Power selling control device, power selling control system, and power selling control method

Abstract

To make a bid so that a power generation site suitably sells generated power and to control the power generation site according to a contract amount of the bid.SOLUTION: A power selling control device 100 comprises: previous day bid planning means 103 and today's bid planning means 107 for determining a transfer amount which can be provided to a power system 180 from a power generation amount prediction time series of a plurality of resources at a bid stage to the previous day before time market or a today's before time market; power generation previous day prediction means 101, power generation before time prediction means 105, and power generation high frequency short-term prediction means 109 for generating a power generation amount prediction time series by predicting a power generation amount of a predetermined cycle of the resources; operation planning means 111 for determining a control command series of each resource for minimizing an error between a contract amount and an actual transfer amount by each resource; and command means 165, 166 for commanding a part of the control command series that can be reflected in the control by each resource.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a bidding for selling electricity from a power generation site including a variable power source such as renewable energy, and a power selling control device, a power selling control system, and a power selling control method for controlling the power generation site according to the conclusion of the bidding.

Background Art

[0002] In recent years, in energy consumption activities such as in houses, it has become common to use a variable power source whose power generation amount varies due to external factors, such as a solar power generation facility, as one of the energy sources. Here, it is desired to minimize the expected value of the energy cost in consideration of the influence of the change in the output of the variable power source and the diversity of the change patterns of the load.

[0003] Patent Document 1 describes a method in which, based on a plurality of weighted load pattern prediction information, the value of an optimal control operation sequence is calculated by simulation, and the next operation that minimizes the evaluation value for a predetermined period is used as a control command.

[0004] Further, Patent Document 2 describes a technique for determining the start / stop and output distribution of a power generation facility capable of load control while considering the uncertainty of the load and the renewable power generation from energy (kWh) supply to frequency adjustment for a generator, a suppressible output fluctuation type renewable energy power generation facility, a load capable of load control, etc.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0006] In the renewable energy power generation business, it is necessary to conclude the delivery volume during the future power delivery period with the market or counterpart by a predetermined time. The future power delivery period is defined by a start time and a period, for example, from 13:00 for 30 minutes. The predetermined time is, for example, a predetermined time on the day before the power delivery period or one hour before the start time of the power delivery period.

[0007] On the other hand, during the actual delivery period, a penalty is imposed according to the error between the contracted amount and the actual supply amount. Therefore, in order to eliminate the difference between the actual power generation amount and the contracted amount of renewable energy power generation facilities that do not necessarily generate power as predicted, it is necessary to activate the procurement adjustment power such as storage batteries, self-generation facilities, and demand response together.

[0008] Patent Document 1 discloses a method for providing optimal control of a storage battery or the like that minimizes the power cost for a load for which a probabilistic deviation from the prediction is assumed. However, it is not a method assuming market transactions, and it cannot be applied to the renewable energy power generation business that requires decision-making at multiple decision-making timings for the same delivery period, such as decisions for the next day or one hour ahead in advance.

[0009] Further, Patent Document 2 discloses a method for determining the start / stop and load distribution of controllable power generation facilities that minimizes the cost while maintaining the supply-demand balance and system frequency within a specified range. However, the control of the power generation facilities is based on the deviation of the frequency from the target caused by the imbalance of the supply-demand balance, and assumes control to increase or decrease the output to eliminate the frequency deviation. On the other hand, the error between the contracted amount and the power generation amount of the renewable energy power generation operator is independent of the fluctuation of the system frequency. Therefore, the method disclosed in Patent Document 2 cannot be applied to the control of storage power and the like for avoiding the error between the power generation amount and the contracted amount.

[0010] Therefore, an object of the present invention is to bid so that the power generated at the power generation site can be preferably sold, and to control the power generation site according to the contracted amount of the bid.

Means for Solving the Problem

[0011] To solve the above problems, the power selling control device of the present invention A solar power generation facility performs Fourier transform on the Power generation output information of the variable power source and and changes it into intensity information and phase information in the frequency space, and the relationship between the intensity information and the phase information in the frequency space and the sky image for a predetermined period is configured to include a neural network that has been machine-learned. At the bidding stage for the previous-day pre-market or the same-day pre-market, using the neural network and inverse Fourier transform a power generation prediction time series that predicts the power generation amount of a resource at a predetermined cycle to generate comprising power generation prediction means; bidding plan means for determining and bidding the transferable amount that can be provided to the power grid from the power generation prediction time series; and resource control series planning means for determining a control command series for each of the resources that minimizes the error between the agreed amount and the actual transferable amount by each of the resources based on the power generation prediction time series, the agreed amount determined from the result of the bidding by the bidding plan means, and the state of each of the resources. Among the control command series determined by the resource control series planning means, command means for commanding the part that each of the resources can reflect in control to the resources. It is characterized by comprising the above.

[0012] The power selling control system of the present invention A solar power generation facility performs Fourier transform on the Power generation output information of the variable power source and and changes it into intensity information and phase information in the frequency space, and the relationship between the intensity information and the phase information in the frequency space and the sky image for a predetermined period is configured to include a neural network that has been machine-learned. At the bidding stage for the previous-day pre-market or the same-day pre-market, using the neural network and inverse Fourier transform a power generation prediction time series that predicts the power generation amount of a resource at a predetermined cycle to generate comprising power generation prediction means; bidding plan means for determining and bidding the transferable amount that can be provided to the power grid from the power generation prediction time series; and resource control series planning means for determining a control command series for each of the resources that minimizes the error between the agreed amount and the actual transferable amount by each of the resources based on the power generation prediction time series, the agreed amount determined from the result of the bidding by the bidding plan means, and the state of each of the resources. Among the control command series determined by the resource control series planning means, command means for commanding the part that each of the resources can reflect in control to the resources. It is characterized by comprising the above.

[0013] The power selling control method of the present invention is such that the power generation prediction means A solar power generation facility performs Fourier transform on thePower generation output information Perform Fourier transform and changes it into intensity information and phase information in the frequency space, and the relationship between the intensity information and the phase information in the frequency space and the sky image for a predetermined period Neural network for machine learning and inverse Fourier transform Using the neural network, generate a power generation prediction time series that predicts the power generation amount of a predetermined cycle of resources at the bidding stage for the pre-day market or the pre-day market of the current day; the bidding planning means determines the deliverable amount available to the power grid from the power generation prediction time series and bids; based on the power generation prediction time series, the contracted amount determined from the result of the bidding by the bidding planning means, and the state of each of the resources, the resource control sequence planning means determines a control command sequence for each of the resources that minimizes the error between the contracted amount and the actual deliverable amount by each of the resources; and among the control command sequences determined by the resource control sequence planning means, the command means commands the resources with the parts that each of the resources can reflect in the control. Other means will be described in the embodiments for carrying out the invention.

Advantages of the Invention

[0014] According to the present invention, it is possible to bid so that the power generated by the power generation site can be preferably sold, and to control the power generation site according to the contracted amount of the bid.

Brief Description of the Drawings

[0015]

Figure 1

Figure 2

Figure 3

Figure 4

Embodiments for Carrying out the Invention

[0016] Hereinafter, embodiments for implementing the present invention will be described in detail with reference to each figure. The power selling control device of the present invention determines the bid amount that requires decision-making at multiple timings such as the day before the delivery start, one hour before the delivery start, or during the delivery period for one delivery period as described above, and controls resources such as a storage battery and a self-generation facility to eliminate the error between the actual delivery amount and the predetermined amount determined in advance.

[0017] FIG. 1 is a block diagram showing the configuration of the power selling control system 10 and the power selling control device 100 according to this embodiment. The power selling control system 10 includes a power selling control device 100 communicably connected to power generation sites 150 and 150a. The power selling control system 10 sells the power generated by the variable power source 151 installed at the power generation site 150.

[0018] The variable power source 151 supplies power to the power grid 180 and is, for example, a renewable power source such as a solar power generation facility, a wind power generation facility, and a tidal power generation facility. The controllable power source 152 is, for example, a self-generation facility that supplies power to the power grid 180 or a storage battery that charges and discharges the power of this power grid 180. The variable power source 151 and the controllable power source 152 function as resources for supplying power to the power grid 180.

[0019] The day-ahead market system 120 and the day-ahead market system 130 are market systems for procuring the power supplied to the power grid 180. The day-ahead market system 120 concludes what the power selling control device 100, the demand response operator 141, and the power generation operator 142 bid on the day before the planned target day.

[0020] The day-ahead market system 130 concludes what the power selling control device 100, the demand response operator 141, and the power generation operator 142 bid by a predetermined time on the planned target day in order to procure the power supplied to the power grid 180.

[0021] The power procured by the day-ahead market system 120 and the real-time market system 130 is purchased in the form of the power bid by the power selling control device 100, the demand response provider 141, and the power generation provider 142. In the figure, the demand response provider 141 is abbreviated as "DR provider".

[0022] The power generation site 150, the load facilities (not shown) operated by the demand response provider 141, and the power generation facilities (not shown) operated by the power generation provider 142 are connected to a single power grid 180.

[0023] The power selling control device 100 includes a day-ahead generation prediction means 101, a day-ahead bidding plan means 103, a pre-generation-time prediction means 105, a real-time bidding plan means 107, a high-frequency short-term generation prediction means 109, an operation plan means 111, an optimization engine 170, and command means 165, 166. The power selling control device 100 is communicably connected to a variable power source 151 installed at the power generation site 150 and a controllable power source 152 installed at the power generation site 150a. Thereby, the power selling control device 100 receives the output 116 of the variable power source 151 and the output 118 of the controllable power source 152. The power selling control device 100 further controls the variable power source 151 with a command value 112 and controls the controllable power source 152 with a command value 113.

[0024] The power selling control device 100 bids in the day-ahead market system 120 and the real-time market system 130 in order to sell the power generated by the variable power source 151 installed at the power generation site 150 and the controllable power source 152 installed at the power generation site 150a. The power selling control device 100 further plans control commands for the variable power source 151 and the controllable power source 152 based on the results agreed upon in the market transaction, and commands these variable power source 151 and controllable power source 152.

[0025] On the day before power generation, the prediction means 101 generates at least one or more power generation prediction series 102 before the day of power generation when bidding to the day-before market system 120, and provides this power generation prediction series 102 to the day-before bidding planning means 103. This process is carried out at the timing when bidding to the day-before market system 120 is possible. The power generation prediction means 101 functions as a power generation prediction means for generating a power generation amount prediction time series obtained by predicting the power generation amount of a resource at a predetermined cycle. Note that the resource includes any one of a storage battery, a self-generation facility, adjustment power by demand response, and output suppression of the variable power source.

[0026] The day-before bidding planning means 103 determines the bidding amount 104 as the amount of power that can be delivered in any of the power generation prediction series 102 on the premise of different facility operations (time series for output suppression of the variable power source 151 and time series for output of the controllable power source 152) for each of the power generation prediction series 102 before the day of power generation, and bids to the day-before market system 120. When determining the bidding amount 104, the day-before bidding planning means 103 uses the assumed power price for each power market commodity (unit) on the planned target day determined by a market price prediction means or an assumed market price setting means (not shown), the assumed penalty price for each unit determined by a penalty price prediction means or an assumed penalty price setting means (not shown), and the operation cost of the controllable power source 152 (unit price per kWh, start-up and stop costs, depreciation cost, etc.). That is, the day-before bidding planning means 103 is a bidding planning means for determining the deliverable amount that can be provided to the power grid 180 from the power generation amount prediction time series of a plurality of resources at the stage of bidding to the day-before market.

[0027] Note that the day-before market system 120 determines the settlement result 114 as a result of the day-before bidding planning means 103 bidding on the previous day, and notifies the same-day bidding planning means 107. The prediction means 105 before power generation predicts one or more same-day power generation prediction series 106. The prediction means 105 before power generation functions as a power generation prediction means for generating a power generation amount prediction time series obtained by predicting the power generation amount of a resource at a predetermined cycle.

[0028] On the day bidding plan means 107 uses the transaction results 114 of the previous day's market and one or more of the day-ahead power generation prediction series 106 predicted by the power generation time prediction means 105 to plan the additional bidding volume for the transfer portion or the amount of power to be purchased from the day-ahead market, determines the day-ahead buy / sell bidding volume 108, and bids in the day-ahead market system 130. At this time, although not shown in the figure, similar to the previous day bidding plan means 103, the assumed transaction price for each day-ahead market product (frame) of the planned target day determined by the day-ahead market price prediction means or the assumed day-ahead market price setting means, and the assumed penalty price for each frame determined by the penalty price prediction means or the assumed penalty price setting means (not shown) are used. The day bidding plan means 107 is a bidding plan means for determining the transfer volume that can be provided to the power grid from the power generation amount prediction time series of a plurality of resources at the stage of bidding in the day-ahead market.

[0029] The previous day bidding plan means 103 and the day bidding plan means 107 also use the output 116 of the variable power source 151 measured by the meter 153, the state 117 of the variable power source 151, the output 118 of the controllable power source 152 measured by the meter 154, and the state 119 of the controllable power source 152. The previous day bidding plan means 103 and the day bidding plan means 107 commonly determine the transfer volume that can be provided on the premise of the operation of different resources, such as the controllable power source 152 whose power generation amount can be controlled and the variable power source 151 whose power generation amount varies due to external factors.

[0030] The high-frequency short-term power generation prediction means 109 functions as a power generation prediction means for generating a high-frequency short-term power generation prediction time series 110 and generating a power generation amount prediction time series for predicting the power generation amount of a resource in a predetermined cycle.

[0031] The operation planning means 111 generates a control command sequence for the variable power source 151 and the controllable power source 152 by using the settlement result 115 for the day's buy and sell bid volume 108 obtained by a predetermined timing, the settlement result 114 for the previous day's bid volume 104, and the power generation prediction time series 110 of the high-frequency short-term power generation prediction means 109. The operation planning means 111 generates a control command sequence by using the optimization engine 170 and various mathematical formulas described later. That is, the operation planning means 111 functions as a resource control sequence planning means for determining a control command sequence for each resource that minimizes the error between the agreed quantity and the actual delivery quantity by each resource based on the power generation quantity prediction time series, the agreed quantity determined from the bid result, and the state of each resource.

[0032] Also, the optimization engine 170 is used when the previous day's bidding plan means 103 determines the bid volume 104 or when the day's bidding plan means 107 determines the day's buy and sell bid volume 108.

[0033] The command means 165 commands the variable power source 151 with the control command sequence of the variable power source 151 generated by the operation planning means 111. The command means 166 commands the controllable power source 152 with the control command sequence of the controllable power source 152 generated by the operation planning means 111. Thereby, the power selling control device 100 can sell the power generation output of the power generation sites 150, 150a most suitably. The command means 165 and 166 function as command means for commanding a part of the control command sequence determined by the operation planning means 111 that can be reflected in the control by each resource to this resource.

[0034] FIG. 2 is a block diagram showing the configuration of the power selling control system 10A according to a modified example. The power selling control system 10A includes a power selling control device 100A communicably connected to the power generation sites 150, 150a. A command device 155 is installed in the power generation site 150, and the operation planning means 111 of the power selling control device 100A commands the variable power source 151 with the control command sequence of the variable power source 151. At the power generation site 150a, a command device 156 is installed, and the control command sequence of the controllable power source 152 generated by the operation planning means 111 of the power selling control device 100A is commanded to the controllable power source 152.

[0035] Moreover, the power selling control system 10A is not limited to such a configuration, and each means may be realized by a plurality of computers and is not limited.

[0036] Returning to FIG. 1, an example of a method for realizing the previous day bidding planning means 103 will be described. Here, the previous day bidding planning means 103 calculates the bidding amount 104 for power selling for the next day, mainly taking as input a plurality of previous day power generation prediction series 102 output by the previous day power generation prediction means 101 and also considering the operation of the controllable power source 152 such as the self-generation facility and the storage battery. As such a calculation method, there is a method of describing it as an optimization problem of profit and determining the bidding amount 104 so as to maximize it.

[0037] Equations (101) to (135) are examples of the objective function and constraint expressions of such an optimization problem. In the present embodiment, a storage battery and a self-generation facility are assumed as the controllable power source 152. Hereinafter, these will be described.

Equation

[0038] Equation (101) is an example of the objective function of the previous day plan. Here, the optimization variables are the following five sets. Previous day bidding amount: bidsDA[t] Power suppression amount of the variable power source: curtailDA[sid,t] Charge amount of the storage battery: actEsCfDA[sid,t] Discharge amount of the storage battery: actEsDfDA[sid,t] Power generation output of the self-generation facility: actLclFDA[t] sid is the identification ID of each prediction series in the day-ahead prediction series 102 output by the day-ahead prediction means 101. Here, the number of prediction series is represented by scnoNum. t is the identification information of the time slot for market trading. The variables constituting the objective function are as follows. Daily sales: earningsDA[sid,t] Premium: premiumDA[sid,t] Operating cost of self-generation equipment: lflexXpnsDA[sid,t] Penalty: penaltyDA[sid,t] What is to be optimized is the average value of the daily earnings for the entire day-ahead prediction series 102, which is the sum of the earnings (the value obtained by subtracting the cost and penalty from the sum of the electricity sales and the premium) calculated from these terms for all time slots. Here, the premium means that due to power shortages, etc., a higher selling price than the normal electricity selling price is set.

Number

[0039] Equation (102) is a constraint equation for the daily sales earningsDA[sid,t]. The variables related to Equation (102) are as follows. Daily sales: earningsDA[sid,t] Predicted market price: mktPrcOfDA[t] Delivery volume at time tt: actualGenDA[sid,tt]

[0040] Equation (102) shows that the value obtained by multiplying the delivery volume at time slot t (the total sum calculation part on the right side) by the predicted market price mktPrcOfDA[t] is the daily sales earningsDA[sid,t] from selling electricity at that time slot t.

[0041] The right side of Equation (102) is the sum of the transfer amounts actualGenDA[sid, tt] at time t = t to t + 30. However, in the case of constraints such as Equation (106) described later, since it essentially becomes one variable, the summation calculation is unnecessary.

[0042] Equation (106) described later is a constraint equation regarding the transfer amount actualGenDA[sid, t] at time period t, and it is the sum of actTtlGenDA[sid, tt] for the same 30 - minute period as the length of the time period. However, when managing it in finer detail, such as 15 minutes, the summation calculation in Equation (102) has meaning.

Number

[0043] Equation (103) is a constraint equation regarding the penalty penaltyDA[sid, t]. The variables related to Equation (103) are as follows. Penalty: penaltyDA[sid, t] Over - penalty at time period t: penaltyPlsDA[sid, tt] Shortage - penalty at time period t: penaltyMnsDA[sid, tt]

[0044] Equation (103) means that the penalty penaltyDA[sid, t] is the sum of the over - penalty and the shortage - penalty at time period t. Similar to the transfer amount, the penalty penaltyDA[sid, t] is defined in such a way that it can be managed in finer detail than the 30 - minute length of the time period. However, the summation calculation in Equation (103) has no meaning in the mathematical formulas described in this embodiment.

Number

[0045] Equation (104) is a constraint regarding the premium premiumDA[sid, t] for time period t. The variables related to Equation (104) are as follows. Premium: premiumDA[sid,t] Predicted or assumed value of the premium unit price: prmmOfDay[t] Value of the predicted series for the day before power generation at time tt: reGenDA[sid,tt] Efficiency during charging of the storage battery: esEffc Discharge amount of the storage battery at time tt: actEsDfDA[sid,tt] Efficiency during charging of the storage battery: esEffc Charge amount of the storage battery: actEsCfDA[sid,t] Power curtailment amount of the variable power source: curtailDA[sid,t]

[0046] The right side of Equation (104) is the value obtained by multiplying the predicted or assumed value prmmOfDay[t] of the premium unit price by the transfer amount derived from the variable power source 151 (renewable energy source).

[0047] In this embodiment, the value of the predicted series 102 for the day before power generation at time tt is set as reGenDA[sid,tt]. Although the storage battery can also store the power derived from the self - power generation facility, assuming that such an operation is not performed, the discharge amount actEsDfDA[sid,tt] of the storage battery at time tt is added as the second term on the right side.

[0048] Also, in order to exclude charging and curtailment from the premium, the power curtailment amount curtailDA[sid,t] of the fourth term is subtracted from the charge amount actEsCfDA[sid,t] of the storage battery in the third term on the right side.

Equation

[0049] Equation (105) is a constraint regarding the operation cost lflexXpnsDA[sid,t] of the self - power generation facility at time t. The variables related to Equation (105) are as follows. Operation cost of the self - power generation facility: lflexXpnsDA[sid,t] Operation cost of on-site power generation equipment: localFexPrice Power generation output of on-site power generation equipment within a time slice tt: actLFlex[tt]

[0050] The operation cost localFexPrice of the on-site power generation equipment is an assumption that does not depend on time and is set as a constant without a time index, but it may also consider time. In the summation calculation, the sum of the power generation output actLFlex[tt] of the on-site power generation equipment within the time slice tt is calculated, converted into the amount of electricity, and then multiplied by the unit price.

Number

[0051] Equation (106) is a constraint equation regarding the transfer quantity. The variables related to Equation (106) are as follows. Transfer quantity at time slice t: actualGenDA[sid,tt] Transfer quantity at time tt: actTtlGenDA[sid,tt] The right side of Equation (106) is the sum of the transfer quantity (electric power) actTtlGenDA[sid,tt] at time tt from time t to t + 30, multiplied by a coefficient and converted into the amount of electricity. In this embodiment, the management unit is from t to t + 30, but it may also be managed in finer granularity such as t + 5 or t + 15.

Number

[0052] Equations (107) to (112) are constraint equations regarding the insufficient transfer quantity. Equation (107) defines the transfer quantity shortage error genMnusDA[sid,t] that takes a positive value when the transfer quantity is insufficient. The variables related to Equation (107) are as follows. Transfer quantity shortage error: genMnusDA[sid,t] Bid volume at time t: reBidsDA[t] Delivery volume deadband: pnltyDeadband Delivery volume at time t: actualGenDA[sid,tt] Equation (107) is the difference between a value that is less than the bid volume reBidsDA[t] at time t by the delivery volume deadband pnltyDeadband and the delivery volume actualGenDA[sid,tt] at time tt.

[0053]

Number

[0054]

Number

[0055] Equations (108) and (109) are constraint equations that constrain the relationship between the delivery volume shortage indicator reUdGnDA[sid,t], which becomes 1 when the delivery volume shortage error genMnusDA[sid,t] is positive and 0 otherwise, and the delivery volume shortage error genMnusDA[sid,t]. The variables related to Equations (108) and (109) are as follows. Delivery volume shortage error: genMnusDA[sid,t] Predetermined value: bigM Delivery volume shortage indicator: reUdGnDA[sid,t] When the delivery volume shortage error genMnusDA[sid,t] is positive, Equation (108) holds regardless of whether the delivery volume shortage indicator reUdGnDA[sid,t] is 1 or 0. However, from Equation (109), the delivery volume shortage indicator reUdGnDA[sid,t] is constrained to 1. On the one hand, when the transfer quantity shortage error genMnusDA[sid,t] is negative, Equation (109) holds regardless of the value of the transfer quantity shortage indicator reUdGnDA[sid,t]. However, from Equation (108), the transfer quantity shortage indicator reUdGnDA[sid,t] is constrained to 0. With such a definition, when the transfer quantity shortage error genMnusDA[sid,t] is positive, the transfer quantity shortage indicator reUdGnDA[sid,t] is constrained to 1, and when it is negative, it is constrained to 0.

[0056] [Number]

[0057] [Number]

[0058] [Number]

[0059] Equations (110) to (112) are constraint equations for the imbalance quantity minusGenDA[sid,t] that takes a value greater than or equal to zero such that when the transfer quantity shortage indicator reUdGnDA[sid,t] is 1, it takes the same value as the transfer quantity shortage error genMnusDA[sid,t], and when the transfer quantity shortage indicator reUdGnDA[sid,t] is 0, it becomes 0. The variables related to Equations (110) to (112) are as follows. Transfer quantity shortage error: genMnusDA[sid,t] Imbalance quantity: minusGenDA[sid,t] Predetermined value: bigM Transfer quantity shortage indicator: reUdGnDA[sid,t] From Equation (110) and Equation (111), when the transfer quantity shortage indicator reUdGnDA[sid,t] is 1, the transfer quantity shortage error genMnusDA[sid,t] and the shortage imbalance quantity minusGenDA[sid,t] are constrained to the same value, and when it is 0, no constraint works. On the other hand, from Equation (112), when the transfer quantity shortage indicator reUdGnDA[sid,t] is 0, the shortage imbalance quantity minusGenDA[sid,t] is constrained to 0.

[0060] In Equations (107) and (113), the bid quantity reBidsDA[t] at time slot t is used. When managing in a period shorter than the length of the time slot, this value may be multiplied by a coefficient considering the period length. For example, when managing every 15 minutes, multiply by 15 / 30.

[0061] Equations (113) to (118) are constraint equations that constrain the relationship between the transfer quantity excess error genPlusDA[sid,t], the transfer quantity excess indicator reOvGnDA[sid,t], and the excess imbalance quantity plusGenDA[sid,t] in the same way as in the case of Equations (107) to (112).

Number

[0062] Equation (113) defines the transfer quantity excess error genPlusDA[sid,t] that takes a positive value when the transfer quantity is insufficient. The variables related to Equation (113) are as follows. Transfer quantity excess error: genPlusDA[sid,t] Bid quantity at time slot t: reBidsDA[t] Transfer quantity deadband width: pnltyDeadband Transfer quantity at time slot t: actualGenDA[sid,tt]

Number

[0063]

Number

[0064] Equations (114) and (115) are constraint equations that constrain the relationship between the transfer quantity excess indicator reOvGnDA[sid,t], which becomes 1 when the transfer quantity excess error genPlusDA[sid,t] is positive and 0 otherwise, and the transfer quantity excess error genPlusDA[sid,t]. The variables related to Equations (114) and (115) are as follows. Transfer quantity excess error: genPlusDA[sid,t] Predetermined value: bigM Transfer quantity excess indicator: reOvGnDA[sid,t]

Number

[0065]

Number

[0066]

Number

[0067] Equations (116) to (118) are constraint equations regarding the excess imbalance quantity plusGenDA[sid,t] that takes a value greater than or equal to zero such that when the transfer quantity excess indicator reOvGnDA[sid,t] is 1, it takes the same value as the transfer quantity excess error genPlusDA[sid,t], and when the transfer quantity excess indicator reOvGnDA[sid,t] is 0, it becomes 0. The variables related to Equations (116) to (118) are as follows. Transfer quantity excess error: genPlusDA[sid,t] Excess imbalance quantity: plusGenDA[sid,t] Predetermined value: bigM Delivery Quantity Excess Indicator: reOvGnDA[sid,t]

Number

[0068] Equation (119) is a constraint equation for deriving the excess penalty penaltyPlsDA[sid,t] from the excess imbalance quantity plusGenDA[sid,t]. The variables related to Equation (119) are as follows. Excess Penalty: penaltyPlsDA[sid,t] Penalty at period t: ppnltOfDay[t] Excess Imbalance Quantity: plusGenDA[sid,t]

Number

[0069] Equation (120) is a constraint equation for deriving the shortage penalty penaltyMnsDA[sid,t] from the shortage imbalance quantity minusGenDA[sid,t]. The variables related to Equation (120) are as follows. Shortage Imbalance Quantity: minusGenDA[sid,t] Penalty at period t: ppnltOfDay[t] Shortage Penalty: penaltyMnsDA[sid,t]

Number

[0070] Equation (121) is a constraint equation for the delivery quantity actTtlGenDA[sid,t] at each time t. The variables related to Equation (121) are as follows. Value of the predicted series of the day before power generation at time t: reGenDA[sid,t] Amount of charge of the storage battery at time t: actEsCfDA[sid,t] Discharge amount of the storage battery at time t: actEsDfDA[sid,t] Power generation output of the on-site power generation facility: actLclFDA[sid,t], Power curtailment amount of the variable power source: curtailDA[sid,t] Efficiency during charging of the storage battery: esEffc Efficiency during discharging of the storage battery: esEffd

Number

[0071] Equation (122) is a constraint regarding the change in the SoC (State of Charge) of the storage battery. The variables related to Equation (122) are as follows. Rated capacity of the storage battery: ratedEsCapacity SoC of the storage battery at time t: esSocDA[sid,t] Amount of charge at the previous moment: actEsCfDA[sid,t-ctrlCycle] Discharge amount at the previous moment: actEsDfDA[sid,t-ctrlCycle] SoC at the previous moment: esSocDA[sid,t-ctrlCycle]

Number

[0072] Equation (123) is a constraint for ensuring that the amount of charge of the storage battery is derived from the power (renewable energy power) of the variable power source 151. This equation restricts the amount of charge of the storage battery at time t to be below the power generation of the variable power source 151. The variables related to Equation (123) are as follows. Efficiency during charging of the storage battery: esEffc Amount of charge of the storage battery at time t: actEsCfDA[sid,t] Value of the predicted series for the day before power generation at time tt: reGenDA[sid,tt]

Number

[0073] Equation (124) is a constraint that ensures the charge amount actEsCfDA[sid,t] of the storage battery does not exceed the rated capacity of the storage battery ratedEsCapacity and the C value esCvC of the storage battery. The variables related to Equation (124) are as follows. Charge amount of the storage battery at time t: actEsCfDA[sid,t] C value of the storage battery: esCvC Rated capacity of the storage battery: ratedEsCapacity Charge execution indicator: esOcDA[sid,t] Here, the optimization variable esOcDA[sid,t] is a charge execution indicator related to time t. If charging is in progress, it is 1; if not, it is 0. When the charge execution indicator esOcDA[sid,t] is 1, the charge amount actEsCfDA[sid,t] of the storage battery at time t has an upper limit determined by the rating and C value of the storage battery, but when it is 0, it is constrained to 0.

Number

[0074] Equation (125) is a constraint similar to Equation (124) for the discharge amount actEsDfDA[sid,t] of the storage battery. The variables related to Equation (126) are as follows. Discharge amount of the storage battery at time t: actEsDfDA[sid,t] C value of the discharge of the storage battery: esCvD Rated capacity of the storage battery: ratedEsCapacity Discharge execution indicator: esOdDA[sid,t]

Number

[0075]

Number

[0076] Equations (126) and (127) are constraint equations representing the charge amount actEsCfDA[sid,t] of the storage battery at time t. The variables related to Equations (126) and (127) are as follows. Charge amount of the storage battery at time t: actEsCfDA[sid,t] Charge execution indicator: esOcDA[sid,t]

[0077] When the charge amount actEsCfDA[sid,t] of the storage battery is positive, from Equation (126), the charge execution indicator esOcDA[sid,t] is constrained to 1. On the other hand, when the charge amount actEsCfDA[sid,t] of the storage battery is 0, Equation (126) holds regardless of the charge execution indicator esOcDA[sid,t].

[0078] In contrast, in Equation (127), when the charge amount actEsCfDA[sid,t] of the storage battery is made sufficiently close to 0, the charge execution indicator esOcDA[sid,t] is constrained to 0. As a result, the charge amount actEsCfDA[sid,t] of the storage battery according to Equation (124) is also constrained to 0. When the absolute value of the charge amount actEsCfDA[sid,t] of the storage battery is made sufficiently large, the constraint on the charge execution indicator esOcDA[sid,t] does not function. Therefore, when the charge amount actEsCfDA[sid,t] of the storage battery is close to 0, the charge execution indicator esOcDA[sid,t] is constrained to 0, and otherwise it is constrained to 1.

Number

[0079]

Number

[0080] Equations (128) and (129) are constraint equations representing the relationship between the discharge amount actEsDfDA[sid,t] of the storage battery and the discharge execution indicator esOdDA[sid,t] at time t. The variables related to Equation (128) are as follows. Discharge amount of the storage battery at time t: actEsDfDA[sid,t] Discharge execution indicator: esOdDA[sid,t]

[0081] When the discharge amount actEsDfDA[sid,t] of the storage battery is positive, from Equation (126), the discharge execution indicator esOdDA[sid,t] is constrained to 1. On the other hand, when the discharge amount actEsDfDA[sid,t] of the storage battery is 0, Equation (126) holds regardless of the discharge execution indicator esOdDA[sid,t].

[0082] In contrast, in Equation (127), when the discharge amount actEsDfDA[sid,t] of the storage battery is made sufficiently close to 0, the discharge execution indicator esOdDA[sid,t] is constrained to 0. As a result, according to Equation (125), the discharge amount actEsDfDA[sid,t] of the storage battery is also constrained to 0.

[0083] On the other hand, when the absolute value of the discharge amount actEsDfDA[sid,t] of the storage battery is made sufficiently large, the constraint on the discharge execution indicator esOdDA[sid,t] does not function. Therefore, when the discharge amount actEsDfDA[sid,t] of the storage battery is close to 0, the discharge execution indicator esOdDA[sid,t] is constrained to 0, and otherwise it is constrained to 1. Equations (130) and (131) are constraints regarding the upper operation limit SoC of the storage battery, opMaxEs, and the lower operation limit SoC of the storage battery, opMinEs.

Number

[0084] Equation (130) is a constraint that restricts charging from the SoC of the battery esSocDA[sid,t] at time t by the charging amount actEsCfDA[sid,t] of the battery over the control cycle ctrlCycle so as not to exceed the operation upper limit. The variables related to Equation (130) are as follows. Charging amount of the battery at time t: actEsCfDA[sid,t] SoC of the battery at time t: esSocDA[sid,t] Rated battery capacity: ratedEsCapacity Operation upper limit SoC of the battery: opMaxEs

Number

[0085] Equation (131) is a constraint that restricts discharging from the SoC of the battery at time t by the discharging amount actEsDfDA[sid,t] of the battery over the period of ctrlCycle so as not to fall below the operation lower limit. The variables related to Equation (131) are as follows. Discharging amount of the battery at time t: actEsDfDA[sid,t] Rated battery capacity: ratedEsCapacity SoC of the battery at time t: esSocDA[sid,t] Operation lower limit SoC of the battery: opMinEs

Number

[0086] Equation (132) is a constraint that prevents simultaneous charging and discharging of the battery. The variables related to Equation (132) are as follows. Charge execution indicator: esOcDA[sid,t] Discharge execution indicator: esOdDA[sid,t]

Number

[0087] Equations (133) and (134) are constraints that restrict the power generation output actLclFDA[sid,t] of the on-site power generation facility within the range of the rated output ratedLocalFlex and the minimum operating output minLocalFlex.

[0088] Equation (133) indicates that the product of the minimum operating output minLocalFlex and the operation indicator lfODA[sid,t] of the on-site power generation facility is the minimum value of the power generation output actLclFDA[sid,t] of the on-site power generation facility. The variables related to Equation (133) are as follows. Minimum operating output: minLocalFlex Operation indicator of the on-site power generation facility: lfODA[sid,t] Power generation output of the on-site power generation facility: actLclFDA[sid,t]

Number

[0089] Equation (134) indicates that the product of the rated output ratedLocalFlex and the operation indicator lfODA[sid,t] of the on-site power generation facility is the maximum value of the power generation output actLclFDA[sid,t] of the on-site power generation facility. The variables related to Equation (134) are as follows. Power generation output of the on-site power generation facility: actLclFDA[sid,t] Rated output: ratedLocalFlex Operation indicator of the on-site power generation facility: lfODA[sid,t] The operation indicator lfODA[sid,t] of the self-generation facility is a variable that becomes 1 when the self-generation facility is operating and 0 when the self-generation facility is stopped. When the operation indicator lfODA[sid,t] of the self-generation facility is 1, the power generation output actLclFDA[sid,t] of the self-generation facility is constrained between the minimum output and the rated value. When the operation indicator lfODA[sid,t] of the self-generation facility is 0, the power generation output actLclFDA[sid,t] of the self-generation facility is constrained to 0.

Number

[0090] Equation (135) is a constraint for avoiding charging the energy derived from the self-generation facility to the storage battery. The variables related to Equation (135) are as follows. Charging execution indicator: esOcDA[sid,t] Operation indicator of the self-generation facility: lfODA[sid,t]

[0091] By generating such optimization variables and constraint equations and providing them to the optimization engine 170, it is possible to generate a series of operation commands for the variable power sources 151 and controllable power sources 152 such as the bid amount reBidsDA[t] at the time t of maximizing the profit, which is the objective function, and actEsCfDA[sid,tt], actEsDfDA[sid,tt], curtailDA[sid,tt], actLclFDA[sid,tt] that realize it.

[0092] The previous day's bidding plan means 103 sets the selling bid price for the bid amount reBidsDA[t] at each time t obtained in this way and bids in the previous day's market. Thereby, the power selling control device 100 can bid so that the power generated by the power generation site 150 can be preferably sold, and can control the power generation sites 150 and 150a according to the contracted quantity of the bid.

[0093] Next, the process of the day-ahead bidding planning means 107, which is carried out using the contract result 114 based on the bids obtained by a predetermined timing, the day-ahead power generation prediction series 106 that is the output of the day-ahead power generation prediction means 105 on the day, and the output 116, state 117 of the variable power source 151, and the output 118, state 119 of the controllable power source 152, will be described.

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[0094] Equation (201) represents an example of the objective function for optimization in the day-ahead plan. Revenue from power market sales: earningsHA[sid,t] Premium: premiumHA[sid,t] Planning period: planHorizon Operating cost of in-house power generation facilities: lflexXpnsHA[sid,t] Penalty: penaltyHA[sid,t] Cost of power procured in the day-ahead market: pcsGenXpnsHA[sid,t] In the day-ahead plan, the objective function is to maximize the average value of the revenue over the planning period for each day-ahead power generation prediction series 106 identified by sid, that is, the value obtained by subtracting the total value over the planning period. The optimization variables in the day-ahead plan are the following five sets. Additional power sales bid volume for each time slot t on the day: reBidsHA[t] Adjustment volume procured in the day-ahead market: pcsGenHA[t] Power curtailment volume of the variable power source at time tt: curtailHA[sid,tt] Charge amount of the storage battery: actEsCfHA[sid,t] Discharge amount of the storage battery: actEsDfHA[sid,tt] Power generation output of in-house power generation facilities: actLclFHA[sid,t]

Number

[0095] Equation (202) is a constraint on the adjustment quantity pcsGenHA[t] procured in the day-ahead market. The variables related to Equation (202) are as follows. Cost of power procured in the day-ahead market: pcsGenXpnsHA[sid,t] Gain in the day-ahead market: MktGain Predicted market price in the day-ahead market: mktPrcOfHA[t] Adjustment quantity procured in the day-ahead market: pcsGenHA[t]

[0096] Since the day-ahead market is a volatile market, it is considered difficult to predict the price itself. Here, the cost of the adjustment quantity pcsGenHA[t] procured in the day-ahead market is calculated by multiplying the predicted market price mktPrcOfHA[t] in the day-ahead market, based on the predicted value and the preliminary report value of the previous day's market price, by the gain MktGain in the day-ahead market, and the cost of power pcsGenXpnsHA[sid,t] procured in the day-ahead market is constrained.

Number

[0097] Equation (203) is a constraint equation for the sales earningsHA[sid,t] from power market sales, similar to Equation (102). The variables related to Equation (203) are as follows. Sales from power market sales: earningsHA[sid,t] Predicted market price in the day-ahead market: mktPrcOfHA[t] Delivery quantity at time slot t: actualGenHA[sid,tt]

Number

[0098] Equation (204) is a constraint equation for the penalty penaltyHA[sid,t] similar to Equation (103). The variables related to Equation (204) are as follows. Penalty: penaltyHA[sid,t] Over - penalty at time t: penaltyPlsHA[sid,tt] Shortage penalty at time t: penaltyMnsHA[sid,tt]

Number

[0099] Equation (205) is a constraint equation for the premium premiumHA[sid,t] similar to Equation (104). The variables related to Equation (205) are as follows. Premium: premiumHA[sid,t] Predicted or assumed value of the premium unit price: prmmOfDay[t] Value of the predicted series of the day before power generation at time tt: reGenHA[sid,tt] Efficiency during battery discharge: esEffd Discharge amount of the battery at time tt: actEsFlexD[sid,tt] Efficiency during battery charging: esEffc Charge amount of the battery at time tt: actEsFlexC[sid,tt] Power suppression amount of the variable power source: curtailHA[sid,t]

Number

[0100] Equation (206) is a constraint equation for the operating cost lflexXpnsHA[sid,t] of the self - generation equipment similar to Equation (105). The variables related to Equation (206) are as follows. Operating cost of the self - generation equipment at time t: lflexXpnsHA[sid,t] Operating cost of on-site power generation equipment: localFexPrice Power generation output of on-site power generation equipment within time tt: actLclFHA[sid,tt]

Number

[0101] Equation (207) is a constraint on the delivered quantity actualGenHA[sid,t] obtained by dividing the within-frame into one or more parts in the same way as Equation (106). The variables related to Equation (207) are as follows. Delivered quantity at frame t: actualGenHA[sid,t] Delivered quantity at time tt: actTtlGenHA[sid,tt] Equations (208) to (213) represent the error between the delivered quantity and the quantity that is less than the contracted quantity considering the contracted part among the bids on the same day by the deadband of the delivered quantity pnltyDeadband, which corresponds to Equations (107) to (112) of the previous day's plan, and define the delivered quantity shortage error genMnusHA[sid,t] that becomes positive when there is a shortage.

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[0102] Equation (208) defines the delivered quantity shortage error genMnusHA[sid,t] that becomes positive when the delivered quantity is insufficient. The variables related to Equation (208) are as follows. Delivered quantity shortage error: genMnusHA[sid,t] Bid quantity at frame t in the previous day's market: reBidsDA[t] Bid quantity at frame t in the current day's market: reBidsHA[t] Deadband of the delivered quantity: pnltyDeadband Delivered quantity at frame t: actualGenHA[sid,tt] Note that the committed quantity reBidsDA[t] of the previous day's bidding is the information included in the trade result 114 notified from the previous day's market system 120, and is treated as a constant in the optimization calculation performed by the same-day bidding planning means 107.

[0103] Also, the part reBidsHA[t] related to the same-day bidding is the aggregated information included in the trade result 115 notified from the same-day market system 130 at the time when the gate is closed. For the time t before the gate is closed, if there is a trade result 115 for the time t, it is formulated in the form of the sum of that value and the sum with the optimization variable for the additional bidding quantity plan.

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[0104]

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[0105] Equations (209) and (210) are the constraints corresponding to Equations (108) and (109) in the previous day's plan. Equations (209) and (210) are constraint equations that constrain the relationship between the under-delivery quantity shortage indicator reUdGnHA[sid,t] that becomes 1 when the under-delivery quantity shortage error genMnusHA[sid,t] is positive and 0 otherwise, and the under-delivery quantity shortage error genMnusHA[sid,t]. The variables related to Equations (209) and (210) are as follows. Under-delivery quantity shortage error: genMnusHA[sid,t] Predetermined value: bigM Under-delivery quantity shortage indicator: reUdGnHA[sid,t]

[0106] When the delivery quantity shortage error genMnusHA[sid,t] is positive, Equation (209) holds regardless of the delivery quantity shortage indicator reUdGnHA[sid,t], so no constraint is imposed. However, Equation (210) constrains the delivery quantity shortage indicator reUdGnHA[sid,t] to 1. On the other hand, when the delivery quantity shortage error genMnusHA[sid,t] is negative, Equation (210) holds regardless of the delivery quantity shortage indicator reUdGnHA[sid,t] and imposes no constraint, but Equation (209) constrains the delivery quantity shortage indicator reUdGnHA[sid,t] to 0.

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[0107]

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[0108]

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[0109] Equations (211) to (213) are constraint equations for the imbalance quantity minusGenHA[sid,t] that takes a value greater than or equal to zero such that it has the same value as the delivery quantity shortage error genMnusHA[sid,t] when the delivery quantity shortage indicator reUdGnHA[sid,t] is 1 and becomes 0 when the delivery quantity shortage indicator reUdGnHA[sid,t] is 0. The variables related to Equations (211) to (213) are as follows. Delivery quantity shortage error: genMnusHA[sid,t] Imbalance quantity: minusGenHA[sid,t] Delivery quantity shortage indicator: reUdGnHA[sid,t]

[0110] Equation (211) and Equation (212) correspond to the previous day's planned equations (101) and (111). When the delivery quantity shortage indicator reUdGnHA[sid,t] is 1, the shortage imbalance quantity minusGenHA[sid,t] is restricted by the delivery quantity shortage error genMnusHA[sid,t]. When the delivery quantity shortage indicator reUdGnHA[sid,t] is 0, the shortage imbalance quantity minusGenHA[sid,t] is restricted to 0 by Equation (213) corresponding to the previous day's planned equation (112).

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[0111]

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[0112]

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[0113]

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[0114]

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[0115]

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[0116] Equations (214) to (219) correspond to equations (113) to (118) in the previous-day plan. Equations (214) to (219) are constraint equations that constrain the relationships among the delivery quantity excess error genPlusHA[sid,t], the delivery quantity excess indicator reOvGnHA[sid,t], and the excess imbalance quantity plusGenHA[sid,t] in the same way as in equations (208) to (213). The variables related to equations (214) to (219) are as follows. Delivery quantity excess error: genPlusHA[sid,t] Delivery quantity at time slot t: actualGenHA[sid,tt] Bid quantity at time slot t in the current-day market: reBidsDA[t] Bid quantity at time slot t in the previous-day market: reBidsHA[t] Delivery quantity deadband width: pnltyDeadband Delivery quantity excess indicator: reOvGnHA[sid,t] Excess imbalance quantity: plusGenHA[sid,t]

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[0117] Equation (220) is a constraint equation for deriving the excess penalty penaltyPlsHA[sid,t] from the excess imbalance quantity plusGenHA[sid,t]. The variables related to equation (220) are as follows. Excess penalty: penaltyPlsHA[sid,t] Excess imbalance quantity: plusGenHA[sid,t]

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[0118] Equation (221) is a constraint equation for deriving the shortage penalty penaltyMnsHA[sid,t] from the shortage imbalance amount minusGenHA[sid,t]. The variables related to Equation (221) are as follows. Shortage imbalance amount: minusGenHA[sid,t] Shortage penalty: penaltyMnsHA[sid,t]

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[0119] Equation (222) is a constraint similar to Equation (121) in the previous day's plan and is a constraint equation for the transfer amount actTtlGenHA[sid,t] at time t. The variables related to Equation (222) are as follows. Daily predicted value of the power generation of the variable power source at time t: reGenHA[sid,t] Power suppression amount of the variable power source at the control time t: curtailHA[sid,t] Adjustment amount procured in the day-ahead market: pcsGenHA[t] Charge amount of the battery: actEsCfHA[sid,t] Discharge amount of the battery: actEsDfHA[sid,t] Power generation output of the on-site power generation facility: actLclFHA[sid,t]

[0120] Note that the adjustment power procured in the market is defined for the times when the day-ahead market is not gate-closed within the planning period planHorizon. For the times before that, the trade result 115 agreed and notified by the day-ahead market system 130 is used as a constant.

[0121] The following equations (223) to (233) are constraint equations for the battery in the time-ahead plan. Equations (223) to (233) correspond to equations (122) to (132) in the previous day's plan.

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[0122] Equation (223) corresponds to Equation (122) in the previous day's plan and is a constraint regarding the change in the State of Charge (SoC) of the storage battery. The variables related to Equation (223) are as follows. Rated capacity of the storage battery: ratedEsCapacity SoC of the storage battery at time t: esSocHA[sid,t] Charge amount of the storage battery at the previous moment: actEsCfHA[sid,t-ctrlCycle] Discharge amount of the storage battery at the previous moment: actEsDfHA[sid,t-ctrlCycle] SoC at the previous moment: esSocHA[sid,t-ctrlCycle]

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[0123] Equation (224) corresponds to Equation (123) in the previous day's plan and is a constraint to ensure that the discharge power from the storage battery is derived from the power of the variable power source (renewable energy power). It restricts the charge amount at time t to be less than or equal to the power generation of the variable power source. The variables related to Equation (224) are as follows. Efficiency during charging of the storage battery: esEffc Charge amount of the storage battery at time t: actEsCfHA[sid,t] Power suppression amount of the variable power source: curtailHA[sid,t] Daily predicted value of the power generation of the variable power source at time t: reGenHA[sid,t]

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[0124] Equation (225) corresponds to Equation (124) in the previous day's plan and is a constraint to ensure that the charged amount of the storage battery actEsCfHA[sid,t] does not exceed the rated capacity of the storage battery ratedEsCapacity and the C value of the storage battery esCvC. The variables related to Equation (225) are as follows. Charged amount of the storage battery at time t: actEsCfHA[sid,t] C value of the storage battery: esCvC Rated capacity of the storage battery: ratedEsCapacity Charge execution indicator: esOcHA[sid,t]

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[0125] Equation (226) corresponds to Equation (125) in the previous day's plan and is a constraint equation similar to Equation (225) for the optimization variable actEsDfHA[sid,t] representing the discharge power. The variables related to Equation (226) are as follows. Discharged amount of the storage battery at time t: actEsDfHA[sid,t] C value of the storage battery discharge: esCvD Rated capacity of the storage battery: ratedEsCapacity Discharge execution indicator: esOdHA[sid,t]

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[0126]

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[0127] Equations (227) and (228) correspond to Equations (126) and (127) in the previous day's plan and are constraint equations representing the charged amount of the storage battery actEsCfHA[sid,t] at time t. The variables related to Equations (227) and (228) are as follows. Amount of charge of the storage battery at time t: actEsCfHA[sid,t] Charge execution indicator: esOcHA[sid,t]

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[0128]

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[0129] Equations (229) and (230) correspond to equations (128) and (129) in the previous day's plan, and are constraint equations representing the relationship between the optimization variable actEsDfHA[sid,t] of the discharge power of the storage battery at time t and the discharge execution indicator esOdHA[sid,t]. The variables related to equations (229) and (230) are as follows. Amount of discharge of the storage battery at time t: actEsDfHA[sid,t] Discharge execution indicator: esOdHA[sid,t]

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[0130] Equation (231) corresponds to equation (130) in the previous day's plan, and is a constraint that restricts that even if charged with the amount of charge actEsCfHA[sid,t] of the storage battery from the SoC esSocHA[sid,t] of the storage battery at time t over the control cycle ctrlCycle, the upper limit SoC of the operation of the storage battery is not exceeded. The variables related to equation (231) are as follows. Amount of charge of the storage battery at time t: actEsCfHA[sid,t] SoC of the storage battery at time t: esSocHA[sid,t] Rated capacity of the storage battery: ratedEsCapacity Upper limit SoC of the operation of the storage battery: opMaxEs

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[0131] Equation (232) corresponds to Equation (131) in the previous-day plan and is a constraint that restricts the current SoC so that even if it discharges by the discharge amount actEsDfHA[sid,t] of the storage battery for the period of ctrlCycle, it does not fall below the lower operation limit SoC of the storage battery. The variables related to Equation (232) are as follows. Discharge amount of the storage battery at time t: actEsDfHA[sid,t] Rated capacity of the storage battery: ratedEsCapacity SoC of the storage battery at time t: esSocHA[sid,t] Lower operation limit SoC of the storage battery: opMinEs

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[0132] Equation (233) corresponds to Equation (132) in the previous-day plan and is a constraint that prevents simultaneous charging and discharging. The variables related to Equation (233) are as follows. Charge execution indicator: esOcHA[sid,t] Discharge execution indicator: esOdHA[sid,t]

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[0133]

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[0134] Equations (234) and (235) are constraint equations regarding the operation of the on-site power generation facility in the time-ahead plan and correspond to Equations (133) and (134) in the previous-day plan.

[0135] Equation (234) indicates that the product of the minimum operating output minLocalFlex and the operation indicator lfOHA[sid,t] of the distributed generation facility is the minimum value of the power generation output actLclFHA[sid,t] of the distributed generation facility. The variables related to Equation (234) are as follows. Minimum operating output: minLocalFlex Operation indicator of the distributed generation facility: lfOHA[sid,t] Power generation output of the distributed generation facility: actLclFHA[sid,t] Equation (235) indicates that the product of the rated output ratedLocalFlex and the operation indicator lfOHA[sid,t] of the distributed generation facility is the maximum value of the power generation output actLclFHA[sid,t] of the distributed generation facility. The variables related to Equation (235) are as follows. Power generation output of the distributed generation facility: actLclFHA[sid,t] Rated output: ratedLocalFlex Operation indicator of the distributed generation facility: lfOHA[sid,t]

Number

[0136] Equation (236) is a constraint for avoiding storing the energy derived from the distributed generation facility in the power to charge the battery in the time-ahead plan, and corresponds to Equation (135) in the previous-day plan. The variables related to Equation (236) are as follows. Charge execution indicator: esOcHA[sid,t] Operation indicator of the distributed generation facility: lfOHA[sid,t]

[0137] From Equation (237) to Equation (242) are constraint equations regarding the indicator esOcHc[sid,t] representing the change in the state of charge of the storage battery, the indicator esOdHc[sid,t] representing the change in the state of discharge, and the indicator lfxOHc[sid,t] representing the change in the operating state of the self-generation facility. From Equation (237) to Equation (242) are constraints regarding the differences in the charge execution indicator esOcHA[sid,t] representing the charging operation of the storage battery, the discharge execution indicator esOdHA[sid,t] representing the discharging operation of the storage battery, and the operation indicator lfOHA[sid,t] of the self-generation facility.

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[0138] For example, consider the indicator esOcHc[sid,t] and the charge execution indicator esOcHA[sid,t]. From Equation (237), at the start of the charging operation, esOcHA[sid,t - ctrlCycle] = 0 and esOcHA[sid,t] = 1. At this time, esOcHA[sid,t] ≥ 1, and the left - hand side indicator esOcHc[sid,t] is constrained to 1. At other times, the indicator esOcHc[sid,t] is not constrained to either 0 or 1.

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[0139] On the other hand, when esOcHA[sid,t] and esOcHA[sid,t - ctrlCycle] are equal, the left - hand side of Equation (238) is constrained to be 1 or more. For this constraint to hold, esOcHc[sid,t] needs to be 0. Therefore, the indicator esOcHc[sid,t] can be constrained to be 1 only at the start of charging by the two constraint equations of Equation (237) and Equation (238).

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[0140] [Number]

[0141] [Number]

[0142] [Number]

[0143] Similarly for expressions (239) to (242), the indicator esOdHc[sid,t] becomes 1 only at the start of discharge, and lfOHc[sid,t] is constrained to become 1 only at the start of the operation of the on-site power generation facility. [Number]

[0144] [Number]

[0145] [Number]

[0146] Expressions (243) to (245) are subject to the constraint that these indicators do not exceed a predetermined number onoffmax within a predetermined period.

[0147] By generating such optimization variables, constraint expressions, and objective functions and providing them to the optimization engine 170 in FIG. 1, it is possible to generate additional power selling bid amounts reBidsHA[t] for each time slot t of the day that maximize the profit, which is the objective function, the adjustment amount pcsGenHA[t] procured in the day's market, and the operation command sequences for variable power sources 151 and controllable power sources 152 such as actEsCfHA[sid,tt], actEsDfHA[sid,tt], curtailHA[sid,tt], and actLclFHA[sid,tt] that achieve them.

[0148] On the day, the bidding planning means 107 sets the selling bid price for the additional power selling bid volume reBidsHA[t] for each frame t of the day thus obtained, and the buying bid price for the adjustment volume pcsGenHA[t] procured in the day-ahead market, and bids them into the day-ahead market system 130. Thereby, the power selling control device 100 can bid so that the power generated by the power generation site 150 can be suitably sold, and can control the power generation sites 150 and 150a according to the agreed quantity of the bid.

[0149] Next, the processing of the operation planning means 111 performed using the agreement result 115 for the day-ahead buying and selling bid volume 108 obtained by a predetermined timing, the agreement result 114 for the previous day's bid volume 104, and the power generation prediction time series 110 of the high-frequency short-term power generation prediction means 109 will be described. Note that the prediction frequency of the high-frequency short-term power generation prediction means 109 is desirably equal to or several times that of the control cycle ctrlCycle. The prediction period needs to be up to the end point of the frame or longer at the start point of the frame (for example, the point in time when the control command at 13:00 is determined), but it is not necessary to predict up to one day or several hours ahead like the previous day's bidding planning means 103 and the day-ahead bidding planning means 107.

Number

[0150] Equation (301) is the objective function of the optimization performed by the operation planning means 111. It is a minimization problem of the optimization variable costRt[t] defined by Equation (302), and determines the following optimization variables. Charge amount of the storage battery: actEsCfRT[tt] Discharge amount of the storage battery: actEsDfRT[tt] Power suppression amount of the variable power source: curtailRT[tt] Power generation output of the on-site power generation facility: actLclFRT[tt] In the previous bidding plan means 103 and the same-day bidding plan means 107, probabilistic optimization was performed using a plurality of predicted power generation amount series. However, in the operation plan means 111 of the present embodiment, one series is used in one planning process. That is, for the plan with time t as the first time of the planning variable and the plan with time t + ctrlCycle as the first time of the planning target, the power generation prediction time series 110 based on the latest prediction each time is used.

[0151] In the planning process, t ∈ CtrlHorizon is set as the period of the integration process in Equation (301), but at least one or more time slots are targeted for planning. [Number]

[0152] The right side of Equation (302) is composed of two major terms. The first term is a term weighted by the setting parameter weightOfActualGen that takes a value from 0 to 1, and it is a term for the imbalance cost with respect to the excess or deficiency of the transfer amount. Predicted value or assumed value of the over - imbalance penalty unit price for time slot t0: ppnltOfDay[t0] Over - imbalance amount: genErrPRT[t] Predicted value or assumed value of the under - imbalance penalty for time slot t0: mpnltOfDay[t0] Under - imbalance amount: genErrMRT[t] The second term is a term weighted by (1 - weightOfActualGen), and it is a term for the cost of the renewable energy suppression amount, the charge amount of the storage battery, the discharge amount of the storage battery, and the power generation output of the self - power generation facility. Power suppression cost of variable power source: curtailCost Power suppression amount of variable power source: curtailRT[tt] Charge cost of storage battery: chargeCost Charge amount of storage battery: actEsCfRT[tt] Discharge cost of storage battery: dischargeC Discharge amount of the storage battery: actEsDfRT[tt] Operating unit price of the self - power generation facility: localFexPrice Power generation output of the self - power generation facility: actLclFRT[tt] For example, the power suppression amount curtailRT[tt] of the variable power source is an optimization variable defined for the control timing tt. The cost is obtained by multiplying the sum of the values obtained by multiplying the value of the variable defined between the start time t0 minutes and t0 + 30 minutes of the target comma by ctrlCycle / 60 and converting it to the power amount by the unit price.

[0153] The possible values of the target time of the planning variable are any of the discrete values as shown in Figure 3. In Figure 3, target times such as time [t0, t0 + ctrlCycle, t0 + 2*ctrlCycle,..., t,..., t0 + div(30, ctrlCycle, 0)*ctrlCycle] are shown.

[0154] Among these, since the part before the first time t of the planning variable is in the past, actual values are used instead of variables. The part after time t is generated as an optimization variable to form a constraint equation.

[0155] The same applies to the charge amount actEsCfRT[tt] of the storage battery, the discharge amount actEsDfRT[tt] of the storage battery, and the power generation output actLclFRT[t] of the self - power generation facility. On the other hand, for the past part, actual values are used as constraint values. Note that div(a, b, c) here means the quotient of the C digits after the decimal point when a is divided by b.

Number

[0156] Equation (303) is a constraint equation that restricts the delivery quantity error genErrorRT[t] to the difference between the delivery quantity actTtlGenRT[t] at the target frame (time t) and the sum of the committed quantities reBidsDA[t] in the previous day's bids and the committed quantity reBidsHA[t] in the same-day bids. The variables related to Equation (303) are as follows. Delivery quantity error: genErrorRT[t] Delivery quantity at time tt: actTtlGenRT[tt] Committed quantity in the previous day's bids: reBidsDA[t] Committed quantity in the same-day bids: reBidsHA[t]

Number

[0157]

Number

[0158] Equations (304) and (305) are constraint equations that, when the delivery quantity excess indicator flgGePRT[t] is 1, restrict the excess imbalance quantity genErrPRT[t] to the delivery quantity error genErrorRT[t], and when the delivery quantity excess indicator flgGePRT[t] is 0, are substantially unconstrained. The variables related to Equations (304) and (305) are as follows. Excess imbalance quantity: genErrPRT[sid,t] Delivery quantity excess indicator: flgGePRT[sid,t] Delivery quantity error: genErrorRT[sid,t]

Number

[0159] Equation (306) does not function as a constraint on the excess imbalance amount genErrPRT[t] when the transfer amount excess indicator flgGePRT[t] is 1. However, when the transfer amount excess indicator flgGePRT[t] is 0, it constrains the excess imbalance amount genErrPRT[t] to 0. The variables related to Equation (306) are as follows. Transfer amount excess indicator: flgGePRT[t] Excess imbalance amount: genErrPRT[t]

Number

[0160]

Number

[0161] Equations (307) and (308) are constraint equations regarding the relationship between the transfer amount error genErrorRT[t] and the transfer amount excess indicator flgGePRT[t]. The variables related to Equations (307) and (308) are as follows. Transfer amount error: genErrorRT[t] Transfer amount excess indicator: flgGePRT[t] When the value of the transfer amount error genErrorRT[t] is positive, Equation (308) holds regardless of the transfer amount excess indicator flgGePRT[t], but from Equation (307), the transfer amount excess indicator flgGePRT[t] is constrained to 1. On the other hand, when the transfer amount error genErrorRT[t] is negative, Equation (307) holds regardless of the value of the transfer amount excess indicator flgGePRT[t], but from Equation (308), the transfer amount excess indicator flgGePRT[t] is constrained to 0.

Number

[0162]

Number

[0163] When the transfer amount shortage indicator flgGeMRT[t] is 1, Equation (309) and Equation (310) constrain the shortage imbalance amount genErrMRT[t] to be the opposite sign of the transfer amount error genErrorRT[t]. When the transfer amount shortage indicator flgGeMRT[t] is 0, they are substantially unconstrained constraint equations. The variables related to Equation (309) and Equation (310) are as follows. Shortage imbalance amount: genErrMRT[t] Transfer amount error: genErrorRT[t] Transfer amount shortage indicator: flgGeMRT[t]

Number

[0164] When the transfer amount shortage indicator flgGeMRT[t] is 1, Equation (311) does not function as a constraint on the shortage imbalance amount genErrMRT[t]. However, when the transfer amount shortage indicator flgGeMRT[t] is 0, it constrains the shortage imbalance amount genErrMRT[t] to 0. The variables related to Equation (311) are as follows. Transfer amount shortage indicator: flgGeMRT[t] Shortage imbalance amount: genErrMRT[t]

Number

[0165]

Number

[0166] Equations (312) and (313) are constraint equations regarding the relationship between the transfer quantity error genErrorRT[t] and the transfer quantity shortage indicator flgGeMRT[t]. The variables related to Equations (312) and (313) are as follows. Transfer quantity error: genErrorRT[t] Transfer quantity shortage indicator: flgGeMRT[t]

[0167] When the transfer quantity error genErrorRT[t] is negative, Equation (313) holds regardless of the value of the transfer quantity shortage indicator flgGeMRT[t], but the transfer quantity shortage indicator flgGeMRT[t] is constrained to 1 by Equation (312). On the other hand, when the transfer quantity error genErrorRT[t] is positive, Equation (312) holds regardless of the value of the transfer quantity shortage indicator flgGeMRT[t], but the value of the transfer quantity shortage indicator flgGeMRT[t] is constrained to 0 by Equation (313).

Number

[0168] From Equation (314), the sum of the transfer quantity excess indicator flgGePRT[t] and the transfer quantity shortage indicator flgGeMRT[t] is constrained to be 1 or less. The variables related to Equation (314) are as follows. Transfer quantity excess indicator: flgGePRT[t] Transfer quantity shortage indicator: flgGeMRT[t]

[0169] In the first term on the right side of Equation (302), penalties for excess and shortage are calculated using these values. Note that ppnltOfDay[t0] in Equation (302) is the predicted or assumed value of the excess imbalance penalty unit price for period t0, and mpnltOfDay[t0] is the predicted or assumed value of the shortage imbalance penalty for period t0.

Number

[0170] Equation (315) is a constraint equation regarding the optimization variable representing the transfer amount actTtlGenRT[t] at time t used in Equation (303). Transfer amount at time t: actTtlGenRT[t] Value of the power generation prediction time series: reGenRT[t] Power suppression amount of the variable power source: curtailRT[t] Power purchase amount contracted in the day-ahead market: pchsGenHA[t] Efficiency during charging of the storage battery: esEffc Charge amount of the storage battery: actEsCfRT[t] Efficiency during charging of the storage battery: esEffc Discharge amount of the storage battery: actEsDfRT[t] Power generation output of the on-site power generation facility: actLclFRT[t] Note that the power generation prediction time series 110 is the output of the high-frequency short-term power generation prediction means 109.

[0171] By generating such optimization variables, constraint equations, and objective functions and providing them to the optimization engine 170 in FIG. 1, a control command series for the variable power source 151 and the controllable power source 152, such as actEsCfRT[tt], actEsDfRT[tt], curtailRT[tt], and actLclFRT[tt], that minimizes the cost, which is the objective function, can be generated.

[0172] Among the calculation results of the operation planning means 111, the optimization result regarding the first time t of the planning target is used as the control command series for the variable power source 151 and the controllable power source 152. Since the on-site power generation facility and the storage battery shown in this embodiment are controllable power sources 152, actEsCfRT[t], actEsDfRT[t], and actLclFRT[t] are transmitted as command values 113 for the controllable power source 152 by the command means 166.

[0173] Since curtailRT[t] is for the variable power supply 151, it is transmitted as a command value 112 for the variable power supply 151 by the command means 165. As a result, the power selling control device 100 can bid so that the power generated by the power generation site 150 can be suitably sold, and can control the power generation sites 150 and 150a according to the contracted amount of the bid.

[0174] 《Second Embodiment》 In the first embodiment of the present invention, it is premised that the power generation high-frequency short-term prediction means 109 generates one power generation prediction series at a high frequency. However, similar to the power generation time prediction means 105 and the power generation day-before prediction means 101, a plurality of prediction series may be generated. In that case, instead of the objective function and constraint expressions from Equation (301) to Equation (315), the objective function and constraint expressions from Equation (401) to Equation (415) may be used.

Equation

[0175] Equation (401) is the objective function of the optimization implemented by the operation planning means 111. It is a minimization problem of the optimization variable costRt[t] defined by Equation (402), and determines the following optimization variables. Charge amount of the storage battery: actEsCfRT[sid,tt] Discharge amount of the storage battery: actEsDfRT[sid,tt] Power suppression amount of the variable power supply: curtailRT[sid,tt] Power generation output of the self-generation facility: actLclFRT[sid,tt]

Equation

[0176] The right side of Equation (402) is composed of two major terms. The first term is a term weighted by the setting parameter weightOfActualGen that takes a value from 0 to 1, and is a term for the imbalance cost with respect to the excess or deficiency of the transfer amount. Predicted or assumed value of the excess imbalance penalty unit price for period t0: ppnltOfDay[t0] Excess imbalance amount: genErrPRT[sid,tt] Predicted or assumed value of the shortage imbalance penalty for period t0: mpnltOfDay[t0] Shortage imbalance amount: genErrMRT[sid,tt] The second term is the term weighted by (1 - weightOfActualGen), and is the term of the power generation suppression amount of the variable power source, the charge amount of the storage battery, the discharge amount of the storage battery, and the cost for the power generation amount of the distributed power generation equipment. Power suppression cost of variable power source: curtailCost Power generation suppression amount of variable power source: curtailRT[sid,tt] Charge cost of storage battery: chargeCost Charge amount of storage battery: actEsCfRT[sid,tt] Discharge cost of storage battery: dischargeC Discharge amount of storage battery: actEsDfRT[sid,tt] Operating unit price of distributed power generation equipment: localFexPrice Power generation output of distributed power generation equipment: actLclFRT[sid,tt]

Number

[0177] Equation (403) is a constraint equation that constrains the delivery amount error genErrorRT[sid,t] to be the difference between the delivery amount actTtlGenRT[sid,tt] at time t, which is the target period, and the sum of the committed amount reBidsDA[t] in the previous day's bid and the committed amount reBidsHA[t] in the same day's bid. The variables related to Equation (403) are as follows. Delivery amount error: genErrorRT[sid,t] Delivery amount at time t: actTtlGenRT[sid,tt] Quantity committed in previous bidding: reBidsDA[t] Quantity committed in current bidding: reBidsHA[t]

Number

[0178]

Number

[0179] Equations (404) and (405) are constraint equations that, when the delivery quantity excess indicator flgGePRT[sid,t] is 1, constrain the excess imbalance quantity genErrPRT[sid,t] to the delivery quantity error genErrorRT[sid,t], and when the delivery quantity excess indicator flgGePRT[sid,t] is 0, substantially unconstrain the excess imbalance quantity genErrPRT[sid,t]. The variables related to Equations (404) and (405) are as follows. Excess imbalance quantity: genErrPRT[sid,t] Delivery quantity excess indicator: flgGePRT[sid,t] Delivery quantity error: genErrorRT[sid,t]

Number

[0180] Equation (406) does not function as a constraint on the excess imbalance quantity genErrPRT[sid,t] when the delivery quantity excess indicator flgGePRT[sid,t] is 1. However, when the delivery quantity excess indicator flgGePRT[sid,t] is 0, it constrains the excess imbalance quantity genErrPRT[sid,t] to 0. The variables related to Equation (406) are as follows. Delivery quantity excess indicator: flgGePRT[sid,t] Excess imbalance quantity: genErrPRT[sid,t]

Number

[0181]

Number

[0182] Equations (407) and (408) are constraint equations regarding the relationship between the transfer amount error genErrorRT[sid,t] and the transfer amount excess indicator flgGePRT[sid,t]. The variables related to Equations (407) and (408) are as follows. Transfer amount error: genErrorRT[sid,t] Transfer amount excess indicator: flgGePRT[sid,t]

Number

[0183]

Number

[0184] Equations (409) and (410) are constraint equations that, when the transfer amount shortage indicator flgGeMRT[sid,t] is 1, constrain the shortage imbalance amount genErrMRT[sid,t] to be the opposite sign of the transfer amount error genErrorRT[sid,t], and when the transfer amount shortage indicator flgGeMRT[sid,t] is 0, are substantially unconstrained. The variables related to Equations (409) and (410) are as follows. Shortage imbalance amount: genErrMRT[sid,t] Transfer amount error: genErrorRT[sid,t] Transfer amount shortage indicator: flgGeMRT[sid,t]

Number

[0185] When the transfer quantity shortage indicator flgGeMRT[sid,t] is 1, Equation (411) does not function as a constraint on the shortage imbalance quantity genErrMRT[sid,t]. However, when the transfer quantity shortage indicator flgGeMRT[sid,t] is 0, the shortage imbalance quantity genErrMRT[sid,t] is constrained to 0. The variables related to Equation (411) are as follows. Transfer quantity shortage indicator: flgGeMRT[sid,t] Shortage imbalance quantity: genErrMRT[sid,t]

Number

[0186]

Number

[0187] Equations (412) and (413) are constraint equations regarding the relationship between the transfer quantity error genErrorRT[sid,t] and the transfer quantity shortage indicator flgGeMRT[sid,t]. The variables related to Equations (412) and (413) are as follows. Transfer quantity error: genErrorRT[sid,t] Transfer quantity shortage indicator: flgGeMRT[sid,t]

[0188] When the transfer quantity error genErrorRT[sid,t] is negative, Equation (413) holds regardless of the value of the transfer quantity shortage indicator flgGeMRT[sid,t], but from Equation (412), the transfer quantity shortage indicator flgGeMRT[sid,t] is constrained to 1. On the one hand, when the delivery quantity error genErrorRT[sid,t] is positive, Equation (412) holds regardless of the value of the delivery quantity shortage indicator flgGeMRT[sid,t]. However, from Equation (413), the value of the delivery quantity shortage indicator flgGeMRT[sid,t] is constrained to 0.

Number

[0189] From Equation (414), the sum of the delivery quantity excess indicator flgGePRT[sid,t] and the delivery quantity shortage indicator flgGeMRT[sid,t] is constrained to be 1 or less. The variables related to Equation (414) are as follows. Delivery quantity excess indicator: flgGePRT[sid,t] Delivery quantity shortage indicator: flgGeMRT[sid,t]

Number

[0190] Equation (415) is a constraint equation regarding the delivery quantity actTtlGenRT[sid,t] at time t used in Equation (403). The variables related to Equation (415) are as follows. Delivery quantity: actTtlGenRT[sid,t] Value of the predicted series of the day before power generation at time t: reGenRT[sid,t] Power curtailment amount of the variable power source: curtailRT[sid,t] Agreed power purchase quantity: pchsGen[t] Efficiency during charging of the storage battery: esEffc Charge amount of the storage battery: actEsCfRT[sid,t] Efficiency during charging of the storage battery: esEffc Power generation output of the on-site power generation facility: actLclFRT[sid,t] Note that the power generation prediction time series 110 is the output of the high-frequency power generation short-term prediction means 109. However, regarding the optimization variables related to the first time point of the planned target, constraints such as those from Equation (416) to Equation (419) are added.

Number

[0191] Equation (416) indicates that any prediction series of the charge amount of the storage battery is equal to other prediction series.

Number

[0192] Equation (417) indicates that any prediction series of the discharge amount of the storage battery is equal to other prediction series.

Number

[0193] Equation (418) indicates that any prediction series of the power suppression amount of the variable power source is equal to other prediction series.

Number

[0194] Equation (418) indicates that any prediction series of the power generation output of the self-power generation facility is equal to other prediction series. Due to these constraints, the variables related to the time t when the output as the control command must be determined can be made to take values independent of the prediction time series, and the output as the command values 112 and 113 can be achieved while maintaining the optimality for multiple prediction series without performing processes such as selection based on some other criteria.

[0195] 《The Third Embodiment》 The power generation high-frequency short-term prediction means 109 of the third embodiment of the present invention is configured to include a neural network that has learned the relationship between the individual measurement data of the variable power source 151 and the power generation amount of the variable power source 151. Thereby, the power generation amount of the variable power source 151 can be predicted at a predetermined cycle.

[0196] FIG. 4 is a diagram showing the configuration of the power generation site 150 where the variable power source 151 is installed. As the power generation high-frequency short-term prediction means 109, the output of a neural network that has learned the relationship between the captured image 202 of the imaging device 201 installed at the power generation site 150 where the variable power source 151 is installed and the power generation output information 221 of the variable power source 151 is used. A combination of the power generation output information 221 for the past 30 minutes and the captured image 202 for the past 30 minutes is used as teacher data, and at the prediction stage, the power generation time series is predicted from the captured image 202 for the past 30 minutes. By using this neural network, it is possible to generate a power generation amount prediction time series in which the power generation amount of the variable power source 151 is predicted at a predetermined cycle.

[0197] Also, instead of directly learning the power generation output information 221, signal processing such as Fourier transform is performed to change it into information such as intensity or intensity and phase in the frequency space, and then the relationship with the sky image is learned. By adding noise due to random numbers to the phase and intensity and performing inverse Fourier transform to return to the signal in the time domain, a plurality of prediction time series may be generated.

[0198] Also, a set of illuminometer data at one or more points may be used instead of the captured image 202 of the sky. Further, the pixel information of one or more specific pixels of the captured image 202 of the sky may be limited and used. By doing so, this embodiment can be applied even when there is little computational resource available for implementing a neural network that learns the relationship between the site weather information and the generated power.

[0199] Note that Fig. 4 assumes solar power generation as the variable power source 151, but it may be a windmill, and an anemometer may be used instead of the imaging device 201. According to the third embodiment, control based on highly accurate power generation prediction according to the site-specific terrain, wind conditions, etc. can be implemented. Also, by using a device that learns the relationship between such sky images and wind speed data and the power generation amount of the variable power source 151, the present invention can be applied to power generation means for which power generation prediction services are not provided, such as tidal power generation.

[0200] 《Fourth Embodiment》 In the first to third embodiments, the previous day's prediction may be made with a coarse time granularity, and the same or finer time granularity as the previous day may be used for the same day's prediction for planning. By doing so, a probability-constrained optimal plan using more prediction series can be implemented.

[0201] 《Fifth Embodiment》 In the first to fourth embodiments, as control means, it has been described on the premise of using power supply by in-house power generation facilities, power generation suppression of variable power sources, and charge and discharge of storage batteries. In the current supply-demand adjustment market, the buyers of adjustment power are limited to grid operation operators, but just like purchasing the insufficient power in the day-ahead market, a service that secures in advance a capacity like the current tertiary adjustment power and activates the required amount during the target period is incorporated.

Number

[0202] For this purpose, the objective function of the previous day's plan in Equation (101) may be modified like Equation (501), and a plan that takes into account the cost pf_xpnsPlsDA[sid,t] of the upward market procurement adjustment power and the cost pf_xpnsMnsDA[sid,t] of the downward market procurement adjustment power may be used. Equation (501) is an example of the objective function of the previous day's plan. The optimization variables here are as follows. Previous day's bid volume: bidsDA[t0] Charge amount of the storage battery: actEsCfDA[t0] Discharge amount of the storage battery: actEsDfDA[t0] Upward power procured from the market: pcsFlxPlsDA[t0] Downward power procured from the market: pcsFlxMnsDA[t0] Power generation output of the on-site power generation facility: actLclFDA[t0] Adjustment amount of market procurement in the upward direction: actPcsFplsDA[t0] Adjustment amount of market procurement in the downward direction: actPcsFmnsDA[t0] The variables that make up the objective function are as follows. Daily sales: earningsDA[sid,t0] Premium: premiumDA[sid,t0] Cost of the downward market procurement adjustment power: pf_xpnsMnsDA[sid,t0] Cost of the upward market procurement adjustment power: pf_xpnsPlsDA[sid,t0] Operating cost of the on-site power generation facility: lflexXpnsDA[sid,t0] Penalty: penaltyDA[sid,t0]

Number

[0203]

Number

[0204] Equations (502) and (503) represent constraint equations regarding the cost of the market procurement adjustment power. Among them, the variables in Equation (502) are as follows. Cost of the downward market procurement adjustment power: pf_xpnsMnsDA[sid,t0] Base cost in the downward direction: flxSageBasePrice[t0] Downward power procured from the market: pcsFlxMnsDA[t0] Unit cost in the downward direction: flxSageUnitPrice[t0] Market procurement adjustment amount in the downward direction: actPcsFmnsDA[t0] The variables in Equation (503) are as follows. Cost of the market procurement adjustment force in the upward direction: pf_xpnsPlsDA[sid,t0] Basic cost in the upward direction: flxAgeBasePrice[t0] Upward power procured from the market: pcsFlxPlsDA[t0] Unit cost in the upward direction: flxAgeUnitPrice[t0] Market procurement adjustment amount in the upward direction: actPcsFplsDA[t0]

Number

[0205] Equation (504) is a constraint equation for the transfer amount actTtlGen[sid,t] considering the market procurement adjustment force. The variables related to Equation (504) are as follows. Transfer amount considering the market procurement adjustment force: actTtlGen[sid,t] Predicted value of the market procurement adjustment amount time series: refVals[sid,t] Efficiency during battery charging: esEffc Battery charge amount: actEsCfDA[sid,t] Efficiency during battery charging: esEffc Battery discharge amount: actEsDfDA[sid,t] Power generation output of the on-site power generation facility: actLclFDA[sid,t] Market procurement adjustment amount in the upward direction: actPcsFplsDA[t] Market procurement adjustment amount in the downward direction: actPcsFmnsDA[t] From Equation (505) to Equation (511) are constraints related to the relationship between the procurement quantity and the activation quantity of the market procurement adjustment power. By adding similar constraints to the daily plan, the market procurement adjustment quantity can also be reflected in the daily plan.

Number

[0206]

Number

[0207] Equations (505) and (506) are constraint equations for the upward market procurement adjustment quantity actPcsFplsDA[t]. The variables related to Equations (505) and (506) are as follows. Upward market procurement adjustment quantity: actPcsFplsDA[t] Upward indicator: pfOplsDA[t]

Number

[0208]

Number

[0209] Equations (507) and (508) are constraint equations for the downward market procurement adjustment quantity actPcsFmnsDA[t]. The variables related to Equations (507) and (508) are as follows. Downward market procurement adjustment quantity: actPcsFmnsDA[t] Downward indicator: pfOmnsDA[t]

Number

[0210] From Equation (509), the sum of the upward direction indicator pfOplsDA[t] and the downward direction indicator pfOmnsDA[t] is constrained to be 1 or less. The variables related to Equation (509) are as follows. Upward direction indicator: pfOplsDA[t] Downward direction indicator: pfOmnsDA[t]

Number

[0211] From Equation (510), the downward power pcsFlxMnsDA[t0] procured from the market is constrained to be equal to or greater than the downward market procurement adjustment amount actPcsFmnsDA[t]. The variables related to Equation (510) are as follows. Downward power procured from the market: pcsFlxMnsDA[t0] Downward market procurement adjustment amount: actPcsFmnsDA[t]

Number

[0212] From Equation (511), the upward power pcsFlxPlsDA[t0] procured from the market is constrained to be equal to or greater than the upward market procurement adjustment amount actPcsFplsDA[t]. The variables related to Equation (511) are as follows. Upward power procured from the market: pcsFlxPlsDA[t0] Upward market procurement adjustment amount: actPcsFplsDA[t]

[0213] Even in the operation plan, by adding the adjustment force activation component like Equation (504) to Equation (315) or Equation (415) and considering the cost of the activation component of the procurement adjustment force for the second term on the right side of Equation (302) or Equation (402), a plan including the utilization of the procurement adjustment force can be made.

[0214] (Modification example) The present invention is not limited to the above-described embodiments, and includes various modification examples. For example, the above-described embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described. It is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. Further, it is also possible to add, delete, or replace a part of the configuration of each embodiment with another configuration.

[0215] Each of the above configurations, functions, processing units, processing means, etc. may be realized by hardware such as an integrated circuit for a part or all of them. Each of the above configurations, functions, etc. may also be realized by software by a processor interpreting and executing a program for realizing each function. Information such as a program, table, file, etc. for realizing each function can be placed in a recording device such as a memory, hard disk, SSD (Solid State Drive), or a recording medium such as a flash memory card, DVD (Digital Versatile Disk).

[0216] In each embodiment, the control lines and information lines show those considered necessary for explanation, and do not necessarily show all the control lines and information lines on the product. In fact, it may be considered that almost all the configurations are interconnected.

Explanation of reference numerals

[0217] 10, 10A Power selling control system 100, 100A Power selling control device 101 Power generation prediction means the day before (Power generation prediction means) 102 Power generation prediction series the day before 103 Bidding plan means the day before (Bidding plan means) 104 Bidding volume 105 Power generation prediction means before power generation time (Power generation prediction means) 106 Power generation prediction series on the day 107 Daily Bidding Planning Means (Bidding Planning Means) 108 Daily Buying and Selling Bidding Volume 109 High-Frequency Short-Term Power Generation Prediction Means (Power Generation Prediction Means) 110 Power Generation Prediction Time Series 111 Operation Planning Means (Resource Control Series Planning Means) 112, 113 Command Values 114, 115 Transaction Results 116, 118 Outputs 117, 119 States 120 Previous Day Market System 130 Current Day Market System 141 Demand Response Operator 142 Power Generation Operator 150, 150a Power Generation Sites 151 Variable Power Source (An Example of a Resource) 152 Controllable Power Source (An Example of a Resource) 153, 154 Meters 155, 156 Command Devices 165, 166 Command Means 170 Optimization Engine 180 Power Grid 201 Imaging Device 202 Captured Image 220 Variable Power Source 221 Power Generation Output Information 222 Variable Power Source

Claims

Claim 1. A power selling control device, comprising: a power generation prediction means for performing a Fourier transform on the power generation output information of a variable power source which is a solar power generation facility, changing it into intensity information and phase information in the frequency space, and training a neural network on the relationship between the intensity information and the phase information in the frequency space and a sky image for a predetermined period, and generating a power generation prediction time series for predicting the power generation amount of a predetermined cycle of a resource by using the neural network and an inverse Fourier transform at the bidding stage for the day-ahead market or the real-time market; a bidding plan means for determining and bidding on the deliverable amount available for supply to the power grid from the power generation prediction time series; a resource control sequence planning means for determining a control command sequence for each of the resources that minimizes the error between the agreed amount and the actual delivered amount by each of the resources based on the power generation prediction time series, the agreed amount determined from the result of the bid by the bidding plan means, and the state of each of the resources; a command means for commanding the part of the control command sequence determined by the resource control sequence planning means that can be reflected in the control of each of the resources to the resources; characterized by comprising the above.

2. Generating a power generation prediction time series of the variable power source to be used at each control time during the power delivery period by using the neural network and an inverse Fourier transform. The power selling control device according to claim 1, characterized by the above.

3. The command means commands the part of the control command sequence determined by the resource control sequence planning means that can be reflected in the control of the resources to the resources at a cycle equal to or less than the predetermined cycle predicted by the power generation prediction means. The power selling control device according to claim 1, characterized by the above.

4. The bidding plan means determines the deliverable amount available for supply to the power grid from the agreed amount determined from the result of the bid by the day-ahead market and the power generation prediction time series of the resources at the bidding stage for the real-time market, and bids on the real-time market. The power selling control device according to claim 1, characterized by the above.

5. The bidding plan means determines the deliverable amount that can be commonly provided on the premise of the operation of different resources. The power selling control device according to claim 4, characterized by the above.

6. The power generation prediction means generates at least one or more power generation prediction series at the bidding stage for the day-ahead market or the real-time market. The power selling control device according to claim 1, characterized by the above.

7. The power generation prediction means generates a power generation prediction series at a predetermined frame in the past and a predetermined frame in the future from the current time. The power selling control device according to claim 1, characterized in that.

8. The resource includes a controllable power source whose power generation amount can be controlled and a variable power source whose power generation amount fluctuates due to external factors. The power selling control device according to claim 1, characterized in that.

9. The variable power source includes any one of solar power generation facilities, wind power generation facilities, and tidal power generation facilities. The power selling control device according to claim 8, characterized in that.

10. The resource includes any one of a storage battery, a self-generation facility, adjustment power by demand response, and output suppression of a renewable energy power source. The power selling control device according to claim 1, characterized in that.

11. A power generation prediction means that performs Fourier transform on the power generation output information of a variable power source that is a solar power generation facility, converts it into intensity information and phase information in the frequency space, and machine-learns the relationship between the intensity information and the phase information in the frequency space and a sky image for a predetermined period. A neural network is included, and at the bidding stage for the previous day's time-ahead market or the current day's time-ahead market, a power generation prediction time series for predicting the power generation amount of a predetermined cycle of resources is generated using the neural network and inverse Fourier transform. Bidding plan means for determining and bidding the delivery amount that can be provided to the power grid from the power generation prediction time series. Resource control sequence planning means for determining a control command sequence for each resource that minimizes the error between the agreed amount and the actual delivery amount by each resource based on the power generation prediction time series, the agreed amount determined from the result of the bidding by the bidding plan means, and the state of each resource. Command means for commanding a part of the control command sequence determined by the resource control sequence planning means that each resource can reflect in control to the resource. A power selling control system, characterized by comprising.

12. A step in which the power generation prediction means performs Fourier transform on the power generation output information of a variable power source that is a solar power generation facility, converts it into intensity information and phase information in the frequency space, and uses a neural network that has machine-learned the relationship between the intensity information and the phase information in the frequency space and a sky image for a predetermined period and inverse Fourier transform to generate a power generation prediction time series for predicting the power generation amount of a predetermined cycle of resources at the bidding stage for the previous day's time-ahead market or the current day's time-ahead market. The bidding planning means determines the deliverable amount available for the power grid from the predicted power generation time series and bids; Based on the predicted power generation time series, the agreed amount determined from the result of the bidding by the bidding planning means, and the state of each of the resources, the resource control sequence planning means determines a control command sequence for each of the resources that minimizes the error between the agreed amount and the actual deliverable amount by each of the resources; Among the control command sequences determined by the resource control sequence planning means, the command means commands the resources with the parts that each of the resources can reflect in control; A power selling control method characterized by comprising the above.

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