CN111047367A - Method, device and storage medium for establishing electricity price area under node marginal pricing mechanism - Google Patents

Method, device and storage medium for establishing electricity price area under node marginal pricing mechanism Download PDF

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CN111047367A
CN111047367A CN201911352010.0A CN201911352010A CN111047367A CN 111047367 A CN111047367 A CN 111047367A CN 201911352010 A CN201911352010 A CN 201911352010A CN 111047367 A CN111047367 A CN 111047367A
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钟声
陈政
张志翔
梁志飞
杨再敏
张翔
杜龙
辜炜德
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Guangzhou Electric Power Trade Center Co ltd
Energy Development Research Institute of China Southern Power Grid Co Ltd
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Abstract

The embodiment of the invention provides a method, a device and a storage medium for establishing an electricity price area under a node marginal pricing mechanism, wherein the method comprises the following steps: based on the electric futures contract financial delivery mode hedging model, a node area model of a power grid in a set range is constructed by adopting a principal component analysis method, and the area clustering quantity is determined according to a transformer substation or a line power supply area; distributing node areas for all nodes in the set range on the principle of minimum Euclidean distance until the total weighted deviation or the total deviation of all node prices and node area prices is minimum; and taking the distribution result of the total weighted deviation or the minimum total deviation of all the node prices and the node area prices in the set range as the electricity price area division result. The output result of the embodiment of the invention is more accurate, the number of the areas does not need to be manually input, and the areas are not blocked.

Description

Method, device and storage medium for establishing electricity price area under node marginal pricing mechanism
Technical Field
The invention relates to the technical field of electric power, in particular to a method, a device and a storage medium for establishing an electricity price area under a node marginal pricing mechanism.
Background
Among three pricing mechanisms of the current spot market, a node marginal pricing mechanism (LMP mechanism) is considered as an optimal pricing mechanism, which defines electricity price settlement nodes according to a network topology, but in the actual spot market, there is also a great limitation in pure node pricing. The LMP price has strong dependence on the geographic position and the power grid structure, and meanwhile, due to the characteristic that the electric energy cannot be stored in a large quantity, the price of the same node fluctuates greatly at different times. Therefore, in many cases, it is important to divide the node area and establish the area node reference price based on the node pricing.
Clustering is carried out based on node electricity prices, a node area center is established, and the average price or weighted average price of all nodes in the area center is calculated to be used as the area price, so that the node electricity prices in the same area can be ensured to be as close as possible, the difference between the area settlement price and the node settlement price is reduced, and the price fluctuation of a node marginal pricing mechanism is reduced. The regional settlement price is used as the settlement reference price of the electric power futures contract, so that market participants can effectively avoid the risk of price fluctuation of spot markets.
At present, research aiming at node electricity price clustering has a plurality of achievements, and from the prior relevant documents, a specific method comprises the following steps: a fuzzy C-means algorithm, a historical electricity price clustering algorithm, an optimized fuzzy clustering algorithm and the like. The fuzzy C-means algorithm needs to manually determine the number of clusters, and the selection of an initial clustering center is also unstable, so that a clustering result easily falls into a local optimal solution; the historical electricity price clustering algorithm clusters nodes with large electricity price relevance based on the historical electricity price data of the nodes, but because the volatility of the electricity price of the nodes is large, the result obtained by calculating the relevance by using the historical electricity price is only a rough estimation value; the optimized fuzzy clustering algorithm overcomes the defect of the original algorithm, namely the clustering result is easy to be a local optimal solution, but the algorithm still needs to manually input the optimal clustering number.
Disclosure of Invention
In view of the above technical problems, an object of the present invention is to provide a method, an apparatus, and a storage medium for establishing an electricity price region under a node marginal pricing mechanism, wherein an output result is more accurate, the number of regions does not need to be manually input, and no block exists inside the regions.
The embodiment of the invention provides a method for establishing an electricity price area under a node marginal pricing mechanism, which comprises the following steps:
s1, constructing a node area model of a fixed-range power grid by adopting a principal component analysis method based on the electric futures contract financial delivery mode hedging model, and determining the area clustering number according to a transformer substation or a line power supply area;
s2, distributing node areas for all nodes in the set range on the principle of minimum Euclidean distance until the total weighted deviation or the total deviation of all node prices and node area prices in the set range is minimum;
and S3, taking the distribution result of the total weighted deviation or the minimum total deviation of all the node prices and the node area prices in the set range as the electricity price area division result.
Further, the node areas are allocated to all the nodes in the set range until the total weighted deviation or the total deviation of the prices of all the nodes and the prices of the node areas in the set range is minimum, specifically,
s2-1, distributing initial state node areas for all nodes in the set range on the principle of minimum Euclidean distance;
s2-2, calculating the settlement price of all node areas in the initial state;
s2-3, calculating the total weighted deviation or the total deviation of all the node prices and the node area prices in the initial state;
s2-4, reallocating node areas for all nodes in the set range;
s2-5, calculating the current settlement price of all node areas;
s2-6, calculating the total weighted deviation or the total deviation of the prices of all the current nodes and the prices of the node areas;
s2-7, judging whether the total weighted deviation of all node prices and node area prices in the initial state is smaller than the total weighted deviation of all current node prices and node area prices, if so, taking the total weighted deviation of all node prices and node area prices in the initial state as the minimum total weighted deviation, if not, returning to the step S2-4, and re-executing S2-4-S2-7;
or judging whether the total deviation of all the node prices and the node area prices in the initial state is smaller than the current total deviation of all the node prices and the node area prices, if so, taking the total deviation of all the node prices and the node area prices in the initial state as the minimum total deviation, if not, returning to the step S2-4, and re-executing the step S2-4-S2-7.
Further, when the set range power grid has n network nodes which respectively belong to m node areas, and m is far smaller than n, the minimum total weighted deviation of the prices of all the nodes and the prices of the node areas in the set range is determined according to the following formula;
Figure BDA0002333885260000021
Figure BDA0002333885260000022
Figure BDA0002333885260000023
Figure BDA0002333885260000024
Figure BDA0002333885260000025
wherein MinD is the minimum total weighted deviation of all node prices and node area prices,
Figure BDA0002333885260000026
a Boolean variable representing the relationship between the i-node and the j-region, and if the i-node belongs to the j-region
Figure BDA0002333885260000031
If not, then,
Figure BDA0002333885260000032
taking the value as 0;
Figure BDA0002333885260000033
representing the weight of each node electricity price between j areas;
Figure BDA0002333885260000034
determining according to the trading volume of the inode at the current time t or according to the total installed capacity or annual average trading volume of each market participant;
Figure BDA0002333885260000035
represents the settlement price of the j area at time t; c. CitThe node price of the inode at T time is shown, and T shows the settlement time point.
Further, the method for establishing the electricity price area under the node marginal pricing mechanism further comprises setting an upper limit of the area cluster quantity.
Further, the method for establishing the electricity price area under the node marginal pricing mechanism further comprises the step of setting a lower limit of the area cluster quantity when the average value of all nodes in the area is used as the node area settlement price.
Further, the method for establishing the electricity price area under the node marginal pricing mechanism further comprises the step of adopting a weighted average value of all nodes in the area as a node area settlement price.
The embodiment of the present invention further provides a device for establishing an electricity price area under a node marginal pricing mechanism, including:
the model building module is used for building a node area model of a power grid in a set range by adopting a main component analysis method based on a power futures contract financial delivery mode hedging model and determining the area clustering number according to a transformer substation or a line power supply area;
the node distribution module is used for distributing node areas for all nodes in the set range on the principle of minimum Euclidean distance until the total weighted deviation or the total deviation of all node prices and node area prices in the set range is minimum;
and the region division module is used for taking the distribution result of the total weighted deviation or the minimum total deviation of all the node prices and the node region prices in the set range as the electricity price region division result.
Further, the device for establishing the electricity price area under the node marginal pricing mechanism allocates the node areas for all the nodes in the set range until the total weighted deviation or the total deviation of all the node prices and the area prices in the set area is minimum, specifically,
s2-1, distributing initial state node areas for all nodes in the set range on the principle of minimum Euclidean distance;
s2-2, calculating the settlement price of all node areas in the initial state;
s2-3, calculating the total weighted deviation or the total deviation of all the node prices and the node area prices in the initial state;
s2-4, reallocating node areas for all nodes in the set range;
s2-5, calculating the current settlement price of all node areas;
s2-6, calculating the total weighted deviation or the total deviation of the prices of all the current nodes and the prices of the node areas;
s2-7, judging whether the total weighted deviation of all node prices and node area prices in the initial state is smaller than the total weighted deviation of all current node prices and node area prices, if so, taking the total weighted deviation of all node prices and node area prices in the initial state as the minimum total weighted deviation, if not, returning to the step S2-4, and re-executing S2-4-S2-7;
or judging whether the total deviation of all the node prices and the node area prices in the initial state is smaller than the current total deviation of all the node prices and the node area prices, if so, taking the total deviation of all the node prices and the node area prices in the initial state as the minimum total deviation, if not, returning to the step S2-4, and re-executing the step S2-4-S2-7.
Further, the device for establishing the electricity price area under the node marginal pricing mechanism adopts the average value of all nodes in the area as the node area settlement price or adopts the weighted average value of all nodes in the area as the node area settlement price.
An embodiment of the present invention further provides a computer-readable storage medium, which includes a stored computer program, where the computer program, when running, controls a device in which the computer-readable storage medium is located to perform the method for establishing an electricity price area under a node marginal pricing mechanism, according to the foregoing claims.
According to the embodiment of the invention, the regional clustering number is determined according to the transformer substation or the line power supply area by adopting a principal component analysis method based on the electric futures contract financial delivery mode hedging model, so that the regional clustering number does not need to be manually input, and meanwhile, the interior of the region is free from blockage. In addition, by calculating the regional electricity prices according to the current node prices, compared with a historical electricity price clustering algorithm, the model output result is more accurate. According to the embodiment of the invention, through iteration of different initial values for multiple times, the total deviation or the total weighted deviation of all the node prices and the area prices in the set range is minimum as an optimization target, and the optimal node distribution result is compared and selected, so that the accuracy of area division can be improved.
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Fig. 1 is a flow chart provided by an embodiment of the present invention.
Fig. 2 is another flow chart provided by the embodiment of the present invention.
Fig. 3 is a schematic structural diagram of an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to any and all possible combinations of one or more of the associated listed items and includes such combinations.
It should be noted that the following section may abbreviated node regions to regions for ease of understanding and distinction and to avoid ambiguity.
Referring to fig. 1, an embodiment of the present invention provides a method for establishing an electricity price area under a node marginal pricing mechanism, which is characterized by comprising the following steps:
s1, constructing a node area model of a fixed-range power grid by adopting a principal component analysis method based on the electric futures contract financial delivery mode hedging model, and determining the area clustering number according to a transformer substation or a line power supply area;
in an embodiment of the invention, the electric futures market comprises a physical delivery mode and a converged delivery mode. In the financial delivery mode, assuming that the buyer and the seller contract for futures, during the physical delivery period, the seller needs to purchase the contract amount of electricity from the electricity retail market to transmit to the buyer at the current price and receive the fund specified in the contract from the buyer. Assuming that the contract stipulates that the total price of electric power is CfAnd C, the total price of the contract amount electric power purchased by the seller from the spot market is C, and the total profit of the seller is as follows:
Ms＝Cf-C (1)
and for the buyer, the total profit is:
Ms＝C-Cf(2)
assuming that a network node t where a certain power plant is located belongs to the H area, the manufacturer is in the spot market at c hoursitWhere t represents the time of the trade, i represents the network node where the plant is located, the revenue the plant receives from the spot market at time t is
Figure BDA0002333885260000051
If the risk of the spot market is countervailed, the power plant is priced as CfA financial settled futures contract is made, the price of the futures contract is relative to the spot price c of the H areaHtThe settlement is carried out, and the income generated when the power plant participates in the futures market is shown as the following formula (1)
Figure BDA0002333885260000052
Suppose that the plant sells hiFutures contracts, in which case the total revenue obtained is:
Figure BDA0002333885260000053
wherein h isitIndicates time t subscribes hiA futures contract.
To enable futures contracts to effectively hedge spot market risks, power plants need to minimize their total revenue variance, i.e.:
Min[σ2(Mit)]＝Min{σ2[cit+hit×(Cf-cHt)]} (4)
since the only factor that the power plant can change during the process of making a futures contract hedge risk is the number of copies of the futures contract, equation (4) can be simplified as follows:
Figure BDA0002333885260000054
taking into account futures contract price CfIs a fixed value, spot market price citAnd cHtFor unknown values, the formula is simplified to obtain:
Figure BDA0002333885260000061
hiσ(cHt)-ρ(cit,cHt)σ(cit)＝0 (7)
Figure BDA0002333885260000062
by substituting formula (8) for formula (3) and calculating the variance thereof, it is possible to obtain:
σ2(Mit)＝[1-ρ2(cit,cHt)]σ2(cit) (9)
where ρ (c)it,cHt) Denotes citAnd cHtCorrelation between hiThe contrast ratio is indicated. From the above analysis, the optimal hedging ratio hiThe variance of the total income can be reduced by 1-rho2(cit,cHt)]And (4) doubling.
Thus, the criteria for selecting the optimal region for a given node is to select a region having the greatest correlation to the node price.
Based on the financial delivery mode hedge model, a node area model of the power grid in a set range is constructed through a Principal Component Analysis (PCA), and the area clustering quantity is determined according to the transformer substation or the line power supply area.
S2, distributing node areas for all nodes in the set range on the principle of minimum Euclidean distance until the total weighted deviation or the total deviation of all node prices and node area prices in the set range is minimum;
referring to fig. 2, in the embodiment of the present invention, the node areas are allocated to all nodes in the set range until the total weighted deviation or the total deviation of the prices of all nodes and the prices of the node areas in the set range is minimum, specifically,
s2-1, distributing initial state node areas for all nodes in the set range on the principle of minimum Euclidean distance;
s2-2, calculating the settlement price of all node areas in the initial state;
s2-3, calculating the total weighted deviation or the total deviation of all the node prices and the node area prices in the initial state;
s2-4, reallocating node areas for all nodes in the set range;
s2-5, calculating the current settlement price of all node areas;
s2-6, calculating the total weighted deviation or the total deviation of the prices of all the current nodes and the prices of the node areas;
s2-7, judging whether the total weighted deviation of all node prices and node area prices in the initial state is smaller than the total weighted deviation of all current node prices and node area prices, if so, taking the total weighted deviation of all node prices and node area prices in the initial state as the minimum total weighted deviation, if not, returning to the step S2-4, and re-executing S2-4-S2-7;
or judging whether the total deviation of all the node prices and the node area prices in the initial state is smaller than the current total deviation of all the node prices and the node area prices, if so, taking the total deviation of all the node prices and the node area prices in the initial state as the minimum total deviation, if not, returning to the step S2-4, and re-executing the step S2-4-S2-7.
It should be noted that, the process returns to step S2-4, and re-executes S2-4-S2-7 until the minimum total weighted deviation or the minimum total deviation state occurs, and takes the state as the final state of the node area division, and the total weighted value of the nodes in each area is the area price.
It should be noted that, since the node positions are fixed, but the node area positions are not fixed, the node areas may be allocated to the nodes in the set range by a different node clustering method of finding "the minimum euclidean distance" based on the different area positions, that is, by using the minimum euclidean distance as a rule, until the total weighted deviation or the total deviation of all the node prices and the node area prices in the set range is minimum. It should be understood that, in the embodiment of the present invention, the total weighted deviation or the total deviation of all node prices and node regional prices in the setting range is the smallest, which means that the total weighted deviation or the total deviation of all node prices and regional prices of the corresponding region in the setting range is the smallest.
And S3, taking the distribution result with the minimum total weighted deviation or the minimum total deviation of all the node prices and the node area prices in the set range as the electricity price area division result.
According to the embodiment of the invention, the regional clustering number is determined according to the transformer substation or the line power supply area by adopting a principal component analysis method through a hedging model based on the electric futures contract financial delivery mode, so that the regional clustering number does not need to be manually input, and meanwhile, the interior of the region is free from blockage. In addition, by calculating the regional electricity prices according to the current node prices, compared with a historical electricity price clustering algorithm, the model output result is more accurate. The number of iterations of the embodiment of the invention is also limited, the final output value of each iteration is based on the initial distribution state of the power grid node, but the optimal solution output each time is not the global optimal solution based on the equations (10) - (14). In order to enable the output value to be closer to the optimal value, through a plurality of iterations of different initial values, the total deviation or the total weighted deviation of the prices of all the nodes in the area and the price of the node area is minimum as an optimization target, and the optimal node distribution result is compared and selected, so that the accuracy of area division can be improved.
In one preferred embodiment, when the set range power grid has n network nodes, each of which belongs to m node areas, m is far smaller than n. For simplicity, it is guaranteed that each node belongs to only one market participant, and each market participant owns only one node. The optimization goal of the node region model is to minimize the total (weighted) deviation of all node prices within the region from the node region prices, i.e.:
Figure BDA0002333885260000071
Figure BDA0002333885260000072
Figure BDA0002333885260000073
Figure BDA0002333885260000074
Figure BDA0002333885260000081
wherein MinD is the minimum total weighted deviation of all node prices and node area prices,
Figure BDA0002333885260000082
a Boolean variable representing the relationship between the i-node and the j-region, and if the i-node belongs to the j-region
Figure BDA0002333885260000083
If not, then,
Figure BDA0002333885260000084
taking the value as 0;
Figure BDA0002333885260000085
representing the weight of each node electricity price between j areas;
Figure BDA0002333885260000086
determining according to the trading volume of the inode at the current time t or according to the total installed capacity or annual average trading volume of each market participant;
Figure BDA0002333885260000087
represents the settlement price of the j area at time t; c. CitThe node price of the inode at T time is shown, and T shows the settlement time point.
In the Guangdong area, the economic development is mainly concentrated in the bead triangle area, the electricity consumption of the economic development center is high, and the line blockage is easily generated in the load gathering area. In order to facilitate grid connection of a power plant, all 500kV high-voltage transmission lines in the Guangdong power grid area adopt a ring network structure, but single-loop connection is still adopted among partial substations, such as a 220kV cis-German substation and a 220kV high-quality substation in a cis-Germany district in the Fushan city. In the peak period of electricity utilization of 'meeting summer' in summer, a single-loop circuit is extremely easy to be blocked, so that the node of a good street district (a 220kV good substation main power supply district) in the Fushan city in the period is higher in electricity price, and the electricity price gradient between districts in the areas is obvious.
Based on the analysis, taking the Guangdong power grid as an example, assuming that the number of nodes of the Guangdong power grid is n, the number of constructed node areas is m, and the weight w of all the nodes to each areaiIs a fixed value, and wiIt is known that since the number m of node areas is much smaller than the number n of nodes, it is easy to know that all nodes are assigned to the appropriate node areas.
MinS means that the total weighted deviation of the node prices of all nodes in the region of the Guangdong power grid from the region price is the smallest.
Figure BDA0002333885260000088
Suppose thatAll nodes i have been assigned to the appropriate region because of the weight wiIt is known that the price of the region in equation (11), and the settlement price of the region j at time t, can be replaced by the following equation:
Figure BDA0002333885260000089
the method for establishing the node area based on the node marginal pricing mechanism in the embodiment of the invention divides all network nodes in the Guangdong power grid area into a plurality of areas by clustering the network nodes with similar Euclidean distances and similar node prices.
In a preferred embodiment, the method for establishing electricity price areas under the node marginal pricing mechanism further includes setting an upper limit of the area cluster number.
Since a large number of futures contracts cannot be circulated in the market at the same time, it is difficult to quantitatively evaluate liquidity of the futures contracts in all defined regions on the market, and therefore, in order to limit liquidity of the futures contracts in the grid, it is necessary to set an upper limit to the number of divided regions.
In one preferred embodiment, when the average value of all nodes in a region is used as the region settlement price, the lower limit of the number of region clusters is set.
In the embodiment of the invention, when the area of the power grid division adopts the average value of all nodes in the area as the area settlement price, the stability of the area price of the power grid in a fault or overhaul state can be ensured by setting the lower limit of the area clustering quantity. In addition, using the average of all nodes as the regional settlement price reduces the variance of the total regional price, thereby making it easier for market participants to predict market prices and increasing the liquidity of futures contracts on the market.
In one preferred embodiment, the lower limit of the number of region clusters is 50. Typically, the lower limit of the number of established regions is set to 50-100.
In one preferred embodiment, the weighted average of all nodes in the region is used as the region settlement price.
In the embodiment of the invention, compared with the method that the average value of all nodes in the region is used as the region settlement price, when the power grid is in a fault or maintenance state, the weight of the related node can be set to be 0, the stability of the settlement price of each region is not influenced, and the lower limit of the number of the region clusters is not required to be set.
Referring to fig. 3, an embodiment of the present invention further provides an apparatus for establishing an electricity price area under a node marginal pricing mechanism, including:
and the model construction module 11 is used for constructing a node area model of the power grid in the set range by adopting a principal component analysis method based on the electric futures contract financial delivery mode hedging model and determining the area clustering number according to the transformer substation or the line power supply area.
In an embodiment of the invention, the electric futures market comprises a physical delivery mode and a converged delivery mode. In the financial delivery mode, assuming that the buyer and the seller contract for futures, during the physical delivery period, the seller needs to purchase the contract amount of electricity from the electricity retail market to transmit to the buyer at the current price and receive the fund specified in the contract from the buyer. Assuming that the contract stipulates that the total price of electric power is CfAnd C, the total price of the contract amount electric power purchased by the seller from the spot market is C, and the total profit of the seller is as follows:
Ms＝Cf-C (1)
and for the buyer, the total profit is:
Ms＝C-Cf(2)
assuming that a network node t where a certain power plant is located belongs to the H area, the manufacturer is in the spot market at c hoursitWhere t represents the time of the trade and i represents the network node where the plant is located, the plant receives revenue from the spot market at time tIs composed of
Figure BDA0002333885260000091
If the risk of the spot market is countervailed, the power plant is priced as CfA financial settled futures contract is made, the price of the futures contract is relative to the spot price c of the H areaHtThe settlement is carried out, and the income generated when the power plant participates in the futures market is shown as the following formula (1)
Figure BDA0002333885260000101
Suppose that the plant sells hiFutures contracts, in which case the total revenue obtained is:
Figure BDA0002333885260000102
wherein h isitIndicates time t subscribes hiA futures contract.
To enable futures contracts to effectively hedge spot market risks, power plants need to minimize their total revenue variance, i.e.:
Min[σ2(Mit)]＝Min{σ2[cit+hit×(Cf-cHt)]} (4)
since the only factor that the power plant can change during the process of making a futures contract hedge risk is the number of copies of the futures contract, equation (4) can be simplified as follows:
Figure BDA0002333885260000103
taking into account futures contract price CfIs a fixed value, spot market price citAnd cHtFor unknown values, the formula is simplified to obtain:
Figure BDA0002333885260000104
hiσ(cHt)-ρ(cit,cHt)σ(cit)＝0 (7)
Figure BDA0002333885260000105
by substituting formula (8) for formula (3) and calculating the variance thereof, it is possible to obtain:
σ2(Mit)＝[1-ρ2(cit,cHt)]σ2(cit) (9)
where ρ (c)it,cHt) Denotes citAnd cHtCorrelation between hiThe contrast ratio is indicated. From the above analysis, the optimal hedging ratio hiThe variance of the total income can be reduced by 1-rho2(cit,cHt)]And (4) doubling.
Thus, the criteria for selecting the optimal region for a given node is to select a region having the greatest correlation to the node price.
Based on the financial delivery mode hedge model, a node area model of the power grid in a set range is constructed through a Principal Component Analysis (PCA), and the area clustering quantity is determined according to the transformer substation or the line power supply area.
And the node distribution module 12 is configured to distribute node areas to all nodes in the set range on the principle that the euclidean distance is the minimum until the total weighted deviation or the total deviation of all node prices and node area prices in the set range is the minimum.
Referring to fig. 2, in the embodiment of the present invention, node areas are allocated to all nodes in the set range until the total weighted deviation or the total deviation of the prices of all nodes and the prices of the node areas in the set range is minimum, specifically,
s2-1, distributing initial state node areas for all nodes in the set range on the principle of minimum Euclidean distance;
s2-2, calculating the settlement price of all node areas in the initial state;
s2-3, calculating the total weighted deviation or the total deviation of all the node prices and the node area prices in the initial state;
s2-4, reallocating node areas for all nodes in the set range;
s2-5, calculating the current settlement price of all node areas;
s2-6, calculating the total weighted deviation or the total deviation of the prices of all the current nodes and the prices of the node areas;
s2-7, judging whether the total weighted deviation of all node prices and node area prices in the initial state is smaller than the total weighted deviation of all current node prices and node area prices, if so, taking the total weighted deviation of all node prices and node area prices in the initial state as the minimum total weighted deviation, if not, returning to the step S2-4, and re-executing S2-4-S2-7;
or judging whether the total deviation of all the node prices and the node area prices in the initial state is smaller than the current total deviation of all the node prices and the node area prices, if so, taking the total deviation of all the node prices and the node area prices in the initial state as the minimum total deviation, if not, returning to the step S2-4, and re-executing the step S2-4-S2-7.
It should be noted that, the process returns to step S2-4, and re-executes S2-4-S2-7 until the minimum total weighted deviation or the minimum total deviation state occurs, and takes the state as the final state of the node area division, and the total weighted value of the nodes in each area is the area price.
It should be noted that, since the node positions are fixed, but the node area positions are not fixed, the node areas may be allocated to the nodes in the set range by a different node clustering method of finding "the minimum euclidean distance" based on the different area positions, that is, by using the minimum euclidean distance as a rule, until the total weighted deviation or the total deviation of all the node prices and the node area prices in the set range is minimum. It should be understood that, in the embodiment of the present invention, the total weighted deviation or the total deviation of all node prices and node regional prices in the setting range is the smallest, which means that the total weighted deviation or the total deviation of all node prices and regional prices of the corresponding region in the setting range is the smallest.
And the area dividing module 13 is configured to use a distribution result with the smallest total weighted deviation or the smallest total deviation of all node prices and node area prices in the setting range as an electricity price area dividing result.
According to the embodiment of the invention, the regional clustering number is determined according to the transformer substation or the line power supply area by adopting a principal component analysis method through a hedging model based on the electric futures contract financial delivery mode, so that the regional clustering number does not need to be manually input, and meanwhile, the interior of the region is free from blockage. In addition, by calculating the regional electricity prices according to the current node prices, compared with a historical electricity price clustering algorithm, the model output result is more accurate.
The number of iterations of the embodiment of the invention is also limited, the final output value of each iteration is based on the initial distribution state of the power grid node, but the optimal solution output each time is not the global optimal solution based on the equations (10) - (14). In order to enable the output value to be closer to the optimal value, the total deviation or the total weighted deviation of the prices of all nodes in the area and the price of the node area is minimum as an optimization target through a plurality of iterations of different initial values, and the optimal node distribution result is selected in a comparing mode, so that the accuracy of area division can be improved.
The number of iterations of the embodiment of the invention is also limited, the final output value of each iteration is based on the initial distribution state of the power grid node, but the optimal solution output each time is not the global optimal solution based on the equations (10) - (14). In order to enable the output value to be closer to the optimal value, the total deviation or the total weighted deviation of all node prices in the set range and the node area prices is minimum as the optimization target through iteration of different initial values, the optimal output is selected as the final output through comparison, and therefore the accuracy of the model output result can be improved.
In one preferred embodiment, when the set range power grid has n network nodes, each of which belongs to m node areas, m is far smaller than n. For simplicity, it is guaranteed that each node belongs to only one market participant, and each market participant owns only one node. The optimization goal of the node area model is to minimize the total (weighted) deviation of all node prices within a set range from the node area prices, i.e.:
Figure BDA0002333885260000121
Figure BDA0002333885260000122
Figure BDA0002333885260000123
Figure BDA0002333885260000124
Figure BDA0002333885260000125
wherein MinD is the minimum total weighted deviation of all node prices and node area prices,
Figure BDA0002333885260000126
a Boolean variable representing the relationship between the i-node and the j-region, and if the i-node belongs to the j-region
Figure BDA0002333885260000127
If not, then,
Figure BDA0002333885260000128
taking the value as 0;
Figure BDA0002333885260000129
representing the weight of each node electricity price between j areas;
Figure BDA00023338852600001210
determining according to the trading volume of the inode at the current time t or according to the total installed capacity or annual average trading volume of each market participant;
Figure BDA00023338852600001211
represents the settlement price of the j area at time t; c. CitThe node price of the inode at T time is shown, and T shows the settlement time point.
In the Guangdong area, the economic development is mainly concentrated in the bead triangle area, the electricity consumption of the economic development center is high, and the line blockage is easily generated in the load gathering area. In order to facilitate grid connection of a power plant, all 500kV high-voltage transmission lines in the Guangdong power grid area adopt a ring network structure, but single-loop connection is still adopted among partial substations, such as a 220kV cis-German substation and a 220kV high-quality substation in a cis-Germany district in the Fushan city. In the peak period of electricity utilization of 'meeting summer' in summer, a single-loop circuit is extremely easy to be blocked, so that the node of a good street district (a 220kV good substation main power supply district) in the Fushan city in the period is higher in electricity price, and the electricity price gradient between districts in the areas is obvious.
Based on the analysis, taking the Guangdong power grid as an example, assuming that the number of nodes of the Guangdong power grid is n, the number of constructed node areas is m, and the weight w of all the nodes to each areaiIs a fixed value, and wiIt is known that since the number m of node areas is much smaller than the number n of nodes, it is easy to know that all nodes are assigned to the appropriate node areas.
MinS means that the total weighted deviation of the node prices of all nodes in the region of the Guangdong power grid from the region price is the smallest.
Figure BDA0002333885260000131
Assume that all nodes i have been assigned to the appropriate region because of the weight wiIt is known that the price of the region in equation (11), and the settlement price of the region j at time t, can be replaced by the following equation:
Figure BDA0002333885260000132
the method for establishing the node area based on the node marginal pricing mechanism in the embodiment of the invention divides all network nodes in the Guangdong power grid area into a plurality of areas by clustering the network nodes with similar Euclidean distances and similar node prices.
An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, where when the computer program runs, a device on which the computer-readable storage medium is located is controlled to execute the above method for establishing an electricity price area under a node marginal pricing mechanism.
The foregoing is directed to the preferred embodiment of the present invention, and it is understood that various changes and modifications may be made by one skilled in the art without departing from the spirit of the invention, and it is intended that such changes and modifications be considered as within the scope of the invention.

Claims (10)

1. A method for establishing an electricity price area under a node marginal pricing mechanism is characterized by comprising the following steps:
s1, constructing a node area model of the power grid in a set range by adopting a principal component analysis method based on the electric futures contract financial delivery mode hedging model, and determining the area clustering number according to the transformer substation or the line power supply area;
s2, distributing node areas for all nodes in the set range on the principle of minimum Euclidean distance until the total weighted deviation or the total deviation of all node prices and node area prices in the set range is minimum;
and S3, taking the distribution result of the total weighted deviation or the minimum total deviation of all the node prices and the node area prices in the set range as the electricity price area division result.
2. The method for establishing electricity price zone under node marginal pricing mechanism according to claim 1, wherein the node zones are allocated to all nodes in the set range until the total weighted deviation or the total deviation of all node prices and node zone prices in the set range is minimum, specifically,
s2-1, distributing initial state node areas for all nodes in the set range on the principle of minimum Euclidean distance;
s2-2, calculating the settlement price of all node areas in the initial state;
s2-3, calculating the total weighted deviation or the total deviation of all the node prices and the node area prices in the initial state;
s2-4, reallocating node areas for all nodes in the set range;
s2-5, calculating the current settlement price of all node areas;
s2-6, calculating the total weighted deviation or the total deviation of the prices of all the current nodes and the prices of the node areas;
s2-7, judging whether the total weighted deviation of all node prices and node area prices in the initial state is smaller than the total weighted deviation of all current node prices and node area prices, if so, taking the total weighted deviation of all node prices and node area prices in the initial state as the minimum total weighted deviation, if not, returning to the step S2-4, and re-executing S2-4-S2-7;
or judging whether the total deviation of all the node prices and the node area prices in the initial state is smaller than the current total deviation of all the node prices and the node area prices, if so, taking the total deviation of all the node prices and the node area prices in the initial state as the minimum total deviation, if not, returning to the step S2-4, and re-executing the step S2-4-S2-7.
3. The method for establishing an electricity price area under the node marginal pricing mechanism according to claim 2, wherein when the set range power grid has n network nodes, which belong to m node areas respectively, and m is much smaller than n, the minimum total weighted deviation of all node prices and node area prices in the set range is determined according to the following formula;
MinD＝min{D}，
Figure FDA0002333885250000011
Figure FDA0002333885250000012
Figure FDA0002333885250000013
Figure FDA0002333885250000021
Figure FDA0002333885250000022
wherein MinD is the minimum total weighted deviation of all node prices and node area prices,
Figure FDA0002333885250000023
is a Boolean variable and represents the relationship between the i node and the j region, if the i node belongs to the j region
Figure FDA0002333885250000024
If not, then,
Figure FDA0002333885250000025
taking the value as 0;
Figure FDA0002333885250000026
representing the weight of each node electricity price between j areas;
Figure FDA0002333885250000027
determining according to the trading volume of the inode at the current time t or according to the total installed capacity or annual average trading volume of each market participant;
Figure FDA0002333885250000028
represents the settlement price of the j area at time t; c. CitThe node price of the inode at the time t is shown, and the settlement time point is shown.
4. The method for establishing electricity price areas under the node marginal pricing mechanism according to claim 3, further comprising setting an upper limit for the number of area clusters.
5. The method for establishing electricity price areas under the node marginal pricing mechanism according to claim 3, characterized by further comprising setting a lower limit for the number of area clusters when taking the average value of all nodes in an area as a node area settlement price.
6. The method for establishing an electricity price zone under a node marginal pricing mechanism according to claim 3, characterized by further comprising adopting a weighted average of all nodes in the zone as a node zone settlement price.
7. An apparatus for establishing electricity price area under node marginal pricing mechanism, comprising
The model building module is used for building a node region model of a power grid in a set range by adopting a principal component analysis method based on a power futures contract financial delivery mode hedging model and determining the number of region clusters according to a transformer substation or a line power supply area;
the node distribution module is used for distributing node areas for all nodes in the set range on the principle of minimum Euclidean distance until the total weighted deviation or the total deviation of all node prices and node area prices in the set range is minimum;
and the region division module is used for taking the distribution result of the total weighted deviation or the minimum total deviation of all the node prices and the node region prices in the set range as the electricity price region division result.
8. The apparatus for establishing electricity price zone under node marginal pricing mechanism according to claim 7, wherein the node zones are allocated to all nodes in the set range until the total weighted deviation or the total deviation of all node prices and zone prices in the set range is minimum, specifically,
s2-1, distributing initial state node areas for all nodes in the set range on the principle of minimum Euclidean distance;
s2-2, calculating the settlement price of all node areas in the initial state;
s2-3, calculating the total weighted deviation or the total deviation of all the node prices and the node area prices in the initial state;
s2-4, reallocating node areas for all nodes in the set range;
s2-5, calculating the current settlement price of all node areas;
s2-6, calculating the total weighted deviation or the total deviation of the prices of all the current nodes and the prices of the node areas;
s2-7, judging whether the total weighted deviation of all node prices and node area prices in the initial state is smaller than the total weighted deviation of all current node prices and node area prices, if so, taking the total weighted deviation of all node prices and node area prices in the initial state as the minimum total weighted deviation, if not, returning to the step S2-4, and re-executing S2-4-S2-7;
or judging whether the total deviation of all the node prices and the node area prices in the initial state is smaller than the current total deviation of all the node prices and the node area prices, if so, taking the total deviation of all the node prices and the node area prices in the initial state as the minimum total deviation, if not, returning to the step S2-4, and re-executing the step S2-4-S2-7.
9. The apparatus for establishing electricity price zone under node marginal pricing mechanism according to claim 6, characterized in that the average value of all nodes in the zone is adopted as node zone settlement price or the weighted average value of all nodes in the zone is adopted as node zone settlement price.
10. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus on which the computer-readable storage medium is located to perform the method of establishing an electricity price area under a node marginal pricing mechanism according to any of claims 1-6.
CN201911352010.0A 2019-12-24 2019-12-24 Method, device and storage medium for establishing electricity price area under node marginal pricing mechanism Pending CN111047367A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111445633A (en) * 2020-03-06 2020-07-24 国电南瑞科技股份有限公司 Method for searching electrified bus node to endow electricity price according to physical connection of uncharged bus

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111445633A (en) * 2020-03-06 2020-07-24 国电南瑞科技股份有限公司 Method for searching electrified bus node to endow electricity price according to physical connection of uncharged bus

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