CN113364043A - Micro-grid group optimization method based on condition risk value - Google Patents
Micro-grid group optimization method based on condition risk value Download PDFInfo
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Abstract
The invention discloses a microgrid cluster optimization method based on condition risk value, which specifically comprises the following steps: historical cost data analysis, historical data rule analysis, scene data simulation generation, scene quantity clustering reduction and condition risk value modeling optimization. Generating enough multiple groups of scene state curves by a scene data simulation generation program based on typical rules and error information; the scene quantity clustering reduction program obtains a plurality of calculable typical scenes and corresponding probabilities thereof by comparing and aggregating the multiple scenes generated in the previous step; and the condition risk value modeling optimization is performed under a multi-probability scene, and the operation strategy of the microgrid group system is obtained through optimization by combining the risk preference of a decision maker or a power grid operator. According to the invention, the comprehensive risk consideration of the microgrid group can be realized under the conditions of uncertainty of new energy power generation output and load fluctuation, and the capability of coordinating output and optimizing operation can be realized.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a microgrid cluster optimization method based on condition risk values.
Background
Facing the increasingly serious problem of environmental pollution, the reduction of carbon emissions has become a widespread consensus in human society. One of the means is to develop renewable energy to replace the traditional fossil energy. With the access of large-scale distributed renewable energy sources in power systems and the application of a large number of related power electronic devices, the complexity of power grids, particularly power distribution networks, is increasing day by day. In order to cope with the inherent characteristics of intermittent and random distributed renewable energy sources and solve the problems of control complexity, stability and the like caused by related power electronic devices, a micro-grid is proposed as a feasible solution. However, the micro-grid has limited capacity and weak anti-interference capability, and has insufficient capability of dealing with transient events such as access or detachment of a large number of instantaneous loads.
For example, in chinese patent CN112103946A, published 2020, 12 months and 18 days, a method for optimizing configuration of energy storage of a micro-grid based on a particle swarm algorithm, a battery energy storage system model is first designed; then, preprocessing daily load data, photovoltaic power generation data and time-of-use electricity price data of the microgrid users to obtain parameter constraints of energy storage capacity, power and investment cost; establishing an energy storage optimization configuration model: establishing an energy storage optimization configuration model by using an objective function with the lowest investment and the largest profit of an energy storage device, and considering constraint conditions including renewable resource power generation, power balance and chargeable and dischargeable times; solving an energy storage optimization configuration model by utilizing a particle swarm algorithm; and finally, evaluating the characteristic indexes, and calculating energy storage optimization parameters to obtain the final microgrid energy storage optimization configuration method. But it does not consider the execution problem regarding the cooperative routing inspection of multiple drones. The problems that the capacity of a microgrid is limited and the anti-interference capability is weak exist, and further improvement needs to be carried out by aggregating a plurality of adjacent microgrids to form a microgrid group.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the existing microgrid optimization method has the technical problem of weak anti-interference capability. The microgrid group optimization method based on the condition risk value is capable of enhancing the anti-interference capability of a microgrid.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a microgrid group optimization method based on conditional risk values comprises the following steps:
s1: historical power data of the microgrid group in the optimization range are counted and input, and a cost power function of each controllable power supply in the microgrid group is deduced;
s2: establishing and obtaining typical power rules and error ranges of renewable energy output and power loads in a microgrid group, and obtaining a characteristic probability distribution function in a mathematical modeling mode;
s3: inputting the corresponding characteristic distribution function into a Monte Carlo simulation program to generate a plurality of groups of power curves;
s4: carrying out scene clustering and reduction on a power curve generated by Monte Carlo simulation to obtain a typical scene and corresponding probability;
s5: establishing a micro-grid group system optimization scheduling model based on condition risk value;
s6: and calculating and solving the optimal scheduling model of the microgrid cluster system according to the confidence level to obtain the optimal scheduling result of the microgrid cluster system. The cost and power of the gas turbine have the following quadratic function relationship:
wherein, PGFor controllable active power output of gas turbines, FG(PG) Is a force PGThe corresponding operating cost.
Preferably, the step S5 of establishing the microgrid group system optimized scheduling model based on the conditional risk value includes establishing the microgrid group system optimized scheduling model based on the conditional risk value by using the minimum weighted sum of the microgrid group operation cost, the upper-level power grid electricity purchase cost, the user load electricity loss cost, and the renewable energy output reduction penalty cost as the objective function of the optimization problem according to the optimized scheduling cycle of the microgrid group, and combining the optimization problem constraint with the typical scenario and the corresponding probability. According to the optimized dispatching cycle of the researched microgrid group, based on the conclusion of the step S1, taking the weighted sum of the running cost of the microgrid group, the electricity purchasing cost of the upper-level power grid, the electricity loss cost of the user load and the output reduction penalty cost of the renewable energy as the objective function of the optimization problem, taking the power balance of the microgrid group, the charging and discharging constraints of the energy storage system, the running constraints of each unit in the microgrid group and the output reduction constraints of the renewable energy as well as combining the typical scene and the corresponding probability thereof obtained in the step S4 to establish a microgrid group system optimized dispatching model based on the condition risk value;
preferably, the optimization problem constraints in step S5 include power balance of the microgrid group, charging and discharging constraints of the energy storage system, operation constraints of each unit in the microgrid group, and output reduction constraints of renewable energy. The optimization objective function of the step S5 is the weighted sum of the operation cost of the microgrid group, the power purchasing cost of the upper-level power grid, the power loss cost of the user load and the punishment cost of the renewable energy output reduction, and the optimization problem constraints are the power balance of the microgrid group, the charging and discharging constraints of the energy storage system, the operation constraints of each unit in the microgrid group and the renewable energy output reduction constraints.
Preferably, the monte carlo method in step S3 is: according to the characteristic probability distribution function, a large number of random numbers are generated and corresponding probability distribution function values are calculated so as to simulate a large number of data which accord with the characteristic probability distribution. According to the typical power law and the error range of the renewable energy output and the electrical load obtained in the step S2, the corresponding characteristic distribution function is input into a monte carlo simulation program to generate enough groups of power curves: based on the characteristic probability distribution function obtained in step S2, a large number of random numbers are generated and their corresponding probability distribution function values are calculated to model a large amount of data that fits the characteristic probability distribution.
Preferably, the scene clustering and reduction algorithm in step S4 adopts a K-MEANS clustering algorithm. In order to relieve the scheduling optimization pressure of the system, scene clustering and reduction are performed on a plurality of power curves generated by Monte Carlo simulation in the step S3, and a typical scene and corresponding probability thereof are obtained.
Preferably, the K-MEANS clustering algorithm is as follows:
based on the already obtained data set (x)1，x2，…，xn) The n data points are divided into K sets (K is less than or equal to n) through a K-MEANS clustering algorithm, so that the square sum in the group is minimum. That is, the K-MEANS clustering aims to find a cluster S that satisfies the following equationi：
Wherein muiIs clustering SiAverage of all data points in (a). Further counting each cluster SiThe number of the data points is calculated to obtain the probability p corresponding to each clusteri. The n data points can be partitioned into K sets (K ≦ n) by the K-MEANS clustering algorithm such that the intra-group sum of squares is minimized.
Preferably, the confidence level in step S6 includes a risk preference coefficient of a decision maker or a power grid operator. And (4) according to the confidence level and the risk preference coefficient of a decision maker or a power grid operator, calculating and solving the micro-grid group system optimization scheduling model based on the condition risk value in the step (5) to obtain the micro-grid group system optimization scheduling result within the acceptable risk. The confidence level is that the characteristic parameters in the condition risk value calculation, namely the risk preference coefficients of the decision maker or the power grid operator, are taken or rejected by the weight of the operation cost and the risk cost in the optimization objective function.
The conditional risk values are specifically expressed as follows:
ηξ≥0
wherein, zeta is risk value, alpha is confidence waterAverage, p (xi) is the probability corresponding to scene xi, etaξRepresenting the cost variation due to scene ξ.
The substantial effects of the invention are as follows: the prediction simulation mode adopted by the invention is added into the research scene of the optimization problem in a probability mode for the extreme operation scene, and the risk of a decision maker or a power grid operator is taken or rejected as a part of the decision making, so that the scheduling of the micro-grid group system is more practical, the flexibility is improved compared with the single micro-grid system aiming at the collective collaborative optimization of the micro-grid group system, the invention aims at the uncertainty caused by renewable energy power generation prediction, user load prediction and the like in the micro-grid group system, the optimal calculation can be carried out until the microgrid cluster optimal scheduling strategy with the lowest cost is adopted by a decision maker or a power grid operator under the condition of the risk preference, the optimal scheduling strategy not only considers the risk cost brought by uncertainty, but also comprises the risk accept and reject of a decision maker or a power grid operator so as to realize the optimal, reasonable and feasible optimal scheduling strategy of the micro-grid system in economy.
Drawings
FIG. 1 is a flow chart showing the steps of the present embodiment;
fig. 2 is a schematic structural diagram of the present embodiment.
Wherein: 1. energy storage battery, 2, alternating current load, 3, direct current load, 4, alternating current sub-network, 5, direct current sub-network.
Detailed Description
The following provides a more detailed description of the present invention, with reference to the accompanying drawings.
The technical solution of the present embodiment is further described in detail with reference to fig. 1 and fig. 2 as follows:
the implementation steps of performing optimized scheduling in this embodiment are shown in fig. 1, the system structure of this embodiment is shown in fig. 2, an example of a microgrid group system includes a plurality of microgrids, and each microgrid includes wind power, photovoltaic, gas turbine, and energy storage unit; each micro-grid is connected to a power exchange device through a corresponding converter, the power exchange device can be provided with an energy storage unit, and the micro-grid group is connected with an upper-layer power grid through the power exchange device. For the example of the microgrid group system, the specific steps of performing optimal scheduling by using the method of the embodiment are as follows:
step 1: and (4) counting historical power data of the microgrid group in the optimization range, and deducing a cost power function of each controllable power supply in the microgrid group.
(1) The cost and power of the gas turbine have the following quadratic function relationship:
wherein, PGFor controllable active power output of gas turbines, FG(PG) Is a force PGThe corresponding operating cost.
(2) The operating cost of the energy storage system in the microgrid group comes from the loss of charging and discharging, namely:
FESS(PESS)＝cESS|PESS|＝cESS|Pd，ESS+Pc，ESS|＝cESS(Pd，ESS+Pc，ESS)
wherein, cESSCost per unit charge-discharge power loss, Pd，ESSFor storing discharge power, Pc，ESSAnd charging power for the stored energy.
(3) Each microgrid in the microgrid group realizes mutual power backup through power exchange equipment, and the running loss cost of the interconnection converter is as follows:
wherein, cconvIn order to cost per unit of exchange power loss,
for the power of the microgrid flowing to the switching devices,
for switching the power flowing to the microgrid by the equipment.
(4) Wind power generation and photovoltaic power generation are renewable energy power generation, no power generation cost is considered, but penalty cost is paid for wind abandonment and light abandonment, and similarly penalty cost for load power loss:
wherein,
and
respectively wind power, photovoltaic abandoned wind power, abandoned light power and lost load power, VWC，VPCAnd VOLLThen the corresponding penalty price.
(5) The electricity purchase and sale cost through the power exchange equipment and the upper-layer power grid is as follows:
wherein λ ispIn order to purchase the price of electricity for sale,
purchasing power to the upper layer power grid for the micro-grid group,
selling electric power to the upper layer power grid for the micro-grid.
Step 2: the method comprises the following steps of counting and inputting historical operating data of a microgrid group in an optimization range, establishing and obtaining typical power rules and error ranges of wind power, photovoltaic and other renewable energy sources in the microgrid group, power loads and the like by using an artificial intelligence method, and obtaining a characteristic probability distribution function by a mathematical modeling mode:
considering that the actual values of wind power output, photovoltaic output and user load and the error between the actual values and the predicted value conform to Gaussian distribution, namely:
and step 3: inputting the corresponding characteristic distribution function into a Monte Carlo simulation program according to the typical power rule and the error range of the renewable energy output and the electrical load obtained in the step 2 to generate enough groups of power curves: and (3) generating a large number of random numbers based on the characteristic probability distribution function obtained in the step (2) and calculating corresponding probability distribution function values thereof so as to simulate a large amount of data which accord with the characteristic probability distribution.
Step 4, in order to relieve the scheduling optimization pressure of the system, scene clustering and reduction are carried out on a plurality of power curves generated by Monte Carlo simulation in the step 3, and a typical scene and a corresponding probability thereof are obtained:
based on the already obtained data set (x)1，x2，…，xn) The n data points are divided into K sets (K is less than or equal to n) through a K-MEANS clustering algorithm, so that the square sum in the group is minimum. That is, the K-MEANS clustering aims to find a cluster S that satisfies the following equationi：
Wherein muiIs clustering SiAverage of all data points in (a). Further counting each cluster SiThe number of the data points is calculated to obtain the probability p corresponding to each clusteri。
And step 5, according to the optimized dispatching cycle of the researched microgrid group, based on the conclusion of the step 1, establishing a microgrid group system optimized dispatching model based on condition risk value by taking the weighted sum minimum of the operating cost of the microgrid group, the electricity purchasing cost of the upper-level power grid, the electricity loss cost of the user load and the output reduction penalty cost of the renewable energy as an optimization problem objective function, and by taking the power balance of the microgrid group, the charging and discharging constraints of the energy storage system, the operating constraints of each unit in the microgrid group and the output reduction constraints of the renewable energy as well as combining the typical scene and the corresponding probability thereof obtained in the step 4.
(1) The optimization objective function of the microgrid group system is as follows:
min(1-β)(CDA+CERT)+β·CVaR
wherein beta is the risk preference coefficient of the decision maker/grid operator, CDADay-ahead scheduling costs for microgrid group systems, CERTFor actual scheduling cost in different scenarios, CVaR is the conditional risk value cost.
(2) The power balance constraint of the microgrid group comprises the following components for each microgrid:
for the power switching device, there are:
Pgrid+Pd，ESS＝Pc，ESS+∑Pconv
(3) the charging and discharging constraints of the energy storage system are as follows:
SOCT＝SOC0
therein, SOCtThe state of charge (SOC) at time t, η is the charge-discharge efficiency of energy storage, SOCTFor storing energy SOC and SOC after the end of the scheduling period0An initial SOC for storing energy.
(4) Besides upper and lower limit constraints, the operation constraints of each unit in the micro-grid group also have constraints on the climbing speed, namely:
-RD≤PG，t-PG，t-1≤RU
and the RD is the maximum reduction rate of the output of the unit, and the maximum climbing rate of the output of the RU unit.
(5) The renewable energy output reduction constraint needs to satisfy that the actual output is not greater than the maximum output in the corresponding scene, namely:
wherein, E (P)WP) For the predicted maximum wind power output in the scene, E (P)PV) The predicted maximum photovoltaic contribution in the scene.
And the load reduction constraint is that the load of a key proportion is required to be satisfied without losing power, namely:
wherein k isvitalIs the proportionality coefficient of the key load to all loads.
And 6, calculating and solving the microgrid group system optimized scheduling model based on the condition risk value in the step 5 according to the confidence level and the risk preference coefficient of the decision maker/power grid operator to obtain the microgrid group system optimized scheduling result within the acceptable risk. The confidence level is that the characteristic parameters in the condition risk value calculation, namely the risk preference coefficients of the decision maker or the power grid operator, are taken or rejected by the weight of the operation cost and the risk cost in the optimization objective function.
The conditional risk values are specifically expressed as follows:
ηξ≥0
where, ζ is risk value, α is confidence level, p (ξ) is probability corresponding to scene ξ, ηξRepresenting the cost variation due to scene ξ.
The system comprises a plurality of alternating current sub-networks 4 and direct current sub-networks 5, wherein the alternating current sub-networks 4 and the direct current sub-networks 5 respectively relate to a plurality of alternating current loads 2 and direct current loads 3, the alternating current sub-networks 4 and the direct current sub-networks 5 are connected with energy storage modules with energy storage batteries 1, the micro-grid group optimization scheduling problem is a secondary planning problem, a general algebraic modeling system GAMS compiling program needs to be called for calculation and solution, and a micro-grid group system optimization scheduling strategy under a given confidence level and a risk preference coefficient of a decision maker/a power grid operator is obtained.
Microgrid group system example effects: the microgrid group optimization scheduling example proves that the microgrid group optimization scheduling strategy with the lowest cost can be optimized and calculated by aiming at the uncertainty caused by renewable energy power generation prediction, user load prediction and the like in a microgrid group system under the condition of risk preference adopted by a decision maker/power grid operator. The optimal scheduling strategy not only considers the risk cost brought by uncertainty, but also comprises the risk accept and reject of a decision maker/power grid operator so as to realize the optimal, reasonable and feasible optimal scheduling strategy of the micro-grid system in economy.
The prediction simulation mode adopted by the embodiment is to add the research scene of the optimization problem in a probability form for the extreme operation scene, and the risk of a decision maker or a power grid operator is taken or rejected as a part of the decision making, so that the scheduling of the micro-grid group system is more practical, the flexibility is improved compared with the single micro-grid system aiming at the collective collaborative optimization of the micro-grid group system, the embodiment aims at the uncertainty caused by renewable energy power generation prediction, user load prediction and the like in the micro-grid group system, the optimal calculation can be carried out until the microgrid cluster optimal scheduling strategy with the lowest cost is adopted by a decision maker or a power grid operator under the condition of the risk preference, the optimal scheduling strategy not only considers the risk cost brought by uncertainty, but also comprises the risk accept and reject of a decision maker or a power grid operator so as to realize the optimal, reasonable and feasible optimal scheduling strategy of the micro-grid system in economy.
The above examples only show some embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (7)
1. A microgrid group optimization method based on conditional risk values is characterized by comprising the following steps:
s1: historical power data of the microgrid group in the optimization range are counted and input, and a cost power function of each controllable power supply in the microgrid group is deduced;
s2: establishing and obtaining typical power rules and error ranges of renewable energy output and power loads in a microgrid group, and obtaining a characteristic probability distribution function in a mathematical modeling mode;
s3: inputting the corresponding characteristic distribution function into a Monte Carlo simulation program to generate a plurality of groups of power curves;
s4: carrying out scene clustering and reduction on a power curve generated by Monte Carlo simulation to obtain a typical scene and corresponding probability;
s5: establishing a micro-grid group system optimization scheduling model based on condition risk value;
s6: and calculating and solving the optimal scheduling model of the microgrid cluster system according to the confidence level to obtain the optimal scheduling result of the microgrid cluster system.
2. The method of claim 1, wherein the step S5 of building the microgrid group system optimization scheduling model based on the conditional risk value includes building the microgrid group system optimization scheduling model based on the conditional risk value by using the weighted sum of the microgrid group operation cost, the upper power grid electricity purchase cost, the user load electricity loss cost and the renewable energy output reduction penalty cost as the objective function of the optimization problem according to the optimization scheduling period of the microgrid group, and combining the optimization problem constraint with the typical scenario and the corresponding probability.
3. The microgrid cluster optimization method based on conditional risk values of claim 2, wherein the optimization problem constraints in the step S5 include power balance of a microgrid cluster, charging and discharging constraints of an energy storage system, operation constraints of units in the microgrid cluster and output reduction constraints of renewable energy sources.
4. The microgrid cluster optimization method based on conditional risk values of claim 1 or 3, characterized in that the Monte Carlo method in the step S3 is as follows: according to the characteristic probability distribution function, a large number of random numbers are generated and corresponding probability distribution function values are calculated so as to simulate a large number of data which accord with the characteristic probability distribution.
5. The microgrid cluster optimization method based on conditional risk values of claim 1, characterized in that the scene clustering and reduction algorithm in the step S4 adopts a K-MEANS clustering algorithm.
6. The microgrid cluster optimization method based on conditional risk values of claim 5, wherein the K-MEANS clustering algorithm is as follows: based on the already obtained data set (x)1，x2，…，xn) Dividing the n data points into K sets (K is less than or equal to n) by a K-MEANS clustering algorithm to minimize the square sum in the group, wherein the K-MEANS clustering aims to find a cluster S satisfying the following formulai：
Wherein muiIs clustering SiAverage value of all data points in (1), and counting each cluster SiNumber of well data pointsMeasuring, calculating to obtain the probability p corresponding to each clusteri。
7. The microgrid cluster optimization method based on conditional risk values of claim 1, 3 or 6, characterized in that the confidence level in the step S6 includes risk preference coefficients of a decision maker or a grid operator.
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