JP2006099432A - Power trading system, method, and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a power trading system, a power trading method, and a power trading program for appropriately pricing a power commodity matching a demand generally given without respect to possession of a power generator. <P>SOLUTION: This power trading system is provided with a first data acquisition part acquiring a time series data of power trade expected with a relative partner, a second data acquisition part acquiring time series data and price data about the power commodity handled in a power market, an allocation processing part allocating the acquired power commodity time series data to the acquired power trade time series data for minimizing a difference between the power total amount of predicted power trade and a power total amount of the allocated power commodity, and a price decision part deciding a power price to be presented to the relative partner based on price data of the allocated power commodity. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to a power trading system, a power trading method, and a power trading program that can be used when trading power products, and in particular, a power trading system and power trading suitable for appropriately pricing the power to be traded. The present invention relates to a method and a power trading program.

  The general merchandise trading system has a trading support function for entering / editing a trade desired by a person who wants to trade (trader), executing a trade, obtaining a trade result, storing and managing the trade result, and a trade result. And a strategy support function for creating future transaction plans. As a strategy support function, there is a function to appropriately manage the risk amount of the company by conducting risk assessment and planning a transaction (hedging transaction) for hedging (defending) the risk. Another important purpose of strategy support is to give an indication of what products should be bought, when, how much and at what price. In general, the latter is much more difficult than the former and often relies on the intuition and experience of traders.

  The power trading system is basically the same, but there are many differences from other product trading systems due to the characteristics of power as a product. Electricity is delivered through transmission lines, and the process needs to satisfy electrical laws. Moreover, since production and consumption are performed at the same time, it is necessary to conduct transactions while considering the balance between buying and selling. In addition, electricity products are difficult to preserve, so price fluctuations are large, especially with risk hedging. For this reason, other commodity transaction systems cannot be used as they are.

  When electric power trading is liberalized, the problem of how to determine the price (unit price per kWh) of electric power products to be traded will arise. In general, power transactions include individual transactions outside the market (relative transactions) and market transactions in the power trading market. Products that are traded in the trading market are standardized, and there are various forms of products that are traded outside the market (relative transactions). Off-market transactions include wholesale electricity transactions and retail transactions.

  If the product to be traded is the same form as the product traded in the market, the price can be determined with reference to the market product. However, especially in the case of retail contracts, the amount of consumption is not known unless it is a delivery date, and there is a daily or weekly consumption (purchase) pattern. There is a big difference in the products and forms of wholesale electricity trading. For this reason, there is a problem of how to determine the price in a retail contract. In addition, the same product is not always traded in the market even in the wholesale trade of wholesale power, and there is a similar problem.

  A method for determining prices in market or relative transactions is a technology called pricing, but there has been little proposed strategic support for this purpose.

  Since the balance between supply and demand is important for power products, in some cases, it is necessary to consider the generator operation plan and transmission method, which complicates the transaction method. For this reason, most of the conventional power trading systems mainly deal with transaction support such as generator operation methods and application to electronic transactions, and there is almost no strategy support. Some examples of strategic support include those that use fuel costs and fuel futures transactions to evaluate electricity prices (Patent Document 1 below), and power trading rights (so-called options) to conduct risk hedging of electricity transactions. Is used (Patent Document 2), and the minimum allowable price is determined so that the loss falls within the specified probability (Patent Document 3).

  The electric power price setting method according to Patent Document 1 is an improvement of a conventional cost-based electric power price evaluation method in consideration of fuel price fluctuations. Japanese Patent Laid-Open No. 2003-228561 is to conduct power transactions taking into account risk hedging by buying and selling power trading rights, but does not take risks by itself. Pricing power products means taking risks in some cases, and this is different from risk hedging. In addition, the strategy based on the trading of power trading rights is not realistic because the power trading rights are not widely traded at present.

  Patent document 3 does not limit the product to electric power, but when applied to electric power, when the demand fluctuates, the electric power price is determined so that the loss becomes a certain probability or less in consideration of the power generation cost and the like. In this case, since the price is determined by the probability given by the seller, there is a problem that it is difficult to evaluate what kind of probability is given and the price is appropriate. Furthermore, in this case, there is a problem that the price is higher than the case where the risk is assumed because the purpose is to make the risk less than the allowable value.

  As described above, as an important function of the power trading system, there is a function of determining the price of a commodity to be bought and sold. This is a technique generally called pricing (pricing). If pricing becomes possible, pricing will be carried out by changing the product brand, trading volume and trading time, and the resulting revenue will be compared to determine which product, when, how much, and what price. You will get basic and useful information on how to buy and sell.

  As a prior art, there is an electric power price setting method as described in Patent Document 1 described above. This is an improvement of the cost-based electricity price evaluation method taking into account the fluctuations in fuel prices. Therefore, there is a problem that it is difficult to apply except for the business owner who owns the generator. An operator who wants to sell electric power does not necessarily have a generator. This is because, for some reason, in the past, power has been purchased and held from another business operator and there are cases where it is desired to sell this in the future. In general, there is a strong correlation between the price of electric power products marketed and the price of fuel in the fuel market, but of course they are not perfectly matched. Therefore, it is not always appropriate to calculate the selling price of electric power using the fuel price. It is ok to calculate the production cost from the fuel price according to the circumstances of each business operator, but if this price (plus profit) is used as the selling price, there is a possibility that the profit that should be obtained cannot be obtained. is there.

  If the electricity sales price is calculated from the fuel price, the electricity price will depend on the type of fuel. The price of electricity sold in the market should not depend on the type of fuel from the perspective of the buyer. In addition, it is difficult to evaluate an appropriate purchase price from the fuel price from the viewpoint of purchasing rather than selling electricity. Therefore, it is desirable to determine the selling price without using the fuel price.

  Normally, the one-of-a-kind principle holds in the market. That is, the price of a product with the same conditions is determined as one. Otherwise, you can easily make a profit if you buy at a cheaper one and sell at a higher one. Such a transaction is called arbitrage, and in an actively traded market (a market with high liquidity), the price is naturally determined so that arbitrage does not occur. Even in the electricity market, electricity prices are considered to be automatically determined by market principles so that arbitrage does not occur.

  Since it can be considered that there is no difference in quality when anyone buys a product called electric power, the electric power price does not differ depending on the means of generating electricity. Production costs are of course different. However, the power price presented to the supplier and the power price put on the market should be the same if the conditions are the same. Since the market price can be known by anyone including the business partner, when the price presented to the business partner is different from the market price, the business partner can judge the profit and loss with the price difference.

  Of course, it can be different from the market price if there is a promise to supply it stably in the future rather than a temporary transaction. In this case, this should not be an ambiguous promise, but should be assessed separately as a royalty on the promise to continue to supply electricity for years to come. In addition, the price may vary depending on the intimacy and dependency relationship with the business partner, but this has a different meaning from the pricing method proposed in the present invention. Good.

  For traders, it is desirable to be able to evaluate electricity prices based on current or past data on electricity products. Of course, when the electric power generated by the generator is combined with the electric power procured from the market, it is necessary to consider the power generation cost. However, even in this case, the price of the electricity sold should not depend on using a generator. From the perspective of the purchaser, the price should be the same from the price-of-item principle, regardless of whether the power is procured from the market and resold or generated by a generator. is there.

  As mentioned above, it is important to use the prices of the products handled in the market to determine the prices of the power products that you are going to trade. For example, if the forward products (or combinations thereof) handled in the market exactly match the products you want to price, the price (total) of the forward products handled in the market will be subject to price evaluation. The price will be reasonable. This is the same as the normal method of market value evaluation. Also. A similar argument can be made when it is replicated in financial engineering (converted with a certain formula). However, in general, a power sales contract has a different demand pattern for each consumer, and cannot always be configured by combining market products.

  When using the prices of commodities handled in the market, it is necessary to use the delivery date price instead of the transaction date price. In some cases, the price of electric power can be estimated roughly according to the season. For example, consider buying and selling power in August of next year. At this time, it is assumed that the power price in August next year is considerably higher than the power price in March. In the case of electric power, it is virtually impossible to buy and store March electricity and sell it in August, so there is not much relation between the price in March and the price in August. The power price evaluated in the present invention is the power price in August next year, and the price in March is not very helpful. The assessment of fluctuations is how electricity prices in August next year will change today, tomorrow, the day after tomorrow, and by next August.

  Nobody knows if electricity prices in August next year will be higher or cheaper tomorrow. If you know that the price will rise tomorrow, you can make a profit if you buy electricity next August and sell it tomorrow. Deciding the electricity price for August next year today is equivalent to taking such a risk. For this reason, it is a natural right to claim the legitimate consideration for taking the risk. Pricing in this application may include consideration for such risks.

As mentioned above, pricing is taking risks. Therefore, the consideration should be received, and the evaluation of the electricity price is made by considering the risk. Otherwise, in the long run you will suffer a big loss. Specifically, it is necessary to evaluate the expected value of profits obtained by trading, taking into account that the price of the product you want to trade fluctuates (there is a risk), and determine the price in consideration of other costs . Of course, the actual market price may rise or fall unexpectedly. Therefore, it is not guaranteed that profits are always obtained even if appropriate pricing is performed. However, when a large number of transactions are made, it means that the price is theoretically calculated so that the balance is statistically zero. Since the balance is zero, the expected value of profit is determined by the additionally calculated profit rate.
JP 2003-6374 A JP 2003-281231 A JP 2004-171180 A

  As described above, one of the important functions of the power trading system is a function for determining the price of a product to be bought and sold. Thus, determining the appropriate price for a given power transaction is a major objective in this application. As a more specific level, it can be considered that there is no difference in quality for anyone who purchases electricity, so appropriate sales or purchase prices are basically based on the fact that electricity prices do not differ depending on the means of power generation. It is necessary to decide. This is related to the fact that it is desirable for the trader to be able to evaluate the power price only from current or past data of the power product.

  In general, a power sales contract has a different demand pattern for each consumer, and cannot necessarily be configured by combining market products. Therefore, it is desirable that a product with a pattern different from the marketed product can be priced.

  The present invention has been made in consideration of the above circumstances, and an electric power transaction system and electric power transaction capable of appropriately determining the price of electric power products corresponding to the generally given demand regardless of whether the generator is owned or not It is an object to provide a method and a power trading program. Here, the transaction with the appropriate price includes not a transaction for risk hedging but a transaction based on a power price obtained by evaluating a reasonable consideration corresponding to the risk.

  In order to solve the above-described problems, a power trading system according to the present invention includes a first data acquisition unit that acquires time-series data of power trading scheduled with a relative business partner, and a transaction in the power market. A second data acquisition unit that acquires time-series data and price data of the electric power product that is being acquired, and assigning the acquired electric power product time-series data to the acquired electric power trading time-series data, and An allocation processing unit for minimizing a difference between the total amount of power traded and the total amount of power of the allocated power product, and the relative transaction based on the price data of the allocated power product And a price determination unit that determines a power price to be presented first.

  That is, the allocation processing unit allocates the power product so that the difference between the total power amount of the scheduled power trading and the total power amount of the allocated power product is minimized, so that the difference is relatively small. By supplementing the power, it is possible to cope with the power demand generally given. Since the price determination is based on the price data of the assigned power product, the price is appropriate.

  Further, the power trading method according to the present invention includes a step of acquiring time series data of power trading scheduled with a relative business partner, and time series data and prices of power products traded in the power market. A step of acquiring data, and assigning the acquired power product time-series data to the acquired power trading time-series data to obtain a total power amount of the scheduled power trading and the allocated power product And a step of determining a power price to be presented to the relative business partner based on price data of the allocated power product. To do. This method is a method corresponding to the above power trading system.

  In addition, the power trading program according to the present invention includes a step of obtaining time series data of power trading scheduled with relative business partners, and time series data and prices of power products traded in the power market. A step of acquiring data, and assigning the acquired power product time-series data to the acquired power trading time-series data to obtain a total power amount of the scheduled power trading and the allocated power product And causing the computer to execute a step of minimizing a difference from the total amount of power and a step of determining a power price to be presented to the relative business partner based on price data of the allocated power product. This computer program is a program for realizing the above-described power trading method on a computer.

  According to the present invention, in a power trading system, a power trading method, and a power trading program that can be used when trading power products, it is possible to respond to generally given demand regardless of whether or not a generator is owned. It is possible to appropriately price electric power products.

  First, preferred embodiments of the power trading system according to the present invention will be sequentially described.

  As one embodiment of the present invention, in the power trading system according to the present invention, power corresponding to the difference between the power product allocated by the allocation processing unit and the planned power trading is generated by a predetermined generator. A power generation cost calculation unit for calculating the power generation cost at the time, and the acquired power trading by adjusting the power amount of the allocated power product so that the total cost including the power generation cost obtained by the calculation is minimized. A reallocation processing unit that performs reallocation processing on the time series data and calculates a power generation amount by the predetermined generator, and the price determination unit includes the price data of the reallocated power product and the calculation A function of determining a power price to be presented to the relative business partner based on price data of a spot product or a forward product corresponding to the generated power generation amount Have to et al., It may be. This is a case where power corresponding to the difference between the power product allocated by the allocation processing unit and the planned power trading is supplemented with a predetermined (for example, self-owned) generator.

  Further, as an embodiment, the allocation processing unit allocates the acquired power product time-series data after preferentially allocating the amount of power generated by a predetermined generator to the acquired power trading time-series data. A function of minimizing a difference between a portion obtained by removing the power generation amount by the generator from the total power amount of the scheduled power trading and the total power amount of the allocated power product, It is good. This prioritizes power generation from a given (eg, self-owned) generator, after which the difference between the planned total amount of power trading and the total amount of power allocated to the power product is minimized. This is a case where electric power products are allocated to.

  Further, as an embodiment, the allocation processing unit further has a function of allocating the acquired power trading time series data to the acquired power product time series data and time series data not related to the spot product. A spot product that allocates a spot product to the power corresponding to the difference between the power product excluding the spot product allocated by the allocation processing unit and the planned power sale and calculates the purchase amount of the spot product At the time of buying and selling power acquired by adjusting the power amount of the allocated power product so that the purchase amount of the spot product obtained by the calculation or the predicted purchase price total by the purchase is minimized. A reallocation processing unit that performs reallocation processing on the sequence data, and wherein the price determination unit is configured to perform the reallocation processing. Further it has a function of determining the electricity price to be presented to the partner of the relative based on the price data of the commodity may be. This is to compensate for the difference between the total power amount of the planned power trading and the total power amount of the allocated power product by purchasing the spot product. Note that “purchase” is a concept that includes sell-buy, where buy is a positive number and sell is a negative number.

  Here, the reallocation processing unit may use, as the predicted purchase price of the spot product, a power price at each time obtained by modeling a price fluctuation of the spot product by a Monte Carlo method or a tree method. . This is a means for predicting the price of spot products.

  In addition, here, it further comprises a risk equivalent price calculation unit that analyzes the spot price fluctuation in the past certain period and calculates the uncertainty of profit given by the analysis as a risk equivalent price, and the price determination unit includes A function of determining a power price to be presented to the relative business partner based on the calculated risk equivalent amount and the price data of the reallocated power product may be further provided. By obtaining a risk-equivalent price from the uncertainty of spot price fluctuations and including this in the power price, it becomes possible to conduct a transaction with a power price that evaluates a reasonable price corresponding to the risk.

  Here, the risk equivalent price calculation unit may use a premium price calculation formula of a European option or a spark spread option to calculate the risk equivalent price. This is an example of calculating a risk equivalent price.

  Here, the risk-equivalent price calculation unit may use a Monte Carlo method or a tree method to calculate the risk-equivalent price. It is another example which calculates a risk equivalent price.

  Here, the risk equivalent price calculation unit calculates the risk equivalent price by using a geometric Brownian motion model, an average regression model, a jump model, a jump diffusion model, an average regression jump diffusion model, or a financial Boltzmann- The model can be used as a fluctuation model of the electric power price. It is another example of calculating the risk equivalent price.

  In addition, the assignment processing unit assigns the acquired power trading time-series data to the acquired power product time-series data and time-series data not related to the spot product within the same period. When there are a plurality of power product time-series data that can be combined, a combination that can further reduce the purchase amount of the spot product calculated by the spot product purchase amount calculation unit may be used for the assignment process. This is to make the difference between the power product allocated by the allocation processing unit and the planned power trading as small as possible.

  Further, as an embodiment, in the allocation unit, the power product time-series data used for the allocation process is allocated to the acquired power trading time-series data having the maximum amount restriction of the power product time-series data. It may be done. This is to cope with the case where there is a maximum amount restriction in the power product time-series data.

  Further, as an embodiment, it is urged to graph the acquired power trading time series data and display it on a display unit, and further superimpose the allocated power product time series data on the graph in the display unit. A display control unit that prompts the user to display the image may be further provided.

  Or, it is urged to graph the acquired power trading time series data and display it on a display unit, and further display the allocated power product time series data superimposed on the graph in the display unit And may further include a display control unit that prompts to display the power product time-series data subjected to the reallocation process superimposed on the graph. Any display control unit makes it easy for the user to visually grasp the display by graphing.

  Here, you may make it further comprise the 2nd display control part which prompts to display the acquired electric power goods time series data on a display part with a name, the present transaction price, and transaction amount. This is for the convenience of the user.

  In addition, here, on the display unit, demand, power generation, power purchase transaction in a relative contract, power sale transaction in a relative contract, a deduction total of relative contract buying and selling, a purchase transaction of forward products in the market, The amount of power for each sale of forward products, total deductions of forward products in the market, buy transactions in the spot market, sell transactions in the spot market, total deductions in the spot market, and total sales power and total purchase power. A third display control unit that prompts to display the average unit price and the total transaction price as a total value for a given period may be further included. This is also for the convenience of the user.

  Based on the above, embodiments of the present invention will be described below with reference to the drawings. The embodiment described below is roughly one of the following. The first is a power trading system capable of evaluating (determining) a power price by appropriately allocating a market product to a given demand. Second, it is a power trading system that can evaluate (determine) a power price in consideration of risks even when it cannot be completely allocated to a given demand only with a forward product as a market product. Furthermore, thirdly, in the case of owning power generation equipment, the power trading system can optimize the power generation amount and evaluate the power price while keeping the production cost to the minimum in combination with the market transaction.

  First, as a comparative example, an example (general example) of obtaining a power unit price for demand will be described with reference to FIG. FIG. 1 is a diagram for explaining an example of obtaining a power unit price for demand. Here, for the sake of simplicity, let us consider a case where power is sold to a certain consumer by combining a product purchased by a certain company from the market and power generation by its own generator.

  In general, the power consumed by the final consumer is a function of time as indicated by a demand curve 71 shown in FIG. The horizontal axis represents time, but it may be a change during a day or a change during a year. Here, it is assumed that the change is one month for convenience of explanation.

  It is assumed that the total demand power is A [kWh]. Here, consider the question “How much should I sell at a unit price (¥ / kWh)?”. Usually it was only calculated from the power generation cost and profit margin. However, the situation is somewhat different when electricity is bought and sold in the market. In other words, it is necessary to determine the price so as not to contradict the market price. No matter how much the power generation cost is, nobody will buy if it is higher than the market price, and if it is lower than the market price, it will result in a loss.

  If exactly the same form of merchandise is traded in the market, the unit price can be determined with reference to the price. However, in many cases, the products traded in the market are block-type products that deliver with a certain amount of power for one month, such as the forward product 72 shown in FIG. . The price of this product is b \ / kWh. Consider buying this product and selling it to a business partner. If the demand curve is completely flat and can be approximated by a block type, you can multiply this unit price by the profit rate. However, since the demand curve is generally not flat, a difference (imbalance portion) from the demand curve occurs.

  If you own a generator, this part can be generated in-house and sold in combination with market products. At this time, since there is no negative value in the power generation amount, it becomes a combination with the purchase amount such as the thin line 74 or the dotted line 75 shown in FIG. There are various combinations, but if the market price is lower than the power generation cost, the amount of the forwarded product is increased as much as possible as shown by the thin line 74 in FIG. If the price of the market product is higher than the power generation cost, it will generate power up to the maximum capacity of the generator and buy the rest from the market. At this time, if the installed capacity of the power generator of the company is D [kW], the purchase amount of the forwarded product 73 is at the level indicated by the dotted line 75 in FIG. Depending on the time of day, the company's generator will be shut down, and the equipment will be wasted. However, it is unavoidable because it is necessary to purchase advanced products at peak times. In this case, since the operation efficiency of the generator is poor, the cost may increase considerably.

In any case, the purchase amount of the forwarded product at this time is B [kWh], and the price is b [¥ / kWh]. Further, the amount of power generated by the company is C [kWh], and the cost is c [¥ / kWh]. Of course, B + C = A. In this case, the cost-based sales price is defined as g
It becomes. Since this price includes the power generation cost c, it cannot be said that it is necessarily appropriate as a selling price by the above discussion.

  Other combinations are also possible when considering buying and selling of electricity in the spot market. The spot market to be considered below is a market in which electric power delivered on the next day is bought and sold in units of 30 minutes or 1 hour, and is also called a previous day market. In some cases, they may be bought and sold on the same day, in this case called the real-time market. In any case, it is the same in that the trading volume can be determined in detail in terms of time, the price cannot be purchased at the time of contracting the relative transaction, and the price is unknown unless it is the previous day or the current day. Although there is a possibility that the desired quantity cannot be bought or sold if there is not enough quantity on the market, this possibility is not taken into consideration here.

  FIG.1 (c) has shown the case where the demand curve 71 is satisfy | filled combining the spot goods and the forward goods. In this case, a problem is how to determine the purchase amount of the forwarded product. First, an example will be described in which it is assumed that the price in the spot market is known and is constant during the delivery period of the contract to be evaluated. If the spot price is lower than the price of the forwarded goods, the purchase amount of the forwarded goods is set to 0 and all of them can be purchased in the spot market. Conversely, if the power price in the spot market is higher than the price of the forwarded product, it is reasonable to buy the forwarded product as much as possible and buy the rest in the spot market. The price of the spot market is usually unknown, but in some circumstances it may be predictable, so it may be appropriate to employ the above method.

  In practice, the spot price is usually unknown. If you do not know, it is natural to think that it is equal to the forward price. Hereafter, for the sake of simplicity, it is assumed that the interest rate is zero. In this case, the total purchase price does not change regardless of the amount of the advanced product. However, considering the risks, the situation is different. There is uncertainty about the planned purchase price of spot products, which needs to be discussed based on expected values and fluctuations. Although there are various definitions of the fluctuation range, as an example, standard deviation can be used.

  Although it is natural to assume that the predicted value of the spot price is equal to the forward price, it is not known how much the spot price will actually be. In this case, as the most appropriate method for determining the purchase amount, it is conceivable to determine the purchase amount of the forward product so that the total of the spot product sales amount during the period becomes zero. In this way, no matter how much the spot price becomes, the transaction price in the spot market is canceled and the total purchase price becomes constant. When considering power trading as a business, it is better not to have uncertainty if the expected value of all purchase prices is the same.

In FIG. 1C, the purchase amount of the forward product is B ^* [kWh], and the purchase amount of the forward product is determined so that the amount of electricity sold in the spot market is equal to the amount of electricity purchased. Assuming that the electricity price in the spot market is constant, no matter how much the value is, it will be canceled by selling and buying, and there will be no trading price in the spot market (the trading fee will be ignored). Therefore, the procurement cost is bB ^* [¥]. Of course, B ^* = B + C = A. As a result, the selling price is
It becomes. This agrees with the result obtained when c = b in Equation 1. This formula does not include the fuel price c. If the products on the market are freely available, the price of Equation 2 should be a common price for all market participants. Therefore, even if the actual procurement cost is the value of Equation 1, the sales price of Equation 2 is more appropriate.

  In the above explanation, there is an assumption that the spot price is uncertain but is constant during the delivery period. This assumption is usually not correct. Spot prices change every hour and every day. Such fluctuation is a risk. For those who procure from the market and sell as retail, it is necessary to decide and sell some price unless the sales price is linked to the market price. Generally, the end consumer does not like the electricity price to fluctuate, so it is usually sold at a fixed price. Therefore, the seller takes the risk of spot price fluctuations. In this case, it is natural to increase the price (add a premium) by taking the risk. The power purchaser will also have to hedge the price fluctuation risk, so there will be no objection to paying the premium.

  Next, this premium evaluation (determination) method will be described. At this time, it is assumed that it is unknown whether the spot price increases or decreases compared to the current price. Actually, there is a price level that is expected depending on the season, but for the sake of simplicity, we exclude only seasonal fluctuations and consider only irregular fluctuation components. Therefore, the spot price cannot be predicted and is considered to change at random.

  A simple way to hedge the risk against spot price fluctuations is to buy spot commodity futures. Unfortunately, there are currently no plans to sell such products. However, assuming that such a product exists, the risk equivalent amount can be evaluated by considering the price of the product. In general, the price of futures products is calculated from the current spot price, storage cost to maturity, interest rate, retained earnings, etc. Futures prices are almost equal to spot prices for products that have no difference in production and consumption depending on the season and are easy to store. However, in the case of electric power, it may be considered that it is practically impossible because it is very difficult or expensive to store. Therefore, the price cannot be evaluated by the conventional future theory based on the storage cost.

  An alternative to this is to use a forecast model for electricity prices. This models the periodicity of the electricity price due to seasonality of the day, week or year, describes the price fluctuations seen in the electricity price by the stochastic differential equation, and at the spot price by the analytical means or Monte Carlo method. This is a method of creating series data for a required period. In this method, a large number of such time-series data is created, the total purchase price is calculated for each of them, and the average value is regarded as the expected value of the purchase price and the standard deviation (or an amount proportional thereto) as the risk. In this case, although it is a purchase price, it is essentially different from a cost-based price because it is calculated based on market products. The price multiplied by an appropriate profit rate can be regarded as the selling price.

  In the above discussion, it was assumed that the amount of transactions in the spot market is sufficiently large and the goods can be bought and sold freely. In the actual spot market, you don't know if you can buy as many items as you want. Another way to reliably hedge the risk in such cases is to have a generator. When assessing spot futures prices, there is a method that considers retained earnings (value by having) in addition to storage costs, but if this is examined in detail, the power plant will be used at the appropriate time. The amount is considered to be equivalent to the right.

  As described above, a realistic way to reliably hedge the price fluctuation risk of the spot price is to own a generator. The right to use the power plant is equivalent to owning the power plant for a limited period. In fact, since it is difficult to store electricity, it is difficult to use a method other than the ownership of the generator as a risk hedging tool when there is no financial derivative. Therefore, a method for calculating a premium for taking a risk from the cost of ownership of the generator can be considered.

  However, it is not realistic to calculate the construction cost of the generator for this purpose. It is difficult to evaluate the cost for a specific driving method for a specific period. In this case, a market product equivalent to a generator may be considered. It can be considered that a business that owns a generator is equivalent to having the right to buy electricity at a desired time, at a price equivalent to fuel costs. Such rights are commonly referred to as “call options”.

  An option is a right to buy (call option) or sell (put option) a target product at a predetermined price (exercise price) at a predetermined time (maturity) in the future. Since this is a right and not an obligation, it can only be exercised if it is profitable. Such convenient rights are naturally not free. Trading at a fixed price. This right fee is called premium. For example, in the case of the right to buy (call), if the market price at the time of a future transaction is less than or equal to the exercise price, the purchase may be made from the market without exercising the right. In this case, the premium is wasted, but this should be considered as the cost of hedging the risk.

  In the example of the above-described generator, the time for generating power is the maturity, and the fuel price is equivalent to the strike price. A pure generator is equivalent to an “American option” in which the maturity can be freely selected, but when used in a relative contract, the maturity is determined on the day when electricity is delivered. It corresponds to “Option”.

  If you have a generator, you can produce (buy) electricity at the time you want by paying fuel. Thus, owning a generator is equivalent to having the right to buy electricity at a certain price. It is a right to the end, and fuel costs must be paid. From this, it is possible to evaluate the price corresponding to the risk of spot price fluctuation from the call option right fee. The fuel cost may also vary with time, but here it is assumed to be constant. When the fuel cost fluctuates, it is necessary to use the “spark spread option”, which will be described later.

  How much should the exercise price be set at this time? When assessing the value of a generator, the strike price is usually considered the fuel cost. However, this is largely in-the-money (profitable), and the option premium calculated from this is the intrinsic value (the value of the difference between the strike price and the spot price regardless of the risk) Is overestimated because it is included. This is not the risk-equivalent part but the value due to the difference between the production cost and the sales price due to the selection of an appropriate production method. However, this value is not constant, but varies depending on spot price fluctuations (power price transaction level). The intrinsic value can always be recovered and does not need to be charged to the buyer. When the spot price is equal to or higher than the strike price (electric power price at the time of contract), the intrinsic value becomes zero.

  On the other hand, the time value part is considered to correspond to the actual risk burden. This means that the longer the time to exercise (power delivery), the greater the risk. Therefore, by setting the exercise price to the current spot price, the value corresponding to the risk can be easily evaluated (determined) using the option theory. However, setting the exercise price does not exclude adjusting the trader's market view. If the spot price is expected to change in the future, it is possible to use that price.

  The risk assessment methods described above need to be selected appropriately according to the products handled in the market, their liquidity, market conditions, etc. It is recommended to adopt the option theory method, which is the most conservative method when it is completely unknown.

  The following explains in detail how to assess the risk premium based on option theory. Here, as an example, a method for calculating a risk premium related to spot purchase on the assumption that the spot purchase is covered by power generation will be shown. In this case, the value of power generation can be generally calculated using a call option formula. Since the risk associated with selling electricity at a spot is basically the same, it is calculated with the value of the put option.

The theoretical prices for call options and put options are given by the Black-Scholes formula used in the financial securities field as shown in Equations 3 to 6 below. That is, the call option premium (right fee) c is the spot price at the time of contract S, the exercise price is K (right to buy in K yen), the non-dangerous interest rate is r, and the price volatility is σ. When the exercise period (time from contracting to power delivery) is τ, it is expressed as follows.
Here, N (d) is a cumulative distribution function of a standard normal distribution.

On the other hand, put option price p is
It is represented by In the above equation, the unknown parameter is volatility σ, which is calculated from past market price fluctuations. Volatility (sigma) is calculated by the following formulas 7-9.
Here, τ is the revenue evaluation interval expressed in units of years, S _i is the price of the i-th evaluation date, and n is the number of data. As τ, one day, one week or the like can be used. For example, when it is desired to easily remove the periodicity in units of one week, one week (7/365) is used as τ. The value of volatility σ can be adjusted based on the trader's market view in addition to the past price data.

  When considering fluctuations in fuel costs, it can be considered that the difference between the spot price and the fuel price (spark spread) corresponds to the underlying asset of the option. While regular stocks do not have negative prices, spark spreads can be positive or negative. In this case, the above formula needs to be modified using the spark spread option formula.

A spark spread option is a type of spread option (an option that uses the price difference between two assets as an underlying asset) and is calculated using the difference between the price of electricity and the price of fuel (natural gas). It is normal to use the geometric Brownian motion model assuming that the electricity price and natural gas price are not negative, but the model assuming that the value is positive is not correct because the spread can be negative. There is a case. In this case, the following formulas 10 to 13 can be used to evaluate the option price c _ss .
Here, H is the heat rate (mmBTU / MWh), _{F E} power price, _{F G} is gas prices, subscripts t and T represent the evaluation date and the maturity date, sigma _E and sigma _G power price and gas Price volatility, ρ, is the correlation coefficient between electricity and gas prices. K is other costs related to power generation.

  When performing risk assessment, complex conditions may be required depending on the contract conditions. Typically, a special contract that returns a certain amount when the market price deviates significantly from the contract price can be considered. Of course, the risk of selling electricity depends on whether there is such a special agreement. Special contracts that correct prices under certain conditions when prices in the electricity market drop significantly or when competitors cut prices are well-considered transactions. In such a complicated transaction, the option premium cannot be calculated by a simple formula, and numerical calculation by the Monte Carlo method or the like is used.

  Formulas 3 to 6 are calculated on the assumption that power price fluctuations follow the geometric Brownian motion model. Although it is convenient as a simple formula, it is possible to adopt a more complicated price fluctuation model.

As the price fluctuation model, it is possible to use a model that takes into account the average regression, a model that takes into account spike-like price fluctuations that are often seen in electricity prices (jump model), and an average regression jump diffusion model that combines these models. it can. The mean regression jump diffusion model is
It is expressed as Here, S is the power price (or its logarithm), κ is the average regression speed, α is the level at which S is regressed, σ is volatility, and dW is the Wiener process. The third term A on the right side is the jump size, and dq is the Poisson process. In order to make the expected value constant, μ−ΦA may be used by using the jump probability Φ and the drift rate μ instead of the average regression level α. Further, a financial Boltzmann model as disclosed in Japanese Patent Application Laid-Open No. 2003-331128 can be adopted as a power price fluctuation model.

  When actually calculating the product price, adjust the purchase amount of the market product to calculate the virtual purchase amount of the call option and the put option, and the market product so that the sum of the option premiums is minimized. Decide how much to purchase. It doesn't matter if you can actually purchase these options. The option price is calculated to evaluate the cost corresponding to the risk. Risk is minimized when the sum of premiums is minimal. A risk can be minimized only by quantitatively evaluating the risk based on a specific option price. Of course, if there is an option market, these options can be purchased to pass on risks. The cost at this time should be the same as the reasonable price if you take risks. Therefore, it can be considered that the option price is an appropriate price for taking the risk on behalf of the consumer.

  Next, the power transaction system according to the embodiment of the present invention will be described with reference to FIG. FIG. 2 is a block configuration diagram showing a schematic configuration of the power trading system 50 according to the embodiment of the present invention.

  As shown in FIG. 1, the power transaction system 50 includes a power transaction processing unit 1 for a power trader to input a power transaction, a power price evaluation unit 2 directly or indirectly connected thereto, and a power transaction processing unit. 1, a power market database 3, a relative contract database 4, and a power generation plan database 5 that are directly connected or connected via an in-house network 51.

  Here, the power market database 3 stores in-house data and public market data other than the in-house data, and stores at least information on products that can be bought and sold at present and past price data that can be used to evaluate power price fluctuations. ing. The relative contract database 4 stores the results of past transactions executed in-house and transaction data scheduled to be executed in the future, and can read information on contracts to be evaluated as necessary. The power generation plan database 5 stores power generation plan data of its own generator.

  The power trading system 50 may be further linked to an external power exchange 6 or a supplier system 7 via the Internet 60 or a telephone line.

  FIG. 3 is a functional block diagram showing functions of the power transaction processing unit 1 shown in FIG. The power transaction processing unit 1 has at least a function 8 for displaying relative contract data, a function 9 for displaying market data, a function 10 for displaying a power generation plan, a function 11 for inputting / editing transaction conditions, and a function for storing transaction conditions. 12 are connected to each other by a function 13 for managing transaction data so that data can be transmitted to each other. The function 13 for managing the transaction data is linked to the power price evaluation unit 2 through the function 14 for evaluating the value of the future transaction based on the result of the power price evaluation unit 2, and is evaluated using the above data. The appropriate contract price can be evaluated (determined). In addition, the function 13 for managing transaction data may include a function 15 for transmitting contract conditions to the other party and a function 16 for evaluating the profits of past transactions.

  FIG. 4 is a functional block diagram showing a functional example of the power price evaluation unit 2 shown in FIG. It has a function to perform (part of) processing based on the concept described above.

  As shown in FIG. 4, the power price evaluation unit 2 receives the designation information of the price evaluation target transaction from the power transaction processing unit 1 (block 17), and reads the evaluation target relative contract condition 18 (first data). The transaction to be evaluated is defined in the system by the acquisition unit). Based on this information, a function 19 (second data acquisition unit) that collects related market data and a function 20 that collects information on related generators collect necessary data. Based on these, the function 21 for calculating the supply and demand imbalance calculates the current supply and demand imbalance expected at each time of the transaction period.

  As a result, when there is excess or deficiency, the function 22 (allocation processing unit; also functions as a power generation cost calculation unit and a reallocation processing unit depending on the case) that allocates standard products to demand allocates standard products as much as possible. The allocation method in this case will be described later. As a result, the function 23 (price determination unit) for evaluating the power price of the relative contract evaluates the price of the relative contract. Further, in response to a request from the power transaction processing unit 1, the price presentation function 24 sends the price information of the power price of the relative contract obtained by the evaluation to the power transaction processing unit 1 through the interface 25 to the power transaction processing unit. .

  In the following, first, a method for allocating market products to a demand profile in consideration of the power generation amount of the generator will be described. As market products, for example, a base product and a peak product in units of one month are considered. The base product is a product that buys and sells electricity with constant power for one month, and the peak product is a product that buys and sells electricity with constant power only during peak hours. The peak time zone is, for example, from 8 am to 10 pm on weekdays, but this is not necessarily this time, and the effect of the present embodiment does not change even at other times. Further, in the following description, for the sake of simplicity, the types of products are limited to one or two, but the number of types may be increased.

  There are certain restrictions when assigning generators to demand. FIG. 5 is a diagram for explaining an example of assigning electric power products to the demand profile. Here, as an example, when selling electricity to a consumer, an example will be described in which the necessary electricity is procured from the market or by generating electricity in-house.

  FIG. 5A shows an example in which a base product and a peak product are allocated to a demand profile, and the rest is procured by own power generation. In this case, the purchase amount of the base product must be smaller than the power at the point A where the demand curve 71 is minimized. On the other hand, the purchase amount of the peak product must be smaller than the amount obtained by subtracting the purchase amount of the base product from the maximum point B that does not exceed the demand curve 71 when the peak product is purchased alone. Since the power generation amount of the generator cannot be made negative, the allocation is as shown in this figure. The actual allocation amount adjusts the purchase amount of the market product so that the total cost is the minimum or the total profit is the maximum within this range.

  Here, when calculating the allocated amount, the power generation cost is used, but when calculating the selling price, the spot price or the forward price is used instead of the power generation cost as the power price of the portion generated by the generator. If there is no risk, either a spot price or a forward price may be used. In practice, the cheaper one is used. In this case, since the risk is not considered, the latest spot price is used. If the spot price at the delivery time can be predicted using a forward price or the like, that value is used. If risk is considered, the portion that can be approximated by forward transactions without risk is enlarged as much as possible, and the spot price with risk is applied to the remaining portion. Originally these prices should be determined appropriately in the market depending on the amount of risk, so you can use either one, but in practice it is convenient for your company (the risk is small or the profit is large) You can choose. The final price determined by taking into account the amount of risk should be at the same level. This is one of the embodiments described above.

  FIG. 5B is an example in which power generation is mainly allocated and the remainder is procured with peak products and spot products. If simply calculated, the selling price is usually different depending on which method shown in FIGS. 5A and 5B is employed. When selling electric power, of course, it is only necessary to sell it by a method convenient to the company. In this case, if there is no competitor, it will be sold with the larger profit, and if the competition is intense, it will be sold with the cheaper one. If a product sells at this price, it will eventually become a market price. In this case, the price of the base product will approach the power generation cost, and eventually it should be the same price with either method. That is why you should not simply adopt the current market price as the selling price. The market mechanism works when all market participants make efforts to determine the selling price by incorporating the market products within a reasonable range. This is another one of the embodiments described above.

  FIG. 6 is a functional block diagram showing another function example (power price evaluation unit 2A) of the power price evaluation unit shown in FIG. 2 on the assumption of the description in FIG. In FIG. 6, the same reference numerals are assigned to the same or the same functional blocks as those shown in FIG. 4. The case shown in FIG. 6 includes a function 102 that adjusts the amount of power generated by its own generator. Further, the function 22 for allocating the standard product to the demand also functions as a spot product purchase amount calculation unit as can be understood from the following description.

  If you have a generator, it is natural to minimize the procurement cost, regardless of the selling price, by using the generator. Therefore, when determining the power generation amount of the generator, the total power generation cost and market procurement cost are determined to be minimized. Furthermore, if there is a risk in the market procurement part, the total including the risk premium should be minimized. The risk premium here is not the risk premium that is passed on to the selling price, but the net risk amount that the company takes. This amount of risk is of course small. Therefore, the difference from the case shown in FIG. 4 is that there is a function 102 for adjusting the power generation amount of the generator, and a function for searching for the power generation amount that minimizes the cost or maximizes the profit (loop by block 101). is there. Others are almost the same as those in FIG. 4, but the function 22 for allocating a standard product also functions as a spot product purchase amount calculation unit. In this case as well, spot prices are used for portions corresponding to power generation costs in the final determined power generation allocation. There is no method other than using spot products for portions other than regular products (peak products and base products).

  With the method described above, there is a possibility that the purchase amount of spot products becomes very large. This is not always desirable considering the fluctuations in the price of spot products (risk). Therefore, in actuality, it is necessary to assign in consideration of the risk of spot products.

  Therefore, a method for allocating regular products to demand in consideration of risks will be described in detail. As mentioned above, there are cases where many types of block-type products are handled in the market. As an example, peak products (products for weekday daytime only) and base products (products supplied at a constant power for one month) ) Is described with reference to FIGS. 7 and 8. FIG. In the following, the case where the risk is not considered will be described first, and then the case where the risk is considered will be described.

  FIG. 7 is an explanatory diagram illustrating an example in which output products are assigned to a demand profile in consideration of a peak time zone and an off-peak time zone. FIG. 7A shows an example in which only a market product is assigned to a demand profile without using a generator. The remaining part to which the base product 81 and the peak product 82 are allocated is assumed to be made up by buying and selling the spot product 83. In this case, the allocation is performed so that the total trading volume of the spot product is zero. Yes. This is yet another one of the embodiments described above.

  It can be shown that such an allocation method always exists. This is illustrated in FIGS. 7B and 7C. In FIG. 7B, first, the time on the horizontal axis is adjusted to shift the peak time zone, and off-peak time zones (portions other than the peak time zone) are collected in one place. In this way, the base product is assigned with attention paid only to the off-peak hours. Next, peak products are allocated so that spot sales are zero for the remaining power demand in the peak time zone. By doing so, it is possible to purchase a combination of base products and peak products, satisfy an arbitrary demand profile, and make net purchases from the spot market zero.

  FIG. 7C shows an allocation example when the demand profiles are different. In this case, since the purchase of the base product is excessive in the peak time zone, the condition is satisfied by selling the peak power (peak product 82A).

  In this way, the price for retail demand can be evaluated (determined) by allocating market products to retail demands having an arbitrary demand profile. In FIG. 7, the peak time zone and the off-peak time zone have been described as an example for the daily demand. If this is one month or one year of demand, peak hours and off-peak hours will alternate, and irregularly due to the presence of holidays. However, as described above, a similar method can be applied by assigning the off-peak time zone together in the first place. FIG. 8 is an explanatory diagram illustrating another example in which an output product is assigned to a demand profile in consideration of a peak time zone and an off-peak time zone. As shown in FIG. 5B, when combining the generator with the marketed product, the method as shown in FIG. 7 and FIG. 8 can be used for the portion exceeding the maximum power generation amount.

  In an actual system, it is preferable to perform allocation by evaluating the risk due to spot price fluctuations. For this purpose, a method for calculating profit when the spot price fluctuates will be described below.

  A theoretical model can be used to calculate the spot price, but in general, a spot price time series data is created by a computer using the Monte Carlo method or the tree method, and the spot purchase price is calculated. FIG. 9 shows an example of the execution, and is a diagram showing an example in which future fluctuations in the price of spot products are generated using the Monte Carlo method, and a diagram showing an example of profit distribution of a relative contract obtained from the result.

  In FIG. 9A, time series data of a large number of spot prices is created by a geometric Brownian motion model. The geometric Brownian motion model is a model that assumes that the logarithm of the power price fluctuates randomly, and is a model used for predicting fluctuations in stock prices and the like. The method of creating spot price data is not limited to the geometric Brownian motion model. For example, the average regression jump diffusion model described in Expression 14 can be used, but here, the geometrical brown model will be described as an example.

A plurality of time series data of future spot prices can be created for the same input parameters (volatility, today's price, interest rate, etc.) by using random numbers. Using a computer to create a large number of time-series data and assuming the electricity sales price to consumers, the following revenues can be obtained from the given demand, power generation, market merchandise sales, and spot merchandise sales: Can be calculated. (Here, interest rates are ignored for the sake of simplicity. If interest rates are taken into account, the total value is calculated by discounting the interest and sales from the point in time when deposits and withdrawals occur and calculating the current value. .)
Revenue [i] = (sales-power generation cost-forward trading cost-spot trading cost [i])
(15)
Sales = Demand x Electricity price (16)

  Here, [i] is a time-series data number. Power generation costs are calculated using generator data, and forwards and spots are calculated by adding net costs from selling and buying. Sales can be obtained from Equation 16, and the power price in Equation 16 is assumed as a temporary parameter. In Equation 15, the uncertain part is only the spot trading cost part, and if the spot price is determined, the profit is determined.

  One profit is determined for one spot time-series data. By calculating for a lot of time-series data, it is possible to obtain a set of profit data having a certain distribution. FIG. 9B shows the frequency distribution graph. In this case, calculation is performed on time-series data of i = 200, and a frequency distribution of a total of 200 profit values is calculated. The curve in the figure is the result of fitting this with a normal distribution. In this figure, the area of the normal distribution is normalized so as to be 1 and is displayed as a probability density function (PDF). From this result, the expected value and variation (variance) of profits can be calculated. The spread (variation) of the profit corresponds to the risk. Specifically, the standard deviation (in monetary unit) of the probability density function corresponding to a given probability (for example, 95%) is defined as the risk.

  By calculating the power (sales) price such that the expected value of the profit is 0, the price calculated backward from the market price can be evaluated. Although the profit ratio is not taken into account in the calculation of profit in Formula 15, after evaluating an appropriate power price by the above method, an appropriate profit is added to determine the final retail price.

  In the above case, if the risk value is too large, adjust the trading volume (or power generation volume) of the forwarded product to reduce the trading volume at the spot and assign the risk value to a value that satisfies the risk value. To change.

  The above description with reference to FIG. 9 corresponds to still another embodiment described above.

  Next, still another case will be described with reference to FIGS. FIG. 10 is a functional block diagram showing still another function example (power price evaluation unit 2B) of the power price evaluation unit shown in FIG. 2, and FIG. 11 shows data by each functional block shown in FIG. It is explanatory drawing which shows the flow. In FIG. 10, the same reference numerals are assigned to the same or equivalent functional blocks as shown in FIG. 4.

  In the case shown in FIG. 10, the spot price varies, that is, the power price evaluation unit (power price evaluation unit 2 </ b> B) when risk is taken into consideration. Therefore, a risk evaluation function 103 is added. The risk evaluation function 103 collects information necessary for evaluating a risk caused by spot purchase. Based on the information, the price calculation function 104 (risk equivalent price calculation unit) corresponding to the risk evaluates the risk premium. Specific examples of the calculation method are shown in Equations 3 to 6, but other methods can be used. Other functions are almost the same as those shown in FIG.

  11 can be said to be a rewrite of the function of the power price evaluation unit 2B shown in FIG. 10 from the viewpoint of data flow. The supply and demand imbalance calculation unit 21P, the standard product allocation unit 22P, and the risk equivalent price calculation unit 104P in FIG. 11 correspond to the same reference numerals without “P” in FIG. As the data flow, the imbalance power amount that needs to be purchased or sold is calculated by the imbalance calculation unit 21P from the demand data 111 of the business partner and the power generation plan 112 of the company. On the other hand, data on market products currently being sold and purchased is acquired from the market product database 113, and products are allocated using the standard product allocation unit 22P. The final imbalance amount 116 is purchased at a spot. The risk equivalent price 119 is calculated by using the risk equivalent price calculation unit 104P based on the spot price data 115 and the risk evaluation parameter 117. The final price 12 is determined from a fixed product price 114, a power generation cost 120, a risk equivalent price 119, and other costs 118.

  FIG. 12 is a flowchart illustrating an example of the processing flow of the “function 104 for calculating the risk equivalent price” shown in FIG. 10, and a calculation method for determining the purchase amount of the standard product so that the total cost is minimized. Is shown. Calculate the total cost including risk premium by giving the trading volume of the standard product (forward delivery product), adjust the purchase amount of the standard product to minimize this, and sell from the procurement cost finally obtained Calculate the price.

  First, the amount of power required within the period is calculated (step S200), and the initial value of the fixed product purchase amount is input (step S201). Then, from these calculated values and initial values, the spot purchase required amount is calculated and determined under the designation of the risk evaluation parameter 202 (step S203), and the risk premium at that time is determined by the method already described (step S204). . Next, the total cost including the calculated risk premium is calculated (step S205), and in order to search whether this is the minimum, the purchase amount of the standard product is changed (step S207), and the process returns to step S203. And so on.

  If this process loop is repeated several times and it is found in step S206 that the total cost is minimum, the fixed product purchase amount and fixed product purchase cost are determined as values at that time (steps S208 and S209).

  FIG. 13 is a flowchart showing another processing flow example of the “function 104 for calculating the risk equivalent price” shown in FIG. 10, and the calculation in the case of determining the purchase amount of the standard product so that the risk is minimized. Shows how. In FIG. 13, the same or equivalent steps as those shown in FIG. The difference from FIG. 12 is that the trading volume of the standard product is determined so that the risk is minimized rather than the total cost.

  For this reason, a minimum risk determination step S210 is provided instead of the total cost minimum determination step S206, and if it is found as a result of the search by the processing loop described above that the risk is minimum in this determination step S210, the value at that time As described above, the fixed product purchase amount, the risk product purchase amount, the fixed product purchase cost, and the risk product purchase cost are respectively determined (steps S211 and S212). Thereby, the total cost is calculated (step S213).

  FIG. 14 is a flowchart showing more specifically the processing example of the main part of the flowchart shown in FIG. 13, and corresponds to the processing from step S200 to step S210 in FIG. In the example shown in FIG. 13, the risk-equivalent price is calculated based on the option price using the above-described equation 3 (step S307).

First, the contract period (i = 1 to N) is set (step S301), and the power supply profile L _i is read corresponding to each i (step S302). Thus, the average power value A ₀ is calculated as A ₀ = ΣL _i / N (step S303), then A ₀ is set as the initial value of the fixed product purchase amount A, and the initial value of the risk equivalent price C is 0. Is set (step S304).

Next, the following processing is performed using each i as a loop variable. First, the spot purchase equivalent amount s _i = L _i −A is obtained (step S 306), and the risk premium R _i is obtained based on the set value by the risk evaluation parameter setting 308 (step S 307). This calculation is based on Equation 3 described above. Next, the risk equivalent cost is calculated as C _i = R _i × s _i (step S309), and the calculated C _i is added to the risk equivalent price C (step S310). Then, i is updated until N is reached (steps S311 and S312), and the processing from step S306 is executed in the same manner.

  When i becomes N, a risk equivalent amount C with respect to a certain standard product purchase amount A is obtained. Therefore, the fixed product purchase amount A is changed (step S314), and the above processing loop is executed to search for the risk equivalent C (step S313). As a result, if it is found in the determination step S313 that the risk is A, at which the risk is minimized, the process is completed.

  FIG. 15 is a diagram illustrating an example of display of allocation of power products to a demand profile in the power trading system according to one embodiment of the present invention. Allocation is made every delivery period (from the start to the end of power transmission) of market transaction products. In this case, marketed products having a delivery period of November are allocated so that the areas of the dot pattern portion and the hatched portion in both directions are equal. This allocation method is called an equal area method. Such an allocation diagram is displayed in the market product list display area 501 on the display screen in the power trading system described below.

  Next, a display screen in the power trading system according to the embodiment of the present invention will be described with reference to FIGS. 16 to 23. In FIG. 16 to FIG. 23, the same reference numerals are assigned to the same equivalent regions. FIG. 16 is a diagram illustrating an example of a display screen (at startup) of the power trading system according to the embodiment of the present invention. Reference numeral 501 in the figure is a demand profile display area, which can display a demand profile of a relative contract and a profile of a market product.

  FIG. 17 is a diagram illustrating a stage where the demand profile is displayed on the display screen illustrated in FIG. 16. The demand profile is displayed with a solid line in the demand profile display area 501 in the figure.

  FIG. 18 is a diagram showing a stage where a list of power products (power product time-series data) is further displayed on the display screen shown in FIG. Reference numeral 511 is a market product list display area, reference numeral 512 is a market product delivery period (start) display area, reference numeral 513 is a market product delivery period (end) display area, reference numeral 514 is a market product price display area, and reference numeral 515 is a product tradeable. It is a flag display area, and products that can be traded in these areas are displayed as a list together with a name, a current trading price, and a trading volume.

  FIG. 19 is a diagram showing a stage where power products are assigned on the display screen shown in FIG. 18 and the result is displayed. In this case, the product is assigned by the equal area method as an initial value. Therefore, risk is not considered. However, this result may be adopted as the electricity sales price. In this display stage, the trade amount is shown for each product type in the area 531 (purchase cost display area), and the sales unit price based on these trade quantities is also displayed. In the area indicated by reference numeral 532 (sales and sales price display area), sales and sales prices based on the area are displayed.

  The purchase cost display area 531 and the sales and sales price display area 532 generally include demand, power generation, power purchase transaction with a relative contract, power sale transaction with a relative contract, a total of deductions of relative contract sales, market Buy-and-sell products on the market, sell transactions on the market in advance, total deductions on the market in advance, buy transactions on the spot market, sell transactions on the spot market, total deductions on the spot market, and total sales About electric power and total purchase electric power, it is good to display electric energy, an average unit price, and a total transaction price as a total value of a given period, respectively.

  When risk is considered in the display of FIG. 19, it is necessary to input volatility based on the analysis result of past data or the market view of the trader. FIG. 20 is a diagram illustrating an example of a dialog box for inputting volatility or the like that is selected from the display screen illustrated in FIG. 19 and displayed.

  If volatility is an input parameter, the selling price will vary depending on volatility. This is contrary to the one-of-a-kind principle, but in principle, volatility is a value determined from past data. Also, it is within the trader's discretion that traders fine-tune volatility. In reality, there are traders who feel that the market price is expensive, and traders who feel that the market price is cheap. The method of expressing such market price uncertainty with a single parameter called volatility is also a practically useful method.

  In the dialog box shown in FIG. 20, the price fluctuation model to be adopted is selected from the area 552 (calculation method designation area) and the optimization execution button 551 is reassigned, and the contract price considering the risk is calculated. Is done. FIG. 21 is a diagram showing a stage where optimization calculation is performed on the display screen shown in FIG. 19 and the result is displayed, and the result of reassignment in consideration of risk is displayed.

  In the display state shown in FIG. 18, in some cases, there may be a market product that cannot be purchased. In this case, restrictions can be added to the choices of products that can be purchased. FIG. 22 is a diagram showing a display example obtained by applying restrictions to the power product on the display screen shown in FIG. 18 and performing subsequent processing. By leaving the check box in the market product list display area 511 blank, FIG. A product that cannot be bought or sold is specified. More specifically, the second product from the top of the product list display area 511 is unchecked, and no assignment is made for this product.

  FIG. 23 is a diagram showing a display example obtained by setting the demand profile as mixed sales on the display screen shown in FIG. 17 and subsequent processing. In this case, it can be seen that the demand profile displayed in the demand profile display area 501 takes positive and negative values. Here, a positive value corresponds to the sale of electric power, and a negative value corresponds to the purchase of electric power.

  The display screens as shown in FIGS. 17 to 23 are realized by the power price evaluation unit 2 (or 2A, 2B) shown in FIG. 2 having a predetermined display control unit.

The figure for demonstrating the example which calculates | requires the power unit price with respect to demand. The block block diagram which shows schematic structure of the electric power transaction system 50 which concerns on one Embodiment of this invention. The functional block diagram which shows the function of the electric power transaction process part 1 shown in FIG. The functional block diagram which shows the function example of the electric power price evaluation part shown in FIG. The figure for demonstrating the example which allocates electric power goods to a demand profile. The functional block diagram which shows another example of a function of the electric power price evaluation part shown in FIG. Explanatory drawing which shows the example which allocates output goods to a demand profile in consideration of a peak time zone and an off-peak time zone. Explanatory drawing which shows another example which allocates output goods to a demand profile in consideration of a peak time zone and an off-peak time zone. The figure which shows the example which produced | generated the future fluctuation | variation of the price of spot goods using the Monte Carlo method, and the figure which shows the profit distribution example of the relative contract calculated | required from the result. The functional block diagram which shows another example of a function of the electric power price evaluation part shown in FIG. Explanatory drawing which shows the flow of the data by each functional block shown in FIG. FIG. 11 is a flowchart showing an example of a processing flow of “function for calculating risk-equivalent price” shown in FIG. 10. FIG. 11 is a flowchart showing another processing flow example of the “function for calculating a risk-equivalent price” shown in FIG. 10. 14 is a flowchart showing more specifically a processing example of a main part of the flowchart shown in FIG. The figure which shows an example of the allocation display of the electric power goods to the demand profile in the electric power transaction system which concerns on one Embodiment of this invention. The figure which shows an example of the display screen (at the time of starting) of the electric power transaction system which concerns on one Embodiment of this invention. The figure which shows the step which displayed the demand profile on the display screen shown in FIG. The figure which shows the step which further displayed the list | wrist of electric power goods on the display screen shown in FIG. The figure which shows the step which performed the allocation of electric power goods on the display screen shown in FIG. 18, and displayed the result. The figure which shows the example of the dialog which inputs the volatility etc. which select and display from the display screen shown in FIG. The figure which shows the step which performed optimization calculation on the display screen shown in FIG. 19, and displayed the result. The figure which shows the example of a display obtained through the process after adding restrictions to electric power goods on the display screen shown in FIG. The figure which shows the example of a display obtained by setting a demand profile as mixing of sales on the display screen shown in FIG. 17, and passing through the subsequent process.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 ... Electric power transaction processing part, 2, 2A, 2B ... Electric power price evaluation part, 3 ... Electric power market database, 4 ... Relative contract database, 5 ... Electric power generation plan database, 6 ... Electric power exchange, 7 ... Supplier system, 21P ... Supply-demand imbalance calculation unit, 22P ... Standard product allocation unit, 104P ... Risk equivalent calculation unit, 50 ... Power transaction system, 51 ... Internal network, 60 ... Internet, 81 ... Base product, 82,82A ... Peak product, 83 ... Spot product, 501 ... Demand profile display area, 511 ... Market product list display area, 512 ... Market product delivery period (start) display area, 513 ... Market product delivery period (end) display area, 514 ... Market product price display area, 515 ... Marketable merchandise available flag display area, 531 ... Purchase cost display area, 532 ... Sales and sales price Display area, 551 ... optimization execution button, 552 ... Calculation designated area.

Claims (14)

A first data acquisition unit for acquiring time series data of power trading scheduled with a relative business partner;
A second data acquisition unit for acquiring time series data and price data of electric power products traded in the electric power market;
A process of assigning the acquired power product time series data to the acquired power trading time series data, and a difference between the total power amount of the scheduled power trading and the total power amount of the allocated power product An allocation processor that minimizes
A power trading system comprising: a price determination unit that determines a power price to be presented to the relative business partner based on price data of the allocated power product. A power generation cost calculation unit that calculates a power generation cost when power is generated by a predetermined generator corresponding to a difference between the power product allocated by the allocation processing unit and the planned power trading;
In order to minimize the total cost including the power generation cost obtained by the calculation, the amount of power of the allocated power product is adjusted and reassigned to the acquired power trading time series data, and the predetermined power A reallocation processing unit for calculating the amount of power generated by the generator,
The price determination unit determines a power price to be presented to the relative business partner based on the price data of the reallocated power product and the price data of a spot product or a forward product corresponding to the calculated power generation amount. The power trading system according to claim 1, further comprising a function of:   The allocation processing unit allocates the acquired power product time-series data after preferentially allocating the amount of power generated by a predetermined generator to the acquired power trading time-series data, and is scheduled. The apparatus further comprises a function of minimizing a difference between a portion obtained by subtracting the power generation amount by the generator from a total power amount of power trading and the total power amount of the allocated power product. 1. The power trading system according to 1. The allocation processing unit further has a function of allocating time series data that is not related to a spot product that is the acquired power product time series data to the acquired power trading time series data,
Spot product purchase amount calculation that allocates a spot product to the power corresponding to the difference between the power product excluding the spot product allocated by the allocation processing unit and the planned power purchase and purchase, and calculates the purchase amount of the spot product And
Processing to reallocate the acquired power trading time-series data by adjusting the power amount of the allocated power product so that the spot product purchase amount obtained by the calculation or the predicted purchase price total by the purchase is minimized. And a reallocation processing unit
The power transaction system according to claim 1, wherein the price determination unit further has a function of determining a power price to be presented to the relative business partner based on price data of the reassigned power product.   5. The electric power price at each time obtained by modeling the price fluctuation of the spot product by a Monte Carlo method or a tree method is used as the predicted purchase price of the spot product by the reallocation processing unit. Power trading system. A risk equivalent price calculation unit that analyzes the spot price fluctuation in the past certain period and calculates the uncertainty of revenue given by the analysis as a risk equivalent price;
The price determination unit further has a function of determining a power price to be presented to the relative business partner based on the calculated risk equivalent amount and the price data of the reallocated power product. The power trading system according to claim 4.   In the allocating unit, the power product time-series data used for the allocation process is allocated to the acquired power trading time-series data having a maximum amount restriction on the power product time-series data. The power transaction system according to claim 1.   Combining within the same period when the allocation processing unit allocates the acquired power trading time series data to the acquired power product time series data and time series data not related to spot products. 5. The power according to claim 4, wherein when there are a plurality of time series data of electric power products, a combination that can make the purchase amount of spot products calculated by the spot product purchase amount calculation unit smaller can be used for the allocation process. Trading system.   The acquired power trading time series data is urged to be graphed and displayed on the display unit, and further, the allocated power product time series data is urged to be displayed superimposed on the graph on the display unit. The power transaction system according to claim 1, further comprising a display control unit.   Prompt to display the acquired power trading time series data in a graph on the display unit, and further to display the allocated power product time series data superimposed on the graph in the display unit The power trading system according to claim 4, further comprising a display control unit that prompts to display the power product time-series data subjected to the reallocation process superimposed on the graph.   11. The electric power according to claim 10, further comprising a second display control unit that prompts the display unit to display the acquired power product time series data together with a name, a current transaction price, and a transaction amount. Trading system.   On the display unit, demand, power generation, power purchase transaction with a relative contract, power sale transaction with a relative contract, deduction total of relative contract buying and selling, purchase transaction of a forward product in the market, sale transaction of a forward product in the market , Total market deductions, spot market buy transactions, spot market sell transactions, spot market deduction totals, and total electricity sold and total electricity purchased, respectively. 12. The power trading system according to claim 11, further comprising a third display control unit that prompts to display the transaction price as a total value for a given period. Obtaining time series data of power trading scheduled with relative business partners;
Obtaining time series data and price data of power products traded in the power market;
A process of assigning the acquired power product time series data to the acquired power trading time series data, and a difference between the total power amount of the scheduled power trading and the total power amount of the allocated power product Steps to minimize
Determining a power price to be presented to the relative business partner based on price data of the allocated power product. Obtaining time series data of power trading scheduled with relative business partners;
Obtaining time series data and price data of power products traded in the power market;
A process of assigning the acquired power product time series data to the acquired power trading time series data, and a difference between the total power amount of the scheduled power trading and the total power amount of the allocated power product Steps to minimize
Determining a power price to be presented to the relative business partner based on price data of the allocated power product.