A power transaction method, device and system based on Markov optimization

Abstract

The application discloses a kind of power transaction method, device and system based on Markov optimization, belong to power transaction technical field, the transaction method includes: in day stage: the Markov chain state transition matrix corresponding Markov unit combination model is constructed, to determine the day output result of two kinds of power generation party;The day start-stop result of conventional energy power generation party is used to fix the start-stop decision in Markov unit combination model to determine day electricity price;The day payment limit under each possible state is calculated using day output result to day electricity price;It reduces the complexity of problem and avoids the excessive conservatism of model and solution.In real-time stage: the real-time output result corresponding to two kinds of power generation party is acquired and determines real-time electricity price;The real-time payment difference corresponding to the difference between real-time output result and day output result is calculated using real-time electricity price, and day-real-time overall settlement is carried out;Day and real-time settlement are connected and unified, so that overall settlement result is more accurate.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of power transaction, and more particularly relates to a power transaction method, device and system based on Markov optimization. BACKGROUND

[0002] Renewable energy generation, such as wind power generation and photovoltaic power generation, is dependent on weather conditions and has uncertainty. In the power market clearing process, renewable energy generation itself cannot be dispatched like conventional generation, and when renewable energy generation is insufficient, conventional energy generation is generally used to meet the corresponding demand. Therefore, in the unit commitment problem of day-ahead power market clearing, the generation power of renewable energy and conventional energy needs to be coordinated.

[0003] Existing methods include deterministic methods, stochastic programming methods and robust optimization methods. The deterministic method considers the expected value of wind or photovoltaic power generation power; the stochastic programming method aims to minimize the expected cost on the probability distribution of uncertainty; and the robust optimization method seeks an optimal solution in the uncertainty set without probability information. In the prior art, after the day-ahead power market clearing is performed, the start-stop decision of the conventional energy generation party is fixed based on the solution of the deterministic method to obtain a corresponding economic dispatch problem, and the marginal price is obtained through the solution of the dual problem to be used for settlement of the power market.

[0004] However, the uncertainty of renewable energy makes the day-ahead power market clearing process more complex and causes inaccurate settlement results. SUMMARY

[0005] In view of the above defects or improvement needs of the prior art, the present application provides a power transaction method, device and system based on Markov optimization, which aims to solve the technical problem that the uncertainty of renewable energy makes the day-ahead power market clearing process more complex and causes inaccurate settlement results.

[0006] To achieve the above-mentioned purpose, according to one aspect of the present application, a power transaction method based on Markov optimization is provided, comprising:

[0007] S1: constructing a state transition matrix of a Markov chain using historical generation power of a renewable energy generation party to predict a corresponding first day-ahead output result of the renewable energy generation party; establishing and solving a Markov unit commitment model corresponding to the first day-ahead output result to obtain a day-ahead start-stop result and a second day-ahead output result of a conventional energy generation party;

[0008] S2: fixing the start-stop decision of the conventional energy generation party in the Markov unit commitment model based on the day-ahead start-stop result to obtain a corresponding first economic dispatch problem; solving the first economic dispatch problem and performing sensitivity analysis to obtain a day-ahead electricity price;

[0009] S3: calculating the day-ahead payment amount of the renewable energy power generation party and the conventional energy power generation party in each possible state by using the day-ahead electricity price, the first day-ahead output result and the second day-ahead output result;

[0010] S4: obtaining the first real-time output result corresponding to the renewable energy power generation party, and establishing a corresponding second economic dispatching problem under the given real-time start-stop state of the conventional energy generator set; solving the second economic dispatching problem to obtain the second real-time output result corresponding to the conventional energy power generation party;

[0011] S5: performing sensitivity analysis on the second economic dispatching problem to obtain the real-time electricity price;

[0012] S6: calculating the real-time payment difference corresponding to the difference between the first day-ahead output result and the first real-time output result by using the real-time electricity price, and performing day-ahead-real-time overall settlement of the renewable energy power generation party; calculating the real-time payment difference corresponding to the difference between the second day-ahead output result and the second real-time output result by using the real-time electricity price, and performing day-ahead-real-time overall settlement of the conventional energy power generation party.

[0013] Further, the S1 comprises:

[0014] S11: aggregating the historical power generation of the renewable energy power generation party, and setting the historical power generation to obey a Markov chain; constructing a state transition matrix of the Markov chain by using the historical power generation of the renewable energy power generation party, for predicting the first day-ahead output result corresponding to the renewable energy power generation party;

[0015] S12: establishing a Markov unit commitment model corresponding to the first day-ahead output result, and selecting a group of start-stop decisions and a plurality of groups of economic dispatching decisions of the conventional energy generator set to minimize the total day-ahead expected cost according to the net demand state within the dispatching time range.

[0016] S13: solving the Markov unit commitment model corresponding to the first day-ahead output result to obtain the day-ahead start-stop result and the second day-ahead output result corresponding to the conventional energy power generation party.

[0017] Further, the objective function of the Markov unit commitment model is:

[0018]

[0019] The system demand constraint in the constraint condition of the Markov unit commitment model is expressed as:

[0020]

[0021] where J DA represents the day-ahead cost, T represents the number of time, t is the index of time, n represents the state in which the net demand of the system is located; I represents the number of conventional energy generation sides, i is the index of the conventional energy generation side, u i (t) represents the binary variable corresponding to the start-stop operation of the generator i, S i is the start cost, x i (t) represents the generation state of the generator i, represents the no-load cost of the generator i when it is online, represents the probability that it is in state n at time t, the electricity energy offer of the generator i contains multiple segments, the price of segment b (1≤b≤B) is C i,b , the maximum power is p i,bmax , and the day-ahead generation power of the generator i in hour t segment b state n is The sum of the powers of the generator i in each segment is D DA (t) represents the predicted value of the total demand of the system at day-ahead, represents the net demand of the system.

[0022] Further, the S12 comprises: solving the Markov unit commitment model by using a branch-cut method to obtain the day-ahead start-stop result and the second day-ahead output result corresponding to the conventional energy generation side.

[0023] Further, the S2 comprises:

[0024] S21: fixing the start-stop decision of the conventional generator unit in the Markov unit commitment model according to the day-ahead start-stop result corresponding to the conventional energy generation side to obtain a corresponding first economic dispatching problem;

[0025] S22: processing the first economic dispatching problem by using a linear programming method and sensitivity analysis to obtain a boundary value, and calculating the day-ahead electricity price of the generation side in each state according to the dual variable in the boundary value.

[0026] Further, the S5 comprises: processing the second economic dispatching problem by using sensitivity analysis to obtain a boundary value, and calculating the real-time electricity price of the generation side in each state according to the dual variable in the boundary value.

[0027] In one embodiment, the S6 comprises:

[0028] The product of the difference between the real-time electricity price and the second day-ahead output result and the second real-time output result corresponding to the renewable energy generation side is taken as the real-time payment difference of the renewable energy generation side, and the day-ahead-real-time overall settlement of the renewable energy generation side is performed.

[0029] The product of the real-time electricity price and the difference between the first day-ahead output result and the first real-time output result of the conventional energy power generation party is taken as the real-time payment difference of the conventional energy power generation party, and day-ahead-real-time overall settlement of the conventional energy power generation party is performed.

[0030] Further, built-in the blockchain platform, the S6 further includes:

[0031] When the renewable energy power generation party files an arbitration application against the penalty charged by the blockchain platform, the arbitration application is voted by using the smart contract in the blockchain platform, wherein the penalty is generated due to too large deviation between the day-ahead output result and the real-time output result;

[0032] The actual penalty amount that the renewable energy power generation party finally needs to pay is determined according to the voting result, and settlement is performed.

[0033] According to another aspect of the present application, a power transaction device based on Markov optimization is provided, comprising:

[0034] A day-ahead output calculation module is configured to construct a state transition matrix of a Markov chain by using historical power generation of a renewable energy power generation party, to predict a first day-ahead output result corresponding to the renewable energy power generation party, and to establish and solve a Markov unit commitment model corresponding to the first day-ahead output result, to obtain a day-ahead start-stop result and a second day-ahead output result corresponding to a conventional energy power generation party;

[0035] A day-ahead electricity price calculation module is configured to fix start-stop decisions of the conventional energy power generation party in the Markov unit commitment model based on the second day-ahead start-stop result, to obtain a corresponding first economic dispatching problem, to solve the first economic dispatching problem and perform sensitivity analysis to obtain a day-ahead electricity price;

[0036] A day-ahead settlement calculation module is configured to calculate day-ahead payment limits of the renewable energy power generation party and the conventional energy power generation party in each possible state by using the day-ahead electricity price, the first day-ahead output result and the second day-ahead output result;

[0037] A real-time output calculation module is configured to obtain a first real-time output result corresponding to the renewable energy power generation party, and to establish a corresponding second economic dispatching problem in a given start-stop state of the conventional energy power generation unit (which can come from the day-ahead start-stop result); the second economic dispatching problem is solved to obtain a second real-time output result corresponding to the conventional energy power generation party;

[0038] A real-time electricity price calculation module is configured to perform sensitivity analysis on the second economic dispatching problem to obtain a real-time electricity price;

[0039] A real-time settlement calculation module is used to use the real-time electricity price to calculate the real-time payment difference corresponding to the difference between the first day-ahead output result and the first real-time output result and to perform the day-ahead-real-time overall settlement of the renewable energy power generation party; and to use the real-time electricity price to calculate the real-time payment difference corresponding to the difference between the second day-ahead output result and the second real-time output result and to perform the day-ahead-real-time overall settlement of the conventional energy power generation party.

[0040] According to another aspect of the present invention, there is provided an electricity trading system, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0041] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0042] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0043] (1) The present invention is directed to a hybrid power system containing renewable energy. In the day-ahead phase, a Markov unit combination model corresponding to a Markov chain is constructed to determine the day-ahead output results corresponding to the two power generators and the day-ahead start / stop results of the conventional energy power generator; the day-ahead start / stop results of the conventional energy power generator are used to fix the start / stop decision of the conventional energy power generator in the Markov unit combination model and a sensitivity analysis is performed to determine the day-ahead electricity price; the day-ahead electricity price is used to calculate the day-ahead payment amount for the two day-ahead output results under various possible states. In the real-time phase, the real-time output results corresponding to the renewable energy power generator are obtained, a real-time economic dispatch problem model is established given the start / stop state of the conventional energy power generator (which can be derived from the day-ahead start / stop results), and the real-time output results corresponding to the conventional energy power generator are calculated, and a sensitivity analysis is performed to determine the real-time electricity price; the real-time electricity price is used to calculate the real-time payment difference corresponding to the difference between the real-time output result and the day-ahead output result and perform a day-ahead-real-time unified settlement of the two power generators. The present invention takes into account the uncertainty of renewable energy in the day-ahead clearing problem, and summarizes the information of all previous moments in a probabilistic sense through the state at a certain moment, thereby reducing the complexity of the problem and avoiding the over-conservatism of the model solution. During the settlement process, the impact of renewable energy uncertainty on day-ahead prices is included, and day-ahead and real-time settlements are connected and unified based on the real-time determined renewable energy status, making the overall settlement results more accurate.

[0044] (2) The Markov unit commitment model in the scheme has the following characteristics: the state transition matrix is used to predict the first day-ahead output result of the renewable energy power generation party; a set of start-stop decisions and multiple sets of economic dispatching decisions of the conventional energy power generation unit are selected according to the net demand state within the dispatching time range to minimize the total day-ahead expected cost; the information of all previous time points is summarized in the probability sense through the state at a certain time point, thereby reducing the complexity of the overall problem compared with the stochastic programming method; and compared with the robust optimization method, the model and the solution are not excessively conservative due to the consideration of the probability information.

[0045] (3) The Markov unit commitment model in the scheme is solved by using the branch-and-cut method. Commercial optimization solvers such as Gurobi and COPT do not provide a solution method for a random process. The state probability is included in the objective function as a weight in the scheme, and the system demand constraint only needs to be true for those states with non-zero probability, and the ramp rate constraint is only true for those state transitions with non-zero probability. Given the state transition probability, the calculated state probability, the objective equation and the constraint condition, the problem is represented as a mixed integer linear programming problem (i.e., Markov optimization) from a Markov decision process, which can be effectively solved by using the branch-and-cut method, thereby ensuring convergence and accelerating the solution.

[0046] (4) The start-stop decisions of the conventional power generation unit are fixed according to the second day-ahead start-stop result of the conventional energy power generation party in the scheme, a corresponding economic dispatching problem is obtained, and the linear programming method and the sensitivity analysis are used to process the economic dispatching problem to obtain boundary values, the day-ahead electricity price of the power generation party in each state is calculated according to the dual variables in the boundary values; the real-time electricity price considers the determined renewable energy state, and also considers the output uncertainty of the renewable energy, so that the calculation result of the electricity price is more accurate.

[0047] (5) The product of the difference between the real-time electricity price and the day-ahead output result and the real-time output result is taken as the real-time payment difference in the scheme, which matches the actual application scene and is simple to operate.

[0048] (6) The scheme arbitrates the possible deviation disputes of the renewable energy. While reducing the cost and improving the system operation efficiency, the arbitration can maintain the interests of the renewable energy power generation party when the renewable energy generation is affected by the unavoidable unexpected weather conditions, or major equipment accidents of the power grid and external power stations due to major maintenance plan adjustment, so as to ensure the enthusiasm of the renewable energy power generation party in participating in the power transaction. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 The flowchart of the power transaction method based on Markov optimization provided for Embodiment 1 of the application;

[0050] Figure 2 A state transition diagram corresponding to the renewable energy power generation party provided for embodiment 1 of the present application is shown in the figure;

[0051] Figure 3 A schematic diagram of the power transaction system provided for embodiment 1 of the present application is shown in the figure;

[0052] Figure 4 A schematic diagram of the hierarchical architecture of the blockchain platform provided for embodiment 1 of the present application is shown in the figure;

[0053] Figure 5 A specific diagram for arbitration in the power transaction method based on Markov optimization provided for embodiment 1 of the present application is shown in the figure;

[0054] Figure 6 A specific flowchart for arbitration in the power transaction method based on Markov optimization provided for embodiment 1 of the present application is shown in the figure. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0056] Embodiment 1

[0057] As shown in the figure, the present embodiment provides a power transaction method based on Markov optimization, which comprises S1-S6. Figure 1

[0058] S1: Construct a state transition matrix of Markov chain using the historical power generation of the renewable energy power generation party to predict the corresponding first day-ahead output result of the renewable energy power generation party; solve the Markov unit commitment model corresponding to the first day-ahead output result to obtain the day-ahead start-stop result and the second day-ahead output result of the conventional energy power generation party.

[0059] S2: Fix the start-stop decision of the conventional energy power generation party in the Markov unit commitment model based on the day-ahead start-stop result to obtain the corresponding first economic dispatching problem; solve the first economic dispatching problem and perform sensitivity analysis to obtain the day-ahead electricity price.

[0060] S3: Calculate the day-ahead payment limit of the renewable energy power generation party and the conventional energy power generation party under each possible state using the day-ahead electricity price, the first day-ahead output result and the second day-ahead output result.

[0061] ​S4: Obtain the first real-time output result corresponding to the renewable energy generator, and establish a corresponding second economic dispatch problem under the given real-time start-stop state of the conventional energy generator (which can come from the day-ahead start-stop result); solve the second economic dispatch problem to obtain the second real-time output result corresponding to the conventional energy generator.

[0062] S5: Perform sensitivity analysis on the second economic dispatch problem to obtain the real-time electricity price.

[0063] S6: Calculate the real-time payment difference corresponding to the difference between the first day-ahead output result and the first real-time output result using the real-time electricity price, and perform day-ahead-real-time overall settlement of the renewable energy generator; calculate the real-time payment difference corresponding to the difference between the second day-ahead output result and the second real-time output result using the real-time electricity price, and perform day-ahead-real-time overall settlement of the conventional energy generator.

[0064] As an optional implementation, S1 includes: S11: aggregate the historical power generation of the renewable energy generator, and assume that the historical power generation distribution obeys a Markov chain; use the historical power generation of the renewable energy generator to construct a state transition matrix of the Markov chain, which is used to predict the first day-ahead output result corresponding to the renewable energy generator. S12: establish a Markov unit commitment model corresponding to the first day-ahead output result, and select a set of start-stop decisions and multiple sets of economic dispatch decisions of the conventional energy generator according to the net demand state within the dispatch time range to minimize the total day-ahead expected cost. S13: solve the Markov unit commitment model corresponding to the first day-ahead output result to obtain the day-ahead start-stop result and the second day-ahead output result corresponding to the conventional energy generator.

[0065] Specifically, in this Markov chain, the capacity of the renewable energy generator is evenly divided into multiple intervals, the mean of each interval is represented by a state, and the states are arranged in ascending order of mean. The transition matrix is denoted by π as follows:

[0066]

[0067] where the (m, n)th element of the matrix π mn represents the ratio of the number of times of observing the transition from state m to state n to the number of occurrences of state m.

[0068] The state probability is calculated as follows:

[0069]

[0070] where, represents the probability that the total power generation of the renewable energy generator is in state n at time t+1, represents the probability that it is in state m at time t. The state transition of the total power generation of the renewable energy generator is as followsFigure 2

[0071] Since the total generation power of renewable energy generators is equal to the sum of generation power of each renewable energy generator, the state of generation power of each renewable energy generator corresponds to the total state, i.e.,

[0072]

[0073] wherein, represents the day-ahead (expected) total generation power of renewable energy generators in state n at hour t; K represents the number of renewable energy generators, and k is the index of the renewable energy generator, is the day-ahead (expected) generation power of renewable energy generator k in state n at hour t.

[0074] As an optional embodiment, S12 comprises: a Markov unit commitment model is used to select a set of unit start-stop decisions and a plurality of sets of economic dispatch decisions to minimize the total day-ahead expected cost within a dispatch time range, such as day-ahead 24 hours. The objective function of the Markov unit commitment model is:

[0075]

[0076] wherein, J DA represents the day-ahead cost, T represents the number of time, t is the index of time, and n represents the state in which the net demand of the system is located; I represents the number of conventional energy generators, i is the index of the conventional energy generator, and u i (t) represents the binary variable corresponding to the start operation of generator i, S i is the start cost, x i (t) represents the start-stop decision of generator i, “1” represents online, and “0” represents offline, represents the no-load cost of generator i when it is online, represents the probability that it is in state n at time t, and the electricity energy offer of generator i contains a plurality of segments, the price of segment b (1≤b≤B) is C i,b , and the maximum power is p i,bmax , and the day-ahead generation power of generator i in segment b state n at hour t is The sum of the powers of generator i in each segment (economic dispatch decision) is

[0077] The constraint conditions include system demand constraints and conventional generator constraints. Specifically, the system demand constraints can be represented as:

[0078]

[0079] wherein, D DA ​(t) represents the forecast value of the total system demand at hour t on the day before, assuming that the system demand is inelastic and ignoring the uncertainty. It is important to note that the economic dispatch decision of conventional generators at hour t is a Markov decision problem. It depends only on the state n of renewable energy generators or system net demand at time t.

[0080] Generator constraints include startup constraints, generator capacity constraints, ramp rate constraints, and minimum rise / fall time constraints.

[0081] Specifically, the startup constraints can be expressed as:

[0082]

[0083] Since the generator quotation contains multiple segments, the power generation of each segment cannot exceed the maximum value of that segment, which can be expressed as:

[0084]

[0085] The sum of the power generation of all quoted segments of an engine is equal to the power generation of the generator, that is:

[0086]

[0087] The generator capacity constraint can be expressed as:

[0088]

[0089] where p imin and p imax are the minimum and maximum power generation of generator i respectively.

[0090] The power generation change of generator i between t and t+1 hours cannot exceed its ramp rate Δ i Since the net demand of the system may be in different states at these two times, the ramp rate constraint should be satisfied for all possible state transitions. Therefore, the ramp rate constraint can be expressed as:

[0091]

[0092] The shortest startup / shutdown time constraint can be expressed as:

[0093]

[0094] As an optional implementation, S13 includes: solving the Markov unit commitment model using a branch-and-cut method to obtain the day-ahead start-stop results and the day-ahead output results corresponding to the conventional energy power generation party.

[0095] Specifically, the branch-and-cut method combines the branch-and-bound method and the cutting plane method. The branch-and-cut method is effective in solving deterministic mixed integer linear problems and has been widely used by the International Organization for Standardization, public utilities, and semiconductor manufacturers. In addition, the existence of commercial optimization solvers such as Gurobi and COPT reduces the programming time and debugging time for solving such problems. However, these software do not provide a solution method for random processes. For the method used in the present invention, the state probability is included as a weight in the objective function (4), and the system demand constraint (5) only needs to be valid for those states with non-zero probability, and the ramp rate constraints (10) and (11) only need to be valid for those state transitions with non-zero probability. Given the state transition probability, the state probability calculated based on (2), the objective equation and the constraints, the problem is expressed from a Markov decision process to a mixed integer linear programming problem (i.e., a Markov optimization problem), which can be effectively solved using the branch-and-cut method, while ensuring convergence and accelerating the solution. Thus, a set of unit start-stop decision x is obtained. i (t) and multiple sets of economic dispatch decisions that depend on state n

[0096] As an optional implementation, S2 includes: S21: fixing the start-stop decisions of conventional power generation units in the Markov unit combination model according to the corresponding day-ahead start-stop results of conventional energy power generation parties, and obtaining the corresponding first economic dispatch problem; S22: using linear programming and sensitivity analysis to process the first economic dispatch problem to obtain boundary values, and calculating the day-ahead electricity price of the power generation party in each state according to the dual variables in the boundary values.

[0097] Specifically, considering a small change in the net demand of the system at time t and state n, based on the sensitivity formula of the linear programming problem, the target value J is relative to The change in can be measured by the dual variable (shadow price) of constraint (5), expressed as Right now:

[0098]

[0099] Among them, the dual variable is the boundary value (sensitivity) of (5), where λ n (t) represents the day-ahead electricity price for n conditions at hour t.

[0100] S3: Calculate the day-ahead payment amount for renewable energy generators and conventional energy generators under each possible state using the day-ahead electricity price, the output result of the first day and the output result of the second day. Based on (14), we can get λ n (t)p i,n (t) is the day-ahead electricity cost of generator i in state n, expressed as So:

[0101]

[0102] Similarly, the day-ahead electricity cost of the renewable energy generator k at state n is represented as:

[0103]

[0104] Note: Since there are multiple possible states in the day-ahead stage, it is difficult to directly settle, so it will be settled in the real-time stage (in S6) according to the real-time state (a state corresponding to the real-time renewable energy generation power).

[0105] S4: Obtain the first real-time output result corresponding to the renewable energy generator, and under the given real-time start-stop state of the conventional energy generator (which can come from the day-ahead start-stop result), establish the corresponding second economic dispatch problem; solve the second economic dispatch problem to obtain the second real-time output result corresponding to the conventional energy generator.

[0106] Specifically, assuming that the generation power (or system net demand) of the renewable energy generator is determined in the real-time situation (t' hours) without considering state changes, under the given real-time start-stop state of the conventional energy generator (which can come from the day-ahead start-stop result), the objective function of the real-time economic dispatch problem can be represented as:

[0107]

[0108] where J RT represents the real-time cost, I' represents the number of online conventional energy generators, i is the index of the conventional energy generator represents the real-time generation power of generator i, represents the real-time generation power of section b.

[0109] The system demand constraint can be represented as:

[0110]

[0111] where D RT (t') represents the total real-time system demand, represents the real-time generation power of the renewable energy generator k.

[0112] The generator capacity constraint can be represented as:

[0113]

[0114] The generation power constraint of each section is represented as:

[0115]

[0116] The total power generation of the generator is expressed as:

[0117]

[0118] The ramp rate constraint can be expressed as:

[0119]

[0120] The second economic dispatch problem is solved by using a linear programming method to obtain the second real-time output result of the conventional energy generator

[0121] As an optional implementation, S5 comprises: using sensitivity analysis to process the second economic dispatch problem to obtain a boundary value, and calculating the real-time electricity price of the generator in each state according to the dual variable in the boundary value.

[0122] Specifically, based on the sensitivity formula of the linear programming problem, the dual variable (shadow price) corresponding to (18) is solved, and λ RT (t') can be obtained, that is, the real-time electricity price.

[0123] In one of the embodiments, S6 comprises: taking the product of the real-time electricity price and the difference between the second day-ahead output result and the second real-time output result of the renewable energy generator as the payment difference of the real-time settlement of the renewable energy generator, and performing the day-ahead-real-time overall settlement of the renewable energy generator; and taking the product of the real-time electricity price and the difference between the first day-ahead output result and the first real-time output result of the conventional energy generator as the payment difference of the real-time settlement of the conventional energy generator, and performing the day-ahead-real-time overall settlement of the conventional energy generator.

[0124] Specifically, it is assumed that the power generation of the renewable energy generator in the real-time situation (t' hours) corresponds to a day-ahead state n' (that is, the real-time state). For the generator i, the total electricity fee can be expressed as the day-ahead electricity fee corresponding to the real-time state plus the electricity fee of the real-time over-generation or minus the electricity fee of the real-time under-generation, and is expressed as follows:

[0125]

[0126] Wherein, W i Tot (t') represents the total electricity fee of the generator i, represents the day-ahead electricity fee of the generator i, W i RT (t') represents the real-time electricity fee (deviation adjustment fee) of the generator i, represents the real-time power generation of the generator i, represents the day-ahead power generation of the generator i.

[0127] Similarly, the renewable energy generator k also needs to pay the deviation adjustment fee. In addition, when the power generation is less than the day-ahead power, the deviation penalty needs to be charged. Then,

[0128]

[0129] wherein, represents the total electricity fee of the renewable energy generator k, represents the day-ahead electricity fee of the renewable energy generator k, represents the real-time electricity fee (deviation adjustment fee) of the renewable energy generator k, represents the deviation penalty of the renewable energy generator k, λ penal represents the unit penalty price, represents the real-time power generation of the renewable energy generator k, represents the day-ahead power generation of the renewable energy generator k in state n’.

[0130] As an optional embodiment, built-in the blockchain platform, S6 further includes: when the renewable energy generator has a dispute with the penalty charged by the blockchain platform, a voting is conducted on the arbitration application by using the smart contract in the blockchain platform; wherein, the penalty is due to the excessive deviation between the day-ahead power generation result and the real-time power generation; the actual penalty amount that the renewable energy generator finally needs to pay is determined according to the voting result and settled.

[0131] wherein, the blockchain platform uses a Hyperledger Fabric consortium chain. The hierarchical architecture of the blockchain platform includes five levels of application layer, smart contract layer, consensus layer, network layer and data layer, as shown in Figure 3 and Figure 4 The power generator, power purchaser and arbitrator call the smart contract on the smart contract layer through the application program provided by the application layer to the parties, as shown in Figure 5 The smart contract is an executable computer program responsible for the automatic execution of various transaction transactions, including power generation contract, settlement contract, arbitration contract and query contract. The network layer is mainly composed of a channel, and the ordering node and the peer node will join the channel, and the peer nodes of the same type will also form an organization. After the smart contract submits a transaction, the ordering node sorts the transaction, the consensus protocol of the consensus layer cooperates with the peer node to verify and execute the transaction, and finally the peer node transmits the transaction information to the data layer through the encryption algorithm, stores it in the ledger and updates the world state.

[0132] Specifically, as shown in Figure 6 After the settlement is completed, the renewable energy generator can initiate an arbitration application by calling the relevant smart contract through the application program. The arbitration application needs to include the information of the transaction and the reason for applying for arbitration.

[0133] Upon receiving the arbitration request, the arbitrators will review the transaction information and vote. Specifically, the arbitrators will check the transaction contract and some more transaction information, especially the relevant weather condition. The relevant weather condition during the transaction period of the power generation party will be recorded. For wind power generation, the weather condition is wind speed; for photovoltaic power generation, the weather condition is irradiance. After checking the information required for arbitration, the arbitrators vote through the smart contract. Further, the vote is Boolean: if the arbitrator believes that the applicant is responsible, the vote is "yes", and the smart contract records it as 0; if the arbitrator believes that the applicant is not responsible, the vote is "no", and the smart contract records it as 1.

[0134] The smart contract determines whether the number of votes exceeds 2 / 3 of the total number of arbitrators. If yes, the voting is completed, the smart contract counts the votes, and decides the amount of penalty that the power generation party needs to pay according to the voting result; otherwise, the voting continues, and the previous step is repeated. Specifically, the arbitrators will check the transaction contract and some more transaction information, especially the relevant weather condition. The relevant weather condition during the transaction period of the power generation party will be recorded. For wind power generation, the weather condition is wind speed; for photovoltaic power generation, the weather condition is irradiance. After checking the information required for arbitration, the arbitrators vote through the smart contract.

[0135] Further, the vote is Boolean: if the arbitrator believes that the applicant is responsible, the vote is "yes", and the smart contract records it as 0; if the arbitrator believes that the applicant is not responsible, the vote is "no", and the smart contract records it as 1.

[0136] After the vote counting is completed, if the number of "yes" votes is more, the applicant's deviation penalty can be waived, otherwise the applicant's deviation penalty will remain as the calculated deviation penalty. At the same time, the applicant needs to pay the arbitration fee after the arbitration is completed. Further, the final deviation penalty that the applicant needs to pay is expressed as follows:

[0137]

[0138] wherein represents the final deviation penalty that the applicant k needs to pay after arbitration, V s represents the number of "yes" votes, V total represents the total number of votes.

[0139] Further, after the arbitration is over, the applicant is required to pay the arbitration fee, which is calculated according to the voting results. Specifically, in order to ensure the fairness and efficiency of the arbitration process, it is stipulated that only the arbitrators who make the vote and whose vote is the same as the final arbitration result are eligible to obtain the arbitration fee, and the arbitrators who make the vote but whose vote is not the same as the final arbitration result or the arbitrators who do not make the vote cannot obtain the arbitration fee. This can encourage arbitrators to participate in voting and encourage them to vote according to the actual situation of the transaction.

[0140] Further, the arbitration fee calculation formula is as follows:

[0141]

[0142] wherein W k V represents the arbitration fee that the applicant k needs to pay, and p0 represents the unit arbitration fee.

[0143] Embodiment 2

[0144] The embodiment provides a power transaction device based on Markov optimization, which comprises a day-ahead output calculation module, a day-ahead electricity price calculation module, a day-ahead settlement calculation module, a real-time output calculation module, a real-time electricity price calculation module and a real-time settlement calculation module.

[0145] The day-ahead output calculation module is configured to construct a state transition matrix of a Markov chain by using historical power generation of a renewable energy power generation party, so as to predict a first day-ahead output result corresponding to the renewable energy power generation party; and establish and solve a Markov unit commitment model corresponding to the first day-ahead output result, so as to obtain a day-ahead start-stop result and a second day-ahead output result corresponding to a conventional energy power generation party.

[0146] The day-ahead electricity price calculation module is configured to fix the start-stop decision of the conventional energy power generation party in the Markov unit commitment model based on the second day-ahead start-stop result, so as to obtain a corresponding first economic dispatching problem; solve the first economic dispatching problem and perform sensitivity analysis, so as to obtain a day-ahead electricity price.

[0147] The day-ahead settlement calculation module is configured to calculate the day-ahead payment amount of the renewable energy power generation party and the conventional energy power generation party in each possible state by using the day-ahead electricity price, the first day-ahead output result and the second day-ahead output result.

[0148] The real-time output calculation module is configured to obtain a first real-time output result corresponding to the renewable energy power generation party, and in the case that the start-stop state of the conventional energy power generation unit is given (which can come from the day-ahead start-stop result), establish a corresponding second economic dispatching problem; solve the second economic dispatching problem, so as to obtain a second real-time output result corresponding to the conventional energy power generation party.

[0149] A real-time electricity price calculation module is configured to perform sensitivity analysis on the second economic dispatching problem to obtain a real-time electricity price.

[0150] A real-time settlement calculation module is configured to calculate a real-time payment difference corresponding to a difference between the first day-ahead output result and the first real-time output result using the real-time electricity price, and perform day-ahead-real-time overall settlement of the renewable energy power generation party; and calculate a real-time payment difference corresponding to a difference between the second day-ahead output result and the second real-time output result using the real-time electricity price, and perform day-ahead-real-time overall settlement of the conventional energy power generation party.

[0151] Embodiment 3

[0152] The embodiment provides a power transaction system, which comprises a memory and a processor. The memory stores a computer program. The processor implements the steps of the above method when executing the computer program.

[0153] Embodiment 4

[0154] The embodiment provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the steps of the above method.

[0155] Those skilled in the art will easily understand that the above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement and improvement within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A power trading method based on Markov optimization, characterized in that: include: S1: Use the historical power generation of renewable energy generators to construct the state transition matrix of the Markov chain to predict the output results of renewable energy generators on the first day before the day. Establish and solve the Markov unit commitment model corresponding to the output results on the first day before the day to obtain the start-stop results and output results on the second day before the day corresponding to conventional energy generators. The objective function of the Markov unit commitment model is: ； in, represents the day-ahead cost, T Indicates the time number, t is the time index, n Indicates the state of the system's net demand; I represents the number of cubic meters of conventional energy generation, i is the index of conventional energy power generation, Indicates a generator i The binary variable corresponding to the start and stop operation, For startup costs, Indicates a generator i The power generation status, Indicates a generator i No-load cost when online, Indicates that at time t In state n The probability that the generator i The electricity energy quotation contains multiple segments. b (1≤ b ≤ B ) is ,dynamo i In hours t part b state n The day-ahead power generation is ; Constraints include system demand constraints and conventional generator constraints; conventional generator constraints include startup constraints, generator capacity constraints, ramp rate constraints, and minimum rise / fall time constraints; S2: fixing the start / stop decisions of conventional energy generators in the Markov unit commitment model based on the day-ahead start / stop results to obtain a corresponding first economic dispatch problem; solving the first economic dispatch problem and performing a sensitivity analysis to obtain a day-ahead electricity price; S3: Calculating the day-ahead payment amounts of the renewable energy power generator and the conventional energy power generator under each possible state using the day-ahead electricity price, the first day-ahead output result, and the second day-ahead output result; S4: Obtain a first real-time output result corresponding to the renewable energy power generation party, and establish a corresponding second economic dispatch problem given the real-time start / stop status of the conventional energy power generation unit; solve the second economic dispatch problem to obtain a second real-time output result corresponding to the conventional energy power generation party; S5: Perform sensitivity analysis on the second economic dispatch problem to obtain a real-time electricity price; S6: Calculate the real-time payment difference corresponding to the difference between the first day-ahead output result and the first real-time output result using the real-time electricity price, and perform the day-ahead-real-time overall settlement of the renewable energy power generation party; Calculate the real-time payment difference corresponding to the difference between the second day-ahead output result and the second real-time output result using the real-time electricity price, and perform the day-ahead-real-time overall settlement of the conventional energy power generation party.

2. The power trading method based on Markov optimization according to claim 1, characterized in that: Said S1 comprises: S11: Aggregating the historical power generation of the renewable energy generators, assuming that the historical power generation distribution obeys a Markov chain, and constructing a state transition matrix of the Markov chain using the historical power generation of the renewable energy generators, for predicting the output result of the renewable energy generators corresponding to the first day before. S12: establishing a Markov unit commitment model corresponding to the first day-ahead output result, wherein the Markov unit commitment model is used to select a set of start-stop decisions and multiple sets of economic dispatch decisions for the conventional energy generating units according to the net demand state within the dispatch time range to minimize the total day-ahead expected cost; S13: Solve the Markov unit commitment model corresponding to the first day-ahead output result to obtain the day-ahead start / stop result corresponding to the conventional energy power generation party and the second day-ahead output result.

3. The power trading method based on Markov optimization according to claim 2, characterized in that: The system demand constraint in the constraint condition of the Markov unit commitment model is expressed as: ； Among them, the generator i The sum of the power in each section is ; express t The forecast value of total hourly system demand on the day before, Represents the net demand on the system.

4. The power trading method based on Markov optimization according to claim 2, characterized in that: The S13 includes: solving the Markov unit combination model using a branch-and-cut method to obtain the day-ahead start-stop result and the day-ahead output result corresponding to the conventional energy power generation party.

5. The power trading method based on Markov optimization according to claim 1, characterized in that: The S2 includes: S21: fixing the start / stop decisions of conventional power generation units in the Markov unit commitment model according to the day-ahead start / stop results corresponding to the conventional energy power generation units, and obtaining a corresponding first economic dispatch problem; S22: Using linear programming and sensitivity analysis to process the first economic dispatch problem to obtain boundary values, and calculating the day-ahead electricity price of the power generator in each state based on the dual variables in the boundary values.

6. The power trading method based on Markov optimization according to claim 5, characterized in that: The S5 includes: processing the second economic dispatch problem by using sensitivity analysis to obtain a boundary value, and calculating the real-time electricity price of the power generator according to the dual variables in the boundary value.

7. The power trading method based on Markov optimization according to claim 1, characterized in that: The S6 includes: The real-time payment difference of the renewable energy generator is calculated by multiplying the real-time electricity price by the difference between the output result of the renewable energy generator on the second day before and the second real-time output result, and performing a day-ahead-real-time overall settlement for the renewable energy generator. The product of the real-time electricity price and the difference between the first day-ahead output result and the first real-time output result corresponding to the conventional energy power generation party is used as the real-time payment difference of the conventional energy power generation party, and the day-ahead-real-time overall settlement of the conventional energy power generation party is performed.

8. The power trading method based on Markov optimization according to any one of claims 1 to 7, characterized in that: Built into the blockchain platform, the S6 also includes: When the renewable energy power generation party objects to the liquidated damages collected by the blockchain platform and files an arbitration application, the smart contract in the blockchain platform is used to vote on the arbitration application; the liquidated damages are caused by a large deviation between the day-ahead output result and the real-time output; the actual amount of liquidated damages that the renewable energy power generation party ultimately needs to pay is determined and settled according to the voting results.

9. A power trading device based on Markov optimization, characterized in that: The method for executing the power trading method based on Markov optimization according to any one of claims 1 to 8 comprises: The day-ahead output calculation module is used to construct a state transition matrix of a Markov chain using the historical power generation of renewable energy generators to predict the day-ahead output results corresponding to the renewable energy generators; establish and solve the Markov unit commitment model corresponding to the first day-ahead output results to obtain the day-ahead start and stop results corresponding to the conventional energy generators and the day-ahead output results; a day-ahead electricity price calculation module configured to fix the start / stop decisions of conventional energy generators in the Markov unit commitment model based on the day-ahead start / stop results, thereby obtaining a corresponding first economic dispatch problem; solve the first economic dispatch problem and perform a sensitivity analysis to obtain a day-ahead electricity price; a day-ahead settlement calculation module, configured to calculate the day-ahead payment amounts of the renewable energy power generator and the conventional energy power generator under various possible conditions using the day-ahead electricity price, the first day-ahead output result, and the second day-ahead output result; a real-time output calculation module configured to obtain a first real-time output result corresponding to the renewable energy power generation party and, given the start / stop status of the conventional energy power generation unit, establish a corresponding second economic dispatch problem; solve the second economic dispatch problem to obtain a second real-time output result corresponding to the conventional energy power generation party; A real-time electricity price calculation module, configured to perform a sensitivity analysis on the second economic dispatch problem to obtain a real-time electricity price; A real-time settlement calculation module is used to use the real-time electricity price to calculate the real-time payment difference corresponding to the difference between the first day-ahead output result and the first real-time output result and to perform the day-ahead-real-time overall settlement of the renewable energy power generation party; and to use the real-time electricity price to calculate the real-time payment difference corresponding to the difference between the second day-ahead output result and the second real-time output result and to perform the day-ahead-real-time overall settlement of the conventional energy power generation party.

10. An electricity trading system comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

Images