JP3982761B2 - Power transaction determination method, apparatus thereof, and program thereof - Google Patents

Description

  According to the present invention, among a plurality of consumers that supply electric power, distribution is made between consumers and consumers that supply the optimum electric power so as to maximize the utility of the electric power company under a predetermined constraint condition. The present invention relates to a power transaction determination method capable of determining a ratio, an apparatus thereof, and a program thereof.

  Electric utilities need to accurately predict parameters that affect revenue in order to maximize revenue from power transactions. Specifically, the demand characteristics of multiple customers receiving power supply, the power generation costs of generators or the power procurement costs of procurement from power sources such as backup power sources, that is, these parameters that affect power procurement. It is necessary to predict accurately.

  These parameters are not necessarily determined and will vary depending on uncertain events at a future time. For example, when a general household is targeted as a consumer, it is conceivable that the amount of power demand for air conditioners or heaters increases or decreases depending on the temperature and season. In addition, fluctuations in fuel prices due to changes in foreign exchange rates and the conditions of crude oil suppliers are expected to affect power generation costs. However, it is not easy for electric utilities to grasp the changes in temperature or fuel prices at a future time.

  Therefore, in order to predict profits from power trading, simulation of operating profit and loss and operational risk of power retail based on the relationship between temperature and power demand based on past statistics, the relationship between fuel price and power procurement cost, etc. A method of performing is disclosed in Patent Document 1.

Further, Patent Document 2 describes a power generation facility that calculates a predicted price of electric power, determines a bid destination market, calculates an optimal operation plan by dividing an operation state of a power plant, and calculates a maximum profit. An optimal operation plan calculation method is disclosed.
Japanese Patent Laying-Open No. 2003-70164 (paragraphs 0014 to 0016, FIG. 1) JP 2001-86645 A (paragraphs 0019 to 0036, FIG. 3)

  However, it is unclear how the simulation system for electric power retail described in Patent Document 1 calculates operating profit / loss and operation risk. The method based on the simulation of the prior art disclosed in Patent Document 2 is a power generation company. Although the future uncertain factors that affect the earnings of the project are reflected, it was not possible to quantitatively grasp the degree of the impact of the uncertain factors on the profits of the power generation companies. In general, when the profit and risk of the power generation company are in a trade-off relationship, it is difficult to quantitatively grasp the trade-off relationship.

  Therefore, the problem to be solved by the present invention is to increase the profit of the power generation company due to the uncertain factor in order to maximize the utility of the power generation company when the profit of the power generation company fluctuates due to the uncertain factor. It is to establish a method for determining power transactions that can quantitatively grasp the degree of impact and can grasp the trade-off relationship between profit and risk quantitatively.

  In order to maximize the utility of selling electric power to a plurality of consumers, the present invention, which has been made to solve the above-described problems, supplies a customer who is an optimal power supply destination and the customer. A power transaction determination method for determining a consumer portfolio including an electric power distribution ratio using a computer having information storage means, wherein the computer stores information on power demand characteristics of a consumer as the information storage means. B) a procedure for storing information related to business revenue in the information storage means; c) a procedure for obtaining an index value indicating a trade-off relationship between revenue and risk; and d) constraints on power supply. A procedure for obtaining a condition, and e) a utility function including a term relating to risk and a term relating to profit, and information on power demand characteristics of a consumer stored in the information storage means and Based on the information related to business revenue and the index value obtained in the step c), a constant for each term of the utility function is calculated and formulated, and an optimal customer portfolio that maximizes the utility function is determined in the step d). And a procedure for deriving using a predetermined optimization method under the constraint condition obtained in step f) and f) a procedure for outputting the optimum customer portfolio derived in step e). This is a method for determining electricity trading.

  Further, the information related to the power demand characteristics of the consumers may include data related to a predicted value of the power demand amount of a plurality of consumers and a fluctuation amount of the predicted value of the power demand amount.

  Further, the constraint condition related to the power supply may be a constraint condition related to a distribution ratio of power supplied to consumers.

  Further, the constraint condition regarding the power supply may be a constraint condition regarding the power supply amount for each time.

  Moreover, the term regarding the profit contained in the said utility function can also be defined by the difference of the income obtained from a consumer, and the output cost of the generator of a generator.

  Moreover, the term regarding the profit contained in the said utility function can also be defined by the difference between the income obtained from a consumer, and the procurement cost of the electric power from a generator or another power supply.

  The term relating to the risk included in the utility function is a change in at least one of the income obtained from the customer, the procurement cost from the generator or other power source, and the output cost of the generator by the electric utility. It can also be defined by quantity.

  In addition, the present invention, which has been made to solve the above-mentioned problems, is intended to maximize the utility of selling electric power to a plurality of consumers, and to a customer who is an optimal power supply destination and this consumer. A power transaction determination device for determining a consumer portfolio including a distribution ratio of power to be supplied, comprising: a consumer data storage means for storing data relating to power demand characteristics of a plurality of consumers; and data relating to profits of the power business. Operator data storage means for storing, utility parameter input means for inputting an index value indicating a trade-off relationship between profit and risk, constraint parameter input means for inputting a constraint condition regarding power supply, a term relating to risk and profit A utility function including a term related to the data stored in the customer data storage means and the operator data storage means, and the utility parameter. On the basis of the index value input by the data input means, a constant for each term of the utility function is calculated and formulated, and an optimal customer portfolio that maximizes the utility function is determined as a constraint condition related to the power supply. An optimal customer portfolio derivation means derived using a predetermined optimization method and a customer portfolio output means for outputting the customer portfolio derived by the optimum customer portfolio derivation means The power transaction determination device.

  Further, the consumer portfolio output means may transmit the consumer portfolio to a terminal device connected to the power transaction determination device via a network.

  In addition, the present invention, which has been made to solve the above-mentioned problems, is intended to maximize the utility of selling electric power to a plurality of consumers, and to a customer who is an optimal power supply destination and this consumer. A power transaction determination program for realizing a power transaction determination device for determining a consumer portfolio including a distribution ratio of power to be supplied by a computer. The computer stores information on power demand characteristics of a plurality of consumers. Means for storing in the storage means, means for storing information relating to the profit of the power business in the information storage means, utility parameter input means for inputting an index value indicating a trade-off relationship between the profit and the risk, and a constraint condition relating to power supply The utility function including the constraint parameter input means to input, the risk terms and the profit terms is stored in the information storage means. Based on the information on the power demand characteristics of a number of consumers, the information on the profit of the power business, and the index value input by the utility parameter input means, the constants of the terms of the utility function are calculated and formulated, and the utility An optimum customer portfolio derivation means for deriving an optimum customer portfolio maximizing a function using a predetermined optimization method under the constraints on the power supply, and a demand derived by the optimum customer portfolio derivation means It is an electric power transaction determination program for functioning as a consumer portfolio output means for outputting a house portfolio.

  According to the present invention, in order to apply a trade-off relationship between risk and profit to an optimization problem, it is formulated as a utility function and solved under a constraint condition, thereby generating profit according to the risk allowed by the electric utility. It is possible to quickly derive the power consumer portfolio that maximizes the utility (maximum utility), quantitatively understand the risk-revenue trade-off relationship, and the electricity supplier's power supply plan Can be planned quickly and accurately.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. First, the outline | summary of the electric power transaction determination method of this Embodiment is demonstrated. The power transaction determination method according to the present embodiment uses a computer and a mathematical programming method such as linear programming or quadratic programming to maximize the utility between the business profit and risk of the power utility. In order to be able to do so, the supply ratio to the demand group of the same industry or the like composed of a power supply destination or a plurality of power supply destinations is determined.

  For this purpose, in this embodiment, a power transaction determination problem that is an optimization problem for determining a power supply destination is solved. This power transaction determination problem is a problem of finding a supply destination that can maximize the formulated utility function under predetermined constraints. For example, as shown in the following formula (1), this utility function is represented by a profit such as a profit for an electric power company, a risk such as a standard deviation indicating a degree of fluctuation of the profit, and a risk avoidance degree.

  Here, the risk avoidance degree is an index value indicating which of the maximization of the profit and the minimization of the risk is favored in the trade-off relationship between the profit and the risk. Further, the risk tolerance is an index value that is inversely related to the risk avoidance level, and is also an index value that indicates whether to maximize the profit or minimize the risk. The risk may be defined by parameters other than the fluctuation amount of the profit, that is, the fluctuation amount of the demand of the customer or the fluctuation amount of the output cost of the electric power company.

  The profit is obtained by subtracting the output cost required for power generation or the procurement cost from other power sources from the income obtained by selling electric power to the supply destination, and is defined as follows, for example.

  In the above formula (2), the first term represents the income obtained from the consumer by the supply of power, the second term and the third term represent the output cost of the power generation company, and the second term represents the generator that supplies the power. The third term represents the fixed cost incurred during power generation. As parameters used in the first term, d (i, t) is the demand power amount of the customer i at a predetermined time t, p (i, t) is a metered charge of the power charged to the customer i at the predetermined time t, fp (i, t) means a fixed electric power charge imposed on the customer i at a predetermined time t. Further, ω (i) indicates a distribution ratio to the customer i, and is standardized so that the total sum of all the consumers becomes 1. In reality, however, the contracted power of consumers is often fixed, and power cannot be allocated according to the distribution ratio. It is necessary to decide whether to supply (or not at all) what the customer needs.

  As parameters used in the second term, n (t) represents the number of generators, and a, b, and c represent characteristic coefficients representing the relationship between the power generation amount and the fuel cost. The relational expression between the power generation amount and the fuel cost uses a model approximated by a quadratic expression. However, when power is procured from an external power source, it can be expressed by a linear expression or the like. The third term represents required expenses other than fuel costs, and it is possible to use a model that takes into account factors such as time and power generation amount, but in this embodiment, a fixed constant d is used. It shall be expressed as

  In addition, the risk can be expressed using the above-described variance of profit or standard deviation. Specifically, the standard deviation of the demand amount at each time point for each consumer who is a candidate for the supply destination, the standard deviation of fuel cost in the future time point, and the like are used as parameters. In addition, since the demand fluctuation of a consumer cannot always be expressed by a normal distribution, it may be considered that it is not appropriate to use a standard deviation as a risk index. In that case, it is also possible to adopt lower half variance, Value-at-Risk, Conditional-Value-at-Risk (expected shortfall), etc. as risk indicators.

  In the present embodiment, Formula (1), which is the utility function described above, is formulated according to a predetermined condition, and a combination of consumers that is a power supply destination that maximizes the formulated utility function is supplied to each consumer. The optimal customer portfolio consisting of the power distribution ratio is obtained. When obtaining this consumer portfolio, according to the needs of the power generation company, restrictions on the distribution ratio ω (i) to specific consumers, and further, the power generation amount Σω (i) × d ( It is also possible to consider the constraints of i, t).

(Electric power transaction decision device)
The power transaction determination apparatus according to the present invention is realized by a computer in which the power transaction determination program according to the present invention is installed. Furthermore, this computer is used when inputting a CPU (Central Processing Unit), RAM (Random Access Memory), a power transaction determination program, a hard disk drive that constitutes each database, data, and the like for executing various operations. Keyboard and mouse, and a display that displays the customer portfolio described later.

  Here, FIG. 1 shows a schematic configuration example of an electric power transaction determining apparatus realized by executing an electric power transaction determining program according to the present invention using a computer. The power transaction determination apparatus shown in FIG. 1 includes a customer data storage unit 101, an operator data storage unit 102, a utility parameter input unit 103, a constraint parameter input unit 104, an optimum customer portfolio derivation unit 105, It consists mainly of customer portfolio output means 106. Next, each component of an electric power transaction determination apparatus is demonstrated.

  The customer data storage means 101 stores data such as demand characteristics of each consumer at a predetermined time, and the data stored in the customer data storage means 101 are illustrated in FIGS. FIG. 2 shows the expected value of the demand amount at each customer's future time point, and FIG. 3 shows the standard deviation of the demand amount at each customer's future time point. FIG. 4 shows data related to contracts such as pay-per-use charges and contract power at each consumer.

  Note that the expected value of the power demand shown in FIG. 2 is data obtained by using a conventional prediction method, and such a prediction means can be included in the power transaction determination device. The standard deviation of the power demand shown in FIG. 3 is derived by statistically processing the expected value of the power demand shown in FIG.

  The business operator data storage means 102 stores data that affects the profit of the business, such as data relating to the characteristics of the generator and data relating to the number of generators operated at each time point. Here, FIG. 5 shows an example of data stored in the provider data storage means 102. FIG. 5A shows the value of the characteristic coefficient of the generator, and FIG. 5B shows the number of operating generators at each time point. FIG. 5A shows that the amount of fuel and the amount of heat used for power generation are approximated by a quadratic equation, and the effective output amount is shown as the rated output. FIG. 5B shows that the number of operating generators is adjusted according to day / night and season.

  The utility parameter input means 103 sets one or a plurality of risk tolerances (or reciprocal risk avoidance degrees) for determining a power trading method having an optimal risk / revenue trade-off relationship. Here, FIG. 6 shows an example of parameters when a plurality of risk avoidance degrees are set. In general, the lower the risk aversion degree, the higher the risky and profitable customer portfolio, and the higher the risk aversion degree, the lower the risky and low profitability customer portfolio.

  The constraint parameter input unit 104 inputs constraint conditions to be considered when determining an optimal power trading method. Here, FIG. 7 shows an example of the data of this constraint condition. FIG. 7A shows the upper and lower limit constraints on the distribution ratio of power between consumers, and FIG. 7B shows the constraint on the amount of power supplied at a specific time point.

  In the optimum customer portfolio deriving unit 105, the demand characteristics stored in the customer data storage unit 101, the data affecting the profit of the business stored in the business operator data storage unit 102, and the utility parameter input unit 103 are input. Using the risk tolerance (or the risk avoidance degree of the reciprocal thereof), the constant of the formula (2) representing the profit is calculated, the utility function represented by the formula (1) is formulated, and the constraint parameter input unit 104 Under the constraint condition input by, a customer portfolio that maximizes this utility function is derived.

  As a method for obtaining the maximum solution of this utility function, if the utility function is a quadratic expression, the main dual interior point method or the effective constraint method, which is a typical solution of the quadratic programming problem, is used. If the function is a linear expression, it is possible to use the simplex method or the principal dual interior point method, which is a typical solution of the linear programming problem.

  The customer portfolio output unit 106 outputs information on the customer portfolio derived by the optimum customer portfolio deriving unit 105 from a display or the like. Here, FIG. 8 shows an output example of the customer portfolio output by the optimum customer portfolio output means 106. In the output example shown in FIG. 8, the table indicated by reference numeral 80 indicates an optimal customer portfolio according to the risk avoidance degree, and the graph indicated by reference numeral 81 indicates a frontier curve indicating the relationship between each risk avoidance degree and revenue. Respectively.

(Electric power transaction determination method)
Next, the procedure for determining the optimum customer portfolio executed in the power transaction determining apparatus shown in FIG. 1 will be described using the flowchart of FIG. 9 (refer to FIG. 1 as appropriate).

  First, in step S <b> 901, data related to demand characteristics of a consumer is input to the consumer data storage unit 101. As the data relating to the demand characteristics, for example, the data shown in FIGS. Next, in step S <b> 902, data related to the characteristics of the electric utility is input to the enterprise data storage unit 102. For example, the data shown in FIG. 5 is input as the data relating to the characteristics of the electric power company.

  In step S903, the function of the utility parameter input unit 103 is used to input a risk avoidance degree (or a risk tolerance of the reciprocal thereof) for determining a power trading method having an optimal risk / revenue trade-off relationship. For example, data for an arbitrary row is input from the table data shown in FIG. 6 as the risk avoidance level. Next, in step S904, a constraint condition to be considered when determining an optimal power trading method is input by the function of the constraint parameter input unit 104. As the constraint condition, for example, the data shown in FIG. 7 is input.

  Next, in step S905, based on the data stored in step S901 and step S902 and the risk tolerance input in step S903, etc., based on the function of the optimal customer portfolio deriving unit 105, for example, The utility parameter of the formula representing the profit shown in Formula (2) is defined, and the utility function shown in Formula (1) is formulated.

  In this embodiment, the revenue is defined by the revenue obtained by subtracting the cost required for power procurement, such as power generation, from the revenue received by the electric utility from the customer, as in the above formula (2). However, regarding the cost of this electricity procurement, it is possible to enter the purchase cost from other power generation companies in addition to the cost generated by power generation.

  Regarding risk, the revenue defined in Equation (2) above depends on uncertain factors due to fluctuations in future customer demand, generator operating conditions, fuel costs generated during power generation, etc. The degree of fluctuation is quantified. For example, the definition based on the standard deviation of revenue as shown in FIG. However, if the distribution of revenue does not follow the normal distribution, there is a risk of underestimating the risk. In this case, as a method for avoiding the underestimation of risk, Value-at- as shown in FIG. It is conceivable to define risk by Risk or Conditional Value-at-Risk.

  In step S906, an optimization method is applied to derive an optimal customer portfolio that maximizes the utility function formulated in step S905. As the optimization method, the interior point method or the effective constraint method is generally used when the utility function is quadratic, and the simplex method or the interior point method is generally used when it is linear. In the present embodiment, an outline of the interior point method will be described on the assumption that the utility function is linear.

  In the interior point method, a point inside a constrained region is used as an initial point, and thereafter, a search direction for searching for the next repetitive point within the constrained region is obtained, and the work for obtaining the repetitive point is repeated sequentially. This is a method for obtaining the optimum point. The feature of the interior point method is that the inside of the constraint area is an object to be updated, and it is empirically known that the optimal point can be reached in several tens of times regardless of the scale of the problem. For this reason, it is often superior to other methods in relatively large-scale problems. Next, the outline of the process of the interior point method will be described using the diagram for explaining the interior point method shown in FIG. 11 and the flowchart shown in FIG.

  First, in step S1201, an arbitrary point inside the restricted area is obtained and set as the initial point 110. Next, in step S1202, a direction in which the optimum solution is searched is obtained. This corresponds to determining the direction of the arrow indicated by reference numeral 111 in FIG. The calculation of the search direction is determined by solving simultaneous linear equations constructed based on information such as the current iteration point, the utility function to be optimized, and the coefficient of the constraint equation.

  In step S1203, a repeat point 112 that is considered advantageous for the next iteration is calculated along the search direction indicated by the arrow 111. Specific methods include a linear search that searches the iteration point that optimizes the objective function along the search direction, or a maximum that can maintain the non-negativeity of the variable to be optimized by minimizing the load in the search. A method of limited movement is conceivable.

  Next, in step S1204, it is checked whether the current iteration point satisfies the optimality condition. In FIG. 11, it is determined whether the repetition point has reached the optimum point indicated by reference numeral 113. The optimality condition is generally called the Kuhn-Tucker condition, and is known to occupy an important position in nonlinear programming. In step S1204, if the optimality condition is satisfied, the series of processes as the optimization technique application in step S906 shown in FIG. 9 is terminated, and if not satisfied, the process returns to step S1202.

  Returning to FIG. 9 again, in step S907, information on the customer portfolio derived in step S906 is output. Here, FIGS. 13A and 13B show output examples of information on the customer portfolio. The example output in FIG. 13A is a customer portfolio corresponding to an arbitrary risk tolerance (or the risk avoidance degree of the reciprocal) input in step S903. Here, although the consumer portfolio shown in FIG. 13A shows the distribution ratio of the supplied power, it is necessary to decide whether to supply or not to supply power in the contract with each consumer. is there. Therefore, it is conceivable to correct these distribution ratios based on the contract power with each consumer as shown in FIG. In addition, as shown in FIG.13 (b), it is good also as a power supply destination displaying the distribution ratio of the supply electric power with respect to a some consumer group.

  Finally, as step S907, based on the information output in step S906, a consumer to be supplied with power is determined. As an example of a method for determining a customer, priority is calculated in the order in which the distribution ratio and customer demand are closer, and the customer is selected in accordance with the priority within an allowable range such as the power generation facility's installed capacity. A method is conceivable. An output example by this determination method is shown in FIG.

(Electric power transaction answering system)
Next, a power transaction answering system according to the present invention in which the above-described power transaction determining device and a client terminal used by a customer are connected by a network will be described. First, FIG. 15 shows a configuration example of the power transaction answering system of the present embodiment. The power transaction answering system 151 shown in FIG. 15 is the above-described power transaction determination device 152 realized by a computer or the like, and a client terminal that is also realized by a computer and incorporates a communication interface program with the power transaction determination device 152. 153 and a network 154 realized by a dedicated line, VPN (Virtual Private Network), or the like. In the power transaction answering system 151 shown in FIG. 15, the power transaction determining device 152 and the client terminal 153 have a built-in network card for communicating via the network 154.

  Here, the client terminal 153 used by the consumer requests the power transaction determination device 152 via the network 154 to request the power demand for the power demand amount for each consumer as shown in FIG. Send as a predicted value of quantity. In addition, the predicted value (expected value) of the power demand amount is not transmitted from the client terminal 153 in this way, and can be created by the power transaction determination device 152 by a conventional technique.

  Then, the power transaction determination device 152 that has received the predicted value derives a customer portfolio by the above-described power transaction determination method, and displays the client terminal 153 as, for example, a customer portfolio screen illustrated in FIG. 14 via the network. Send to. With reference to this screen, the customer can make a power supply plan.

  As described above, according to the power transaction determination apparatus and method therefor according to the present invention, the trade-off relationship between the risk and the profit is represented by the utility function, and the optimization problem using the utility function as the objective function is solved. Thus, it is possible to quickly derive a power consumer portfolio that maximizes profits according to the risk allowed by the electric power company. As a result, the operator can quantitatively grasp the trade-off relationship between profit and risk. In addition, according to the power transaction answering system according to the present invention, which can distribute a power consumer portfolio to a consumer via a network, the consumer can make a quick power sales contract in a power trading market that changes in real time. Can do.

It is a block diagram which shows the internal structure of an electric power transaction determination apparatus. (A), (b) It is an example of the expected value of the electric power demand amount stored in the consumer data storage means. (A), (b) It is an example of the standard deviation of the electric power demand amount stored in the consumer data storage means. It is an example of the data regarding the contract stored in the provider data storage means. (A) It is an example of the characteristic coefficient of a generator. (B) An example of a generator operation plan. It is an example of the risk and profit corresponding to a risk aversion degree. (A) It is an example of the data regarding restrictions of the distribution ratio to a consumer. (B) It is an example of the data regarding restrictions of electric power supply amount. It is an example of the screen output by the customer portfolio output means. It is a flowchart which shows the outline | summary of the process in an electric power transaction determination method. It is an example regarding an index as a risk, and (a) is an example representing a standard deviation. (B) It is an example showing Value at Risk and Conditional Value at Risk. It is a conceptual diagram explaining the interior point method. It is a flowchart which shows the outline | summary of the process at the time of using an interior point method. (A) It is an example of the customer portfolio output by the customer portfolio output means. (B) An example of a customer group portfolio. It is an example of a customer portfolio reflecting the contents of a contract with a customer. It is a block diagram which shows an electric power transaction reply system.

Explanation of symbols

DESCRIPTION OF SYMBOLS 101 Customer data storage means 102 Business operator data storage means 103 Utility parameter input means 104 Restriction parameter input means 105 Optimal customer portfolio derivation means 106 Customer portfolio output means 107 Optimal portfolio output means 152 Electric power transaction determination apparatus 153 Client terminal

Claims (17)

In order to maximize the utility of selling electric power to multiple consumers, a customer portfolio that includes the customers who are the optimal power supply destinations and the distribution ratio of the electric power supplied to these consumers is provided with an information storage means. A power transaction determination method for determining using a computer having:
The computer is
a) a procedure for storing information on power demand characteristics of a consumer in the information storage means;
b) a procedure for storing information on business revenue in the information storage means;
c) a procedure for obtaining an index value indicating a trade-off relationship between revenue and risk;
d) a procedure for obtaining constraints on power supply;
e) Based on the utility function including a term relating to risk and a term relating to profit, based on the information relating to the power demand characteristics of the consumer and the information relating to business profit stored in the information storage means and the index value obtained in the procedure c). A constant for each term of the utility function is calculated and formulated, and an optimal customer portfolio that maximizes the utility function is obtained using a predetermined optimization method under the constraints obtained in step d). The derivation procedure;
f) comprising a procedure for outputting the optimum customer portfolio derived in the procedure e).
A power transaction determination method characterized by the above. The information on the power demand characteristics of the consumer includes data on the predicted value of the power demand amount of the plurality of consumers and the fluctuation amount of the predicted value of the power demand amount,
The power transaction determination method according to claim 1, wherein: The constraint on the power supply is a constraint on the distribution ratio of power supplied to consumers,
The power transaction determination method according to claim 1, wherein: The constraint condition regarding the power supply is a constraint condition regarding the power supply amount for each time,
The power transaction determination method according to any one of claims 1 to 3, wherein: The term on revenue included in the utility function is:
Defined by the difference between the revenue from the consumer and the output cost of the generator's generator,
The power transaction determination method according to claim 4, wherein: The term on revenue included in the utility function is:
Defined by the difference between the revenue earned from the consumer and the cost of procuring power from the generator or other power source,
The power transaction determination method according to claim 4, wherein: The risk terms included in the utility function are:
To be defined by the amount of change in at least one of the following items: revenue from consumers, procurement costs from generators or other power sources, and output costs of generators by electric utilities,
The power transaction determination method according to any one of claims 4 to 6, wherein: In order to maximize the utility of selling power to multiple customers, the power transaction decision that determines the customer portfolio that includes the customer to whom the optimal power is supplied and the distribution ratio of the power supplied to this customer A device,
Consumer data storage means for storing data relating to power demand characteristics of a plurality of consumers;
A provider data storage means for storing data relating to profits of the power business;
Utility parameter input means for inputting an index value indicating a trade-off relationship between profit and risk,
Constraint parameter input means for inputting constraint conditions regarding power supply;
A utility function including a term relating to risk and a term relating to profit is obtained based on the data stored in the customer data storage means and the business operator data storage means and the index value input by the utility parameter input means. An optimal customer that calculates and formulates constants for each term of the function, and derives an optimal customer portfolio that maximizes the utility function using a predetermined optimization method under the constraints on the power supply Portfolio derivation means;
Customer portfolio output means for outputting the customer portfolio derived by the optimum customer portfolio derivation means;
It is comprised from these, The electric power transaction determination apparatus characterized by the above-mentioned. In the customer data storage means, data relating to the predicted value of the power demand amount of a plurality of consumers and the fluctuation amount of the predicted value of the power demand amount is stored.
The power transaction determination device according to claim 8, wherein: In the constraint parameter input means, inputting constraints relating to a distribution ratio to consumers as the constraint conditions;
The power transaction determination device according to claim 8 or 9, wherein: In the constraint parameter input means, inputting a constraint on the amount of power supply per hour as the constraint condition,
The power transaction determination device according to any one of claims 8 to 10, characterized by: The optimum customer portfolio derivation means includes:
The utility function is a formula including terms relating to risk and profit, based on the data stored in the customer data storage means and the provider data storage means, and the index value input by the utility parameter input means. And
Deriving an optimal customer portfolio that maximizes the utility function under the constraints input by the constraint parameter input means;
The power transaction determination device according to any one of claims 8 to 11, wherein The term on revenue included in the utility function is:
Defined by the difference between the revenue from the consumer and the output cost of the generator's generator,
The power transaction determination device according to claim 12, wherein: The term on revenue included in the utility function is:
Defined by the difference between the revenue earned from the consumer and the cost of procuring power from the generator or other power source,
The power transaction determination device according to claim 12, wherein: The risk terms included in the utility function are:
To be defined by the amount of change in at least one of the following items: revenue from consumers, procurement costs from generators or other power sources, and output costs of generators by electric utilities,
The power transaction determination device according to any one of claims 12 to 14, characterized by: The customer portfolio output means transmits the customer portfolio to a terminal device connected to the power transaction determination device via a network;
The power transaction determination device according to any one of claims 8 to 15, characterized by: In order to maximize the utility of selling power to multiple customers, the power transaction decision that determines the customer portfolio that includes the customer to whom the optimal power is supplied and the distribution ratio of the power supplied to this customer A power transaction determination program for realizing a device by a computer,
Computer
Means for storing information on power demand characteristics of a plurality of consumers in an information storage means;
Means for storing in the information storage means information relating to profits of the power business;
Utility parameter input means for inputting an index value indicating a trade-off relationship between profit and risk,
Constraint parameter input means for inputting constraint conditions regarding power supply,
A utility function including a term relating to risk and a term relating to revenue is inputted by the utility parameter input means, information relating to power demand characteristics of the plurality of consumers stored in the information storage means, information relating to profits of the power business, and the utility parameter input means. Based on the index value, a constant for each term of the utility function is calculated and formulated, and an optimal customer portfolio that maximizes the utility function is determined under a predetermined optimization method under the constraints on the power supply. Optimal customer portfolio derivation means derived using
Customer portfolio output means for outputting the customer portfolio derived by the optimum customer portfolio derivation means;
Electricity trading decision program to function as.

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