JP7480647B2 - Driving assistance device, driving assistance method, and driving assistance program - Google Patents

Description

The present invention relates to a driving assistance device, a driving assistance method, and a driving assistance program.

In recent years, technology related to so-called microgrids has been considered. In a microgrid, multiple energy devices are placed in a specific area, and the energy demand in the area is met by each energy device. Patent Documents 1 and 2 and Non-Patent Documents 1 and 2 disclose technologies related to the operation of energy systems such as microgrids.

In a microgrid that buys and sells electricity in the wholesale electricity market, the amount of electricity transmitted and received for each future time period is bought and sold in the market based on a power supply and demand plan that was formulated in advance. In each time period, the planned amount of electricity transmitted and received may not match the actual amount of electricity transmitted and received. The difference between the planned amount of electricity transmitted and received and the actual amount of electricity transmitted and received is called an imbalance. When the planned amount of electricity transmitted and received and the actual amount of electricity transmitted and received do not match, the general electricity transmission and distribution business operator, which is ultimately responsible for the supply of electricity, uses an adjustment power source to eliminate the imbalance. When the imbalance is eliminated, the operator of the microgrid must bear part of the costs incurred in eliminating the imbalance according to the amount of the imbalance.

In order to achieve economical power supply and demand in a microgrid that buys and sells electricity in the wholesale electricity market, the following two issues must be addressed: optimizing the power supply and demand plan while taking into account the risk of imbalances and issuing buy and sell orders based on the optimized power supply and demand plan (problem (1)); and avoiding and suppressing demand imbalances by replanning and controlling the supply and demand of the microgrid (problem (2)).

For example, Patent Document 1 discloses a technology that attempts to solve the above problems (1) and (2). For problem (1), a penalty cost is predicted based on statistical processing of the difference between past power receiving plans and actual results. The predicted information is then used to optimize the supply and demand plan. For problem (2), Patent Document 1 also proposes a control method that solves an optimization problem that includes, as an objective function, a penalty cost according to the difference between a determined power receiving plan and actual results, and suppresses power procurement costs, including the burden associated with imbalance adjustment, by reoptimizing the supply and demand plan and performing control based on the reoptimization.

In addition to imbalances and the associated penalty costs, there are many situations in which future uncertainties must be taken into consideration. For example, if a supply and demand plan is formulated based on an inaccurate forecast, costs will increase depending on the magnitude of the difference between the forecast and actual results. There is also a risk that the plan will fail and supply and demand will not be established. Patent Document 2 broadly discloses technology that takes future uncertainties into consideration. The technology in Patent Document 2 proposes a method for solving optimization problems in a microgrid with storage batteries, taking into account future forecasts as probability distributions, and further incorporating periodic plan corrections.

Patent No. 6059328 JP 2019-97267 A

Yuji Oguma, Akinobu Inamura, "Mathematical Models and Algorithms for Optimizing Energy System Configuration and Operation," IHI Technical Report, Vol. 59, No. 4, pp. 24-35 (2019). Ryohei Yokoyama, Yasushi Hasegawa, and Koichi Ito, "Equipment Configuration Optimization of Energy Supply Systems Using a Decomposition Method of Mixed Integer Linear Programming," Transactions of the Japan Society of Mechanical Engineers, Series C, Vol. 66, No. 652, pp. 4016-4023 (2000).

When a difference occurs between a predetermined plan and actual energy demand, adjustments are made using adjustable power sources. Using adjustable power sources is disadvantageous in terms of cost. Therefore, it is desirable to reduce the difference between actual energy demand and the predetermined plan.

The present invention aims to provide a driving assistance device, a driving assistance method, and a driving assistance program that can reduce the difference between a determined plan and actual energy demand.

One embodiment of the present invention is an operation assistance device that supports the operation of a microgrid that includes an energy transfer device capable of transferring energy and is also capable of transferring energy from an external source, and includes an optimization calculation unit that calculates control information for controlling the transfer of energy in the energy transfer device, and an optimization result output unit that outputs the control information. When solving the optimization problem for calculating the control information, the optimization calculation unit treats a function that indicates the difference between a predetermined plan, which is indicated by a time period and an energy demand associated with the time period, and the actual energy demand, as a constraint condition and/or objective function.

Another aspect of the present invention is an operation support method for supporting the operation of a microgrid that includes an energy transfer device capable of transferring energy and is also capable of transferring energy from an external source, the method comprising the steps of calculating control information for controlling the transfer of energy in the energy transfer device and outputting the control information, and in the step of calculating the control information, a function indicating the difference between a predetermined determined plan, which is indicated by a time period and an energy demand associated with the time period, and the actual energy demand is treated as a constraint condition and/or objective function when solving an optimization problem for calculating the control information.

Yet another aspect of the present invention is an operation assistance program that causes a computer to assist in the operation of a microgrid that includes an energy transfer device capable of transferring energy and is also capable of transferring energy from an external source, and that operates the computer to perform a step of calculating control information for controlling the transfer of energy in the energy transfer device and a step of outputting the control information, and in the step of calculating the control information, the computer is operated to solve the optimization problem for calculating the control information by treating, as a constraint and/or objective function, a function indicating the difference between a predetermined plan, which is indicated by a time period and an energy demand associated with the time period, and the actual energy demand.

The above driving assistance device, driving assistance method, and driving assistance program can reduce the difference between the determined plan and the actual energy demand.

In one embodiment of the driving assistance device, the optimization calculation unit may employ a function including a weighted sum of the absolute value of the difference between the determined plan and the actual energy demand at each time included in the planning period as a function indicating the difference.

In one embodiment of the driving assistance device, the optimization calculation unit may employ a function including a weighted sum of squares of the difference between the determined plan and the actual energy demand at each time included in the planning period as a function indicating the difference.

In one embodiment of the driving assistance device, the optimization calculation unit may use a function including the maximum value of the difference between the determined plan and the actual energy demand at each time included in the planning period as a function indicating the difference.

In one embodiment of the driving assistance device, the optimization calculation unit may treat energy as electric power.

In one embodiment of the driving assistance device, the optimization calculation unit may calculate, as the control information, information for controlling the charging and discharging of a storage battery, which is an energy transfer device.

In one embodiment of the driving assistance device, the optimization calculation unit may calculate, as the control information, information for controlling the start, stop, and amount of generated power of a power generation device, which is an energy transfer device.

In one embodiment of the operation assistance device, the optimization calculation unit may calculate, as control information, information for controlling the start, stop, and amount of generated power of the waste-to-energy device, which is an energy transfer device.

In one embodiment of the driving assistance device, the optimization calculation unit may calculate, as control information, information for controlling the start, stop, and amount of generated power of the biomass power generation device, which is an energy transfer device.

In one embodiment of the driving assistance device, the optimization calculation unit may calculate, as control information, information for controlling the start, stop, and load of the water electrolysis device, which is an energy transfer device.

In one embodiment of the driving assistance device, the optimization calculation unit may calculate, as the control information, information for suppressing the output of a renewable energy power generation system, which is an energy transfer device.

The present invention provides a driving assistance device, a driving assistance method, and a driving assistance program that can reduce the difference between a determined plan and actual energy demand.

FIG. 1 is a diagram for explaining the concept of re-planning. FIG. 2 is a diagram illustrating an example of the overall configuration of a microgrid. Fig. 3A is a diagram showing an example of an internal configuration of a driving support device, and Fig. 3B is a diagram showing a hardware configuration of the driving support device. Fig. 4(a) shows an operation procedure for manual execution using the driving support device, and Fig. 4(b) shows an operation procedure for automatic periodic execution using the driving support device. FIG. 5 is a table showing definitions of symbols in the mathematical expressions used in the embodiment. FIG. 6 is an illustration of a microgrid having a storage battery. FIG. 7 is a graph showing power demand forecasts at the time of purchase order and at the time of rescheduling. 8A is a graph showing a battery charge/discharge plan and a power receiving plan, which are results of applying the technology of the comparative example. FIG. 8B is a graph showing a power receiving plan and a power receiving plan, which are results of applying the technology of the comparative example. 9A is a graph showing a battery charge/discharge plan and a power receiving plan, which are results of applying the driving support device of the embodiment. FIG. 9B is a graph showing a power receiving plan and a power receiving plan, which are results of applying the driving support device of the embodiment.

Below, the embodiment of the present invention will be described in detail with reference to the attached drawings. In the description of the drawings, the same elements are given the same reference numerals, and duplicated explanations will be omitted.

In addition, in the following description, the following documents will be cited as appropriate.
Reference 1: Japanese Patent No. 6059328, "Supply and demand control device, power storage device, charge and discharge control device, supply and demand control system, and supply and demand control method."
Reference 2: JP 2019-97267 A, “Energy management system, power supply and demand plan optimization method, and power supply and demand plan optimization program.”
Reference 3: Yuji Oguma, Akinobu Inamura, "Mathematical Models and Algorithms for Optimizing Energy System Configuration and Operation," IHI Technical Report, Vol. 59, No. 4, pp. 24-35 (2019).
Reference 4: Ryohei Yokoyama, Yasushi Hasegawa, and Koichi Ito, "Equipment Configuration Optimization of Energy Supply Systems Using a Single Decomposition Method of Mixed Integer Linear Programming," Transactions of the Japan Society of Mechanical Engineers, Series C, Vol. 66, No. 652, pp. 4016-4023 (2000).

An embodiment will be described taking the perspective of a consumer who purchases electricity as an example. It is easy to think of a similar embodiment for a power generation business that has generators and sells electricity. As a means of suppressing the imbalance, (1) some method is used to determine the planned amount of electricity to be received at each time in the future, and a purchase order is placed in the wholesale electricity market. (2) After the amount of electricity to be received is agreed upon, the supply and demand plan is updated appropriately based on the supply and demand performance of the microgrid and the latest demand forecast, etc., and reflected in operation. At that time, a framework is considered to suppress the difference (imbalance) between the planned and actual amount of electricity to be received. The driving assistance device of this embodiment relates to (2) of the above-mentioned framework.

Figure 1 shows a conceptual diagram of replanning. In the example shown in Figure 1, the power receiving plan from 0:00 to 0:00 the next day has already been determined. Furthermore, the purchase order for the required amount of power receiving has also been contracted. The microgrid operates from 0:00 to 1:00, and the difference between the actual amount of power receiving and the contracted amount of power receiving is determined as the actual imbalance. Now, let us assume that we are considering replanning the supply and demand plan from 1:00 to 1:00 the next day based on the latest demand forecast, etc. However, since there is no contracted amount of power receiving from 0:00 to 1:00 the next day during the replanning period, there is no need to consider the imbalance for this period. Note that in Figure 1, the replanning period is from 1:00 to 1:00 the next day, but the range of replanning can be any range. For example, replanning may be performed up to the day after the next day, or conversely, replanning may be performed for the most recent period from 1:00 to 1:30.

Figure 2 shows the overall configuration of a microgrid 20 to which the driving assistance device 1 of this embodiment can be applied. Figure 3(a) shows an example of the internal configuration of the driving assistance device 1. Figure 3(b) shows the hardware configuration of the driving assistance device 1. In this embodiment, electricity is mainly assumed as the form of energy in the microgrid 20 shown in Figure 2. In the following description, electricity is used as an example, but the driving assistance device 1 of this embodiment can achieve the same effect as when the energy is electricity, even if the energy is, for example, gas or steam. In addition, when the driving assistance device 1 of this embodiment is applied, there are no restrictions on the form of energy, the type of energy equipment, or the number of energy equipment. The driving assistance device 1 may be applied to a microgrid that considers gas, steam, etc. in addition to electricity.

The energy devices 21, 22, and 23 shown in FIG. 2 include the energy devices (a) to (d) exemplified below.
Energy equipment (a): Equipment capable of storing and releasing electricity, such as a storage battery.
Energy equipment (b): Power generation equipment capable of generating electricity from fuel, such as gas turbines, gas engines, waste-to-energy generation equipment, and biomass power generation equipment.
Energy Equipment (c): Equipment that converts electricity into other resources or forms of energy, such as water electrolyzers and electric boilers.
Energy equipment (d): Renewable energy power generation equipment such as solar power generation and wind power generation.

From the perspective of preventing and avoiding imbalances, energy equipment (a) can be used to make up for power shortages at another time by using surplus power at one time. Energy equipment (b) and energy equipment (c) can be used as compensation means when there is a power shortage or surplus, respectively. The power generated by energy equipment (d) is determined depending on the weather. Therefore, controlling the power generated by energy equipment (d) is more difficult than that of energy equipment (a) to (c). However, energy equipment (d) can be used as a means of preventing imbalances by, for example, operating a power conditioner connected to these equipment when there is a power surplus, thereby cutting off and limiting the power supply.

The external energy supply source 25 specifically refers to a grid connection point (power receiving point). The imbalance is the difference between the planned and actual amount of power received at the grid connection point. In the microgrid 20, the power demand and power load distribution at each time slice can be measured or calculated in some way. In FIG. 2, measuring devices B to E are simply provided for the output of the energy devices 21 to 23 and the energy demand 24. However, in the configuration shown in FIG. 2, another method may be used, for example, to calculate the energy demand 24 from the sum of the external energy supply amount and the output of each energy device.

<Driving assistance device>
The driving assistance device 1 determines an optimal operating state of the energy equipment (group) of the microgrid 20. The driving assistance device 1 is used to assist in the operation of the energy equipment 21, 22, and 23. FIG. 3B is a block diagram showing a hardware configuration of the driving assistance device 1. As shown in FIG. 3B, the driving assistance device 1 is physically configured as a computer including one or more processors 101, a storage device 102 such as a RAM (Random Access Memory) and a ROM (Read Only Memory), an input device 103 such as a keyboard, a display device 104 such as a display, and a communication device 105 which is a communication interface for transmitting and receiving data. The driving assistance device 1 reads a predetermined computer program into hardware such as the processor 101, thereby operating each piece of hardware under the control of the processor 101 and reading and writing data in the storage device 102. As a result, each function of the driving assistance device 1 shown in FIG. 3A is realized.

As shown in FIG. 3(a), the driving assistance device 1 includes as its components a predicted value specification unit 11, a constraint condition specification unit 12, an objective function specification unit 13, an equipment characteristics database 14, a power receiving plan storage unit 16, an optimization calculation unit 15, an optimization result output unit 17, and an input terminal for the measured value φ (or calculated value) of each of the above-mentioned items.

The forecast value specification unit 11 specifies forecast values of power demand corresponding to each time period (e.g., 30-minute intervals) during the period for which the supply and demand plan is considered. The period for which the supply and demand plan is considered is typically the most recent day. If the microgrid 20 includes renewable energy power generation equipment, a forecast value of power generation for the same period can also be specified.

The constraint condition specification unit 12 and the objective function specification unit 13 are provided in the form of loading a file or inputting from a screen. In the constraint condition specification unit 12, for example, upper and lower limits of the power generation of each energy device 21 to 23, upper and lower limits of the remaining charge of the storage battery, and other constraints related to imbalance can be specified.

The objective function specification unit 13 can indirectly specify the objective function for optimization by specifying parameters related to the electricity rate, fuel price, and imbalance.

The equipment characteristics database 14 stores the model of the energy equipment to be optimized and its parameters. For example, this includes formulas related to efficiency and their parameters.

The power receiving plan storage unit 16 stores the determined power receiving plan. There are no particular limitations on the means for creating this determined power receiving plan. The determined power receiving plan may be, for example, a plan that has been manually determined and stored. In this case, an input unit for the manually determined plan is required. The determined power receiving plan may be a plan obtained as a result of optimization calculations within the driving assistance device 1. In the latter case, the intended plan can be obtained by performing calculations without considering the constraint conditions and objective function related to the imbalance in the optimization problem (P or Q) described below. Hereinafter, a description of the method for creating the power receiving plan to be stored in the power receiving plan storage unit 16 will be omitted.

The optimization calculation unit 15 obtains an energy supply and demand plan that maximizes or minimizes a predetermined objective function while satisfying various constraint conditions. The energy supply and demand plan is a load allocation of the energy devices 21 to 23 in response to the energy demand at each time. The optimization calculation unit 15 uses the following exemplary data (a) to (f) in the operation of obtaining the energy supply and demand plan. Details of the optimization calculation will be described later.
Data (a): A predicted value designated by the predicted value designation unit 11 .
Data (b): constraint conditions specified by the constraint condition specification unit 12 .
Data (c): Objective function specified by the objective function specification unit 13.
Data (d): Energy equipment model and its parameters stored in the equipment characteristics database 14.
Data (e): Current status values measured by measuring instruments A to E. Alternatively, it may be a value obtained by appropriately processing the values measured by measuring instruments A to E, such as a time average value of the current status values for a certain period of time.
Data (f): The determined power receiving plan stored in the power receiving plan storage unit 16.
The constraint conditions may also include a constraint regarding an imbalance with the previous supply and demand plan.

The optimization result output unit 17 outputs the energy supply and demand plan calculated by the optimization calculation unit 15 in an appropriate manner, such as on a screen or as a file.

In the above embodiment, it is also assumed that information on current values is used in energy supply and demand planning. This is assumed for online supply and demand planning. For example, when used in next-day supply and demand planning, it is not necessary to use current values. Furthermore, the driving assistance device 1 is typically realized in a single computer. However, there is a degree of freedom in its form, for example, each component may be placed on a different computer. The effect of the driving assistance device 1 does not change depending on the physical configuration that realizes the driving assistance device 1.

<Driving assistance method and driving assistance program>
Next, an example of the optimization process executed in the optimization calculation unit 15 of the driving assistance device 1, that is, a driving assistance method according to the present embodiment, will be described.

In each of Figures 4(a) and 4(b), two operation procedures of the driving assistance device 1 according to the embodiment are shown: manual execution (hereinafter also referred to as procedure (a)) and automatic periodic execution (hereinafter also referred to as procedure (b)). In manual execution, the optimization calculation is performed at the operator's discretion. In automatic periodic execution, the optimization calculation is automatically performed periodically (for example, every hour) without being instructed by the operator. In the case of online supply and demand planning, for example, the calculation results may be used to automatically control the energy equipment 21 to 23.

Manual execution and automatic periodic execution may be provided as operation modes that can be switched between in the same driving assistance device 1, for example. In this case, switching is performed, for example, by an operator. Note that the processes in steps 1 (S11) to 6 (S16) in FIG. 4(a) and FIG. 4(b) are independent of each other. These steps 1 (S11) to 6 (S16) do not necessarily have to be as shown in the figures. For example, the processes in steps 1 (S11) to 6 (S16) may be performed in reverse order. The processes may also be performed in parallel.

The driving assistance method based on manual operation shown in FIG. 4(a) has, as its main steps, step S11 of specifying a predicted value, step S12 of specifying a constraint condition, step S13 of specifying an objective function, step S14 of reading a determined power receiving plan, step S15 of reading device characteristics, step S16 of inputting current values, step S17 of performing an optimization calculation, and step S18 of outputting the results of the optimization calculation.

The driving assistance method based on automatic operation shown in FIG. 4(b) has, as its main steps, step S11 of specifying a predicted value, step S12 of specifying a constraint condition, step S13 of specifying an objective function, step S14 of reading a determined power receiving plan, step S15 of reading equipment characteristics, step S16 of inputting a current value, step S17 of performing an optimization calculation, and step S18 of outputting the result of the optimization calculation. Furthermore, in addition to the above steps S11 to S18, the driving assistance method based on automatic operation shown in FIG. 4(b) has step S9 of stabilizing whether or not to end the operation, and step S10 of determining the timing of performing the calculation.

The optimization process in the optimization calculation unit 15 is performed by the processor 101 reading and executing a program stored in the storage device 102. The flowchart shown in FIG. 4(a) illustrates an example in which each of steps S11 to S17 is started in response to a manual operation by an operator.

In the example shown in FIG. 4(a), first, the forecast value designation unit 11 designates a forecast value for power demand (S11).

Next, the constraint condition specification unit 12 sets the constraint conditions (step S12). In step S12, the constraint conditions are expressed, for example, by equations (2) to (3) described below. The constraint condition specification unit 12 transmits constraint condition data indicating the constraint conditions expressed by equations (2) to (3) to the optimization calculation unit 15.

Next, the objective function designation unit 13 sets an objective function (step S13). In step S13, the objective function is expressed, for example, by equation (1) described below. The objective function designation unit 13 transmits the objective function expressed by equation (1) to the optimization calculation unit 15.

Next, the optimization calculation unit 15 reads the determined power reception plan to perform the optimization calculation (step S14).

Next, the optimization calculation unit 15 reads the equipment characteristic data from the equipment characteristic database 14 to perform the optimization calculation (step S15).

Next, the optimization calculation unit 15 inputs the current status values provided by the measuring instruments A to E in order to perform the optimization calculation (step S16). The current status values are included in the measurement data from the measuring instruments A to E. The current status values refer to the power supply (or power demand).

Next, the optimization calculation unit 15 executes the optimization calculation (step S17). That is, the optimization calculation unit 15 solves the optimization problem of the objective function set in step S13 under the constraint conditions set in step S12. The device characteristic data and the measurement data are used as parameters in the optimization problem.

Next, the optimization result output unit 17 outputs the optimization calculation result (i.e., the calculation result data) calculated in step S17 (step S18). In step S18, the optimization result output unit 17 may present the calculation result data from the optimization calculation unit 15 to the operator. It may also be output as electronic data that can be directly input to each of the energy devices 21 to 23. As a result, the output values of all the energy devices 21, 22, and 23 are optimized.

<Operation of the optimization calculation unit>
The driving support device 1 devises a formulation of the optimization problem in "Step 7 (S17): Execute optimization calculation". A method for solving the optimization problem executed by the optimization calculation unit 15 of the driving support device 1 of this embodiment will be described in detail below. The optimization calculation unit 15 of the driving support device 1 solves either the optimization problem (P) or (Q) shown below. Note that the meanings of the formulas and symbols exemplified in the following description are as shown in the table of FIG. 5.

最適化問題（Ｑ）は、式（４）～（８）によって示される。なお、最適化問題（Ｑ）における式（５）、式（６）はいずれか一方のみとしてもよい。

The optimization problem (P) is given by equations (1)-(3).

The optimization problem (Q) is represented by the formulas (4) to (8). Note that the optimization problem (Q) may include only one of the formulas (5) and (6).

Alternatively, an optimization problem (R) including features of both optimization problems (P) and (Q) may be solved. The optimization problem (R) is expressed by equations (9) to (13).

In the operation performed by the optimization calculation unit 15 of the driving assistance device 1, the key point in the formulation is to consider the imbalance itself as an objective function or constraint condition, rather than the imbalance penalty cost. Specific formulation of the characteristics of various energy equipment may use the techniques disclosed in, for example, References 3 and 4.

Specific functions for p(r) and v(r) can be considered. For example, we will show some examples of functions that are effective in replanning to reduce imbalances.

次に、式（１５）に示す例として、計画対象期間のインバランスの二乗和を考えるものがある。

さらに、式（１６）に示す例として、計画対象期間のインバランスの最大値を考えるものがある。

Let p(r) be an example of an objective function term corresponding to the imbalance over the entire planning period. First, as an example shown in equation (14), the total amount of imbalance over the planning period is considered.

Next, as an example shown in equation (15), the sum of squares of the imbalance over the planning period is considered.

A further example shown in equation (16) considers the maximum value of the imbalance over the planning period.

In the formulas (14), (15), and (16), w _k is a non-negative weighting coefficient for the imbalance at each time. For example, if you want to suppress the imbalance at a time closer to the current time (where k is smaller), you can set the w _k value to satisfy the following formula.

In addition, it is also effective to give a large weight to a time when the prediction uncertainty is high. Although no particular distinction is made regarding the sign of the imbalance in equations (14) and (15), weight coefficients may be set separately for the positive and negative sides. Specifically, the weight coefficients for the positive and negative imbalances may be set as follows. Then, equation (14) can be expressed as equation (17), and equation (15) can be expressed as equation (18).

Regarding equation (16), by introducing the above-mentioned positive and negative weights shown below, equation (16) can be transformed into equation (19), whereby the sign of the imbalance can be taken into consideration.

この場合は、式（１７）～式（１９）は、以下に示す式（２０）～式（２２）のように示される。

Furthermore, for the positive and negative imbalances, it is possible to consider only those imbalances equal to or greater than the threshold values shown below.

In this case, equations (17) to (19) can be expressed as equations (20) to (22) shown below.

Equation (14), which is an objective function according to the imbalance over the entire planning period, is obtained by setting the imbalance penalty unit price _UPR in Reference 1 as follows:

When there is an imbalance that cannot be avoided even by using a storage battery, it is reasonable to concentrate the imbalance at a time when the penalty unit price _UPR or _wk is small in both the objective function described in Reference 1 and the above formula (14). However, the penalty unit price is determined ex post facto, and if the unit price at that time is higher than expected, there is a risk that a high penalty cost payment will be required due to the concentration. In contrast, both the formula (15) that considers the sum of squares of the imbalance for the planning period and the formula (16) that considers the maximum value of the imbalance for the planning period have a structure in which the value of u(r) becomes large when the imbalance is concentrated at a specific time. In this case, optimization can be performed to obtain a solution (plan) that avoids the concentration of the imbalance at a specific time.

In other words, the formula (15) which considers the sum of squares of the imbalance for the planning period and the formula (16) which considers the maximum value of the imbalance for the planning period have the advantage that the peak value of the imbalance distribution over time is likely to be lower than formula (14), which is an objective function according to the imbalance for the entire planning period. Reference 1 etc. makes no mention of the uncertainty in the penalty cost unit price and the resulting risk of increased payment costs. In other words, there is no description corresponding to formulas (15) and (16) in Reference 1 etc.

When this approach is adopted, the weighted sum of the objective functions shown in equations (14), (15), and (16) may be used as the objective function. The weighted sum of equations (14) and (15) may be adopted as the objective function, or the weighted sum of equations (14) and (16) may be adopted as the objective function, or the weighted sum of equations (15) and (16) may be adopted as the objective function. Furthermore, the weighted sum of equations (14), (15), and (16) may be adopted as the objective function. For example, by adopting the weighted sum of equations (14) and (16) as the objective function, both the total amount and maximum value of the imbalance can be suppressed.

Compared to an approach that considers imbalance as a constraint condition (solving optimization problem (Q)), which will be described later, an advantage of the approach that considers imbalance as an objective function (solving optimization problem (P)) is that it is easier to obtain a feasible solution (a solution that satisfies all constraint conditions). When solving optimization problem (Q), depending on the severity of the imbalance constraint, it may be possible to fall into a situation where there is no solution. In contrast, when solving optimization problem (P), there is no solution due to imbalance. This is an effective property in situations where the energy supply and demand plan is frequently updated when the optimization problem is solved periodically and automatically in the control of energy equipment (Figure 4 (b)), and imbalances in the latter half of the plan (for example, 12 hours after the start of the plan) are not a concern (it is sufficient to suppress imbalances when re-planning in the future).

Next, we will provide examples of constraint terms according to the imbalance over the entire planning period. For v(r), we can use a function of the same form as p(r), as shown below. Of course, we can also consider extensions such as equations (17) to (22).

次に、式（２４）に示す例として、計画対象期間のインバランスの二乗和を考えるものがある。

さらに、式（２５）に示す例として、計画対象期間のインバランスの最大値を考えるものがある。

First, as an example shown in equation (23), the total amount of imbalance for the planning period is considered.

Next, as an example shown in equation (24), the sum of squares of the imbalance over the planning period is considered.

A further example shown in equation (25) considers the maximum value of the imbalance over the planning period.

The expected effect of adopting each function is roughly the same as p(r). When considering it as an objective function, a large imbalance may remain due to the balance with other objective function terms (such as electricity charges). When considering it as a constraint condition, the first priority is to obtain a solution that observes the constraints of equations (23) to (25), regardless of the number of other objective function terms.

The advantage of adopting this approach (which solves optimization problem (Q)) is that it is easy to perform economic evaluation during operation. If the maximum unit value of excess imbalance cost per year is X yen/kWh and the maximum unit value of shortage imbalance cost is Y yen/kWh, the worst-case expected amount caused by imbalance is as shown in the following formula.

Also, the annual average of the imbalance can be used to calculate an approximate penalty amount. On the other hand, in the case of the method shown in the reference literature, when calculating the worst-case or average value of the penalty cost on an annual basis, the above-mentioned simple method cannot be used, and a complex simulation must be performed. The method adopted by the driving assistance device 1 of this embodiment has the advantage of making it easy to estimate the penalty amount that may occur in a business plan, and therefore contributes to understanding business risks.

When adopting this approach, it is possible to adopt a method of gradually expanding the tolerance range, for example by initially allowing an imbalance of up to 1%, and if no solution is found under this condition, allowing an imbalance of up to 2% and recalculating. With such a method, it becomes easier to avoid "no solution" depending on the strictness of the constraints.

<Action and effect>
In short, the driving assistance device 1 of the present embodiment supports the operation of a microgrid that includes an energy transfer device capable of transferring energy and is also capable of transferring energy from the outside. The driving assistance device 1 includes an optimization calculation unit 15 that calculates control information for controlling the transfer of energy in the energy transfer device, and an optimization result output unit 17 that outputs the control information. When solving an optimization problem for calculating the control information, the optimization calculation unit 15 handles a function indicating a difference between a predetermined determined plan, which is indicated by a time period and an energy demand associated with the time period, and an actual energy demand, as a constraint condition and/or an objective function.

The driving assistance device 1, driving assistance method, and driving assistance program described above can reduce the difference between the determined plan and the actual energy demand.

The effects of the driving assistance device 1 of this embodiment will be described below in comparison with the method of the comparative example. For the purpose of explanation, the microgrid 20A shown in FIG. 6 is used as an example. The microgrid 20A has a storage battery 21A with a capacity of 1000 kWh and an output of 1000 kW.

Now, let us assume that a purchase order has been placed and agreed to in the wholesale electricity market for the forecasted amount of demand based on the forecasted amount of electricity demand from 0:00 to 24:00 the following day. Figure 7 is an example of an electricity demand forecast. Graph G7a shows the electricity demand forecast at the time of the purchase order. Graph G7b shows the electricity demand forecast at the time of replanning. As the electricity demand forecast is updated, it becomes necessary to replan the supply and demand plan (see graph G7b), and at that time, it is considered to optimize the charge and discharge plan for storage battery 21A so as to reduce the imbalance with the agreed amount (or the penalty cost related to the imbalance).

Figure 8(a) shows the results of a simulation using the method of the comparative example, and shows the charge/discharge plan for storage battery 21A. The horizontal axis shows time, and the vertical axis shows the charge/discharge power of storage battery 21A. Graph G8a shows the charge/discharge power of storage battery 21A, and graph G8b shows the remaining charge of storage battery 21A. Figure 8(b) shows the results of a simulation using the method of the comparative example, and shows the power receiving plan. The horizontal axis shows time, and the vertical axis shows the power receiving plan and imbalance. Graph G8c shows the power receiving plan, graph G8d shows the determined and confirmed power receiving plan, graph G8e shows the imbalance, and graph G8f shows the imbalance penalty unit price.

In the comparative example method, a charging/discharging plan and a power receiving plan that minimize the total penalty cost for the day are determined based on the (predicted) imbalance penalty cost unit price (see graph G8f) also shown in Figure 8(b). As shown in graph G8e in Figure 8(b), it can be seen that, according to the comparative example method, imbalances are concentrated around 3:00, when the (predicted) imbalance penalty unit price is low. As described above, if the actual penalty unit price for this time period is significantly higher than predicted, it becomes necessary to pay a large imbalance penalty cost.

In short, in the method shown in Reference 1, the imbalance penalty is considered as an objective function in the optimization problem. As already mentioned above, in many actual cases, the penalty unit prices UPR _{and UPS} are determined ex post through the electricity market, and it is difficult to predict them accurately because the penalty cost unit price is variable. If one tries to minimize the imbalance cost under uncertain predictions, the result may be that the imbalance is concentrated in the time period when the predicted imbalance cost unit price is the lowest. If the actual imbalance cost unit price for that time period is higher than predicted, a large imbalance penalty will have to be paid, which is a high risk.

In contrast, in the driving assistance device 1 of this embodiment, the imbalance penalty cost unit price is not used, and the charging/discharging plan and the power receiving plan are calculated using the objective function (16) so that the maximum imbalance in one day is minimized.

Figure 9(a) shows the charging and discharging plan for storage battery 21A, which is the result of a simulation using the method executed by driving assistance device 1. The horizontal axis shows time, and the vertical axis shows the charging and discharging power of storage battery 21A. Graph G9a shows the charging and discharging power of storage battery 21A, and graph G9b shows the remaining charge of storage battery 21A. Figure 9(b) shows the power receiving plan, which is the result of a simulation using the method executed by driving assistance device 1. The horizontal axis shows time, and the vertical axis shows the power receiving plan and imbalance. Graph G9c shows the power receiving plan, graph G9d shows the determined and confirmed power receiving plan, graph G9e shows the imbalance, and graph G9f shows the imbalance penalty unit price.

Referring to graph G9e, it can be seen that the imbalance is distributed throughout the day. Even if the imbalance penalty cost unit price is extremely high at any time, the burden amount will not be large, that is, it can be said that the risk is distributed. In short, the ingenuity of the formulation, which is the key point of the method executed by the driving assistance device 1, is not specialized to either electricity reception (purchasing electricity) or electricity transmission (selling electricity). In this embodiment, an example of suppressing (demand) imbalance has been described using the charging and discharging of a storage battery as an example on the demand side (electricity purchasing side). Assuming a power generation company, suppressing (supply) imbalance by optimizing the operation plan of the generators owned is also included in the scope of application of the method of this embodiment.

One application of the driving assistance device 1 of this embodiment is a microgrid 20 that is connected to a system and buys and sells electricity in the wholesale electricity market. In the microgrid 20 that buys and sells electricity in the wholesale electricity market, the amount of electricity transmitted and received in each future time slot is bought and sold in the market based on a power supply and demand plan that was formulated in advance. If the planned amount of electricity transmitted and received does not match the actual amount of electricity transmitted and received in each time slot (this difference is called an imbalance), the general electricity transmission and distribution company that is ultimately responsible for the supply of electricity will use an adjustable power source to eliminate the imbalance, and the microgrid will need to bear part of the costs incurred in this process according to the imbalance.

Therefore, in order to achieve economical power supply and demand in a microgrid that buys and sells electricity in the wholesale electricity market, the challenges are to optimize the power supply and demand plan taking into account the risk of imbalances, to place buy and sell orders based on that plan, and to avoid and suppress demand imbalances by replanning and controlling the supply and demand of the microgrid.

Reference 1 is an example of a conventional technique for addressing this issue. Reference 1 targets a microgrid with storage batteries, and proposes a control method that solves an optimization problem that includes, as an objective function, a penalty cost according to the difference between a determined power receiving plan and actual results, and suppresses power procurement costs, including the burden of imbalance adjustment, by reoptimizing the supply and demand plan and performing control based on the reoptimization (see claims 1 to 8 in Reference 1). Reference 1 also proposes a method that predicts penalty costs based on statistical processing of the difference between past power receiving plans and actual results, and uses this information to optimize the supply and demand plan (the battery charge and discharge plan and the power receiving plan based on it) (see claims 9 to 13 in Reference 1).

In Reference 1, an optimization problem is solved that includes a penalty cost according to the difference between the already determined power receiving plan and the actual results as an objective function, but there is no description of how to calculate the penalty cost unit price. This unit price cannot be known in advance because it is determined by the supply and demand situation of the entire system and the market price difference between regions. A method of predicting this based on past data is also possible, but accurate prediction is difficult because the penalty cost unit price depends not only on the microgrid itself, but also on the power generation forecast and actual power generation of the power generation company, the demand forecast and actual demand of other microgrids, the adjustment capacity of thermal power generation, which is the adjustment capacity, and prices in the spot market and the one-hour-ahead market. If an attempt is made to minimize the imbalance cost when the penalty cost unit price is uncertain, the result may be that the imbalance is concentrated in the time period when the (predicted) imbalance cost unit price is the lowest.

If the actual imbalance cost unit price for that time period is higher than predicted, a large imbalance penalty will have to be paid, which is a high risk. For example, electricity market prices are prone to fluctuations during abnormal weather and disasters such as floods, typhoons, and earthquakes, which can cause the imbalance cost unit price to soar. In such an emergency, it is easy for the microgrid itself to have difficulty maintaining supply and demand due to a decrease in renewable energy generation, which can lead to excessive imbalances. In the case of conventional technology, this can result in the payment of imbalance penalty costs that are greater than expected. In addition, electricity market prices can soar without warning even in the absence of a specific disaster or other event, which is difficult to predict, and this is one of the business risks for power generation companies.

In response to the above problem, the driving assistance device 1 of this embodiment reoptimizes the supply and demand plan under the condition that, when solving the optimization problem, a function related to the imbalance itself is included in the objective function of the optimization, or a constraint is imposed to keep the imbalance in each time period or the value of the function related to the imbalance below a certain value, rather than minimizing the penalty cost related to the imbalance. This eliminates the risk caused by the uncertainty in the prediction accuracy of the penalty cost unit price.

This configuration can reduce the risk of imbalance penalty costs when operating the microgrid 20.

In summary, the gist of the technology disclosed in this embodiment is as follows:

The first gist is that the driving assistance device, driving assistance method, or program therefor optimizes an energy supply and demand plan for a microgrid capable of receiving and sending energy to and from the outside, and is characterized in that the driving assistance device, driving assistance method, or program includes, in one or both of the constraint conditions and the objective function, a function related to the difference between the external energy supply and demand plan obtained by the optimization and a previously determined plan.

The second aspect is a driving assistance device, driving assistance method, or program as described in the first aspect, which includes a weighted sum of the absolute values of the difference at each time during the planning period as a function related to the difference between the energy transfer plan with the outside obtained by optimization and the determined plan.

The third aspect is a driving assistance device, driving assistance method, or program as described in the first aspect, which includes a weighted sum of squares of the difference at each time during the planning period as a function related to the difference between the energy transfer plan with the outside obtained by optimization and the determined plan.

The fourth aspect is a driving assistance device, driving assistance method, or program as described in the first aspect, which includes a maximum value of the difference at each time during the planning period as a function related to the difference between the energy transfer plan with the outside obtained by optimization and the determined plan.

Another aspect is a driving assistance device, driving assistance method, or program described in any one of the first to fourth aspects, which employs energy as a variable.

The fifth aspect is a driving assistance device, a driving assistance method, or a program described in any one of the first to fourth aspects, which includes electric power as the energy.

The sixth aspect is a driving assistance device, a driving assistance method, or a program according to any one of the first to fifth aspects, which includes charging and discharging a storage battery as a means for supply and demand planning.

The seventh aspect is a driving assistance device, driving assistance method, or program described in any one of the first to fifth aspects, which includes starting and stopping equipment having power generation capacity and changes in generated power as a means of supply and demand planning.

The eighth gist is the operation assistance device, operation assistance method, or program described in the sixth gist, which considers waste-to-energy generation as an example of equipment with power generation capacity.

The ninth aspect is an operation assistance device, an operation assistance method, or a program described in the sixth aspect, which considers biomass power generation as an example of equipment with power generation capacity.

The tenth aspect is an operation assistance device, an operation assistance method, or a program according to any one of the first to fifth aspects, which includes starting and stopping the water electrolysis device and increasing and decreasing the load as a means for supply and demand planning.

The eleventh aspect is a driving assistance device, a driving assistance method, or a program according to any one of the first to fifth aspects, which includes output suppression of a renewable energy power generation system as a means for supply and demand planning.

<Modification>
Although the embodiment of the present invention has been described above, the driving assistance device, the driving assistance method, and the driving assistance program of the present invention are not limited to the above embodiment.

In the above embodiment, the system that exchanges energy with the outside world is called a "microgrid" for convenience. The aspect that a microgrid refers to is not necessarily limited to, for example, a single factory or business site. For example, a microgrid may refer to an industrial complex that bundles together multiple factories, or may be a bundle of physically isolated factories. A microgrid may also be a form in which the supply and demand plan for energy equipment is left to an external party, and the microgrid only intermediates in the power purchase and sale contracts and financial transactions.

In the above embodiment, the time span per frame is 30 minutes. The time span per frame is not limited to 30 minutes. The time span per frame may be as short as 5 minutes or as long as one day.

<Additional comments>
Incidentally, the main power source for adjusting the imbalance to be eliminated is thermal power generation. In order to eliminate the imbalance, it is necessary to operate multiple units in a partial output state with a margin for both increasing and decreasing the output. This is not desirable from the viewpoint of power generation efficiency, and may lead to an increase in the unit price of power generation and an increase in _CO2 emissions. Therefore, suppressing and eliminating the imbalance does not only relate to the economics and business profits of a specific microgrid, but also relates to the economic energy supply and environmental load reduction of society as a whole. Therefore, the above-mentioned operation assistance device, operation assistance method, and operation assistance program contribute to Goal 7 "Ensure access to affordable, reliable, sustainable and modern energy for all" and Goal 13 "Take urgent measures to mitigate climate change and its impacts" of the Sustainable Development Goals (SDGs) led by the United Nations. The above-mentioned operation assistance device, operation assistance method, and operation assistance program, which aim to suppress the imbalance amount itself, not the cost, are excellent in this respect.

REFERENCE SIGNS LIST 1 Driving support device 11 Prediction value designation unit 12 Constraint condition designation unit 13 Objective function designation unit 14 Equipment characteristic database 15 Optimization calculation unit 16 Power receiving plan storage unit 17 Optimization result output unit 20, 20A Microgrid 21-23 Energy equipment 21A Storage battery 25 External energy supply source

Claims (13)

An operation assistance device that supports operation of a microgrid, the operation assistance device including an energy transfer device capable of transferring energy and also capable of transferring the energy from an external source,
an optimization calculation unit that calculates control information for controlling the energy transfer in the energy transfer device;
an optimization result output unit that outputs the control information;
A driving assistance device, wherein, when solving an optimization problem to calculate the control information, the optimization calculation unit treats a function indicating the difference between a predetermined determined plan, which is indicated by a time period and the energy demand associated with the time period, and the actual energy demand, as a constraint condition and/or an objective function. The driving assistance device according to claim 1, wherein the optimization calculation unit adopts a function including a weighted sum of absolute values of the difference between the determined plan and the actual energy demand at each time included in the planning period as a function indicating the difference. The driving assistance device according to claim 1, wherein the optimization calculation unit adopts a function including a weighted sum of squares of the difference between the determined plan and the actual energy demand at each time included in the planning period as a function indicating the difference. The driving assistance device according to claim 1, wherein the optimization calculation unit adopts a function including the maximum value of the difference between the determined plan and the actual energy demand at each time included in the planning period as a function indicating the difference. The driving assistance device according to any one of claims 1 to 4, wherein the optimization calculation unit treats the energy as electric power. The driving support device according to any one of claims 1 to 5, wherein the optimization calculation unit calculates, as the control information, information for controlling charging and discharging of the storage battery that is the energy transfer device. The driving support device according to any one of claims 1 to 5, wherein the optimization calculation unit calculates, as the control information, information for controlling the start, stop, and amount of generated power of the power generation device, which is the energy transfer device. The driving support device according to claim 6, wherein the optimization calculation unit calculates, as the control information, information for controlling the start, stop, and amount of generated power of the waste-to-energy device, which is the energy transfer device. The driving support device according to claim 6, wherein the optimization calculation unit calculates, as the control information, information for controlling the start, stop, and amount of generated power of the biomass power generation device, which is the energy transfer device. The driving support device according to any one of claims 1 to 5, wherein the optimization calculation unit calculates, as the control information, information for controlling the start, stop, and load of the water electrolysis device, which is the energy transfer device. The tenth aspect is a driving assistance device according to any one of claims 1 to 5, which calculates, as the control information, information for suppressing the output of the renewable energy power generation system that is the energy transfer device. An operation assistance method for assisting operation of a microgrid that includes an energy transfer device capable of transferring energy and is also capable of transferring the energy from an external source, comprising:
calculating control information for controlling the energy transfer in the energy transfer device;
and outputting the control information.
A driving assistance method in which the step of calculating control information involves treating a function indicating a difference between a predetermined determined plan, which is indicated by a time period and the energy demand associated with the time period, and an actual energy demand, as a constraint condition and/or an objective function when solving an optimization problem for calculating the control information. An operation assistance program that causes a computer to assist in the operation of a microgrid that includes an energy transfer device capable of transferring energy and is also capable of transferring the energy from an outside source,
calculating control information for controlling the energy transfer in the energy transfer device;
outputting the control information;
In the step of calculating control information, the driving assistance program causes the computer to operate in such a way that, when solving the optimization problem for calculating the control information, a function indicating the difference between a predetermined determined plan, which is represented by a time period and the energy demand associated with the time period, and the actual energy demand is treated as a constraint condition and/or an objective function.

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