JP2016042748A - Energy management system and power demand plan optimization method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an energy management system and a power demand plan optimization method capable of obtaining a solution reliably while shortening the time required for acquiring a solution.SOLUTION: An energy management system 20 includes a demand plan calculation unit 22c for obtaining the solution of a demand plan, including a control value for an apparatus at a plurality of times in a planning period, by solving the optimization problem of an objective function that is an index of optimization. The demand plan calculation unit 22c acquires a primary optimal solution S1 by searching a plurality of first discrete values obtained by dividing the value of independent variables, i.e., the variables of control value, while furthermore, acquires a secondary optimal solution S2 by searching a plurality of second discrete values obtained by dividing the value of independent variables in a range including the primary optimal solution S1 finer than the plurality of first discrete values.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an energy management system and a power supply and demand plan optimization method.

  As described in Patent Literature 1, a power supply system including a power storage unit, a charging stand for charging an in-vehicle power storage device, a solar power generator, and a general load is known. These devices are provided in a house or the like, and are connected to an AC power line via an integrated power control device. The integrated power control device determines a charging / discharging schedule of the power device based on the prediction data of the power consumption amount, the prediction data of the power generation amount, and the use schedule of the in-vehicle power storage device. The integrated power control device controls the power charged to the power storage unit and the in-vehicle power storage device and the power discharged from the power storage unit and the on-vehicle power storage device to the AC power line according to the charge / discharge schedule.

  The integrated power control apparatus formulates and calculates a charge / discharge schedule into a mixed integer programming problem so that a preset evaluation index becomes a predetermined value. In mixed integer programming problems, conditional branches are expressed by linear inequality constraints using logical variables. By expressing conditional branches in this way, a solution is obtained using an optimization tool.

JP2013-27214A

  By the way, the supply and demand planning problem related to power supply and demand and battery charge and discharge is generally a nonlinear mixed integer problem. Especially in the integer problem, it is difficult to obtain an analytical solution. Conventionally, an approximate solution is obtained by a technique such as continuous relaxation, and then an integer problem is obtained by a technique such as metaheuristics. However, with such a conventional method, it may take time to obtain a solution or an optimal solution may not be obtained.

  It is an object of the present invention to provide an energy management system and an electric power supply and demand plan optimization method that can shorten the time required to obtain a solution and can reliably obtain the solution.

  The present invention provides energy management for optimizing energy supply and demand plans in a microgrid that includes a power-controllable device including a power storage device, a generator that generates power using renewable energy, and a load, and that is connected to an external power system The system is equipped with a supply and demand plan calculation unit that obtains a solution to supply and demand plans including control values for equipment at multiple times within the planning period by solving the optimization problem of the objective function that is an optimization index. The plan calculation unit obtains a primary optimal solution by searching for a plurality of first discrete values obtained by dividing the value of the independent variable that is a variable of the control value, and further, an independent variable within a certain range including the primary optimal solution. A second optimal solution is obtained by searching for a plurality of second discrete values obtained by dividing the value of more finely than the plurality of first discrete values.

  According to this energy management system, an optimization problem of an objective function that is an optimization index is solved by the supply and demand plan calculation unit, and a supply and demand plan solution at a plurality of times within the planning period is obtained. This solution contains control values for the device, which controls the device in the microgrid. First, the demand-and-supply plan calculation unit obtains a first optimal solution by searching for a plurality of first discrete values obtained by dividing the value of an independent variable that is a variable of a control value. Therefore, even when the independent variable is in a continuous state, the value of the independent variable is divided (that is, discretized) and replaced with the first discrete value, and the search method is used, so that a solution can be reliably obtained. For example, even if the optimization problem is a nonlinear mixed integer problem, a solution can be obtained reliably. Furthermore, the demand-and-supply plan calculation unit obtains the secondary optimal solution by searching for a plurality of second discrete values obtained by dividing the value of the independent variable within a certain range including the primary optimal solution. These second discrete values are discrete values divided more finely than the first discrete value around the first-order optimal solution. In this way, the value of the independent variable is finely subdivided around the first-order optimal solution, and the same search is performed again, thereby improving the accuracy of the solution. According to the above two-stage search method, the same search may be performed twice, so that the time required for obtaining the solution is shortened. Moreover, a solution with a certain accuracy can be obtained with certainty.

  In some aspects, the supply and demand plan calculation unit obtains the primary optimal solution and the secondary optimal solution by applying dynamic programming to each of the search for the first discrete value and the search for the second discrete value. . In this case, a solution can be obtained more reliably by a search using dynamic programming.

  In some aspects, the supply and demand plan calculation unit searches a one-dimensional search space composed of one independent variable. In this case, since the amount of calculation is relatively small, it is possible to obtain a solution of the supply and demand plan for a certain independent variable in a short time.

  In some embodiments, the supply and demand plan calculation unit searches a multidimensional search space including a plurality of independent variables. In this case, it is possible to obtain a solution of the supply and demand plan for a plurality of independent variables, and to enable flexible operation control of the equipment in the microgrid.

  The present invention provides a power supply and demand that optimizes an energy supply and demand plan in a microgrid that includes a power-controllable device including a power storage device, a generator that generates power using renewable energy, and a load, and that is connected to an external power system A plan optimization method in which an energy management system solves an optimization problem of an objective function that is an optimization index, thereby obtaining a supply and demand plan solution including control values for devices at multiple times within the planning period. Including a supply and demand plan calculation step, wherein the energy management system obtains a first optimal solution by searching for a plurality of first discrete values obtained by dividing the value of the independent variable that is a variable of the control value; Further, a plurality of second separation values obtained by dividing the value of the independent variable including the first-order optimal solution more finely than the plurality of first discrete values. Acquiring secondary optimal solution by searching the values. According to this electric power supply and demand plan optimization method, the same operation effect as the above-mentioned energy management system is produced.

  According to the present invention, the time required to obtain a solution can be shortened, and a solution with a certain accuracy can be obtained with certainty.

It is a block diagram which shows schematic structure of the electric power supply system to which the energy management system of 1st Embodiment of this invention was applied. It is a functional block diagram of the energy management system in FIG. It is a flowchart which shows the process performed in the energy management system of FIG. It is a flowchart which shows the supply-and-demand plan process in FIG. It is a schematic diagram which shows the search space of the primary search in a supply-and-demand plan process. It is a schematic diagram which shows the mode of a search. It is a schematic diagram which shows an example of the primary optimal solution obtained by a primary search. It is a schematic diagram which shows the search space of the secondary search in a supply-and-demand plan process. It is the elements on larger scale of FIG. 8, and is a schematic diagram which shows an example of the secondary optimal solution obtained by secondary search. It is a flowchart which shows the supply-and-demand balance control in FIG.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the description of the drawings, the same elements are denoted by the same reference numerals, and redundant descriptions are omitted.

  With reference to FIG. 1, the power supply system 1 to which the energy management system according to the first embodiment is applied will be described. As shown in FIG. 1, the power supply system 1 includes a microgrid G including a plurality of types of generators and power consuming devices, and an energy management system 20 that calculates control values of the devices in the microgrid G. Prepare. In the following description, “energy management system” is abbreviated as “EMS”. In FIG. 1, a solid line arrow indicates the flow of electricity, and a broken line arrow indicates the flow of information.

  The power supply system 1 can be used by a consumer who owns a building or a factory, for example. The power supply system 1 can be used by, for example, a power sales company that owns a renewable energy generator. The EMS 20 provides an optimized energy supply and demand plan to such a consumer or a power sales company.

  The microgrid G includes a renewable energy generator 5 including a solar power generator 3 and a wind power generator 4, and a prime mover 7 that generates power using fossil fuel. For example, a gas turbine can be used as the prime mover 7. Further, a gas turbine for cogeneration can be used as the prime mover 7. The microgrid G further includes a load 6 that consumes power in the microgrid G, and an electric vehicle (power storage device) 8 that travels using the power in the microgrid G. The load 6 may include a plurality of devices that consume power. The electric vehicle 8 includes a storage battery (not shown) and can store and discharge electric power. The electric vehicle 8 may include a charging station, for example. The microgrid G further includes a storage battery (power storage device) 9 capable of storing and discharging electric power. As the storage battery 9, a lithium ion battery can be used, for example. The microgrid G includes a grid control device 10 to which the plurality of power devices described above are electrically connected. The grid control device 10 is connected to the external power system 2. In other words, each of the plurality of power devices described above is connected to the external power system 2 via the grid control device 10.

  The electric power used in the microgrid G is covered by the renewable energy generator 5 or the prime mover 7, the electric vehicle 8 or the storage battery 9. In addition, in the microgrid G, it is possible to purchase power (that is, purchase power) from the external power system 2 or sell power (that is, sell power) by a reverse flow to the external power system 2. Generally, the power purchase price may depend on a contract with an electric power company or may vary depending on a time zone. The power selling price is defined by, for example, the “Special Measures Law concerning Renewable Energy Electricity Procurement by Electric Power Companies”. The power purchase price or the power sale price may be determined separately from the above system.

  The grid control device 10 includes hardware such as a CPU (Central Processing Unit), ROM (Read Only Memory), and RAM (Random Access Memory), and software such as a program stored in the ROM. The grid control device 10 comprehensively controls power devices connected to the grid control device 10. The grid control device 10 includes a control circuit and a storage battery for controlling the flow of electricity. The grid control device 10 controls received power from the external power grid 2 and reverse power flow to the external power grid 2. The grid control device 10 and each power device can perform information communication with each other. The grid control device 10 acquires information on the current state of each power device. The grid control device 10 outputs a control value to each power device. The grid control device 10 stores information about each acquired power device. The grid control device 10 transmits information regarding each power device to the EMS 20.

  Among the power devices of the grid control device 10 described above, the prime mover 7, the electric vehicle 8, and the storage battery 9 correspond to devices whose output can be controlled by the grid control device 10. The grid control device 10 receives a control value from the EMS 20 and outputs a start, stop, output command, and the like to the prime mover 7 based on the control value. The grid control device 10 inputs a control value from the EMS 20 and outputs a charge / discharge command or the like to the storage battery 9 based on the control value.

  The EMS 20 is a computer configured by hardware such as a CPU, a ROM, and a RAM, and software such as a program stored in the ROM. The EMS 20 and the grid control device 10 can perform information communication with each other. The EMS 20 and the grid control device 10 may be communicable via the Internet, or may be communicable via a wired or wireless LAN. The EMS 20 has a function of calculating a control value for each power device in the microgrid G. In other words, the EMS 20 controls each power device in the microgrid G through the grid control device 10 by transmitting a control value to the grid control device 10.

More specifically, the EMS 20 optimizes an energy supply and demand plan for a certain period in the future in the microgrid G using a certain index as an objective function. As an optimization index, that is, an objective function, for example, grid operation costs, that is, electricity charges are minimized during a planning period (for example, one day), received power fluctuation amount is minimized during a planning period, and emission CO ₂ is minimized. Is mentioned. The EMS 20 calculates the supply and demand plan by solving the optimization problem of the objective function. The objective function may be an index other than the above three indices. The index used as the objective function is not particularly limited. The objective function may be defined by a weighted sum of a plurality of objective functions.

Examples of the independent variable of the objective function include variables shown in Table 1 below. The independent variable may be anything as long as it can be controlled in the microgrid G. The number of prime movers 7, electric vehicles 8, and storage batteries 9, which are controllable devices in the microgrid G, may be arbitrary as long as it is 1 or more as a whole, and may have different performance for each device.

  On the other hand, the power supply system 1 includes a demand / power generation amount prediction database 12 that stores a predicted amount of power demand and power generation amount in the microgrid G. The EMS 20 and the demand / power generation amount prediction database 12 can perform information communication with each other. The EMS 20 acquires the demand power generation amount and the power generation amount prediction value at a specific date and time from the demand / power generation amount prediction database 12. The demand power amount and the predicted value of the power generation amount stored in the demand / power generation amount prediction database 12 are periodically updated.

  The EMS 20 includes the latest information (for example, device status) regarding each power device of the microgrid G acquired from the grid control device 10 and the latest information regarding the power generation amount and the power demand amount acquired from the demand / power generation amount prediction database 12 ( For example, the supply and demand plan is corrected (that is, the supply and demand plan is updated) based on the predicted value and the like. Information (for example, device status) regarding each electric power device of the microgrid G and / or information (for example, predicted value) regarding the power generation amount and the power demand amount are used as parameters in the calculation of the supply and demand plan. That is, the parameter includes a current value or a predicted value related to at least one of the renewable energy generator 5, the prime mover 7, the storage battery 9, and the load 6. Specific parameters include, for example, various parameters shown in Table 2 below.

上表において、ＳＯＣは充放電状態（Ｓｔａｔｅ ｏｆ Ｃｈａｒｇｅ）を意味し、満充電の状態を１００％としたときの充電率を表す。ＰＣＳはパワーコンディショナ（Ｐｏｗｅｒ Ｃｏｎｄｉｔｉｏｎｉｎｇ Ｓｕｂｓｙｓｔｅｍ）を意味する。

In the above table, SOC means a charge / discharge state (State of Charge), and represents a charging rate when the fully charged state is 100%. PCS stands for Power Conditioning Subsystem.

  In the microgrid G, the independent variables and parameters depend on how the optimization problem is constructed, and therefore cannot be uniquely determined for the same grid. For example, if charging / discharging power of the storage battery 9 is an independent variable, the SOC becomes a dependent variable. On the other hand, when the SOC is an independent variable, a formulation with the charge / discharge power of the storage battery 9 as a dependent variable is also possible.

  The EMS 20 calculates the value of an independent variable that minimizes the objective function under given constraints in the calculation of the supply and demand plan. For example, the EMS 20 calculates the generated power at each time in the prime mover 7 by solving the optimization problem of the objective function. The EMS 20 calculates charge / discharge power at each time in the electric vehicle 8. The EMS 20 calculates the charge / discharge power at each time in the storage battery 9. The EMS 20 may calculate the SOC at each time in the storage battery 9. The EMS 20 calculates the received power at each time of purchase from the external power system 2 depending on the generated power of the prime mover 7, the charge / discharge power of the electric vehicle 8, and the charge / discharge power of the storage battery 9.

  A functional configuration of the EMS 20 will be described with reference to FIG. The EMS 20 includes an overall control unit 20a, a communication unit 21, a calculation unit 22, a storage unit 23, and a display unit 24. The overall control unit 20a performs overall control of processing in the EMS 20. The overall control unit 20a determines, for example, whether or not it is the time of supply and demand prediction processing. The overall control unit 20a determines whether it is time for supply-demand balance control. The communication unit 21 is an input / output unit that performs information communication with the grid control device 10 and the demand / power generation amount prediction database 12. The calculation unit 22 calculates a supply and demand plan.

  The calculation unit 22 includes a supply / demand prediction calculation unit 22a, a discrete value determination unit 22b, a supply / demand plan calculation unit 22c, and a supply / demand balance control unit 22d. The supply and demand prediction calculation unit 22a executes a supply and demand prediction process based on various data such as past statistical information, weather forecasts, and demand predictions. The supply and demand prediction calculation unit 22a generates supply and demand prediction information by executing a supply and demand prediction process. The supply and demand prediction calculation unit 22a generates and outputs supply and demand prediction information within a planned time (for example, 24 hours) at a predetermined time interval (for example, every 30 minutes).

  The discrete value determination unit 22b determines a first discrete value and a second discrete value used for calculation by the supply and demand plan calculation unit 22c. The EMS 20 performs a two-stage search when calculating the supply and demand plan. In each of the two-stage searches, the first discrete value and the second discrete value, which are the values of the independent variables, are used. The discrete value determination unit 22b generates a plurality of first discrete values by dividing the value of the independent variable into a predetermined number. Note that the first discrete value may be determined in advance. The discrete value determination unit 22b determines the second discrete value based on the first-order optimal solution obtained by the first stage search. In other words, the discrete value determination unit 22b generates a plurality of second discrete values by dividing a predetermined range of independent variable values including the primary optimal solution into a predetermined number. As described above, the discrete value determination unit 22b converts the value of the independent variable that is originally a continuous state (for example, the charge / discharge power amount or the SOC value in the storage battery 9) into the discrete state. The second discrete value is a discrete value that is divided more finely than the first discrete value around the first-order optimal solution.

  The supply and demand plan calculation unit 22c executes the supply and demand planning process by solving the optimization problem (calculates the supply and demand plan). More specifically, the supply and demand plan calculation unit 22c performs a two-stage search using dynamic programming (search method) for the first and second discrete values discretized by the discrete value determination unit 22b. . The supply and demand plan calculation unit 22c solves the optimization problem by performing a two-stage search, and obtains an optimal solution for the supply and demand plan at a plurality of times within the planning period. In other words, first, the supply and demand plan calculation unit 22c acquires a first optimal solution by searching for a plurality of first discrete values. Furthermore, the demand-and-supply plan calculation unit 22c acquires a secondary optimal solution by searching for a plurality of second discrete values generated around the primary optimal solution. This secondary optimal solution includes plan information (control values) for devices capable of output control, such as the prime mover 7, the electric vehicle 8, and the storage battery 9. The supply and demand plan calculation unit 22c generates and outputs plan information within the planned time (for example, 24 hours) at a predetermined time interval (for example, every 30 minutes).

  The supply / demand balance control unit 22d executes supply / demand balance control. The supply and demand balance control unit 22d may execute supply and demand balance control using a known method. The supply / demand balance control unit 22d executes supply / demand balance control for the device to be controlled using, for example, hysteresis control. The supply and demand balance control unit 22d generates control information (control value) that is corrected plan information by executing supply and demand balance processing. The supply and demand balance control unit 22d generates and outputs control information at a predetermined time interval (for example, every one minute).

  The storage unit 23 includes a hard disk device or a flash memory. The storage unit 23 includes a supply / demand prediction storage unit 23a, a supply / demand plan storage unit 23b, and a control value storage unit 23c. The supply and demand prediction storage unit 23a stores the supply and demand prediction information generated by the supply and demand prediction calculation unit 22a. The supply and demand plan storage unit 23b stores the plan information calculated by the supply and demand plan calculation unit 22c. The supply and demand plan storage unit 23b includes a primary optimal solution storage unit 23ba and a secondary optimal solution storage unit 23bb. The primary optimal solution storage unit 23ba stores the primary optimal solution acquired and generated by the supply and demand plan calculation unit 22c. The secondary optimal solution storage unit 23bb stores the secondary optimal solution acquired and generated by the supply and demand plan calculation unit 22c. The control value storage unit 23c stores the control information generated by the supply / demand balance control unit 22d.

  The display unit 24 is a display, for example, and displays parameters, supply and demand plans, and the like. The display unit 24 may display detailed information included in parameters, supply and demand plans, and the like. For example, the display unit 24 includes a touch panel or the like, and may display an operation schedule (such as a charge / discharge schedule or a power generation schedule) of the power device when the power device in the microgrid G is selected. The display unit 24 may be controlled by the calculation unit 22 to display a predetermined message (for example, a guidance message or a warning message) to the user of the EMS 20.

  With reference to FIG. 3, the process performed in EMS20, ie, the power supply and demand plan optimization method of this embodiment, is demonstrated. The following processing is periodically executed while the EMS 20 is activated. As shown in FIG. 3, the supply and demand prediction calculation unit 22a acquires various data such as past statistical information, weather forecasts, and demand forecasts (step S01). The supply and demand prediction calculation unit 22a executes a supply and demand prediction process based on the acquired various data (step S02). The supply and demand prediction calculation unit 22a generates and outputs supply and demand prediction information within the planned time (for example, for 24 hours) (step S03).

  Subsequently, the supply and demand plan calculation unit 22c acquires the actual measurement values of the devices of the microgrid G via the grid control device 10 (step S04). Then, the supply / demand plan calculation unit 22c executes a supply / demand plan process (step S05; supply / demand plan calculation step). This supply and demand planning process is executed, for example, according to the processing procedure shown in FIG.

  Hereinafter, the supply and demand planning process will be described in detail with reference to FIGS. 4 and 5 to 9. In the following, in order to simplify the explanation, a supply and demand plan for the purpose of peak cut in the storage battery 9 is assumed. In this case, the operation of the microgrid G that realizes the peak cut is required because the electricity charge due to power purchase is the lowest. That is, the optimization problem becomes a problem of determining the charge / discharge power amount of the storage battery 9 at each time. This optimization problem is a problem of planning when the storage battery 9 should be charged / discharged based on the supply and demand prediction information. Hereinafter, a charging / discharging plan of the storage battery 9 for reducing the power purchase cost (that is, making it the cheapest) with the peak cut as a constraint is considered.

ここで、Ｃ_Ｅｒ（ｉ）は、時刻ｉでの電気料金（円／ｋＷｈ）であり、Ｅ_ｒ（ｉ）は、時刻ｉでの３０分間の買電電力である。Ｅ_ｂ（ｉ）は、時刻ｉでの蓄電池９の充放電電力であり、Ｅ_Ｌ（ｉ）は、時刻ｉでの負荷である。Ｅ_ｒｍａｘ（ｉ）は、時刻ｉにおける買電電力の上限（すなわちピークカット値）である。 The evaluation function and the constraint condition are expressed by the following formula (1), formula (2), and formula (3).

Here, C _Er (i) is the electricity bill (yen / kWh) at time i, and E _r (i) is the purchased power for 30 minutes at time i. E _b (i) is the charge / discharge power of the storage battery 9 at time i, and E _L (i) is the load at time i. E _rmax (i) is the upper limit (that is, the peak cut value) of the purchased power at time i.

ここで、ＳＯＣ（ｉ）は、時刻ｉでの電池残量（％）、係数ｋは、充放電電力量に対する蓄電池９のＳＯＣの変化量を示す。ここでは、手法の説明を簡単にするため、充電量（ＳＯＣ）を決定変数にとる。 Further, the following expressions (4) and (5) are established.

Here, SOC (i) indicates the remaining battery level (%) at time i, and the coefficient k indicates the amount of change in the SOC of the storage battery 9 with respect to the charge / discharge power amount. Here, in order to simplify the description of the method, the amount of charge (SOC) is taken as a decision variable.

  As shown in FIG. 4, the discrete value determining unit 22b determines the first discrete value (step S51). Dynamic programming is a method applied to discrete state search. As shown in FIG. 5, the discrete value determining unit 22b defines the SOC value by discretizing it every 10%. That is, the discrete value determination unit 22b divides the SOC value in the range of 0 to 100% into ten values. The time on the horizontal axis is also defined by being discretized at 30-minute intervals. In this case, the only independent variable is SOC, and the search space is one-dimensional.

  Next, the supply and demand plan calculation unit 22c performs a primary search by a dynamic programming method for the plurality of first discrete values and the plurality of times (step S52). Here, the problem is the same as the route search from the start state to any state at the end time, and the route having the smallest evaluation function value is the optimal solution. Thus, in the primary search, a search algorithm is applied to the tree structure. The initial value in the primary search can be, for example, the current SOC value (actual value) at the time of calculation execution.

ここで、ｆ_Ｎ（ｓ_Ｎ）は、状態Ｓ_Ｎでのコスト値、Ｓ_ｉは、時刻iでのＳＯＣ、ｘ_ｉは、時刻ｉでの充放電電力量であり、ｉ＝１，２，３，４，…，Ｎとする。上記式（６）は、時刻iに関して、下記式（７）のように表される。

Dynamic programming will be described. As a formulation of dynamic programming, for example, the following formula (6) is given as an example.

Here, f _N (s _N ) is the cost value in the state S _N , S _i is the SOC at the time i, x _i is the charge / discharge power amount at the time i, and i = 1, 2, 3, 4, ..., N. The above formula (6) is expressed as the following formula (7) with respect to the time i.

以上のようにして、需給計画計算部２２ｃは、最適な蓄電池９の充放電の量と時刻を決定することができる。 In Expression (7), the minimum value of f ₂ (x ₂ ) + f ₁ (S ₂ , x ₂ ) is obtained. This is because, if determined to a value that can take x _2, since the optimization of one variable for s _i, can be relatively easily calculated. This is repeated until i = N-1. f _N (s _N ) is expressed as in the following formula (8).

As described above, the supply and demand plan calculation unit 22c can determine the optimal charge and discharge amount and time of the storage battery 9.

As shown in FIG. 6, each of Ax, Bx, Cx, Dx, and Ex is a discretized SOC value that can be taken at each time. Cxx is a cost for a possible transition (a power purchase cost in this embodiment). The variable x corresponds to which route has passed to the state at the next time. As can be seen from FIG. 6, at time t2, the state transitions from the state B1 to B5 to the state s2 = {C1, C2,..., C5}. The lowest cost in this state is expressed as the following formula (9).

Supply Planning calculation unit 22c, by repeating the above optimization l to f _N, obtain the lowest costs. In addition, if x _{i at} which the minimum cost is obtained is recorded, this is the SOC behavior to be realized in the storage battery 9. As described above, the supply and demand plan calculation unit 22c acquires the primary optimal solution (step S53; see the primary optimal solution S1 shown in FIG. 7). The supply and demand plan calculation unit 22c stores the primary optimal solution in the primary optimal solution storage unit 23ba.

  Subsequently, the discrete value determining unit 22b determines the second discrete value (step S54). The discrete value determining unit 22b sets a certain width up and down around the first-order optimal solution. In the example shown in FIG. 8, a fixed range of 10% in the positive (plus) direction and 10% in the negative (minus) direction is set around the first-order optimal solution. Then, the discrete value determination unit 22b defines and defines the SOC value in the range of 20% by every 2%. That is, the discrete value determination unit 22b divides (re-divides) the SOC value in the range of 20% into ten. The number of first discrete values and the number of second discrete values may be the same as described above, but may be different.

  And the demand-and-supply plan calculation part 22c performs the secondary search by a dynamic programming method with respect to several 2nd discrete values and several time (step S55). Here, a search using dynamic programming is performed by the same method as described above. Then, the supply and demand plan calculation unit 22c acquires a secondary optimal solution within a certain range including the primary optimal solution S1 (step S56; see the secondary optimal solution S2 shown in FIG. 9). The supply and demand plan calculation unit 22c stores the secondary optimal solution in the secondary optimal solution storage unit 23bb.

  In this way, the discrete value determination unit 22b sets a fixed range in the positive direction and the negative direction with the first-order optimal solution S1 as the center, and subdivides the range. After the subdivision, the supply and demand plan calculation unit 22c performs the same search again, so that the resolution is increased and the accuracy of the solution is improved. In this case, it is desirable that there is no optimal solution that differs greatly from the primary optimal solution.

  Then, the supply and demand plan calculation unit 22c generates plan information (step S57) and outputs the plan information (step S06 shown in FIG. 3).

  Subsequently, the supply and demand balance control unit 22d acquires the actual measurement values of the devices of the microgrid G via the grid control device 10 (step S07). Then, the supply / demand balance control unit 22d executes supply / demand balance control (step S08). This supply-demand balance control is executed according to the processing procedure shown in FIG. 10, for example.

First, the supply-and-demand balance control part 22d acquires the difference value Ds of the planned value and actual value of SOC in the storage battery 9 (step S81). Next, the supply and demand balance control unit 22d determines whether or not the difference value Ds is larger than the allowable value ε (step S82). Here, the allowable value ε can be a value of 5% or less. If it is determined in step S82 that the difference value Ds is greater than the allowable value ε, the supply and demand balance control unit 22d determines that the actual measurement value is greater than the planned value, and increases the charge / discharge power E _b of the storage battery 9 to increase the load _Er and Control information that reduces the output E _{GT of the} prime mover 7 is generated (step S83).

If it is determined in step S82 that the difference value Ds is less than or equal to the allowable value ε, the supply and demand balance control unit 22d determines whether or not the difference value Ds is smaller than the allowable value −ε (step S84). If it is determined in step S84 that the difference value Ds is smaller than the allowable value −ε, the supply and demand balance control unit 22d determines that the actual measurement value is smaller than the planned value, and decreases the charge / discharge power E _b of the storage battery 9 to thereby reduce the load E _r. Then, control information that increases the output E _{GT of the} prime mover 7 is generated (step S85).

If it is determined in step S84 that the difference value Ds is greater than or equal to the allowable value −ε, the supply and demand balance control unit 22d assumes that the actual measurement value substantially matches the plan value and does not change the charge / discharge power E _b of the storage battery 9. Then, control information that does not change the load _Er and the output E _{GT of the} prime mover 7 is generated (step S86). Then, the supply and demand balance control unit 22d outputs the generated control information (step S09 shown in FIG. 3).

  The overall control unit 20a determines whether or not it is time for the supply and demand prediction process (step S10). The time of the supply and demand prediction process is set, for example, at 30 minute intervals. When the overall control unit 20a determines that it is the time of the supply / demand prediction process in step S10, the overall control unit 20a sends the supply / demand prediction calculation unit 22a, the discrete value determination unit 22b, the supply / demand plan calculation unit 22c, and the supply / demand balance control unit 22d to the steps S01 to S09. Execute the process.

  If the overall control unit 20a determines in step S10 that it is not the time of supply and demand prediction processing, it determines whether or not it is the time of supply and demand balance control (step S11). The time of the supply and demand prediction process is set at intervals of 1 minute, for example. If the overall control unit 20a determines in step S11 that the supply / demand balance control time is reached, the overall control unit 20a causes the supply / demand balance control unit 22d to execute the processes of steps S07 to S09. If the overall control unit 20a determines in step S11 that the supply / demand balance control time is not reached, the overall control unit 20a ends the series of processes shown in FIG.

  According to the EMS 20 and the power supply and demand plan optimization method described above, the supply and demand plan calculation unit 22c solves the optimization problem of the objective function that is an optimization index, and solves the supply and demand plan at a plurality of times within the planning period. Is obtained. This solution includes control values for the devices (the prime mover 7, the electric vehicle 8, the storage battery 9, etc.), and the devices are controlled by this solution. The demand-and-supply plan calculation unit 22c acquires the first optimal solution S1 by searching for a plurality of first discrete values obtained by dividing the value of the independent variable that is the variable of the control value (see FIGS. 5 and 7). Therefore, even when the independent variable is in a continuous state, the value of the independent variable is divided (that is, discretized) and replaced with the first discrete value, and the search method is used, so that a solution can be reliably obtained. For example, even if the optimization problem is a nonlinear mixed integer problem, a solution can be obtained reliably. Furthermore, the demand-and-supply plan calculation unit 22c obtains the secondary optimal solution S2 by searching for a plurality of second discrete values obtained by dividing the value of the independent variable within a certain range including the primary optimal solution S1 (see FIG. 8 and FIG. 8). (See FIG. 9). These second discrete values are discrete values divided more finely than the first discrete value around the first-order optimal solution S1. Thus, the accuracy of the solution is improved by finely subdividing the value of the independent variable around the first-order optimal solution S1 and performing the same search again. According to such a two-stage search method, the same search may be performed twice, so that the time required for obtaining the solution is shortened. Moreover, a solution with a certain accuracy can be obtained with certainty.

  The supply and demand plan calculation unit 22c applies dynamic programming in each of the primary search and the secondary search. A search using dynamic programming provides a more reliable solution.

  The supply and demand plan calculation unit 22c searches a one-dimensional search space composed of one independent variable. In this case, since the amount of calculation is relatively small, it is possible to obtain a solution of the supply and demand plan for a certain independent variable in a short time. Further, the amount of memory used in the EMS 20 is reduced.

  Subsequently, a second embodiment of the present invention will be described. In the EMS 20 of the second embodiment, the supply and demand plan calculation unit 22c searches for a multidimensional search space including a plurality of independent variables. For example, when the prime mover 7 is added to the optimization problem dealt with in the first embodiment and the start and stop times of the prime mover 7 and the amount of power generation are determined, the optimization problem is a nonlinear mixed integer problem. Become.

ここで、Ｃ_Ｅｒ（ｉ）は、時刻ｉでの電気料金（円／ｋＷｈ）であり、Ｅ_ｒ（ｉ）は、時刻ｉでの３０分間の買電電力である。また、Ｃ_ＥＧＴ（ｉ）は、原動機７にて１ｋＷｈを発電するための燃料コスト（円／ｋＷｈ）であり、Ｅ_ＧＴ（ｉ）は、時刻ｉでの３０分間の原動機７の発電電力である。 In this case, the evaluation function is represented by the following formula (10).

Here, C _Er (i) is the electricity bill (yen / kWh) at time i, and E _r (i) is the purchased power for 30 minutes at time i. C _EGT (i) is the fuel cost (yen / kWh) for generating 1 kWh at the prime mover 7, and E _GT (i) is the generated power of the prime mover 7 for 30 minutes at time i. .

Moreover, a constraint condition is represented by following formula (11)-Formula (15).

In each of the above equations, E _b (i) is the charge / discharge power of the storage battery 9 at time i, and E _L (i) is the load at time i. E _rmax (i) is the upper limit (that is, the peak cut value) of the purchased power at time i. E _GTmax (i) is an upper limit value of the output of the prime mover 7, and E _GTmin (i) is a lower limit value of the output of the prime mover 7. SOC (i) indicates the remaining battery level (%) at time i, and coefficient k indicates the amount of change in the SOC of the storage battery 9 with respect to the charge / discharge power amount.

  The EMS 20 can perform a two-stage search similar to the first embodiment for the evaluation function and the constraint conditions of the second embodiment. Even if the optimization problem is a nonlinear mixed integer problem, the time required to obtain the solution is reduced, and a solution with a certain accuracy can be obtained with certainty. Moreover, the solution of the demand-and-supply plan regarding a plurality of independent variables (that is, control values of the prime mover 7 and the storage battery 9) can be acquired, and flexible operation control of devices in the microgrid G becomes possible.

  As mentioned above, although embodiment of this invention was described, this invention is not limited to the said embodiment. For example, in the first embodiment, the case where the search is performed twice has been described, but the same search may be repeated three or more times. In that case, a discrete value for the next search may be determined based on the optimal solution obtained sequentially. Moreover, although the said embodiment demonstrated the case where the supply-and-demand plan calculation part 22c searches by a dynamic programming method, it is not restricted to this, You may apply another search method (search algorithm). For example, a greedy method or a branch and bound method may be employed.

  The fixed range around the first-order optimal solution may be determined in any way. It may be a fixed range where the first-order optimal solution is located at the center, but may be a fixed range where the first-order optimal solution is not located at the center. The size of the certain range can be changed as appropriate.

  Further, the fineness of division (step size, number of divisions) may be determined in any way. The number of divisions in the primary search and the number of divisions in the secondary search may be the same or different. The number of divisions of the planning period, that is, a plurality of times at which solutions can be obtained, may be determined in any manner according to the operation policy of the microgrid G.

  In the above embodiment, the case where the EMS 20 is provided separately from the microgrid G has been described, but the grid control device 10 of the microgrid G may have the same function as the EMS20. The microgrid G may include a fuel cell system, for example.

DESCRIPTION OF SYMBOLS 1 Electric power supply system 2 External power system 3 Solar power generator 4 Wind power generator 5 Renewable energy generator 6 Load 7 Engine 8 Electric vehicle 9 Storage battery 20 Energy management system 20a General control part 22 Calculation part 22a Supply-demand prediction calculation part 22b Discrete Value determining unit 22c Supply / demand plan calculation unit 22d Supply / demand balance control unit 23 Storage unit 23a Supply / demand prediction storage unit 23b Supply / demand plan storage unit 23ba Primary optimal solution storage unit 23bb Secondary optimal solution storage unit 23c Control value storage unit G Microgrid

Claims (5)

An energy management system for optimizing energy supply and demand plans in a microgrid that includes a power-controllable device including a power storage device, a generator that generates power using renewable energy, and a load, and that is connected to an external power system ,
A demand-and-supply plan calculation unit that obtains a solution of the demand-and-supply plan including control values for the equipment at a plurality of times within a planning period by solving an optimization problem of an objective function that is an optimization index;
The supply and demand plan calculation unit obtains a primary optimal solution by searching for a plurality of first discrete values obtained by dividing the value of an independent variable that is a variable of the control value, and further includes a constant including the primary optimal solution. The energy management system which acquires a secondary optimal solution by searching the several 2nd discrete value which divided | segmented the value of the said independent variable of the range finer than the said several 1st discrete value.   The supply and demand plan calculation unit obtains the primary optimal solution and the secondary optimal solution by applying dynamic programming to each of the search for the first discrete value and the search for the second discrete value. The energy management system according to claim 1.   The energy management system according to claim 1, wherein the supply and demand plan calculation unit searches for a one-dimensional search space including the one independent variable.   The energy management system according to claim 1, wherein the supply and demand plan calculation unit searches a multidimensional search space including a plurality of the independent variables. Electric power supply and demand plan optimization method for optimizing energy supply and demand plan in a microgrid equipped with an output-controllable device including a power storage device, a generator that generates power by renewable energy, and a load, and connected to an external power system Because
The energy management system includes a supply and demand plan calculation step for obtaining a solution of the supply and demand plan including control values for the equipment at a plurality of times within a planning period by solving an optimization problem of an objective function that is an optimization index. ,
In the supply and demand plan calculation step, the energy management system obtains a first-order optimal solution by searching for a plurality of first discrete values obtained by dividing the value of the independent variable that is the variable of the control value, and further, the first-order solution A power supply and demand plan optimization method for obtaining a secondary optimal solution by searching a plurality of second discrete values obtained by dividing a value of the independent variable in a certain range including the optimal solution more finely than the plurality of first discrete values. .

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