Multi-market transaction assisting device and multi-market transaction assisting method

Abstract

To efficiently create a bidding plan in a complicated power / regulation power market transaction.SOLUTION: A multi-market transaction assisting device according to the present invention includes a bid path imitator that inputs, to an objective function that inputs a variable group including a bid price and a bid volume as separate variables in a plurality of markets in which electric power and / or regulation power are traded and outputs a transaction profit, a candidate of a state that is a variable group including the bid price and lacking at least the bid volume, and acquires the state that maximizes the transaction profit.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a multi-market trading support device and a multi-market trading support method, and more particularly to a multi-market trading support device and a multi-market trading support method for trading the amount of electricity and adjustment capacity generated by distributed energy sources such as renewable energy power sources and storage battery systems. [Background technology]

[0002] Hereinafter, renewable energy sources are also referred to simply as "renewable energy." Battery storage systems are also referred to simply as "batteries." Distributed energy resources are also referred to as "DERs (Distributed Energy Resources)." Recent electricity system reforms have increased the purpose and opportunities for power generation companies that own or operate renewable energy sources or storage batteries to trade their energy or balancing capacity in the market. For example, apart from trading energy, it is possible to trade balancing capacity with short response times in the primary balancing market or the secondary balancing market, or to trade energy and balancing capacity together in a simultaneous market. Furthermore, it is also possible to trade balancing capacity in a local flexibility market with the aim of alleviating regional grid congestion. The present invention provides a means for creating trading plans in increasingly complex electricity and balancing capacity markets.

[0003] Background art in this technical field is the invention of Patent Document 1. The purpose of this invention is to "provide a technology that can create a trading plan for balancing power," and the solution is to "create a generator operation plan and trading plans in the spot market and the supply-demand balancing market by solving an optimization problem of an objective function that represents the profit or loss obtained by subtracting the profit of the electricity business from the loss of the electricity business, and that minimizes the profit or loss represented by the objective function." [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2021-136832 Summary of the Invention [Problem to be solved by the invention]

[0005] For example, suppose a power generation company trades energy or balancing capacity in a certain time slot (bidding slot) in the future. The timing when the power generation company bids its volume and unit price into the market is not necessarily a single point in time prior to the time slot. As is well known, multiple markets exist depending on the target product (e.g., energy or balancing capacity), the contract time, or the length of the response time. Over a period of, say, about one week prior to the time slot, the demand for energy or balancing capacity, weather forecast, etc. for that time slot gradually becomes clear. As a result, many power generation companies will bid sequentially in multiple markets in a decentralized manner over that period as the accuracy of the demand, weather forecast, etc. increases.

[0006] In such a complex market, when a computer tries to determine a bidding plan that optimizes profits, the increase in constraints often results in a longer solution time, or the number of variables required for optimization tends to exceed the memory capacity of the computer, making it difficult to create a bidding plan within working hours.

[0007] Although Patent Document 1 describes a method for creating a trading plan in the spot market and the balancing market, it does not assume that bidding will be conducted sequentially in multiple rounds in a distributed manner across multiple markets. Therefore, an object of the present invention is to efficiently create a bidding plan in the complex electricity and balancing market transactions. [Means for solving the problem]

[0008] The multiple market trading support device of the present invention is characterized by including a bidding path simulation unit that inputs a set of variables including bid prices and bid volumes as separate variables in multiple markets where electricity and / or adjustment capacity are traded, and inputs candidate states that are sets of variables that include the bid prices but lack at least the bid volumes to an objective function that outputs trading profits, and obtains the state that maximizes the trading profits. Other means will be described in the detailed description of the invention. [Effects of the Invention]

[0009] According to the present invention, it is possible to efficiently create a bidding plan in the complicated electricity and adjustment capacity market transactions. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating bidding paths in multi-market trading. [Figure 2] FIG. 2 is a diagram illustrating the configuration of a multiple market trading support device. [Figure 3] FIG. 10 is a diagram illustrating narrowing down the number of variable sets. [Figure 4] This is an example of a situation in a multi-market transaction. [Figure 5] 1 is an example of a bidding plan format. [Figure 6] 1 is an example of an operation plan format. [Figure 7] FIG. 10 is a diagram illustrating a bid recommendation flow. [Figure 8] FIG. 10 is a diagram illustrating an agent simulation. [Figure 9] FIG. 1 is a diagram illustrating the configuration of a multi-market trading device with an automatic bidding function. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, an embodiment of a multiple market trading support device of the present invention (hereinafter also referred to as "the present embodiment") will be described in detail with reference to the drawings and the like.

[0012] (Trade profit maximization and bidding planning) In complex electricity and balancing capacity market transactions, bidding for a target product on a target date is conducted across multiple markets with different contract times. Because the market price forecast values ​​used in the bidding plan are updated for each market transaction in accordance with weather forecasts, etc., it is not possible to create a bidding plan that maximizes trading profits all at once. Therefore, the multi-market trading support device of this embodiment maximizes trading profits in each market from market transactions onward, as shown in the following equations 1 and 2, and maximizes overall trading profits by repeating this process sequentially across multiple markets.

[0013]

number

[0014]

number

[0015] TIFF2026014636000004.tif13158TIFF2026014636000005.tif58158

[0016] Figure 1 is a diagram explaining the bidding path in multi-market trading. In Figure 1, time progresses from the bottom left to the top right of a plane divided into contour lines. The top right area is "Intraday (current day)." The area to the bottom left of that is "Day-ahead (1 day ahead)." The area further below that is "Week-ahead (1 week ahead)." The bottom left area is "Other Future (more than 2 weeks ahead)."

[0017] ● and ○ represent bids. Arrows represent the transition from one bid to the next. Of the ● and ○, ● represents a bid that was actually selected (passed through). ○ represents a bid that was available as an option but was not actually selected. "Energy" represents energy trading. "FCR (Frequency Containment Reserve)" represents adjustment capacity trading. "S" written in white inside ● represents the start of bidding. "GC (Gate Close)" written in white inside ● represents the closing of bidding.

[0018] Focusing on bidding path 101a and tracing the dots, we can see that from "S" to "GC," bidding for the week-ago trading, the day-ago trading, the day-ago trading, and the current day's trading were all conducted in this order. Focusing on bidding path 101b and tracing the dots, we can see that from "S" to "GC," bidding for the day-ago trading and the current day's trading were conducted simultaneously, and then bidding for the current day's trading and the current day's trading were conducted simultaneously. The bids available at a given time depend on the history of previous bidding transactions. For example, if a generator has an output of 100,000 kWh and makes a selling bid for 80,000 kWh of electricity for a specific time slot, the amount of electricity sold at a given time will be limited to 20,000 kWh or less, and the (upward) balancing capacity of the selling bid will also be limited to 20,000 kWh or less. The conditional probabilities in Equations 1 and 2 illustrate this.

[0019] The multi-market trading support device of this embodiment creates a bidding plan for recommended market transactions (hereinafter also simply referred to as a "bidding plan") and a DER operation plan (hereinafter also simply referred to as an "operation plan").

[0020] 2 is a diagram illustrating the configuration of the multiple market trading support device. The multiple market trading support device 1 is a general-purpose computer, and includes a central control device 11, an input device 12 such as a keyboard, an output device 13 such as a display, a main memory device 14, and an auxiliary memory device 15. The auxiliary memory device 15 stores a database 207.

[0021] The setting conversion unit 201, agent-based recommendation unit 202, optimization solver unit 203, bidding path simulation unit 204, bid quantity optimization unit 205, and contract simulation unit 206 in the main memory device 14 are programs. The optimization solver unit 203 includes a combinatorial optimization solver 203a and a mixed integer optimization solver 203b. In the following description, when an entity is described as "XX unit," it means that the central control unit 11 reads a program from the auxiliary memory device 15 to the main memory device 14 and executes the processing previously described in the program. With this configuration, the multiple market trading support device 1 can create bidding plans for different target markets by changing the processing in accordance with the market rules of each market.

[0022] Fig. 3 is a diagram for explaining narrowing down the number of variable sets. As a premise for explaining Fig. 3, for convenience of explanation, the objective function of Equation 1 is reinterpreted as Equation 3 below. Expected trading profit =F(target product, target date, trading market, bid price, bid slot, bid amount) (Formula 3)

[0023] In other words, the expected value of trading volume is a function with variables: target product, target date, trading market, bid price, bid slot, and bid volume. There are six types of variables, which constitute a "variable set." Each of the six variables can take many different values, either discrete or continuous. These possible values ​​are also called "candidates." A combination of candidates is also called a "variable set candidate." For example, suppose the number of target product candidates is p1, the number of target date candidates is p2, the number of trading market candidates is p3, the number of bid price candidates is p4, the number of bid slot candidates is p5, and the number of bid volume candidates is p6. Then, theoretically or arithmetically, there are "P = p1 × p2 × p3 × p4 × p5 × p6" variable set candidates. The multi-market trading support system 1 calculates the expected value of trading profit for each of the P variable set candidates. The feature of this embodiment is to minimize P.

[0024] If no special measures are taken, the number of candidates for each of the target product 501, target date 502, trading market 503, bid price 504, bid slot 505, and bid amount 506 in FIG. 3 will be as follows:

[0025] The number of candidates for the target product 501 is equal to the sum of the number of trading sides for energy trading (for example, two, "sell" or "buy") and the number of types of adjustment power, such as primary, secondary, and tertiary, for adjustment power trading (for example, a total of six, in the up and down directions of each system frequency). The number of candidates for the target date 502 is equal to the number of days (for example, 3 days) targeted by the trading plan. The number of candidates for the trading market 503 is equal to the number of markets for trading of power and adjustment capacity (for example, two markets each). The number of candidates for the bid price 504 is equal to the number of market price prediction bands (for example, 10).

[0026] The number of candidates for the bidding slot 505 is equal to the number of time slots in a given day (for example, 24 per day). Note that the bidding slot means a "time slot." The number of candidates for the bid amount 506 is equal to the product of the number of generators that can be operated and the number of output increments, for example.

[0027] Additionally, in the case of balancing power trading, the market trading rules stipulate that there is only one bid price for each target product and each target date, so the market price prediction range for each bidding slot can become the bid price for other bidding slots. Here, the combination of target product 501, target date 502, trading market 503, and bid price 504 is called a "state 507." The candidate combinations of each variable that make up state 507 are also called "candidate states."

[0028] The number of expected trading profits calculated by the multi-market trading support system 1 is also P. The time required to calculate the expected trading profit for one of the generated variable set candidates is T. Then, the time required to calculate the expected trading profit for all variable set candidates is P×T. Usually, P×T far exceeds the working time. Narrowing down in this embodiment is a method of reducing P×T.

[0029] To reduce P×T even slightly, it is sufficient to reduce P. Possible methods for reducing P include reducing at least one of p1, p2, p3, p4, p5, and p6 (reducing the number of candidates) and reducing the number of dimensions by discarding (excluding) at least one of p1, p2, p3, p4, p5, and p6. If P is reduced, T also becomes smaller (reducing the computational burden). Therefore, if P is reduced, P×T becomes smaller in two senses. Specifically, discarding (excluding) at least one of p1, p2, p3, p4, p5, and p6 means substituting a temporary constant for the discarded variable or deleting a term including the discarded variable from the calculation formula (objective function) for calculating the expected value of trading profits.

[0030] In the first stage, the multi-market trading support system 1 of this embodiment identifies candidates for four-dimensional states 507 that maximize the expected value of trading profits, after ignoring bid slots 505 and bid quantities 506 from the six dimensions. In the second stage, the multi-market trading support system 1 identifies candidates for six-dimensional variable sets that maximize the expected value of trading profits, taking the identified candidates for four-dimensional states 507 as given. In the second stage, the variable sets are essentially two-dimensional.

[0031] 4 shows an example of a state in a multi-market transaction. In the upper state 507a, the value of the bid price (unit price) 504 is "40.24ΔkWh." The value of the target product 501 is "increase primary control capacity," the value of the target date 502 is "the first day of a certain month," and the value of the trading market 503 is "the day-ahead market."

[0032] In the lower state 507b, the value of bid price (unit price) 504 is "132.15 kWh." The value of target product 501 is "electricity to sell," the value of target date 502 is "the 2nd of a certain month," and the value of trading market 503 is "current day market."

[0033] In the first stage, a large number of candidate states such as state 507a and state 507b are generated, and the expected value of the trading profit is calculated for each of them. Among these candidate states, one candidate state that maximizes the expected value of the trading profit is subsequently used in the second stage.

[0034] Fig. 5 shows an example format of the bidding plan 303. The bidding plan 303 has multiple (four in the example of Fig. 5) bidding plan sections 303a, 303b, 303c, and 303d. At the beginning of each bidding plan section, a combination of a target product 501, a target date 502, and a trading market 503 is written. Next, one or more combinations of a bid price 504 and a bid quantity 506 are written. The bid quantity 506 is a 24-dimensional vector, and each element corresponds to 24 bidding slots per day.

[0035] For example, when the bidding plan portion 303a is looked at, the following can be seen. A power generation company plans to submit a bid to sell electricity on the day-ahead market on the first day of a certain month. The power producer plans to sell 0.1 of energy at a bid price (unit price) of 91.45 in the 16th bidding slot (15:00-16:00). The power producer plans to sell 0.7 of energy at a bid price (unit price) of 147.73 in the 21st bidding slot (8:00 PM to 9:00 PM). The power producers do not plan to trade in any bidding slots other than those mentioned above.

[0036] Similarly, looking at the bidding plan portion 303d, the following can be seen: A power generation company plans to bid for downward primary control reserve on the market on the 2nd of a certain month. The power producer plans to sell 0.3 of downward adjustment capacity at a bid price (unit price) of 14.89 in the first bidding slot (12:00-1:00), and to sell 0.3 of downward adjustment capacity at a bid price (unit price) of 14.89 in the ninth bidding slot (8:00-9:00). The power producers do not plan to trade in any bidding slots other than those mentioned above.

[0037] Note that "limit_order" indicates that the bid price (unit price) 504 is a fixed limit price. On the other hand, "price_independent_order" indicates that the bid price (unit price) 504 is not fixed and can be changed to another value depending on supply and demand at the time of bidding.

[0038] Fig. 6 is an example format of the operation plan 304. The operation plan 304 has storage battery availability 801, storage battery charge / discharge amount 802, imbalance amount 803, renewable energy availability 804, and renewable energy power generation amount 805. Fig. 6 shows the operation plan 304 for a power generation company that has a renewable energy generator and a storage battery as DERs.

[0039] Storage battery availability 801 indicates whether the storage battery is available for use for each bidding slot on the target day. "1" means available, and "0" means unavailable. The storage battery charge / discharge amount 802 indicates the amount of charge / discharge of the storage battery for each bid slot on the target day. A positive value indicates the amount of discharge, and a negative value indicates the amount of charge. The imbalance amount 803 indicates the power supply and demand for each bidding slot on the target day. A positive value indicates a surplus, and a negative value indicates a shortage. Renewable energy availability 804 indicates whether renewable energy is available for use for each bidding slot on the target day. "1" indicates availability, and "0" indicates availability. The renewable energy power generation amount 805 indicates the power generation amount of the renewable energy generator for each bidding slot on the target day.

[0040] FIG. 7 is a diagram illustrating the bid recommendation flow. In step S 301 , the setting conversion unit 201 converts the input data 301 into setting data 302 having an optimizable data type, and stores the converted data in the database 207 .

[0041] The input data 301 includes the following information: Market trading rules such as target date, target products, and minimum trading unit of target products -Predicted market price of the target product on the target date Battery capacity, output size, maximum allowable output depth, charge / discharge efficiency, and State of Charge (SOC) - Renewable energy power generation forecast and controllable amount DER specification data such as DER availability - System specification data such as interconnection capacity ·History of past bids and contracts on the bidding route

[0042] The setting data 302 mainly includes parameters such as the state of the bidding path simulation, constraints for optimizing the bid volume, and the like, which will be described later.

[0043] In step S302, the agent-based recommender 202 creates a bidding plan 303 and an operating plan 304 from the setting data 302 by executing an agent simulation (described in detail later with reference to FIG. 8).

[0044] 8 is a diagram for explaining the agent simulation, and also shows details of step S302 in FIG. In step S401, the agent-based recommender 202 acquires the history of past bid agreements on the bidding path.

[0045] In step S402, the agent-based recommendation unit 202 updates the operation plan using the setting data 302. Through this process, the agent-based recommendation unit 202 updates, for example, the SoC of the storage battery, the predicted renewable energy power generation amount, the availability of DER, etc. to the latest values. Note that the database 207 is assumed to always store the latest operation plan 304.

[0046] As a prerequisite for entering the subsequent repeat loop (steps S403 to S407), the bidding path simulation unit 204 creates state candidates by substituting constants for the bid slot and bid volume variables in the variable set, or by abstracting the bid slot and bid volume variables. As a result, a large number of state 507 candidates, which are combinations of four variables: target product 501, target date 502, trading market 503, and bid price 504, are stored in the database 207. Random values ​​are substituted for each variable in the state 507 candidates. These values ​​may not have any practical meaning.

[0047] In step S403, the agent-based recommendation unit 202 calls the bidding path simulation unit 204. The bidding path simulation unit 204 then acquires candidates for state 507 that maximize the expected value of transaction profit. In other words, the bidding path simulation unit 204 executes the following first to fourth processes.

[0048] First, the bidding path simulator 204 reads all candidates for the state 507 from the database 207 . Second, the bidding path simulator 204 determines the upper and lower limits of the range of appropriate bid prices (unit prices) from among all candidates in state 507. Specifically, the bidding path simulator 204 considers contract risk (the risk that a bid will not be concluded), and determines an "upper limit" on the selling side that is sufficiently lower in the distribution of predicted market prices, and a "lower limit" on the buying side that is sufficiently higher in the distribution of predicted market prices. These upper and lower limits avoid the risk that power generation companies and power transmission and distribution companies will bid unrealistic bid prices in their excessive pursuit of profit maximization, resulting in a contract not being concluded (not being awarded).

[0049] Third, the bidding path simulation unit 204 executes the following exclusion processes (1) to (5) on the read state 507. (1) The bidding path simulator 204 excludes candidates for the state 507 having the target date 502 on which DER cannot be used. (2) The bidding path simulation unit 204 excludes candidates for the state 507 having the target date 502 on which it is determined that renewable energy power generation is impossible based on the predicted renewable energy power generation amount, and candidates for the state 507 having the target date 502 on which it is determined that arbitrage trading is inappropriate based on the predicted market price value. (3) The bidding path simulation unit 204 excludes candidates for state 507 that have a selling adjustment power bid price 504 using a market price prediction range that is greater than the upper limit, and a buying adjustment power bid price 504 using a market price prediction range that is smaller than the lower limit. (4) The bidding path simulation unit 204 excludes candidates for state 507 having a bid price 504 for selling electricity using a market price prediction range greater than the upper limit and a bid price for buying electricity using a market price prediction range less than the lower limit. (5) The bidding path simulator 204 excludes candidates for state 507 that have the second or subsequent bid price for adjustment capacity and bid price for energy for the same target product and the same target date in accordance with the market trading rules.

[0050] Fourth, from among the candidate states 507 remaining after the elimination processes (1) to (5), the bid path simulator 204 selects a candidate state 507 that maximizes the expected value of the transaction profit in Equation 1. At this time, the bid path simulator 204 uses the combinatorial optimization solver 203a (first optimization solver) of the optimization solver unit 203. As the combinatorial optimization solver 203a, a metaheuristic method such as ant colony optimization can be used, but is not limited to this. Note that the bid path simulator 204 may further narrow down the states 507 using a state likelihood, which will be described later. Fifth, the bidding path simulator 204 displays on the output device 13 the state 507 that maximizes the expected value of the transaction profit.

[0051] The state 507 obtained in step S403 maximizes the expected value of the trading profit, but the bid slot 505 and the bid amount 506 are omitted.

[0052] In step S404, the bid quantity optimization unit 205 determines the bid quantity that maximizes the expected value of transaction profit for the candidates for state 507 acquired in step S403. That is, the bid quantity optimization unit 205 executes the following first to fourth processes.

[0053] First, the bid quantity optimizer 205 randomly generates candidate bid quantities 506 for each bid slot 505 used in the bidding plan. Second, the bid quantity optimization unit 205 generates a candidate bidding plan 303 by adding the candidate bid quantity 506 for each generated bid slot 505 to the candidate state 507 acquired in step S403. Third, the bid quantity optimization unit 205 reads the latest imbalance amount 803 , the battery charge / discharge amount 802 , and the renewable energy power generation amount 805 from the database 207 .

[0054] Fourth, the bid quantity optimization unit 205 determines candidates for bidding plans 303 that optimize the expected value of the transaction profit in Equation 1. At this time, the bid quantity optimization unit 205 uses the mixed integer optimization solver 203b (second optimization solver) of the optimization solver unit 203. As the mixed integer optimization solver 203b, a metaheuristic method such as particle swarm optimization can be used, but is not limited to this. Furthermore, at this time, the bid quantity optimization unit 205 generates constraint conditions based on the latest imbalance amount 803, storage battery charge / discharge amount 802, and renewable energy power generation amount 805 that have been read.

[0055] The following (11) to (15) are examples of constraints used by the bid quantity optimization unit 205. (11) As a constraint on the interconnection capacity, both the forward and reverse currents at the interconnection points of renewable energy sources and storage batteries must be less than the interconnection capacity. (12) As a supply-demand balance constraint, the net power generation calculated using the renewable energy power generation and the battery charge / discharge amount must match the net power sales amount calculated from the bid amount and imbalance amount in the electricity trading. Here, the renewable energy power generation is equal to or greater than the value obtained by subtracting the renewable energy controllable amount from the renewable energy power generation forecast value, and is equal to or less than the renewable energy power generation forecast value. (13) As a capacity allocation constraint, the available battery capacity for balancing power trading is greater than the battery capacity required for balancing power supply on both the power generation and demand sides. (14) As a minimum bid volume constraint, all bid volumes for both adjustment capacity trading and power trading must be an integer multiple of the minimum trading unit. (15) As a battery output constraint, the maximum discharge and maximum charge amounts possible in the battery charging and discharging for the activation of adjustment power and power trading must be less than the battery output size and maximum output depth of the battery.

[0056] Fifth, the bid volume optimization unit 205 displays on the output device 13 the bidding plan 303 that maximizes the expected value of the transaction profit and the bid volume 506 for each bid slot 505 thereof. By adopting such a configuration, it is possible to exclude the range where optimization of the bid volume is not necessary, and to efficiently create a trading plan even for complicated market transactions.

[0057] In step S405, the bidding path simulator 204 calls the contract simulator 206. The contract simulator 206 then calculates the net trading profit by deducting necessary costs such as imbalance costs and battery charging / discharging costs from the expected trading profit value related to the bidding plan 303 determined in step S404. These costs are considered to be part of the operation plan 304 identified by the previous iterative process.

[0058] In step S406, the agent-based recommender 202 calls the bid quantity optimizer 205. The bid quantity optimizer 205 then obtains the bidding plan 303 and the management plan 304 that maximize the trading profit, and stores them in the database 207.

[0059] In step S407, the agent-based recommender 202 calls the bidding path simulator 204. The bidding path simulator 204 then uses the expected value of the net trading profit to calculate the likelihood of the obtained state 507 being selected (hereinafter also referred to as "state likelihood") and stores the result in the database 207. The state likelihood is used by the bidding path simulator 204 to further narrow down the states 507 in the "fourth" step of step S403 in the iterative process.

[0060] Thereafter, the process returns to step S403. Then, the processes of steps S403 to S407 are repeated for the new variable set candidates in which the variable values ​​have been swapped. After steps S403 to S407 have been repeated a sufficiently large number of times designated in advance by the user, the process proceeds to step S409. At this stage, the database 207 has been overwritten with the bidding plan 303 that maximizes the expected value of trading profits.

[0061] In step S408, the agent-based recommendation unit 202 stores in the database 207 the bidding plan 303 obtained as a result of the repeated processing of steps S403 to S407, and the management plan 304 on which the bidding plan 303 is based.

[0062] (summary) As is clear from the above, the multi-market trading support device of this embodiment includes a bid path simulation unit that inputs a set of variables including bid prices and bid volumes as separate variables in multiple markets where electricity and / or power are traded, and inputs candidate states that are sets of variables that include bid prices but lack at least bid volumes to an objective function that outputs trading profits, thereby obtaining a state that maximizes trading profits. Furthermore, the bid path simulation unit limits the bid prices among the set of variables input to the objective function. Furthermore, the multi-market trading support device of this embodiment includes a bid volume optimization unit that inputs a set of variables in which the candidate bid volumes are added to the obtained states to the objective function, and determines the bid volume that maximizes trading profits.

[0063] Figure 9 is a diagram illustrating the configuration of a multiple market trading support device 1 with an automatic bidding function. The multiple market trading support device 1 in Figure 9 is an advanced version of the multiple market trading support device 1 in Figure 2. Compared to the multiple market trading support device 1 in Figure 2, the multiple market trading support device 1 in Figure 9 additionally includes a market price prediction unit 208, a DER remote monitoring unit 209, a bid recommendation unit 210, and a bid execution unit 211.

[0064] The market price prediction unit 208 predicts the market price of the trading product. The DER remote monitoring unit 209 constantly monitors the DER and collects DER specification data. When there are multiple bidding plans 303 created by the bid recommendation flow of Figure 7, the bid recommendation unit 210 selects a bidding plan suitable for actual bidding from among those bidding plans 303 based on the following information and recommends it to the user.

[0065] The market price of the trading product predicted by the market price prediction unit 208 Market trading rules DER specification data collected by the DER remote monitoring unit 209 Bid and Execution History

[0066] In response to the user's approval, the bidding execution unit 211 submits bids based on the bidding plan 303. Specifically, the bidding execution unit 211 transmits the bidding plan 303 to a server or the like operated by the exchange market.

[0067] (Effects of the embodiment) (1) The multi-market trading support device can save computer resources and shorten processing time by first obtaining the state including the optimal bid price and then determining the optimal bid amount. (2) The multi-market trading support device can acquire a time-series bidding plan. (3) The multi-market trading support system can reduce the dimension of a set of variables by a simple method such as substituting constants. (4) The multi-market trading support device can use a solver that demonstrates performance in each of the two stages of optimization. (5) The multi-market trading support device can actually place bids on the market. (6) The multi-market trading support device can display the results of optimization.

[0068] The present invention is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations.

[0069] Furthermore, the above-mentioned configurations, functions, processing units, processing means, etc. may be partly or entirely implemented in hardware, for example, by designing them as integrated circuits. The above-mentioned configurations, functions, etc. may also be implemented in software, with a processor interpreting and executing a program that implements each function. Information such as the programs, tables, and files that implement each function can be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD. In addition, the control lines and information lines shown are those that are considered necessary for the explanation, and do not necessarily show all the control lines and information lines in the product. In reality, it can be assumed that almost all components are interconnected. [Explanation of symbols]

[0070] 1. Multi-market trading support device 11 Central control unit 12 Input Devices 13 Output Devices 14 Main memory 15 Auxiliary storage 101, 101a, 101b bidding routes 201 Setting conversion unit 202 Agent-based Recommendation 203 Optimization Solver 203a Combinatorial Optimization Solver 203b Mixed Integer Optimization Solver 204 Bidding Route Simulation Section 205 Bid Quantity Optimization Department 206 Execution simulation part 207 Database 208 Market Price Forecasting Department 209 DER monitoring and control unit 210 Bid Recommendation Department 211 Bidding Department 303 Bidding Plan 304 Operational Plan 507 Status

Claims

1. A group of variables including bid prices and bid volumes as separate variables in multiple markets in which electricity and / or adjustment capacity are traded is input, and a candidate state is input to an objective function having trading profit as an output, the candidate state being a group of variables including the bid prices but lacking at least the bid volumes; a bidding path simulator for acquiring the state that maximizes the trading profit; A multiple market trading support device characterized by:

2. The bidding path simulation unit limiting the bid price among the set of variables input to the objective function; 2. The multi-market trading support device according to claim 1,

3. inputting a set of variables obtained by adding the candidate bid quantity to the acquired state into the objective function; a bid volume optimization unit that determines the bid volume that maximizes the transaction profit; 3. The multiple market trading support device according to claim 2.

4. The objective function is: outputting trading profits for a plurality of time-series bids made for future time frames in the plurality of markets where the bid contract times are different from each other; 4. The multiple market trading support device according to claim 3.

5. The bidding path simulation unit creating the state candidates by substituting a constant for a bid volume variable in the group of variables or by deleting a term including the bid volume variable in the objective function; 5. The multiple market trading support device according to claim 4.

6. The bidding path simulation unit using a first optimization solver to obtain the state that maximizes the trading profit; The bid volume optimization unit using a second optimization solver different from the first optimization solver to determine the bid quantity that maximizes the trading profit; 6. The multi-market trading support device according to claim 5.

7. a bid execution unit that executes a bidding plan including a bid amount that maximizes the trading profit for the multiple markets; 7. The multiple market trading support device according to claim 6.

8. The bidding path simulation unit displaying the state that maximizes the trading profit on an output device; The bid volume optimization unit displaying on an output device a bidding plan including the bid amount that maximizes the trading profit; 8. The multiple market trading support device according to claim 7.

9. The bidding path simulation unit of the multi-market trading support device A group of variables including bid prices and bid volumes as separate variables in multiple markets in which electricity and / or adjustment capacity are traded is input, and a candidate state is input to an objective function having trading profit as an output, the candidate state being a group of variables including the bid prices but lacking at least the bid volumes; obtaining the state that maximizes the trading profit; A method for supporting trading in multiple markets, comprising:

Images