a population, and determining the parameters of a genetic algorithm by a selection, crossing and variation operation method;
wherein the adaptive crossover rate function is as follows:
in the above formula, favgIs the average fitness of each generation population; f. ofmaxIs the maximum fitness among the individuals to be crossed; f is the greater fitness of the two individuals to be crossed; pc1Value of 0.9, Pc2The value is 0.6;
wherein the adaptive variability function is as follows:
Pm＝Pm1-Pm1×i/N
in the above formula, Pm1The initial value of the variation rate is 0.08; i is the current iteration number; n is the total number of iterations;
(5) changing the cooling, heating and power configuration of the corresponding node, judging the configuration of the cooling, heating and power supply one by one according to the constraint condition of the objective function with the minimum regional cooling, heating and power comprehensive energy cost in the step 1, and entering the step (6) if the configuration of the cooling, heating and power supply meets the condition; otherwise, adding 1 to the iteration number N, and returning to the step (4);
(6) comprehensively considering an optimization target with minimum comprehensive energy cost of the cold-heat-electricity hybrid energy in the region and constraint conditions thereof, and constructing a fitness function shown as the following;
in the above formula, CmaxIs a given constant, f (x) is the objective function after normalization;
(7) replacing the optimal value generated by the adaptive function, adding 1 to the iteration number N, returning to the step (4), and performing maximum iteration number termination judgment;
the objective function of the optimization model with the minimum comprehensive energy cost of the regional cooling, heating and power is as follows:
in the above formula, EiThe minimum electricity consumption cost of the ith user of the regional cooling, heating and power comprehensive energy cost; qiThe minimum value of the heat energy cost of the ith user is the comprehensive energy cost of the regional cooling, heating and power; wiThe minimum value of the cooling energy cost of the ith user of the regional cooling, heating and power comprehensive energy cost; n is the total number of regional cold, heat and electricity hybrid energy users;
the constraint conditions comprise:
(1) regional combined cooling, heating and power energy flow constraint
In the above formula, NsIs the total node number set; gij、BijIs the admittance coefficient between the nodes i, j; vi，VjIs the voltage amplitude of node i, j; pGi、QGiAre respectively asActive output and reactive output of the generator of the node i; pDi、QDiRespectively the active and reactive loads of node i; thetaijIs the power angle between node i and node j;
(2) output power constraint of regional cooling, heating and power comprehensive energy node
Pimin≤Pi≤Pimax
In the above formula, PiminAnd PimaxRespectively is the lower limit and the upper limit of the output power of the cooling, heating and power integrated energy node;
(3) mutual coupling constraint of regional cooling, heating and power comprehensive energy
Pinmax≤Pc≤Poutmax
In the above formula, PinmaxAnd PoutmaxRespectively, a lower limit and an upper limit of the power sold to the large power grid.
5. The device for optimizing the configuration of regional combined cooling, heating and power energy based on graph theory as claimed in claim 4, wherein: the optimized configuration model construction module is specifically configured to: considering the actual network topology connection of the cooling, heating and power comprehensive energy, the optimal configuration of the regional cooling, heating and power comprehensive energy is equivalent to a undirected weighted graph G (V, E, W), and the minimum weight W of the undirected weighted graph G is searched, so that the cooling, heating and power comprehensive energy is optimally configured;
wherein, the element in the set V is a fixed point or a node or a point of the undirected authorized graph G, which represents an actual cooling, heating and power supply in the regional scope, and the element is a finite nonempty node set consisting of regional cooling, heating and power supplies, and the nodes are numbered from 1 in sequence according to the quantity sequence of the cooling, heating and power supplies until all the cooling, heating and power supplies are numbered;
wherein, the element of the set E is the edge or line of the undirected authorized graph G, which represents the communication switch or PCC switch set between the cold and heat power sources, and can be used as EijIs represented by eijIs 1 and 0; wherein, 1 represents that the connection exists between the cold and heat power supply i and the cold and heat power supply j, and 0 represents that the connection does not exist; e represents a V middle edge set;
wherein, the element of the set W is the active work between any two nodesRate-exchanged values, called weights, W, of undirected weighted graph GijAnd the active power value which is exchanged between the node i and the node j is represented, and the value is positive when the active power flows in, and is negative when the active power flows out.
6. The device for optimizing the configuration of regional combined cooling, heating and power energy based on graph theory as claimed in claim 4, wherein: the optimal configuration module is specifically configured to: firstly, selecting a chromosome with the total coding length of 13, respectively forming chromosome strings by the three coding decision variables, and then connecting the chromosome strings into a complete chromosome; the first two represent grid-connected PCC switches of the cold and heat power supply, the middle five represent the operating cost of a single cold and heat power supply, and the last six represent the capacity of the single cold and heat power supply.
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