Distributed energy management system, apparatus and method

Abstract

A distributed energy management system, a device, and a distributed energy management method are provided. The device includes an optimization problem generation section that generates an optimization problem for solving based on information including power distribution system information and demand / output prediction information of power in the power distribution system; a cost gradient estimation section that acquires a cost and an output value from a site having a power supply device and a power consumption device, and estimates a cost gradient with respect to power output for each site; and an output instruction determination section that calculates an output instruction for each site based on the optimization problem obtained by the optimization problem generation section and the cost gradient estimated by the cost gradient estimation section. The present invention can easily perform control of output power in a site.

Description

Technical Field

[0001] This invention relates to the technology of distributed energy management systems, devices, and methods. Background Technology

[0002] In the future, the introduction of distributed energy resources (DERs) such as solar photovoltaic (PV), electric vehicles (EV), combined heat and power (CHP), batteries (BT), and heat pumps (HP) is expected to increase in power distribution systems. As a result, the following problems of power distribution system instability are expected to become more pronounced: Locally, voltage disconnection and overcurrent caused by PV power backflow are considered significant issues. Furthermore, in urban areas, overcurrent caused by the rapid charging of EVs is considered a significant issue.

[0003] In contrast, by coordinating the use of distributed energy resources (DERs) scattered throughout the power distribution system, structures are needed to prevent voltage deviations and overcurrents within the system. Multiple sites (residential buildings, factories, etc.) possessing DERs can, for example, resolve overload or voltage deviation issues by coordinating the use of multiple DERs within a site to output or absorb (hereinafter, output) power (active or reactive power) into the power distribution system, for example, at the request or instruction of the power distribution system operator (DS). While each DER can only guarantee a small capacity individually, their aggregation ensures a large capacity, which can then be used for system stabilization. Therefore, the demand for distributed energy resource mapping systems (hereinafter, DERMS) has increased, serving as the foundation for the appropriate coordination and use of a large number of DERs.

[0004] In DERMS, the coordinated operation planning of DERs is defined as an optimization problem that minimizes objective functions such as energy cost, and is solved under the constraints of DER equipment. When all DERs exhibit linear characteristics, it becomes a linear programming problem, thus it is easy to solve even with a large number of DERs and a large-scale optimization problem. However, if we consider DERs with nonlinear characteristics, such as combined heat and power systems, it becomes a large-scale nonlinear programming problem, making it difficult to solve.

[0005] To address this issue, the technology described in Patent Document 1 exists. Patent Document 1 discloses the following microgrid operation planning apparatus and method, as well as a regional energy management device and an energy management device used in the microgrid operation planning apparatus (see abstract): "It comprises: an energy management device (EMS) that manages power and heat within a site; and a regional energy management device (AEMS) that obtains power and heat information from the energy management device at each site and determines the allocation of power and heat for the entire microgrid. The energy management device generates a power and heat supply and demand plan for the site, as well as an operation plan for heat utilization operation that maximizes the use of heat exhaust within the site, and sends these plans to the regional energy management device. The regional energy management device generates a power supply and demand plan that negotiates the supply of power to other sites from sites where power generation has increased through maximum heat exhaust utilization operation. The energy management device verifies the power and heat supply and demand plans provided by the regional energy management device and determines the power and heat to be supplied to users within the site." According to the patent document 1, even if the number of stations in the microgrid increases, overall optimization can be achieved and energy costs can be reduced.

[0006] Existing technical documents

[0007] Patent documents

[0008] Patent Document 1: Japanese Patent Application Publication No. 2017-200311 Summary of the Invention

[0009] The problem this invention aims to solve is as follows: In handling large-scale nonlinear programming problems involving multiple sites with nonlinear DERs (Energy Management Systems), primarily combined heat and power (CHP) systems, the information relevant to the optimization problem consists of constraints related to the equipment managed by the Energy Management Systems (EMS) at each site and their operation. Therefore, this sometimes includes operational information for each site, which is itself difficult to obtain. Furthermore, to properly obtain the necessary information, modifications to each EMS are required, demanding improvements in reducing unnecessary costs.

[0010] The present invention was made in view of the following background, and the problem it solves is to easily control the power output to the power distribution system in the site.

[0011] The means used to solve the problem:

[0012] To address the aforementioned problems, the present invention comprises: an optimization problem generation unit that generates an optimization problem for solving based on information including the topology of the distribution system (i.e., distribution system information) and power demand / output prediction information in the distribution system; a gradient estimation unit that obtains a publicly available objective function value that is minimized in the overall system from stations equipped with power supply equipment and power consumption equipment, and obtains a power output value of the destination distribution system corresponding to the objective function value, and estimates the gradient of the objective function value relative to the power output for each station; and an output command determination unit that calculates the command output power for commands to each station based on the optimization problem obtained by the optimization problem generation unit and the gradient of the objective function value estimated by the gradient estimation unit.

[0013] Other solutions are described appropriately in the implementation details.

[0014] Effects of the invention: According to the present invention, it is possible to easily control the output power to the power distribution system in the site. Attached Figure Description

[0015] Figure 1 This is a diagram showing the structure of the distributed energy management system in the first embodiment.

[0016] Figure 2 Figure 1 is an example of the processing content of the cost gradient estimation department.

[0017] Figure 3 Figure 2 is an example of the processing content of the cost gradient estimation department.

[0018] Figure 4 This is a flowchart illustrating the steps of the processing performed in this embodiment.

[0019] Figure 5 This is a diagram illustrating the convergence conditions.

[0020] Figure 6 This is a diagram illustrating the structure of the distributed energy management system in the second embodiment.

[0021] Figure 7 This is a diagram showing the structure of the distributed energy management system in the third embodiment.

[0022] Figure 8 It is a diagram showing the hardware structure of the computing device.

[0023] Figure 9 This is a diagram illustrating a variation of the calculation of the characteristic curve.

[0024] Figure 10This is a diagram showing the structure of the distributed energy management system in the fourth embodiment.

[0025] Explanation of reference numerals in the attached figures

[0026] 1, 1a, 1b: Distributed Energy Management System

[0027] 100: Computing device (distributed energy management device)

[0028] 101: Optimization Problem Generation Department

[0029] 102: Cost Gradient Estimation Section (Gradient Estimation Section)

[0030] 103: Output Instruction Determination Unit

[0031] 111: Power Distribution System DB (Stores power distribution system information)

[0032] 112: Demand / Output Forecasting DB (stores demand / output forecasting information)

[0033] 113: Cost

[0034] 114: Output values ​​(output values ​​of active power and reactive power)

[0035] 115: Output commands (output commands for active power and reactive power)

[0036] 116: Output range can be changed

[0037] 121: Power transmission system operation device

[0038] 122: Power Trading Support System

[0039] 200: Site

[0040] 320: Characteristic Curve

[0041] 321: Cost gradient (gradient of the objective function value)

[0042] S102: Generation of Optimization Problems (Steps for Generating Optimization Problems)

[0043] S112: Estimation of the cost gradient (gradient estimation steps)

[0044] S124: Sending the output command (Command output power sending steps)

[0045] S132: Decision / Send (Final Instruction Sending Step). Detailed Implementation

[0046] Next, the methods for carrying out the present invention (referred to as "implementation methods") will be described in detail with appropriate reference to the accompanying drawings.

[0047] First Implementation Method

[0048] System structure:

[0049] Figure 1 This is a diagram showing the structure of the distributed energy management system 1 in the first embodiment.

[0050] The distributed energy management system 1 includes a computing unit 100, a power distribution system DB111, and a demand / output forecasting system DB112. Furthermore, the distributed energy management system 1 has an EMS (Energy Management System) 210 at each site 200 to manage the power demand at that site. The EMS 210 also includes an equipment operation planning unit 211. The site 200 includes power supply equipment and power consumption equipment, and includes residences, buildings, factories, and DHC (District Heat and Cooling) systems.

[0051] The arithmetic unit 100 obtains the operating cost (hereinafter referred to as cost 113) and output value 114 from each station 200 (EMS 210) and calculates the instruction output value of the station 200. Furthermore, the arithmetic unit 100 has an output instruction determination unit 103 that outputs the calculated instruction output value as an output instruction 115 to each station 200.

[0052] The computing unit 100 is connected to the power distribution system DB111 and the demand / output prediction DB112, which will be described later.

[0053] The power distribution system DB111 stores information such as the power distribution system topology at each site 200, i.e., power distribution system information. The demand / output forecast DB112 stores information related to the power demand and output forecast in the power distribution system, i.e., demand / output forecast information.

[0054] The computing device 100 includes an optimization problem generation unit 101, a cost gradient estimation unit 102, and an output instruction determination unit 103.

[0055] The optimization problem generation unit 101 generates the problem based on power distribution system information, demand / output forecast information, and input device 135 (see reference). Figure 8 The input information is used to generate an optimization problem to be solved within the power distribution system. The optimization problem will be discussed later.

[0056] The cost gradient estimation unit 102 obtains the cost 113 and output value 114 output by the equipment operation planning unit 211 of each station 200 equipped with power supply equipment and power consumption equipment. Furthermore, the cost gradient estimation unit 102 estimates a cost gradient 321 relative to the output value 114 for each station 200 (see reference). Figure 3 The assumption about cost gradient 321 will be discussed later.

[0057] The output command determination unit 103 calculates the command output value for each station 200 based on the optimization problem generated by the optimization problem generation unit 101 and the cost gradient 321 estimated by the cost gradient estimation unit 102. At this time, the output command determination unit 103 calculates the power flow of the power distribution system and the command output value for each station 200 with power supply equipment, etc. The command output value is sent as a temporary output command 115 to the EMS 210 installed at each station 200.

[0058] Based on the calculated results, the output command determination unit 103 repeatedly calculates the command output values ​​for each station 200, thereby obtaining a convergent solution. The method for obtaining the convergent solution will be described later. The convergent solution, as the determined output command 115, is sent to the EMS 210 installed at each station 200. Here, the output command 115 is the output (increase) and absorption (decrease) of active and reactive power from the station 200 to the power distribution system. Specifically, the output (increase) increases the power supply to the power distribution system through generators, batteries, etc., while the absorption is achieved through charging in the batteries, power consumption in demanding equipment, and a decrease in generator output. Hereinafter, power output means both the output (increase) and absorption (decrease) of power.

[0059] The following details the structure and processing of each component in the distributed energy management system 1.

[0060] Power distribution system information:

[0061] The power distribution system information stored in the power distribution system DB111 contains information needed to predict the state of the power distribution system. For example, the power distribution system information stores information related to the topology (connection method of each distribution line) of the power distribution system that is managed by the distributed energy management system 1. In addition, the power distribution system information stores information such as the impedance, inductance, and allowable current value of each distribution line.

[0062] Demand / output forecast information:

[0063] The demand / output forecast information stored in the demand / output forecast DB112 stores forecast values ​​of demand and output (active power and reactive power) for each node in the planned time period of the power distribution system of each of the managed sites 200. Here, a node is a node in the power distribution system. Demand is the power demand of users existing at each node, including the power consumption of EV stations, etc., existing therein. Output is the output of renewable energy sources such as PV (solar power generation) and the output of generators, EVs, BTs, HPs, etc., owned by residences, buildings, factories, etc. These values ​​can also be predicted and used by the operator who owns the computing device 100 in this embodiment. Alternatively, information provided by an operator different from the operator who owns the computing device 100 can be used as forecast values ​​or outputs. In addition, regarding the output of generators, the operator who owns the computing device 100 can also obtain the planned values ​​in advance from the operator who owns the generators and use the obtained planned values ​​as forecast values. In addition, the time intervals in the planning period are generally given in units of simultaneous and equal control, i.e., 30-minute intervals, but they can also be set in wider time intervals, which are then consistent with the time intervals of the operation plan calculated by the equipment operation planning department 211.

[0064] Optimization Problem Generation Department 101:

[0065] The optimization problem generation unit 101 uses power distribution system information and demand / output forecast information to set (generate) an optimization problem during the operation planning period. In this embodiment, power flow equations used for optimal power flow calculation are used as the optimization problem. The optimization problem generation unit 101 uses the output values ​​114 of each station 200 during the time period in which the output command 115 of the optimization problem can be increased or decreased as variables. Furthermore, the optimization problem generation unit 101 sets parameters other than the output values ​​114 of the station 200 as fixed parameters. Alternatively, the optimization problem generation unit 101 can also use the following method: for fixed parameters related to the power distribution system, information is not obtained each time the optimization problem is solved, but rather pre-obtained information is embedded in the formula and only retrieved when necessary. Additionally, the optimization problem generation unit 101 specifies constraints such as upper and lower current limits and upper and lower voltage limits for each distribution line based on the power distribution system information.

[0066] Cost gradient estimation section 102:

[0067] As described above, the cost gradient estimation unit 102 estimates the cost gradient 321 (described later) based on the costs 113 and output values ​​114 obtained from the equipment operation planning units 211 at each site 200. Here, the equipment operation planning unit 211 calculates the costs 113 and output values ​​114 for each planned time period at each site 200. The cost gradient estimation unit 102 obtains the costs 113 and output values ​​114 calculated by the equipment operation planning unit 211.

[0068] The cost 113 corresponding to the power supply from the generator or battery generally varies over time. The cost gradient estimation unit 102 first calculates the cost 113 corresponding to the power supply using the obtained value, and then processes it as a fixed value, for example, after converting it to an average value.

[0069] Output instruction determination unit 103:

[0070] The output command determination unit 103 calculates the desired active and reactive power output values, i.e., the command output values, at each station 200 equipped with power supply equipment by performing optimal power flow calculation. The optimal power flow calculation method can be any commonly used method. Representative methods for optimal power flow calculation include the interior-point method, successive quadratic programming, successive linear programming, and simplified gradient method. Furthermore, the objective function for the optimal power flow calculation specifies the sum of the costs 113 required for the operation of the distribution system and for each station 200. Moreover, the output command determination unit 103 generally performs optimization to minimize the sum of costs 113.

[0071] In this embodiment, the Benders Decomposition Method is used for optimal power flow calculation. In this method, the sum of costs 113 (total cost) is used as the objective function value. Then, the upper and lower bounds of the objective function value (total cost in this embodiment) are calculated, and final optimization is achieved by bringing the upper and lower bounds to converge.

[0072] Furthermore, as described above, in this embodiment, the total cost is used as the objective function value for optimal power flow calculation, but emissions of greenhouse gases such as carbon dioxide can also be used. In this case, for example, the sum of greenhouse gas emissions for all sites 200 can be calculated in steps S112 and S123, and the sum can be used for optimization (minimization). That is, the objective function value for optimal power flow calculation can be a function that can be disclosed by the operator and can be minimized in the overall system. Here, the system refers to all sites 200 managed by the distributed energy management system 1 and the power distribution system.

[0073] Here, "minimizeable in the overall system" refers to a value that can be evaluated (optimized) based on the sum of values ​​across all stations 200. For example, voltage, etc., cannot be minimized in the entire system. Furthermore, the objective function value that the operator can disclose refers to a value that the operator can easily disclose. For example, the operator's transaction amount, etc., is information that cannot be disclosed. Moreover, similar to the output value when calculating cost 113, the cost gradient estimation unit 102 obtains from the equipment operation planning unit 211 "the objective function value that can be minimized in the overall system and disclosed, and the corresponding output value 114".

[0074] Furthermore, the time interval for the output command determination unit 103 to perform optimal power flow calculation to calculate the command output value is generally a 30-minute interval, which is the unit of simultaneous and equal control. However, it is not limited to this and other intervals are also possible. The command output value is calculated for each station 200. The command output value calculated by the output command determination unit 103 is sent as output command 115 to the EMS 210 set at each station 200.

[0075] Equipment Operation Planning Department 211:

[0076] The equipment operation planning unit 211 generates operation plans for each piece of equipment within the site 200 based on the output commands 115 for active and reactive power sent to the EMS 210. Furthermore, the equipment operation planning unit 211 calculates the cost 113 based on the operation plans. Then, the equipment operation planning unit 211 sends the calculated cost 113, the output values ​​114 in the site 200, and the output changeability range 116 to the cost gradient estimation unit 102.

[0077] Processing in cost gradient estimation unit 102:

[0078] Figure 2 and Figure 3 This is a diagram illustrating an example of the processing steps of the cost gradient estimation unit 102. Refer to the diagram as appropriate. Figure 1 .

[0079] exist Figure 2 and Figure 3 In the figure, the horizontal axis represents the output value of active or reactive power of each station 200, 114 (MW), and the vertical axis represents the cost, 113 (JPY).

[0080] The reference numeral 301a (301) shown in the figure is used to indicate the cost 113 relative to the output value 114 of each station 200, which is calculated for the first time at each station 200. Next, the reference numeral 301b (301) is used to indicate the cost 113 relative to the output value 114 of each station 200, which is calculated for each station 200. The cost gradient estimation unit 102 obtains the slope (straight line 311) of the cost 113 relative to the output value 114 by connecting these two points.

[0081] Figure 3 It indicates repetition. Figure 2 The diagram shows the set of slopes for cost 113 obtained through the process shown. The cost gradient estimation unit 102 repeats this process. Figure 2 The process shown is used to calculate the slopes of lines 311a to 311c representing cost 113. Lines 311a to 311c pass through points respectively with... Figure 2 The same process is used to calculate the straight line 311. The cost gradient estimation unit 102 calculates the characteristic curve 320 by calculating the curves that are tangent to the straight lines 311a to 311c respectively. This characteristic curve 320 becomes the cost gradient 321. The cost gradient estimation unit 102 calculates such a characteristic curve 320 (cost gradient 321) for each station 200. The output command determination unit 103, by using the characteristic curve 320 (cost gradient 321), can, as described later... Figure 5 A better total cost is derived from the processing.

[0082] Figure 3 The information of the cost gradient 321, as shown by characteristic curve 320, can be used as sensitivity information for each station 200 in the Benders decomposition method. Alternatively, it can be omitted. Figure 2 as well as Figure 3 The method shown uses the same gradient as the λ-method in the generator to solve the optimization problem.

[0083] flow chart:

[0084] Figure 4 This is a flowchart illustrating the steps of the processing performed in this embodiment. Refer as appropriate. Figure 1 .

[0085] First, the user accesses the input device 135 (refer to...) Figure 8(S101) Set the parameters for optimal power flow calculation of the distribution system. Here, the Benders decomposition method is used for optimal power flow calculation. The parameters input in step S101 are the upper and lower limits of line power flow, the upper and lower limits of active power output from each station 200 to the distribution system, and the upper and lower limits of reactive power output, etc. In addition, the user specifies the cost as the objective function via the input device 135.

[0086] Next, the optimization problem generation unit 101 generates an optimization problem for optimal power flow calculation (S102). In step S102, the optimization problem generation unit 101 generates the optimization problem based on power distribution system information and demand / output forecast information. The generated optimization problem is, for example, a power flow equation for performing optimal power flow calculation.

[0087] Then, the cost gradient estimation unit 102 obtains the cost 113 calculated by the equipment operation planning unit 211 in each station 200, the output values ​​114 of active power and reactive power in each station 200, and the output changeable range 116 (S111).

[0088] Here, the EMS of each site 200 generates an operation plan for the equipment within the site by the equipment operation planning unit 211 in a manner that satisfies the output command value 115 from the computing unit 100 of the distributed energy management device, and outputs the cost 113, active power and reactive power output values ​​114 and the output changeable range 116 to the computing unit 100.

[0089] Next, the cost gradient estimation unit 102 estimates the cost gradient 321 based on the cost 113 and output value 114 obtained in step S111 (S112). The estimation of the cost gradient 321 in step S112 is achieved through... Figure 2 and Figure 3 The method described in the text is used to make an inference.

[0090] Next, the output command determination unit 103 performs optimal power flow calculation using the power flow equation (S121), calculating the command output values ​​of active and reactive power for each station 200 (S122). At this time, the output command determination unit 103 performs optimal power flow calculation based on the output values ​​114 of active and reactive power for each station, the output changeable range 116, the cost 113, the cost gradient 321 estimated in step S112, the parameters set in step S101, and the optimization problem (power flow equation) generated in step S101. In step S122, the output command determination unit 103 calculates the cost 113 for each station 200 that minimizes the sum of costs 113 through the optimal power flow calculation. Afterwards, the output command determination unit 103 applies the results of the optimal power flow calculation to... Figure 3The cost gradient 321 shown is used to calculate the instruction output value.

[0091] Then, the output instruction determination unit 103 determines whether the result of the optimal power flow calculation satisfies the convergence condition (S123). Regarding the convergence condition, refer to... Figure 5 To be described later.

[0092] If the convergence condition is met (S123 → Yes), the output instruction determination unit 103 determines the instruction output value, and then sends the final instruction output value as output instruction 115 to the EMS 210 (S131). Then, the arithmetic unit 100 ends the processing. In step S131, the output instruction determination unit 103 determines the instruction output value of each station 200 when the convergence condition is met as the final instruction output value for each station 200, and sends it to the EMS 210.

[0093] If the convergence condition is not met (S123 → No), the output instruction determination unit 103 sends the instruction output values ​​of each station 200 calculated in step S121 as output instructions 115 to each station 200 (S124). Incidentally, the instruction output values ​​output in step S124 are temporary instruction output values. Afterwards, the arithmetic unit 100 returns the processing to step S111.

[0094] Convergence condition:

[0095] Figure 5 This means that in Figure 4 The graph shows the convergence conditions performed in step S123.

[0096] exist Figure 5 In the , represents the convergence condition described later.

[0097] exist Figure 5 In the diagram, the horizontal axis represents the number of calculations, and the vertical axis represents the total cost. The number of calculations is equivalent to... Figure 4 The number of times the optimal power flow calculation is performed in step S121. Furthermore, the total cost is the sum of the costs 113 across the entire system (all 200 sites). Thus, in Figure 5 In the process shown, the entire system (i.e., all sites 200) becomes an object.

[0098] Moreover, in Figure 5 In the attached figure, reference numeral 401 indicates the upper bound of the total cost. Additionally, in... Figure 5 In the attached figure, reference numeral 411 indicates the lower limit of the total cost. Here, the upper limit 401 and the lower limit 411 are the values ​​used in this embodiment for optimal power flow calculation, i.e., the Benders decomposition method.

[0099] The total cost exists in region 421, sandwiched between line 402 connecting the upper bounds 401 and line 412 connecting the lower bounds 411. Then, as... Figure 5 As shown, by repeatedly performing the calculation, region 421 narrows. The output instruction determination unit 103 sets the width of the upper bound 401 and the lower bound 411 in region 421 as the convergence condition and determines whether the width of region 421 is below a predetermined value. When the width of region 421 is below the predetermined width, the output instruction determination unit 103 determines in step S123 that the convergence condition (i.e., a converged solution) is met.

[0100] Therefore, operational planning can be formulated without requiring information about the equipment managed by EMS210 or the constraints related to its operation, and without the need for modifications to each EMS210 to obtain the necessary information. That is, the command output value of station 200 can be determined using information that is publicly available and easily obtained by the operator, such as output value 114, cost 113 (or emissions of greenhouse gases such as carbon dioxide). Furthermore, by calculating... Figure 5 The converged solution shown can send stable command output values ​​to each station 200.

[0101] also, Figure 2 , Figure 3 , Figure 5 The displayed chart can be output to the output device 136 of the computing device 100. This allows the user to assess the appropriateness of the processing.

[0102] Second Implementation Method

[0103] Next, refer to Figure 6 The second embodiment of the present invention will be described.

[0104] Figure 6 This is a diagram showing the structure of the distributed energy management system 1a in the second embodiment.

[0105] exist Figure 6 In the middle, to and Figure 1 The same structures are labeled with the same reference numerals and the descriptions are omitted.

[0106] exist Figure 6 In, with Figure 1 The difference is that the optimization problem generation unit 101 obtains information from the power transmission system operation device 121.

[0107] In the second embodiment, the optimization problem generation unit 101 obtains information input by the transmission system operator (TSO) from the power transmission system operating device 121. This allows for additional power adjustments from the distribution system (which targets the distributed energy management system 1a) to the transmission system, adjusting the active and reactive power at each station 200 for stabilization within the distribution system.

[0108] The information sent from the power transmission system operating device 121 contains the amount of power adjustment to be output from the distribution system managed by the distributed energy management system 1a to the power transmission system for the purpose of power transmission system operators adjusting the supply and demand of the power transmission system. This information is obtained by the output command determination unit 103 at each time interval when the solution to the optimal power flow calculation is obtained. However, it is not limited to this; information such as the amount for one day may also be sent from the power transmission system operating device 121 in summary before the start of the optimal power flow calculation. Alternatively, the equipment operation planning unit 211 may also obtain information from the power transmission system operating device 121 at each time interval when formulating the operation plan. In addition, the optimization problem generation unit 101 may also obtain information from the power transmission system operating device 121 as needed.

[0109] Generally speaking, the time interval for the equipment operation planning department 211 to formulate operation plans is a unit of simultaneous and equal control, namely a 30-minute interval, but it is not limited to this.

[0110] According to the second embodiment, the power adjustment amount to be output from the distribution system managed by the distributed energy management system 1a is sent from the power transmission system operation device 121 to the optimization problem generation unit 101. Thus, in the equivalent of Figure 4 In step S102, the optimization problem generation unit 101 adds the constraint that the output power flow to the target power transmission system (site 200) exceeds the power adjustment amount to the parameters of the optimal power flow calculation. Alternatively, the optimization problem generation unit 101 substitutes the value of the power adjustment amount into a pre-set formula for optimal power flow calculation (power flow formula). As a result, the equipment operation planning unit 211 can formulate an operation plan for active and reactive power to each site 200 for the purpose of stabilization within the power distribution system based on the power adjustment for the power transmission system.

[0111] Thus, according to the second embodiment, the distributed energy management system 1a can take into account the power adjustment of the power transmission system and formulate an operation plan for the active and reactive power of each station 200 for the purpose of stabilization within the power distribution system.

[0112] Third Implementation Method

[0113] Next, refer to Figure 7 The third embodiment of the present invention will be described.

[0114] Figure 7 This is a diagram showing the structure of the distributed energy management system 1b in the third embodiment.

[0115] exist Figure 7 In the middle, to and Figure 1 The same structures are labeled with the same reference numerals and the descriptions are omitted.

[0116] exist Figure 7 In, with Figure 1 The difference is that the optimization problem generation unit 101 obtains information from the power trading auxiliary system 122.

[0117] In the third embodiment, based on the electricity price of electricity trading, power adjustment can be performed on the distribution system that targets the distributed energy management system 1b.

[0118] The optimization problem generation unit 101 obtains information from the power trading support system 122, which is the predicted power price for each time period within the time frame for which the equipment operation planning unit 211 formulates the operation plan. This time interval is generally, as mentioned above, a unit of simultaneous and equal control, i.e., a 30-minute interval, but is not limited to this.

[0119] The optimization problem generation unit 101 is equivalent to Figure 4 In step S102, the predicted electricity price obtained from the power trading auxiliary system 122 is set as the price information included in the objective function value (total cost in this case) of the optimal power flow calculation. Therefore, in the output instruction determination unit 103, based on the electricity price, a power adjustment amount, such as reducing the objective function value of the optimal power flow calculation (i.e., the overall system cost 113), can be calculated as the solution for the optimal power flow calculation.

[0120] Thus, according to the third embodiment, power adjustment can be performed by the distribution system, which targets the distributed energy management system 1b, based on the electricity price based on electricity trading.

[0121] Furthermore, both the power transmission system operating device 121 described in the second embodiment and the power trading support system 122 described in the third embodiment can be connected to the optimization problem generation unit 101. In this case, during periods when there is a request for power adjustment by the power transmission system operating device 121, the arithmetic unit 100 outputs an instruction output value corresponding to the requested power adjustment. Additionally, during periods when there is no request for power adjustment by the power transmission system operating device 121, the arithmetic unit 100 adjusts the instruction output value based on the power trading price sent from the power trading support system 122 to reduce the overall system cost 113.

[0122] Fourth Implementation Method

[0123] Next, refer to Figure 10 The fourth embodiment of the present invention will be described.

[0124] Figure 10 This is a diagram showing the structure of the distributed energy management system 1c in the fourth embodiment.

[0125] exist Figure 10 In the middle, to and Figure 1 The same structures are labeled with the same reference numerals and the descriptions are omitted.

[0126] exist Figure 10 In, with Figure 1 The difference is that the cost gradient estimation unit 102 of the computing device 100 is moved into the EMS210.

[0127] In the first embodiment, the cost gradient 114 of each site 200 is calculated using the computing device 100. However, in the fourth embodiment, a cost gradient estimation unit 211 for calculating the cost gradient 114 of each site 200 is provided in the EMS 210. Here, the method for calculating the cost gradient 114 is the same as described above.

[0128] In the fourth embodiment, the location for calculating the cost gradient 114 of each site 200 is different compared to the first embodiment, but the overall calculation process is the same, and the same effect can be achieved.

[0129] Hardware Structure

[0130] Figure 8 This is a diagram showing the hardware structure of the arithmetic unit 100.

[0131] The computing device 100 includes a memory 131, a CPU (Central Processing Unit) 132, a storage device 133 such as an HD (Hard Disk Drive). Additionally, the computing device 100 includes a communication device 134 such as a NIC (Network Interface Card), an input device 135 such as a keyboard and mouse, and an output device 136 such as a display and a printer.

[0132] Furthermore, the program stored in storage device 133 is loaded into memory 131, and the loaded program is executed by CPU 132. Thus, [the following is achieved / implemented]: Figure 1 , Figure 6 , Figure 7 The optimization problem generation unit 101, cost gradient estimation unit 102, and output instruction determination unit 103 are shown.

[0133] Variations in the processing of the cost gradient estimation unit 102:

[0134] Figure 9 It means Figure 2 and Figure 3 The figure shows a modified example of the calculation of characteristic curve 320.

[0135] exist Figure 2 and Figure 3 In the example shown, the cost gradient estimation unit 102 connects two points—the output value 114 obtained from each site more than 200 times and the marker point 301 of the cost 113—with a straight line 311. Then, the cost gradient estimation unit 102 calculates the characteristic curve 320 by calculating a curve tangent to each straight line 311. However, the method for calculating the characteristic curve 320 is not limited to this. For example, in… Figure 9 In, with Figure 2 Similarly, marker 301 indicates the output value 114 and cost 113 in station 200. Moreover, as... Figure 9 As shown, the cost gradient estimation unit 102 can also calculate the characteristic curve 320A (cost gradient 321A) by calculating an approximate curve based on optimization for multiple marker points 301.

[0136] This invention is not limited to the embodiments described above, and includes various modifications. For example, the embodiments described above are those that have been explained in detail for the purpose of easily understanding and illustrating the invention, and are not limited to having all the structures described. Furthermore, a part of the structure of a certain embodiment can be replaced with the structure of another embodiment, and the structure of another embodiment can be added to the structure of a certain embodiment. In addition, for a part of the structure of each embodiment, other structures can be added, deleted, or replaced.

[0137] In this embodiment, the power distribution system DB111 and the demand / output prediction DB112 are connected to the computing device 100, but are not limited thereto. For example, the power distribution system DB111 and the demand / output prediction DB112 may be located in the cloud. In this case, the computing device 100 obtains power distribution system information and demand / output prediction information from the power distribution system DB111 and the demand / output prediction DB112 located in the cloud.

[0138] Furthermore, the aforementioned structures, functions, parts 101-103, DB111, 112, etc., can also be partially or entirely designed using, for example, integrated circuits, thereby achieving hardware implementation. Additionally, as... Figure 8As shown, the aforementioned structures and functions can also be implemented in software by having a processor such as CPU132 interpret and execute the programs that implement each function. In addition to being stored in HD, the programs, tables, files, and other information that implement each function can also be stored in memory 131, SSD (Solid State Drive), or other recording devices, or in recording media such as IC (Integrated Circuit) cards, SD (Secure Digital) cards, and DVD (Digital Versatile Disc).

[0139] Furthermore, in each embodiment, the control lines and information lines represent what is necessary for the description, but may not represent all control lines and information lines in the product. In reality, it can be assumed that almost all structures are interconnected.

Claims

1. A distributed energy management system, characterized in that, include: The optimization problem generation unit generates optimization problems that are solved based on information including the topology of the power distribution system, i.e., power distribution system information, and power demand / output prediction information in the power distribution system. The gradient estimation unit obtains the objective function value that is minimized and publicly available in the whole system from the stations equipped with power supply equipment and power consumption equipment, and obtains the power output value corresponding to the objective function value. For each of the stations, it estimates the gradient of the objective function value with respect to the power output. as well as The output command determination unit calculates the command output power for each of the stations based on the optimization problem obtained by the optimization problem generation unit and the gradient of the objective function value estimated by the gradient estimation unit. The output command determination unit calculates the command output power by performing optimal power flow calculation. The objective function value is either the cost at which the power output value is achieved at the site, or the amount of greenhouse gas emissions. The output command determination unit further sends the command output power to each of the stations based on the result calculated by the output command determination unit. When the distributed energy management system obtains the objective function value based on the output power of the transmitted command and the power output, it repeats the processing of the gradient estimation unit and the processing of the output command determination unit. The repeated processing is stopped when the result of the repeated processing meets the convergence condition. The output command determination unit takes the command output power that meets the convergence condition as the final command output power and sends it to the station.

2. The distributed energy management system according to claim 1, characterized in that, The gradient estimation unit is included in the site.

3. A distributed energy management device, characterized in that, include: The optimization problem generation unit generates optimization problems that are solved based on information including the topology of the power distribution system, i.e., power distribution system information, and power demand / output prediction information in the power distribution system. The gradient estimation unit obtains the objective function value that is minimized and publicly available in the whole system from the stations equipped with power supply equipment and power consumption equipment, and obtains the power output value corresponding to the objective function value. For each of the stations, it estimates the gradient of the objective function value with respect to the power output. as well as The output command determination unit calculates the command output power for each of the stations based on the optimization problem obtained by the optimization problem generation unit and the gradient of the objective function value estimated by the gradient estimation unit. The output command determination unit calculates the command output power by performing optimal power flow calculation. The objective function value is either the cost at which the power output value is achieved at the site, or the amount of greenhouse gas emissions. The output command determination unit further sends the command output power to each of the stations based on the result calculated by the output command determination unit. When the distributed energy management device obtains the objective function value based on the output power of the transmitted command and the power output, it repeats the processing of the gradient estimation unit and the processing of the output command determination unit. The repeated processing is stopped when the result of the repeated processing meets the convergence condition. The output command determination unit takes the command output power that meets the convergence condition as the final command output power and sends it to the station.

4. The distributed energy management device according to claim 3, characterized in that, The output instruction determination unit uses the Benders decomposition method to terminate the repeated processing if the upper and lower bounds obtained from the results of the repeated processing are below a predetermined value.

5. The distributed energy management device according to claim 3, characterized in that, The objective function value is the carbon dioxide emissions.

6. The distributed energy management device according to claim 3, characterized in that, The optimization problem generation unit obtains information related to the power adjustment amount from the power transmission system operation unit, and adds constraints based on the information related to the power adjustment amount to the parameters of the optimal power flow calculation, or substitutes the value of the power adjustment amount into the power flow equation of the pre-set optimal power flow calculation.

7. The distributed energy management device according to claim 3, characterized in that, The optimization problem generation unit obtains information related to power price and includes the information related to power price in the objective function value of the optimization problem.

8. The distributed energy management device according to claim 3, characterized in that, The distributed energy management device has an output unit that outputs information related to the gradient of the objective function value and information related to the repeated processing.

9. A distributed energy management method, which controls the output power of a site equipped with power supply equipment and power consumption equipment, characterized in that, include: The optimization problem generation step generates an optimization problem that is used to solve based on information including the power distribution system topology, i.e., power distribution system information, and power demand / output prediction information in the power distribution system. The gradient estimation step involves obtaining the objective function value that can be minimized and disclosed for the entire system from the site, and obtaining the power output value corresponding to the objective function value. For each of the sites, the gradient of the objective function value with respect to the power output is estimated. as well as The output command determination step calculates the command output power for each of the stations based on the optimization problem generated in the optimization problem generation step and the gradient of the objective function value estimated by the gradient estimation step. In the output command determination step, the command output power is calculated by performing optimal power flow calculation. The objective function value is the cost when the power output value is output at the station, or the amount of greenhouse gas emissions. In the distributed energy management method, based on the result calculated in the output command determination step, a command output power transmission step is performed to send the command output power to each of the stations. When the objective function value based on the output power of the transmitted command and the power output are obtained, the gradient estimation step, the output command determination step, and the command output power transmission step are repeated. The final instruction sending step involves stopping the repeated processing when the result of the repeated processing meets the convergence condition, and sending the instruction output power at the time of meeting the convergence condition as the final instruction output power to the station.

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