CN114757580A - Active power distribution network expansion planning method for multi-subject incomplete information non-cooperative game - Google Patents
Active power distribution network expansion planning method for multi-subject incomplete information non-cooperative game Download PDFInfo
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Abstract
The invention discloses an active power distribution network expansion planning method for a multi-subject incomplete information non-cooperative game, which comprises the following steps: s1, establishing an extended planning target model of each benefit subject; s2, establishing a mathematical model of the incomplete information game relation based on the co-sorting theory; s3, establishing a target function by taking the balance point of the planning strategies of all game parties as an optimization target; and S4, solving the objective function, and searching game balance to obtain an expansion planning result. The result obtained by the invention not only accords with the actual situation, but also enables each main body to give a more conservative and objective planning strategy, and the conservative strategy can also avoid the occurrence of extreme conditions of network operation as far as possible while maximizing the own benefits of each main body, thereby improving the operation stability and avoiding the economic loss possibly caused.
Description
Technical Field
The invention relates to the technical field of power distribution networks, in particular to an active power distribution network expansion planning method of a multi-main-body incomplete information non-cooperative game.
Background
For convenience of explanation, the terms are first explained as follows:
active power distribution network: the active power distribution network is a power distribution network which is internally provided with distributed or decentralized energy sources and has control and operation capabilities;
hasani transformation (the Harsanyi transformation): refers to converting a selection under uncertainty conditions to a selection under risk conditions. Wherein under risk conditions, B, although not knowing the type of A, can know the distribution probabilities of different types;
reinforcement learning: one area in machine learning emphasizes how to act on an environmental basis to achieve maximum expected benefit. The inspiration comes from the theory of behavioral senses in psychology, namely how an organism develops an expectation of stimulation under the stimulation of reward or punishment given by the environment, resulting in habitual behaviors that can obtain the maximum benefit.
The active power distribution network is a novel power distribution solution which appears in recent years, integrates advanced communication technology and power electronic technology, takes an active network management strategy as a core, and plays a key role in the aspects of consuming grid-connected renewable energy sources, managing demand side response resources and the like.
With the further deepening of the power system innovation, the planning of the active power distribution network also faces new problems. The business of the electricity selling side is further opened to the social capital, and the difference of investment subjects leads to the separation of the electricity selling side, the power transmission and distribution side and the electricity utilization side, and participates in the network planning decision as different benefit subjects. Under the premise of limited network resources, each subject must compete for the limited resources from the perspective of individual rationality to maximize the benefits of the subject. How to fully consider the non-cooperative game relationship among different benefit subjects in the planning process and provide a planning scheme under the condition of balanced benefits of all parties is one of the problems to be solved urgently in the planning process of the active power distribution network.
Given that often the information disclosure between different stakeholders is not transparent, the gaming relationship between the holders often is an incomplete information non-cooperative gaming relationship. In terms of description of incomplete information, common methods include a hasani (hasanyi) conversion method, a reinforcement learning method, and the like. However, most of the existing research methods need to give a priori probability distribution to describe initial incomplete information and correct the initial incomplete information in a later iteration process, and the priori probability distribution is often difficult to obtain directly or subjectivity is introduced to a model under a human-made specified condition, so that the accuracy of a modeling result is influenced.
It is to be noted that the information disclosed in the above background section is only for understanding the background of the present application and thus may include information that does not constitute prior art known to a person of ordinary skill in the art.
Disclosure of Invention
The invention aims to overcome the defect of low accuracy of a modeling result of the background technology and provide an active power distribution network expansion planning method of a multi-subject incomplete information non-cooperative game.
In order to achieve the purpose, the invention adopts the following technical scheme:
the active power distribution network expansion planning method of the multi-subject incomplete information non-cooperative game comprises the following steps:
s1, establishing an extended planning target model of each interest subject;
s2, establishing a mathematical model of the incomplete information game relation based on the co-sorting theory;
s3, establishing a target function by taking the balance point of the planning strategies of all game parties as an optimization target;
and S4, solving the objective function, and finding game balance to obtain an expansion planning result.
In some embodiments, in step S1, the benefit agent includes DNO, DGO, and DRO, the constraints that need to be satisfied by the DNO expansion planning include power flow constraints, safety constraints, and topology constraints, the constraints that need to be satisfied by the DGO optimization model include permeability constraints and capacity constraints, and the constraints that need to be satisfied by the DRO optimization model include project engagement rate constraints and load constraints.
In some embodiments, the step S2, the mathematical model of the incomplete information gambling relationship includes establishing a regression equation for decision strategies of different subjects having incomplete information relationships based on a least squares method.
In some embodiments, in step S2, if there is a historical data set of the relevant planning strategy, performing regression analysis based on the historical data set; otherwise, modeling and regression analysis are carried out for many times, and the regression model is continuously corrected according to the result obtained each time.
In some embodiments, the performing regression analysis based on the historical data set in step S2 includes performing stationarity check on the historical data set using ADF verification.
In some embodiments, the balancing of the gaming party planning strategies in step S3 includes gaming balancing of the subject DNO and DGO at the system planning level and gaming balancing of the power transaction process at the system operational level.
In some embodiments, in step S4, the objective function is solved using standard genetic algorithms.
In some embodiments, step S4 further includes the following steps:
s41, generating an initial population of each decision main body planning decision strategy, wherein each individual representing the decision strategy in the population is randomly generated, and the iteration number is set to be 0;
s42, calculating the fitness according to the objective function and the constraint condition of each benefit subject;
s43, setting the maximum iteration times, and exiting the loop if the result of the current round meets the iteration termination condition; otherwise updating the population based on the pattern of the following genetic algorithm: selecting individuals with high fitness in the population to form a new population through a championship strategy, pairing the individuals in the new population pairwise to form a filial generation population in a crossed manner, carrying out variation with a specific probability to form the new population, then carrying out iteration times +1, and returning to the step S42;
and S44, outputting the obtained solution as the expanded planning strategy in the current group.
In some embodiments, in step S43, the maximum number of iterations selects the maximum number of iterations allowed by the algorithm.
The invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor can implement the steps of the active power distribution network extension planning method for the multi-subject incomplete information non-cooperative game as described in any one of the above.
The invention has the following beneficial effects:
compared with other incomplete information methods (such as the Haisani conversion method and the like), the method can provide more objective results without introducing artificial designated factors. The result obtained by using the method not only accords with the actual situation, but also enables each main body to give a more conservative and objective planning strategy, and the conservative strategy can also avoid the extreme situation of network operation as far as possible while maximizing the self benefit of each main body, thereby improving the operation stability and avoiding the possible economic loss. The method provided by the invention is beneficial for a network decision maker to fully consider the requirements of different subjects when planning, and from the perspective of individual rationality, a network planning strategy capable of fully meeting the benefits of multi-party subjects is obtained.
Furthermore, when incomplete information is described by a common Haisani conversion method in the prior art, probability distribution needs to be manually specified to express the prediction of one game party to the other game party, and the probability distribution is corrected in the subsequent game iteration process, so that the subjectivity is inevitably introduced into a final model; the model is established on the basis of historical data, and human factor intervention is avoided as far as possible, so that the solving result can more reasonably reflect the result of individual rational decision, and a more reasonable planning strategy which fully meets the benefit of multiple subjects can be obtained.
Drawings
FIG. 1 is a flowchart of an active power distribution network expansion planning method of a multi-subject incomplete information non-cooperative game in an embodiment of the present invention;
FIG. 2 is a flowchart of specific steps of an active power distribution network extension planning method of a multi-subject incomplete information non-cooperative game in the embodiment of the present invention;
FIG. 3 is a schematic diagram of an 8-node power distribution network testing system in an experimental example of the present invention;
fig. 4 is a schematic diagram of an expansion planning result of an active power distribution network of a multi-subject incomplete information non-cooperative game in an experimental example of the present invention;
fig. 5 is a schematic diagram of an expansion planning result of an active power distribution network of a multi-subject complete information non-cooperative game in an experimental example of the present invention;
FIG. 6 is a flow chart of multi-agent complete information static gaming in the experimental example of the present invention;
FIG. 7 is a graph showing a change in electricity prices on a typical day in the experimental example of the present invention;
FIG. 8 is a graph showing the change in daily load at the node 5 in the experimental example of the present invention.
Detailed Description
The embodiments of the present invention will be described in detail below. It should be emphasized that the following description is merely exemplary in nature and is not intended to limit the scope of the invention or its application.
The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of the present invention, "a plurality" means two or more unless specifically limited otherwise.
Examples
According to the embodiment of the invention, firstly, the main bodies in the system and the incomplete information non-cooperative game relationship between the main bodies are analyzed according to the benefit requirements of different investment main body representatives in the power distribution network planning process; then modeling is carried out on the incomplete information relation based on a co-integration theory, and a co-integration equation between main body decisions with the incomplete information relation is established based on a least square method; and finally, establishing a planning-operation optimization model based on the incomplete information non-cooperative game relation, and solving through a standard genetic algorithm to obtain an expansion planning result. The method provided by the embodiment of the invention is beneficial for a network decision maker to fully consider the requirements of different subjects when planning, and obtains a network planning strategy which can fully meet the benefits of multi-party subjects from the perspective of individuality.
As shown in fig. 1 and fig. 2, the specific steps of the embodiment of the present invention are as follows:
(1) the method comprises the steps of inputting network data of a power distribution network, Distributed Generation (DG) equipment data accessed to the power distribution network, various items of data of loads in the power distribution network, including network topology, parameters of existing lines and lines to be newly built, nodes for installing DG equipment, upper and lower limits of DG equipment capacity, load capacity and the like, and investment cost of various items of equipment.
(2) According to different investment conditions in a power Distribution Network, separating benefit agents representing different benefit requirements, wherein the benefit agents comprise a Distribution Network Operator (DNO), a DG equipment Operator (DGO) and a Demand side Response service Operator (DRO), and the three are respectively responsible for Distribution side service, electricity selling side service and electricity consumption load management service, wherein the DNO is responsible for new establishment, upgrading and modification of a Distribution system feeder line, the DGO is responsible for site selection and capacity determination of DG equipment in the Network, and the DRO is responsible for adjusting real-time electricity consumption load of a power user according to load Distribution and electricity price conditions. In the aspect of system planning, a competitive relationship exists between DNO and DGO: the distribution of the capacity of the DG equipment can affect the power flow distribution, so that the network loss condition and the electric quantity purchased from the DNO to the main network are affected, and the DNO line planning strategy can also be changed correspondingly; the change of the line strategy influences the network topology and the power flow distribution, thereby also influencing the equipment construction strategy of the DGO. Both of which aim to maximize their own benefits and thus create a competitive relationship with respect to the decision strategy. In terms of system operation, DNO and DGO as power price formulating parties have a competitive relationship with DRO as represented by power consumers, namely, the power price condition necessarily affects real-time power utilization loads. Three stakeholders were modeled as follows:
the DNO maximizes the total revenue as an extended planning objective, as shown in equation (1):
in the formula, FDNORepresenting the total profit obtained by the DNO during the planning period, the meaning of the formula being the implementation ruleMaximization of total DNO profit within the planning period. The details of each term to the right of the equation are shown in equation (2):
wherein the content of the first and second substances,
representing the total income obtained by the DNO to sell electricity to the power consumer; t represents a planning period; n represents the number of nodes in the system; rhoS,tRepresenting the price of electricity sold at the time t; p isLoad,n,tRepresenting the active load at the node n at the moment t;
representing the total cost of the system network loss; l represents the number of power lines in the system; ρ is a unit of a gradientB,tRepresenting the electricity price of the DNO for purchasing electricity to the upper-level power transmission network at the time t; pLoss,l,tRepresenting the active network loss of the line l at the time t;
representing the total cost of the DNO to purchase electricity to the DGO and the upper-level power transmission network; k represents the kind of DG in the system; pDG,k,n,tThe active output value of the generation of the kth type DG equipment at the node n at the time t is represented; rhoDG,k,tShowing the price of electricity sold by the kth DG to the DNO at the time t;
represents the investment cost of the network; r represents a discount rate; y represents the total years of planning; rhoLineThe investment cost of a unit length line is expressed; slRepresents the length of line l;
representing the operation and maintenance cost of the system line; rhoLine,O&MAnd the operation and maintenance cost of the line with the unit length in unit time is expressed.
Constraint conditions which need to be met by the extension planning of the DNO include power flow constraint, safety constraint and topological constraint, which are respectively shown as formulas (3), (4) and (5):
the constraint (3) is a power equation in the field of the power system, is derived from kirchhoff voltage law and kirchhoff current law, and is a basis for carrying out load flow calculation on the power system; the power system must satisfy this constraint in the case of stable operation.
Constraint (4) is a system safety constraint, meaning that there are an upper limit and a lower limit on the voltage amplitude of any node, and the active power of any line cannot exceed the upper limit; the voltage and power must be limited to a certain range or else the lines or equipment may be damaged, causing instability in the operation of the power system.
The constraint (5) is a topological constraint, and the number of the total lines is 1 less than that of the total nodes because the power distribution network has to meet a tree structure and no isolated nodes can exist; in addition, when performing route planning, DNO needs to ensure that no ring network or island structure exists in the system.
Wherein, Pi,t，Qi,tRespectively representing active power and reactive power injected into the node i at the time t; u shapei,t，Uj,tRespectively representing the voltage amplitudes at the node i and the node j at the time t; gijRepresents the conductance of the line between node i and node j; b isijSusceptance representing the line between node i and node j; thetaij,tRepresenting the phase angle difference between the voltages of the node i and the node j at the time t; u shapei,tRepresents the voltage at node i at time t; u shapei,min，Ui,maxRespectively represents the minimum value and the maximum value of the voltage amplitude at the node i at the time tA large value; pij,tRespectively representing the active power of a line between the node i and the node j at the moment t; p isij,maxRepresents the maximum value of the active power of the line; r isijIndicating the existence of a direct connection line between the node i and the node j, wherein 1 represents the existence of the line and 0 represents the nonexistence of the line; n represents the number of nodes in the system.
The DGO maximizes the total electricity sales revenue as an extended planning objective, as shown in equation (6):
in the formula, FDGOThe DGO obtains the total profit of electricity sale in the planning period, and the meaning of the formula is to realize the maximization of the DGO total profit in the planning period. The details of each term to the right of the equation are shown in equation (7):
wherein the content of the first and second substances,
representing the total electricity sales revenue of the DGO; t represents a planning period; n represents the number of nodes in the system; rhoDG,tRepresenting the DG electricity selling price at the time t; p isDG,n,tRepresenting DG active power output at a node n at the time t;
representing the renewable energy power generation subsidy price of DGO; ρ is a unit of a gradientsubRepresents the unit subsidy price of the corresponding DG output;
represents the total investment cost of the DG equipment; r represents a discount rate; y represents the total years of planning; rhodevRepresenting the commissioning cost of a unit capacity DG device; cDG,nRepresenting the installed capacity of the corresponding DG at node n;
representing the operation and maintenance subsidy cost of the DG equipment; l represents the number of power lines in the system; rhoDG,O&MThe equipment operation and maintenance cost converted by the DG output is expressed;
the punishment cost of the renewable energy power generation is represented, such as the cost of wind abandoning and light abandoning; ρ is a unit of a gradientpenRepresents the penalty cost per unit DG power generation; ppen,tRepresenting the active power abandoned by the DG output exceeding the load demand in the active distribution network at time t.
The constraint conditions to be met by the DGO optimization model comprise permeability constraint and capacity constraint, which are respectively shown in formulas (8) and (9):
CDG,n,min≤CDG,n≤CDG,n,max (9)
wherein, CDG,nRepresenting the installed capacity of the corresponding DG at node n; sigmaPENE,kRepresents the maximum permeability of the kth DG; cLoad,nRepresents the load capacity at node n; cDG,n,minRepresents the minimum value of DG capacity at node n; cDG,n,maxRepresenting the maximum value of DG capacity at node n.
The constraint (8) is a DG permeability constraint, and because the total load amount in the system is limited, and the power generation of the DG equipment has randomness and volatility, the load requirement can not be met all the time, the total access capacity of the DG equipment needs to be limited within a certain range, and the total access capacity is not higher than the load capacity by a certain percentage, so that the main position of power supply of a superior power transmission network is ensured, and the running stability of the system is improved.
The constraint (9) is the DG capacity constraint at each node, and is limited by multiple factors such as node geographical position, site space size, natural resource distribution and the like, and the DG equipment installation capacity at any node has upper and lower limits, so that the capacity of the DG equipment needs to meet the constraints. The benefit of the DRO to charge the electricity consumer for the service fee is maximally an optimization goal, as shown in equation (10):
in the formula, FDROThe overall profit obtained by the DRO for charging the service fee to the electricity user during the planning period is expressed, and the meaning of the formula is to achieve the maximization of the overall profit of the DRO during the planning period. The details of each term to the right of the equation are shown in equation (11):
wherein the content of the first and second substances,
represents service revenue for a DRO implemented electricity rate based DR project; t represents a planning period; n represents the number of nodes in the system; omeganIs between [0,1 ]]Represents a specific proportion of the service fee charged by the DRO at the node n according to the total electricity fee of the electric power users participating in the project; rhoS,tThe price of electricity sold at the time t is represented; pDRLoad,n,tRepresenting the active load of the power users participating in the DR project at the node n at the time t;
the total installation cost of equipment such as a demand side intelligent instrument is represented; rhoMeterExpressing the installation cost of the intelligent instrument equipment converted to the unit capacity load in the DR project; sigmaDR,nRepresenting the percentage of the load of the power consumer participating in the DR project at node n to the total load capacity; cLoad,nRepresenting the load capacity at node n;
representing the total operation and maintenance cost of the intelligent instrument equipment; c. CDR,O&M,nAnd the operation and maintenance cost of the intelligent instrument equipment at the node n in the planning operation period is represented.
The constraint conditions to be met by the DRO optimization model comprise project participation rate constraint and load constraint, which are respectively shown in formulas (12) and (13):
σDR,n≤σDR,n,max (12)
wherein σDR,nRepresenting the percentage of the load of the power consumer participating in the DR project at node n to the total load capacity; sigmaDR,n,maxRepresenting the maximum percentage of the load of the power consumer participating in the DR project at node n to the total load capacity; n represents the number of nodes in the system; DRP represents the number of hours encompassed by one DR cycle; m represents
A random integer within the range; p isLoad,n,tRepresenting the active load at the node n at the moment t; pItrLoad,n,tRepresenting the load shedding amount in the project at the node n at the time t, and belonging to the interruptible load category;
and representing the original real-time active load at the node n at the time t.
The constraint (12) is a constraint on the participation rate of the demand side response project, and considering that all participation of power consumers cannot be guaranteed by related projects proposed by the DRO, and excessive load adjustment is not beneficial to maintaining stable operation of the power system, the participation rate of the demand side response project should be limited to a certain extent.
The constraint (13) is a load constraint, is an equality constraint and represents the implementation condition of a load adjustment strategy; since the total amount of electrical load and demand remain constant, the total amount of load before adjustment should be equal to the sum of the total amount of load after adjustment and the total amount of interrupt load.
(3) The incomplete information game relation among all benefit subjects is analyzed, and the method mainly comprises the following steps:
in the system planning level, the access of DG equipment enables the DNO to perform line expansion planning and upgrade so as to adapt to the requirements of voltage class and power flow, which inevitably has a negative impact on the economy of network planning, and in the system planning aspect, there is a competitive relationship between the DNO and the DGO: the distribution of the capacity of the DG equipment can affect the power flow distribution, so that the network loss condition and the electric quantity purchased from the DNO to the main network are affected, and the DNO line planning strategy can also be changed correspondingly; the change of the line strategy influences the network topology and the power flow distribution, thereby also influencing the equipment construction strategy of the DGO. Both of which are targeted to maximize their own benefits and therefore compete with decision strategies. The DRO is a load management party and is mainly responsible for adjusting real-time loads of power consumers and the like according to the change condition of the electricity price, but the DRO does not participate in the formulation of a system planning strategy and cannot influence the total capacity of the electricity loads, so that no direct game relationship exists between the DRO and other subjects on a planning level.
However, the DRO can influence the network operation condition by influencing the electric load and directly influence the total benefits of the DNO and the DGO; therefore, the DNO and the DGO also need to be simulated and operated in a simulation mode when the planning strategy is formulated, and the decision possibility of the DRO is fully considered, which means that the decision of the DRO can indirectly influence the planning strategy. Namely, the DGO and the DNO have a non-cooperative game relationship in the aspect of planning strategies; and both can not know the investment cost and the interest demand of the other side completely and accurately, namely, an incomplete information game relation exists.
In the trading level of system operation and power market, a certain pricing right is mastered by DNO and DGO, but DRO represents a power user, and can adjust the power load according to the power price, so that the trading volume of power is influenced, and the power selling income of a pricing main body is influenced. Therefore, the DRO has a non-cooperative game relationship with the DNO and the DGO which master the pricing right of the electricity price. Similarly, the game subject cannot accurately know the cost and appeal of other subjects, and an incomplete information game relationship also exists.
In addition, in a certain planning period, the load capacity in the active power distribution network is relatively fixed, so that the DRO does not have a direct game relationship with other benefit agents in a system planning level, but can indirectly influence a planning strategy through a strategy influencing an operation level.
(4) And establishing a mathematical model of incomplete information based on a co-sorting theory. If the historical data set of the relevant planning strategy exists, performing regression analysis according to the historical data set; otherwise, modeling and regression analysis are carried out for many times, and the regression model is continuously corrected according to the result obtained each time. The essence of the model is based on a least square method, a regression equation is established for decision strategies of different subjects with incomplete information relation, the decision results of other subjects can be predicted according to the regression equation when the subsequent subjects make decisions, the optimization target of the subsequent subjects is realized under the condition, and a relatively reasonable decision result is given.
First, the stationarity of the historical data set was examined using the ADF test (extended dicky-Fowler test, Unit root test) function provided by Eviews 10(x64) software, a specialized economics of metrology software introduced by IHS. The method firstly assumes that unit roots exist in a numerical sequence, namely the sequence is not a stable sequence; if the result of the t-test statistic can be less than the criterion value at a certain confidence level, the sequence of values can be considered to reject the original hypothesis at the corresponding confidence level, i.e., the sequence of values is a stationary sequence. The principle is as follows: the historical data set can be regarded as a time series, and a time series can be regarded as a regression process, i.e. can be expressed in the form of a difference equation, such as:
at a given sequence initial value
In the case of (2), the following can be derived by estimation:
at this time, the value of rho will influence the predicted value of the sequence: if ρ is 1, the error result of the actual value and the predicted value at the final time t is the sum of all errors from the time 0 to the time t, which means that the error is time-dependent, the variance of the system increases with time, i.e. a unit root process occurs, and therefore
Is a non-stationary sequence. The ADF test is to assume that a sequence has a unit root, if the obtained significance test statistic is less than a specific confidence coefficient, the result of rejecting the original hypothesis under the corresponding confidence level is obtained, and the sequence is ensured to be stable. A set of standard mathematical models exist in the ADF testing process, corresponding statistics can be obtained through a numerical method, and stability testing is carried out. Eviews provides a mature ADF checking function and can independently complete numerical calculation. In some embodiments, other ways of performing the stationarity check may also be used.
Then, performing regression analysis on the strategy sequence represented by the main body with the incomplete information game relation by a least square method to obtain a regression equation, namely a co-integral equation representing the incomplete information; the calculation process is as follows:
firstly, carrying out logarithm processing on all data to avoid possible heteroscedastic difference, and setting m groups of data in total; for the decision variable Y needing prediction, a decision main body is assumed to have a plurality of decision variables marked as X1，X2，…，XnThe model can be built as follows:
all the parameters a are then solvediAnd b, making a loss function
And minimizing to obtain a parameter estimation result of the least square method, and substituting the parameter estimation result into the model to obtain a regression equation.
And finally, performing the co-integration test on the obtained equation, calculating the model residual error and testing the stability of the residual error sequence according to the obtained co-integration equation and the original data, and if the residual error sequence is a stable sequence, determining that the co-integration test is passed by the co-integration model, and determining that the co-integration model is an effective model. The calculation process is as follows: substituting independent variables of original data into a regression model, and taking the difference between the obtained result and dependent variables of the original data as residual errors; the corresponding obtained of a plurality of groups of original data is a residual sequence. The sequence stationarity test is consistent with the stationarity test process.
(5) Further analyzing the incomplete information game relationship established in the step (3) respectively, taking the balance point of the planning strategies of all the game parties as an optimization target, and establishing an objective function, wherein the specific form and the constraint conditions thereof are respectively shown as a formula (14) and a formula (15):
wherein the content of the first and second substances,
a decision vector representing the final DGO capacity planning, i.e. the capacity of the DG equipment installed at each node;
representing a final decision vector of DNO line planning, namely the commissioning condition of each line to be newly built, namely the commissioning conditions of all lines which can be newly built, wherein 1 represents that the line is commissioned, and 0 represents that the line is not commissioned;
the total gain of the DGO is represented,
represents the total benefit of DNO;
representing a final decision vector of a DRO load adjustment strategy, namely the final load at each node;
representing the final decision vector of the DNO and the DGO in the aspect of the power price strategy, namely the time-of-use power price strategy in a certain period;
the total profit of the DRO is represented,
representing the total benefit of DNO.
The formula (14) realizes the game balance of the subject DNO and the DGO in the system planning level, namely, any party can not obtain greater benefit by changing own strategy under the condition that the other party does not change the strategy.
The formula (15) realizes the game balance of the electric power transaction process in the system operation layer, namely, any party of the electricity price formulating party and the load party can not obtain larger income by changing the strategy of the party under the condition that the strategy of the other party is not changed
The decision vectors for DNO, DGO and DRO are specifically shown in equation (16):
NL represents the number of lines to be newly built; n represents the number of nodes in the system; t isDRThe number of hours included in one period of load planning adjustment of the DRO is represented; l. theiShowing the new establishment of the line i, wherein 1 represents that the line is established, and 0 represents that the line is not established; c. CiRepresenting the capacity of newly-built DG equipment at a node i; p is a radical ofijAnd the real power of the load at the node j at the moment i is represented. sDNOThe decision result of DNO represents the putting-in and putting-out situation of each newly-built line; sDGOIs the decision result of the DGO, indicating the projected capacity of the DG device at the corresponding node; sDROIs the decision result of DRO, which indicates the specific timeThe active load at a particular node of the time segment.
Under the premise, a standard genetic algorithm is adopted to find game balance. The method comprises the following specific steps:
(6) firstly, generating an initial population of planning decision strategies of each decision main body, wherein each individual representing the decision strategy in the population is randomly generated, and the iteration frequency is set to be 0.
(7) Then, calculating the fitness according to the objective function and the constraint condition of each benefit subject, introducing a corresponding planning strategy into the system for load flow calculation for the individuals meeting the constraint condition, and calculating the final objective function of each benefit subject as the final fitness result according to the obtained result and the economic model of the benefit subject; and for individuals not meeting the constraint condition, a penalty function method is adopted, and the fitness of the individuals is set to be a maximum negative value.
(8) Setting the maximum iteration number to be 200, and exiting the loop if the result of the current round meets the iteration termination condition; otherwise updating the population based on the pattern of the following genetic algorithm:
selecting individuals with high fitness in the population to form a new population through a championship strategy, pairing the individuals in the new population pairwise to form a filial generation population in a crossed manner, and carrying out variation according to a specific probability to form the new population.
Then the number of iterations +1 and return to step (7).
(9) And outputting the optimal planning strategy in the current group.
In order to guarantee the calculation performance and avoid falling into local optimization as much as possible, the number of iterations set as described above should be as large as the calculation power allows. In some embodiments, other computational intelligence methods may be used to solve, and in other embodiments, conventional numerical methods may be used.
Examples of the experiments
As shown in fig. 3, the effectiveness of the proposed method is verified through simulation in this experimental example by taking an 8-node power distribution network system as an example. In the system of the calculation example, a solid line and a solid node form an original distribution network structure (numerical symbols represent the number of the node), two load nodes of 7 and 8 are additionally arranged on the basis, and lines 1-7, 2-7, 5-7, 2-8, 3-8 and 4-8 are provided as newly-built distribution lines. Nodes 2, 4 are nodes for installation of photovoltaic equipment, and nodes 3, 4, 6 are nodes for installation of wind power equipment. DNO needs to be selectively built in a line to be newly built, DGO needs to plan the capacity of DG equipment at a corresponding node, and relevant parameters are shown in tables 1, 2 and 3 below, where table 1 below gives existing branch parameters of an 8-node system:
TABLE 1
The following table 2 shows the parameters of the branch to be newly built in the 8-node system:
TABLE 2
The following table 3 gives the node parameters of the 8-node system:
TABLE 3
The network topology corresponding to the capacity planning result of the line and the DG device obtained by using the optimization method provided by the experimental example of the present invention is shown in fig. 4, wherein a number symbol represents a number of a node, arrows indicate photovoltaic devices and wind power devices which are accessed to the node, and a number frame represents access capacity of corresponding devices. The network topology corresponding to the planning result obtained in the scenario of the full information game is shown in fig. 5. The complete information game is essentially a game scene without incomplete information modeling, so that the game relation is basically the same as the content mentioned above, and only in the game process, each subject masters the policy sets and benefit appeal of other subjects, and meanwhile, the decision of each subject does not have primary or secondary or precedence relation, and the specific flow is shown in fig. 6. The specific planning results are shown in table 4 below:
TABLE 4
Wherein, the result column respectively represents the DNO line commissioning strategy and the DGO installed equipment capacity at each node, and if 1-7 represents that a direct connection line between the node 1 and the node 7 is finally selected to be newly established; 1-7 and 4-8 indicate that the two lines are built, while other newly-built line schemes are abandoned, and the nodes and the capacities in the DGO result are corresponding, which indicates the DG equipment installation capacities at the corresponding nodes, such as {2, 4} and {73.8, 366.2} indicate that photovoltaic equipment with capacities of 73.8kVA and 366.2kVA is installed at the node 2 and the node 4, respectively.
In addition, in terms of network operation, a typical day is selected, and the change of the electricity price and the change of the load at the node 5 in this day are shown in fig. 7 and 8, respectively. Fig. 7 is a power rate change situation in one day, which is divided into an initial state (a standard power rate state before any game planning is not performed) and states in two different planning scenes, and is used for revealing the influence of a game process on the power rate change, in terms of network operation, when a game relation between a power selling side and a power transmission and distribution side is considered, DNO and DGO serve as power rate makers, original prices are adjusted, the power rate in a power utilization peak period is properly reduced, and the power rate in a power utilization valley period is increased, on one hand, the demand in a power utilization peak period is further stimulated, and the power supply and demand balance is ensured; on the other hand, the system also conforms to the power generation characteristics of DG equipment, and improves the electricity selling income of the user in the valley period by improving the electricity price in the power generation valley period (such as at night). Fig. 8 shows the load change situation in a day, which is divided into an initial state (a standard load state before any game planning is not performed, i.e., data selected during initialization) and states in two different planning scenarios, and is used to reveal the influence of the game process on the adjustment change of the power consumption load, and the DRO is used as a representative of the power consumption side and needs to adapt to the power rate change situation to make a reasonable power consumption policy for the power consumers. Because the electricity price is only slightly changed on the basis of the original peak-flat-valley distribution trend, the DRO establishes an electricity utilization strategy which still conforms to the policy of 'electricity cost saving', namely, the load at the peak time of the electricity price is reduced, and part of the load is transferred to the low-valley time of the electricity price, so that the electricity utilization expenditure of a user is saved, and the loss of the user is compensated.
Compared with a complete information scene, the DNO under the incomplete information scene is more prone to selecting and building a line capable of ensuring power supply stability so as to reduce investment risk and operation risk; DGO also tends to deploy equipment in areas of concentrated load to increase power generation utilization, thereby ensuring its own profit. In terms of network operation, DNO and DGO tend to properly reduce the electricity price to stimulate the electricity demand of users, thereby ensuring the self electricity selling income; the DRO tends to reduce the load during the peak period of power consumption, so as to save the power consumption expense of the user and make up the loss of the user. The planning strategy is conservative on the whole, the stability of network operation is improved, and unstable network operation and economic loss possibly caused by individual rational subject competition in the market are avoided to a certain extent, so that the effectiveness of the experimental example is demonstrated.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is a more detailed description of the invention in connection with specific/preferred embodiments and is not intended to limit the practice of the invention to those descriptions. It will be apparent to those skilled in the art that various substitutions and modifications can be made to the described embodiments without departing from the spirit of the invention, and these substitutions and modifications should be considered to fall within the scope of the invention. In the description of the present specification, reference to the description of "one embodiment," "some embodiments," "preferred embodiments," "example," "specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction. Although embodiments of the present invention and their advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the scope of the claims.
Claims (10)
1. The active power distribution network expansion planning method of the multi-subject incomplete information non-cooperative game is characterized by comprising the following steps of:
s1, establishing an extended planning target model of each interest subject;
s2, establishing a mathematical model of the incomplete information game relation based on the co-sorting theory;
s3, establishing a target function by taking the balance point of the planning strategies of all game parties as an optimization target;
and S4, solving the objective function, and finding game balance to obtain an expansion planning result.
2. The method for the active power distribution network extension planning of the multi-agent incomplete information non-cooperative game as claimed in claim 1, wherein in step S1, the benefit agent includes DNO, DGO and DRO, the constraints to be satisfied by the extension planning of DNO include power flow constraint, safety constraint and topology constraint, the constraints to be satisfied by the DGO optimization model include permeability constraint and capacity constraint, and the constraints to be satisfied by the DRO optimization model include project participation rate constraint and load constraint.
3. The active power distribution network expansion planning method for the multi-subject incomplete information non-cooperative game as claimed in claim 1, wherein in step S2, the mathematical model of the incomplete information game relationship comprises establishing a regression equation for decision strategies of different subjects with incomplete information relationship based on a least square method.
4. The active power distribution network expansion planning method for the multi-subject incomplete information non-cooperative game as claimed in claim 1, wherein in step S2, if there is a historical data set of the related planning strategy, regression analysis is performed according to the historical data set; otherwise, modeling and regression analysis are carried out for many times, and the regression model is continuously corrected according to the result obtained each time.
5. The active power distribution network expansion planning method for the multi-subject incomplete information non-cooperative game as recited in claim 4, wherein in step S2, the performing regression analysis based on the historical data set includes performing stationarity check on the historical data set by using an ADF test method.
6. The active power distribution network expansion planning method for the multi-subject incomplete information non-cooperative game as recited in claim 1, wherein in step S3, the balancing of the game party planning strategies includes game balancing of the subject DNO and DGO at a system planning level and game balancing of the power transaction process at a system running level.
7. The active power distribution network expansion planning method for the multi-subject incomplete information non-cooperative game as claimed in claim 1, wherein in step S4, the objective function is solved by using a standard genetic algorithm.
8. The active power distribution network expansion planning method for the multi-subject incomplete information non-cooperative game as recited in claim 1, wherein the step S4 further comprises the following steps:
s41, generating an initial population of each decision main body planning decision strategy, wherein each individual representing the decision strategy in the population is randomly generated, and the iteration number is set to be 0;
s42, calculating the fitness according to the objective function and the constraint condition of each benefit subject;
s43, setting the maximum iteration times, and exiting the loop if the result of the current round meets the iteration termination condition; otherwise updating the population based on the pattern of the following genetic algorithm: selecting individuals with high fitness in the population to form a new population through a championship strategy, pairing the individuals in the new population pairwise to form a filial generation population in a crossed manner, carrying out variation with a specific probability to form the new population, then carrying out iteration times +1, and returning to the step S42;
and S44, outputting the obtained solution as the expanded planning strategy in the current group.
9. The active power distribution network expansion planning method for the multi-subject incomplete information non-cooperative game as claimed in claim 8, wherein in step S43, the maximum number of iterations is selected as the maximum number of iterations allowed by the calculation power.
10. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, is capable of implementing the steps of the method for the active power distribution network extension planning for the multi-subject incomplete information non-cooperative game as claimed in any one of claims 1 to 9.
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