Distributed resource market trading device, distributed resource market trading method, and program

Abstract

To achieve a market transaction of power so that power distribution system reinforcement investment is suppressed within a power distribution area.SOLUTION: A distributed resource market transaction device comprises: a system model creation section that creates a power distribution system model on the basis of power distribution system information including connection information on a power distribution system and specification information on facilities constituting the power distribution system; a market transaction section that acquires bid information including a desired operation schedule of distributed resources from information management devices of a plurality of market participants which manage the distributed resources connected to the power distribution system; a simulation section that applies the acquired bid information to the power distribution system model to perform power flow and voltage calculation of the power distribution system by time zones and by points, and derives an optimum operation schedule which is an operation schedule satisfying constraint conditions of the facilities constituting the power distribution system; and a notification section that notifies the information management devices of the market participants of the optimum operation schedule.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a distributed resource market trading device, a distributed resource market trading method, and a program. [Background technology]

[0002] Due to the falling prices of distributed resources (distributed power sources) such as renewable energy sources like PV (solar panels), load devices like EV chargers, and storage batteries, the introduction of distributed resources in the power distribution sector is progressing. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2016-082869 A Summary of the Invention [Problem to be solved by the invention]

[0004] When a large number of distributed resources are introduced in a distribution area, it is expected that localized constraints such as system congestion and voltage violations will occur at the distribution system level. This will lead to increased investment in strengthening the distribution system to deal with these constraints, which may lead to higher electricity prices as they are reflected in electricity rates.

[0005] In addition, there is no system for managing and monitoring distributed power sources, which is necessary to optimize the operation of distributed resources.

[0006] In addition, while it is possible for distribution system operators (DSOs) to procure equipment to alleviate the anticipated distribution constraints in some way, there is no mechanism to do this economically.

[0007] In addition, power distribution facilities have been updated when they have reached a certain age or when they do not meet the standards upon on-site inspection, but these have basically been based on the assumption that the previous specifications will be continued or upgraded, and there is no system to support the consideration of more economical alternatives.

[0008] In addition, there is no system in place to grasp the above-mentioned power distribution constraints and the state of power distribution equipment, and to support the development, manufacture, deployment, and operation of equipment (distributed power sources) appropriate for resolving these constraints.

[0009] An object of the present disclosure is to provide a distributed resource market trading device, a distributed resource market trading method, and a program that can realize market trading of electricity within a distribution area so as to suppress investment in strengthening the distribution system. [Means for solving the problem]

[0010] A distributed resource market trading device according to one embodiment of the present disclosure includes a system model creation unit that creates a distribution system model based on distribution system information including wiring information of a target distribution system and specification information of equipment that constitutes the distribution system; a market trading unit that acquires bidding information including desired operation schedules of the distributed resources connected to the distribution system from information management devices of a plurality of market participants that manage the distributed resources; a simulation unit that applies the acquired bidding information to the distribution system model to perform time-zone and location-specific power flow and voltage calculations of the distribution system and derives an optimal operation schedule, which is the operation schedule that satisfies constraints of the equipment that constitutes the distribution system; and a notification unit that notifies the information management devices of the market participants of the optimal operation schedule. Effect of the Invention

[0011] According to the above-described aspects, it is possible to realize market trading of electricity within a distribution area so as to suppress investment in strengthening a distribution system. [Brief description of the drawings]

[0012] [Figure 1]FIG. 2 is a diagram showing the functional configuration of a distributed resource market trading platform according to the first embodiment. [Diagram 2] FIG. 2 is a diagram showing a processing flow (day-ahead market) of the distributed resource market trading platform according to the first embodiment. [Diagram 3] FIG. 2 is a diagram showing a processing flow (real-time market) of the distributed resource market trading platform according to the first embodiment. [Figure 4] FIG. 2 is an image diagram of the effect of visualizing DLMP by the economic optimization system simulation unit 13 according to the first embodiment. [Diagram 5] FIG. 11 is a diagram showing a processing flow (medium- to long-term plan) of the distributed resource market trading platform according to the second embodiment. [Figure 6] FIG. 13 is a diagram showing a processing flow (real-time market) of a distributed resource market trading platform according to the third embodiment. [Figure 7] FIG. 13 is a diagram showing a processing flow of a distributed resource market trading platform according to the fourth embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] <First embodiment> Hereinafter, the distributed resource market trading platform according to the first embodiment will be described in detail with reference to FIGS.

[0014] (Functional configuration of distributed resource market trading platform) FIG. 1 is a diagram showing a functional configuration of a distributed resource market trading platform according to the first embodiment.

[0015] As shown in Fig. 1, a distributed resource market trading platform 1 (distributed resource market trading device) is a system used in an electricity trading market operated by a market operator A (DMO operator). In this electricity trading market, trading takes place between a distribution system operator B (DSO) that manages a certain distribution system and a market participant C. The market participant C is an owner of a distributed resource (DER) connected to the distribution system, or an aggregator that bundles and manages multiple distributed resources.

[0016] The market participant terminal device 2 is a terminal device used by each of the market participants such as an aggregator.

[0017] The DSO system 3 is an associated system operated by a distribution system operator B.

[0018] A DERMS4 (Distributed Energy Resource Management Systems) is a system that manages distributed resources connected to a target power distribution system, and remotely controls each distributed resource and acquires operation information.

[0019] The day-ahead market and real-time market5 provide market prices for substations, higher-level grids, JEPX, etc.

[0020] ADMS6 (ADMS; Distribution Intermediate Feeding System) provides measurement information for each feeder in the target distribution system and the open / closed status of switches installed in the distribution system.

[0021] The settlement system 7 performs settlement processing based on electricity market transactions between the power distribution system operator B and the market participants C.

[0022] Next, the functions of the distributed resource market trading platform 1 shown in FIG. 1 will be described in detail.

[0023] As shown in Figure 1, the distributed resource market trading platform 1 includes a system model creation unit 10, a registration processing unit 11, a power supply and demand forecasting unit 12, an economic optimization system simulation unit 13 (simulation unit), a market trading unit 14, and a notification unit 15.

[0024] The system model creation unit 10 creates a model (system model) of a distribution system to be a target of electricity market trading. More specifically, the system model creation unit 10 creates the distribution system model based on distribution system information including wiring information of the target distribution system and specification information of equipment that constitutes the distribution system.

[0025] The registration processing unit 11 accepts information on market participants and registers them.

[0026] The power supply and demand prediction unit 12 performs simple prediction of future (in this embodiment, the next day and the current day) power demand.

[0027] The economic optimization system simulation unit 13 applies operations based on the bidding information (desired operation schedule) provided by the market participant C to the power distribution system model, and performs power flow and voltage calculations (simulations) for each time period and location of the power distribution system. The economic optimization system simulation unit 13 performs such simulations for multiple combinations of bidding information, and derives an operation schedule (optimal operation schedule) that satisfies the constraints of the power distribution system and is also economically optimized. A "constraint condition" is a condition that must be satisfied due to the specifications of the equipment that constitutes the power distribution system, such as the upper limit of the transmission power and voltage of the distribution lines, transformers, and various voltage regulating equipment that constitute the power distribution system.

[0028] The market trading unit 14 acquires bidding information including a desired operation schedule and a desired price of the distributed resources from a plurality of market participants C who manage the distributed resources.

[0029] The notification unit 15 notifies the power distribution system operator B and the market participant C of the optimal operation schedule.

[0030] (Processing flow of distributed resource market trading platform) 2 and 3 are diagrams showing the processing flow of the distributed resource market trading platform according to the first embodiment. Here, FIG. 2 shows the processing flow in the day-ahead market, and FIG. 3 shows the processing flow in the real-time market.

[0031] (Day before market) The processing flow in the day-ahead market will be described with reference to FIG.

[0032] First, the registration processing unit 11 of the distributed resource market trading platform 1 performs participant registration (step S10). This participant registration is a process performed for each market participant C in order to use this platform. When registering a participant, the market participant C provides participant information and information on the distributed resources it owns or manages (DER information). The distributed resource information includes the type (solar panel, storage battery, EV charging equipment, etc.), grid connection point, rated output, etc.

[0033] The power supply and demand forecasting unit 12 of the distributed resource market trading platform 1 forecasts the power supply and demand for the next day (step S11). Here, the DSO system 3 (Fig. 1) provides the feeder current in its own distribution system, smart meter data, and planned power generation values ​​for the DSO area. The DSO system 3 also provides short-term weather forecasts, renewable energy fluctuation forecasts, and JEPX prices as information imported from outside. Based on this information, the power supply and demand prediction unit 12 outputs a power demand prediction for each time period for the next day, a PV power generation prediction, and a JEPX price prediction to the economic optimization system simulation unit 13.

[0034] The economic optimization system simulation unit 13 of the distributed resource market trading platform 1 accepts bidding information from each market participant C (through the market trading unit 14 described later). The bidding information is information including the desired operation schedule and desired price of the distributed resources owned by each market participant C. Specifically, the bidding information includes 30-minute values ​​of receiving point power (demand suppression amount) by time zone and node, price by time zone and node, 30-minute values ​​of reserve power (demand suppression amount) by time zone and node, and standby price by time zone and node.

[0035] The economic optimization system simulation unit 13 has a system model created in advance by the system model creation unit 10 .

[0036] The economic optimization system simulation unit 13 acquires the power demand forecast for the next day obtained in step S11 and the bidding information received from each of the multiple market participants C. Then, the economic optimization system simulation unit 13 applies the operation based on the desired operation schedule of the bidding information acquired from the market participants C to the power distribution system model created by the system model creation unit 10, and performs power flow and voltage calculation (simulation) for each time period and location of the power distribution system (step S12). The economic optimization system simulation unit 13 repeats such simulations for multiple combinations of bidding information to find a combination that satisfies the constraint conditions of the equipment that constitutes the power distribution system. Furthermore, when there are multiple candidates for a combination that satisfies the constraint conditions, the economic optimization system simulation unit 13 selects the candidate that will result in the lowest power price. Through the above process, matching (selection of a bidder) corresponding to the power demand for the next day is performed, and an optimal operation schedule is derived. This optimal operation schedule is an operation schedule that satisfies the constraint conditions of the power distribution equipment while obtaining a supply and demand amount that satisfies the power supply and demand forecast for the next day, and is also an operation schedule that will result in the lowest power price.

[0037] The acquisition of the bidding information in step S12 is performed by the market trading unit 14 of the distributed resource market trading platform 1. The functions of the market trading unit 14 are as follows. First, distribution system operator B (DSO) enters a bid to resolve the constraints. The bid has fields for entering information such as location, date and time, duration, and desired output, and the bid details are published on a screen that includes a map. Market participant C, who is a bidder who has registered DERs, can check the details. Market participant C decides on and enters an offer (bidding information) based on the published bid details. The offer has fields for entering the desired price, in addition to facility information such as output and response time.

[0038] Next, the economic optimization system simulation unit 13 calculates a DLMP (Distribution locational marginal price) according to the matching (bidding information), and outputs the analysis result of the optimal operation schedule to the DSO system 3. The analysis result of the optimal operation schedule includes, for example, 30-minute values ​​of power receiving point power (demand suppression amount) by time period and node, 30-minute values ​​of price by time period, etc.

[0039] The economic optimization system simulation unit 13 performs processing related to visualization of the DLMP (step S13a). This processing makes the electricity price by location after matching publicly available. In this processing, for example, a schematic diagram of the power distribution system and the electricity price by location of the power distribution system are superimposed on a map of the target range. The calculation method of the DLMP and the effect obtained by "visualization" will be described later.

[0040] The market trading unit 14 of the distributed resource market trading platform 1 performs a commitment confirmation process for the market participant C selected by matching (economic optimization simulation) (step S13b).

[0041] Next, the power distribution system operator B checks the analysis result through the DSO system 3 and performs an approval process for the result (step S14a). The distributed resource market trading platform 1 (market trading unit 14), which has received the approval of the result, notifies the market participant C of the bidding result (step S14b).

[0042] The market trading unit 14 performs settlement processing based on the bidding results (step S15) and issues an invoice (step S16). Meanwhile, the DSO system 3 prepares for dispatch communication to the market participant C (aggregator, etc.) (step S17), and then issues a settlement instruction (step S18). The market participant C confirms receipt of the settlement amount through the terminal device 2 (step S19). Here, we will explain the dispatch function of the DSO system 3. The dispatch function is a function that executes concluded distributed resource operation contracts, and when the operation time arrives, a standby command is issued to the distributed resource. If distribution system operator B (DSO) determines that the distributed resource needs to be operated, an operation command is issued to the distributed resource. A stop command is issued when distribution system operator B (DSO) decides to stop the resource or when the contract expiration time comes. The system can monitor the status of distributed resources for all contracts, and the execution status of the contract can be confirmed.

[0043] (Real-time market) Next, the processing flow in the real-time market will be described with reference to FIG.

[0044] The power supply and demand forecasting unit 12 of the distributed resource market trading platform 1 predicts the power supply and demand for the day (step S20). Here, the DSO system 3 (Fig. 1) provides the feeder current in its own distribution system, smart meter data, and planned power generation values ​​for the DSO area. The DSO system 3 also provides short-term weather forecasts, renewable energy fluctuation forecasts, and JEPX prices as information imported from outside. Based on this information, the power supply and demand forecasting unit 12 outputs the power demand forecast by time period for the day, the PV power generation forecast, and the JEPX price forecast to the economic optimization system simulation unit 13. Note that the forecast results here are more accurate than the forecasts made in the day-ahead market.

[0045] The economic optimization system simulation unit 13 acquires the power demand forecast for the day obtained in step S20 and the bidding information received from each of the multiple market participants C. Then, the economic optimization system simulation unit 13 applies the operation based on the desired operation schedule of the bidding information acquired from the market participants C to the power distribution system model created by the system model creation unit 10, and performs power flow and voltage calculation (simulation) for each time period and location of the power distribution system (step S21). The economic optimization system simulation unit 13 repeats such simulations for multiple combinations of bidding information to find a combination that satisfies the constraint conditions of the equipment that constitutes the power distribution system. Furthermore, when there are multiple candidates for a combination that satisfies the constraint conditions, the economic optimization system simulation unit 13 selects the candidate that will result in the lowest power price. Through the above process, matching (selection of bidders) corresponding to the power demand for the day is performed, and an optimal operation schedule is derived. In principle, this optimal operation schedule is an operation schedule that satisfies the constraint conditions of the power distribution equipment while obtaining a supply and demand amount that satisfies the power supply and demand forecast for the day, and is also an operation schedule that will result in the lowest power price. However, in a real-time market, there may be cases where there is no solution to resolve a violation of grid constraints.

[0046] Therefore, the economic optimization system simulation unit 13 judges whether or not there is a solution that resolves the violation of the system constraint (step S23). If there is a solution that resolves the violation of the system constraint (step S23; NO), the market trading unit 14 receives approval of the analysis result from the DSO system 3 and notifies the bidding result (step S24). On the other hand, if there is no solution that resolves the violation of the grid constraint (step S23; YES), the economic optimization grid simulation unit 13 formulates a power generation / demand reduction plan in the order of high power generation cost and low demand cost according to the bid price (step S25). Next, the notification unit 15 proposes the power generation / demand reduction plan formulated in step S25 to the DSO system 3 (a simple command system for DSO) (step S26). The proposal contents include the bidding results and the power generation / demand reduction proposal. The DSO system 3 (a simple command system for DSO) sends a dispatch notification to the market participant C (such as an aggregator) in response to the power generation / demand reduction proposal (step S28). The DER equipment (distributed resources) owned and managed by the market participant C receives or supplies power in accordance with the dispatch command received from the DSO system 3 (step S28). This resolves the violation of the grid constraint in the target distribution grid.

[0047] (Action, effect) As described above, according to the distributed resource market trading platform 1 of the first embodiment, the economic optimization system simulation unit 13 derives an optimal operation schedule that satisfies the constraints of the distribution system equipment, so that the distribution system operator B can reduce investment in expanding the distribution system equipment.

[0048] For example, assume that a large power demand is predicted in a factory facility connected to a certain feeder in a power distribution system. At this time, if no measures are taken, it is assumed that the power transmitted from the upstream of the power distribution system to meet the large power demand will be excessive, and a constraint violation (over capacity) will occur at a specific point of the power distribution system equipment. In this case, the economic optimization system simulation unit 13 according to this embodiment performs matching so as to actively adopt bids from distributed resources connected to the same feeder as the factory equipment, and derives an operation schedule for them. In this way, the power supply and demand between the distributed resources and the factory equipment is completed within the same feeder. Therefore, the power transmitted from the upstream of the power distribution system becomes small, and it becomes possible to operate the power distribution system equipment to meet the overall power supply and demand while satisfying the constraint conditions at a specific point of the power distribution system equipment (without making any investment in equipment expansion).

[0049] In addition, the distributed resource market trading platform 1 according to the first embodiment further includes an electric power supply and demand prediction unit 12 that predicts electric power supply and demand for the future (the next day, the current day). Then, an economic optimization system simulation unit 13 derives an operation schedule that is adaptable to the electric power supply and demand prediction as an optimal operation schedule. In this way, in the economic optimization system simulation, it is possible to accurately balance and match the power demand and power supply for the next day or that day.

[0050] Moreover, the economic optimization system simulation unit 13 according to the first embodiment further derives, as the optimal operation schedule, an operation schedule that minimizes the electricity price. This reduces the amount of money exchanged in electricity supply and demand.

[0051] Furthermore, when there is no solution that satisfies the constraint conditions of the power distribution system, the economic optimization system simulation unit 13 according to the first embodiment formulates a power generation suppression / demand suppression plan based on the desired price (step S25 in FIG. 3). In this way, dispatch commands are issued appropriately so that the constraints of the power distribution system are satisfied.

[0052] Moreover, the economic optimization system simulation unit 13 according to the first embodiment performs processing related to visualization of the distribution line marginal price (DLMP). The action and effect of this process will be described with reference to FIG.

[0053] Fig. 4 is an image diagram of the effect of visualizing DLMP by the economic optimization system simulation unit 13 according to the first embodiment. Fig. 4 shows a schematic diagram of a power distribution system K that is a target of power trading. As an example, the power distribution system K has feeders F21, F22, F23, and F24.

[0054] The electricity price (20 yen) of the electricity transmitted from upstream is displayed at the receiving point of distribution system K. This electricity price (20 yen) is determined based on the energy cost (power generation cost) of the upstream power generation equipment, and serves as the basis for the electricity price at each point (node) within distribution system K.

[0055] For example, at node A of feeder F23, there are no constraints related to voltage violations or system congestion (excessive heat capacity). In this case, as a result of matching by the economic optimization system simulation unit 13, the power price at node A is the same as that at the receiving point (20 yen).

[0056] At node B of feeder F21, there is an excess supply from PV power generation, and grid congestion results in a situation where the grid is constrained by the power distribution facility (capacity 3 MW). In this case, the economic optimization simulation of the economic optimization system simulation unit 13 selects the bid of market participant C, which offers the lowest price among many candidates (bids) connected to node B, within the range that does not exceed the power distribution facility constraint (capacity 3 MW). As a result, the power price at node B becomes lower (10.5 yen) than at the receiving point.

[0057] At node C of feeder F24, the power consumption by the EV charger at the end of the grid is excessive. As a result, the voltage here tends to drop, and it is assumed that the voltage constraints (for example, maintaining a voltage of -2% or more of the reference voltage of 6.6 kV) will not be met. In this case, there are only a few candidates (bids) around node C, and the predicted power demand will be matched from among the few candidates. As a result, the power price will be higher than at the receiving point (30.3 yen).

[0058] At node D of feeder F22, the power supply from the PV generator at the end of the grid is excessive. As a result, the voltage here tends to rise, and it is assumed that the voltage constraints (for example, maintaining the reference voltage of 6.6 kV at +2% or less) will not be met. In this case, there are many candidates (bids) around node D, and matching will be performed from among the many candidates against the predicted power demand. As a result, the power price will be lower (8.7 yen) than at the receiving point.

[0059] In the visualization process of marginal prices by distribution point, the prices at nodes A to D determined in the above manner are made public. Then, DER owners and electricity consumers who see these prices will take actions that are economically advantageous for them. For example, the owner of a factory facility connected to node B will consider increasing electricity demand in response to the low electricity price. Also, for example, EV users will discharge electricity at node C, where the electricity price is high, and charge it at node D, where the electricity price is low.

[0060] In this way, the DLMP calculated as a result of the economic optimization simulation will have a lower price for nodes with excess supply. If this information is made public (visible), it will encourage the introduction and use of load equipment and the reduction in the use of power generation equipment at those nodes. Similarly, the DLMP calculated as a result of the economic optimization simulation will have higher prices in nodes with excess demand, which will encourage the introduction and use of power generation facilities and the reduction in the use of load facilities in those nodes. In this way, making DLMP visible will encourage behavioral change among DER owners and electricity consumers, helping to alleviate challenges related to distribution grid constraints.

[0061] <Second embodiment> Next, the distributed resource market trading platform according to the second embodiment will be described in detail with reference to FIG. The functional configuration of the distributed resource market trading platform according to the second embodiment is similar to that of the first embodiment (FIG. 1), and therefore a description thereof will be omitted.

[0062] (Medium- to long-term plan) FIG. 5 is a diagram showing a process flow (medium- to long-term plan) of the distributed resource market trading platform according to the second embodiment.

[0063] The processing flow of the mid- to long-term plan will be described with reference to FIG. First, the registration processing unit 11 of the distributed resource market trading platform 1 registers a participant (step S30). Here, the registration processing unit 11 mainly accepts participant information and various information on distributed resources (DER information). The market operator A performs participant and DER authentication, credit checks, and operation / capacity confirmation. If there are no problems with these, the market operator A concludes a basic contract with the market participant C (step S31), and registers the participant in the distributed resource market trading platform 1 via the registration processing unit 11 (step S32).

[0064] Meanwhile, the system model creation unit 10 of the distributed resource market trading platform 1 receives information on the distribution system selected as the area to be considered for the mid- to long-term plan (single-connection diagram of the distribution system, type of distribution line / number of circuits / length, specifications of transformers and various voltage adjustment equipment, etc.), and creates a system model of the distribution system (step S33). Here, information on the mid- to long-term system plan for the distribution system is provided by the distribution system operator B. Therefore, the distribution system model created here incorporates future specifications for the mid- to long-term.

[0065] Next, the power supply and demand forecasting unit 12 of the distributed resource market trading platform 1 performs a mid- to long-term power supply and demand forecast (step S34). Here, the power supply and demand forecasting unit 12 receives information such as feeder current, smart meter data, planned power generation values ​​entering the DSO area, and maximum energy connection plans from the DSO system 3. The power supply and demand forecasting unit 12 also receives long-term weather forecasts, JPEX prices, electrification promotion plans, EV demand forecasts, and the like as information to be received from outside. The power supply and demand forecasting unit 12 performs a mid- to long-term power supply and demand forecast based on this information.

[0066] The power demand and supply forecasting unit 12 estimates the forecast error (step S35), and outputs the demand forecast, PV power generation forecast, JEPX price forecast, etc. obtained in step S34, and the demand forecast error, PV power generation forecast error, etc. obtained in step S35. An NWA scenario is formulated (step S36). Here, NWA (Non-Wire Alternatives) scenario formulation refers to formulating scenarios that avoid new investment by utilizing distributed energy resources (DER) within the area when the power distribution system is congested or is expected to become congested in the future, and new investment in capacity expansion of power transmission and distribution facilities is expected to be necessary. The NWA formulation scenario determined in step S36 is a scenario in which only the participating DERs accepted in step S30 are used and no investment in facility expansion is made.

[0067] The economic optimization system simulation unit 13 performs an economic optimization system simulation under conditions according to the NWA scenario of step S36 (i.e., conditions under which only participating DERs are connected) (step S37). Here, a simulation is performed based on the future power distribution system incorporating the mid- to long-term plan created in step S33 and the mid- to long-term power supply and demand forecast calculated in step S34.

[0068] The economic optimization system simulation unit 13 judges whether or not there is a solution that resolves the violation of the system constraints as a result of the simulation in step S37 (step S38). If there is a solution that resolves the violation of the system constraints (step S38; NO), it is determined that the NWA scenario formulated in step S36 (i.e., a scenario in which only participating DERs are utilized and no investment in facility expansion is made) is not problematic as a mid- to long-term plan. Therefore, the processing flow of the mid- to long-term plan is terminated.

[0069] If there is no solution that resolves the violation of the grid constraint (step S38; YES), the NWA scenario is formulated again (step S39). The scenario here includes the expansion of distribution facilities or the procurement of new DER facilities by the distribution system operator B (DSO).

[0070] The economic optimization system simulation unit 13 performs an economic optimization system simulation under conditions in accordance with the NWA scenario formulated in step S39 (i.e., conditions under which new power distribution facilities are expanded or DER facilities are procured) (step S40).

[0071] The economic optimization system simulation unit 13 judges whether or not there is a solution that resolves the violation of the system constraints as a result of the simulation in step S40 (step S41). If there is no solution that resolves the violation of the system constraints (step S41; YES), the process returns to step S39 and a new NWA scenario is formulated. If there is a solution that resolves the violation of the grid constraint (step S41; NO), the NWA scenarios in which the solution was found are compared (step S42). Here, the market operator A (DMO) compares a scenario in which new DER equipment is procured with a scenario in which distribution grid equipment is expanded, and evaluates the costs of each (step S43).

[0072] Based on the result of the cost evaluation in step S43, the market operator A (DMO) determines whether the DER procurement cost or the grid reinforcement cost is cheaper (step S44). If the "DER procurement cost > grid reinforcement cost" (step S44; NO), the market operator A proposes grid reinforcement based on the NWA scenario to the distribution system operator B (step S45b). If the "DER procurement cost < grid reinforcement cost" (step S44; YES), the market operator A proposes DER procurement based on the NWA scenario to the distribution system operator B (step S45b).

[0073] As described above, the distributed resource market trading platform 1 according to the second embodiment includes a system model creation unit that creates a distribution system model based on distribution system information including wiring information of a target distribution system and specification information of equipment that constitutes the distribution system, a registration processing unit that acquires and registers distributed resource information (DER information) from participants who own or manage distributed resources (DER), and a simulation unit that applies operation using the distributed resources of the participants to the distribution system model under conditions in accordance with a given NWA scenario, and calculates power flow and voltage by time zone and location in the distribution system, while deriving an optimal operation schedule that satisfies the constraints of the equipment that constitutes the distribution system.

[0074] According to the above configuration, the distributed resource market trading platform 1 can appropriately determine for the distribution system operator B (DSO) whether or not to make investments to enhance facilities in the distribution system in a medium- to long-term plan.

[0075] <Third embodiment> Next, with reference to FIG. 6, a distributed resource market trading platform according to a third embodiment will be described in detail. The functional configuration of the distributed resource market trading platform according to the third embodiment is similar to that of the first embodiment (FIG. 1), and therefore a description thereof will be omitted.

[0076] FIG. 6 is a diagram showing a process flow (real-time market) of the distributed resource market trading platform according to the third embodiment. In Fig. 6, the same processes as those in the first embodiment (Fig. 3) are denoted by the same reference numerals and the description thereof is omitted. Also, in Fig. 6, the description of the processes of the terminal devices of the market participants (aggregators, etc.) is omitted.

[0077] The distributed resource market trading platform 1 according to the third embodiment differs from the first embodiment in that it has a function of proposing appropriate power distribution and securing an emergency power source as an emergency response in the event of a disaster or the like.

[0078] Specifically, in the economic optimization simulation (step S21A), the economic optimization system simulation unit 13 receives information provided about distributed resource (DER) facilities owned by the distribution system operator B (DSO). The distributed resources owned by the distribution system operator B are facilities (reserve capacity) used for the purpose of satisfying constraint conditions only in emergencies. The information about the reserve capacity includes, for example, the reserve capacity (demand suppression amount) by time zone and node and the standby price by time zone and node as shown in FIG. 6.

[0079] In addition, in normal times, the economic optimization system simulation unit 13 performs the economic optimization system simulation under the assumption that the reserve power (distributed resources owned by the DSO) is not used (same as step S21 (FIG. 3) in the first embodiment). However, when a part of the equipment of the power distribution system is damaged due to the occurrence of a disaster, the economic optimization system simulation is performed including the reserve power (step S21A).

[0080] The distribution intermediate supply system (ADMS) of the DSO system 3 constantly monitors each point of the distribution system (step S50). If some equipment is damaged due to a disaster or the like, causing an overload (step S51; YES), emergency control is immediately performed (step S52). In this emergency control, power generation suppression and reserve DER control are performed.

[0081] In step S52, first, a notification of emergency control is sent to the distributed resource market trading platform 1 of market operator A (DMO). The economic optimization system simulation unit 13 of the distributed resource market trading platform 1 performs an economic optimization system simulation taking into account the smart meter data and feeder current at the current time (when the disaster occurs) and the DSO-owned DER equipment (step S21A). This process derives an optimal operation schedule for the DSO-owned DER equipment that satisfies the constraints of the power distribution system at the time the disaster occurs. This optimal operation schedule is transmitted to the DSO simple command system. The DSO simple command system issues a dispatch command to the DSO-owned DER equipment based on the optimal operation schedule (i.e., the operation schedule for eliminating the overload caused by the disaster) obtained in step S21A (step S53), which results in the emergency control in step S52.

[0082] As described above, the distributed resource market trading platform 1 according to the third embodiment includes: a system model creation unit 10 that creates a distribution system model based on distribution system information including wiring information of the distribution system and specification information of equipment that constitutes the distribution system; a market trading unit 14 that acquires bidding information including desired operation schedules and desired prices of the distributed resources connected to the distribution system from a plurality of market participants that manage the distributed resources connected to the distribution system; a simulation unit 13 that applies operation based on the desired operation schedules of the acquired bidding information to the distribution system model, and performs time-zone and location-specific power flow and voltage calculations of the distribution system, while deriving an optimal operation schedule, which is the operation schedule that satisfies constraints of the equipment that constitutes the distribution system; and a notification unit 15 that notifies market participants of the optimal operation schedule. The economic optimization system simulation unit 13 according to this embodiment is further characterized in that it receives information regarding the reserve capacity owned by the power distribution system operator B, and in the event of an emergency response, derives the optimal operation schedule including the reserve capacity.

[0083] By doing this, during an emergency response such as a disaster, even if an overload occurs due to damage to the distribution equipment, an operation schedule (optimal operation schedule) that eliminates such overload can be derived, including the DER equipment (reserve capacity) owned by distribution system operator B.

[0084] The distributed resource market trading platform 1 according to the third embodiment may further include the following functions.

[0085] That is, when a disaster occurs, some wiring may be cut or equipment may be damaged, and as a result, a wiring state different from the initial single-line diagram may be assumed in order to maintain power supply to downstream. In this case, the system model creation unit 10 may receive the state (ON / OFF state) of switches installed at various locations in the distribution system during an emergency response, and create a new distribution system model that matches the single-line diagram after the disaster occurs. In this case, the economic optimization system simulation unit 13 uses the distribution system model newly created (after the disaster occurs) by the system model creation unit 10, and performs an economic optimization system simulation by taking into account the receiving point power bid by the market participant C as well as the reserve power owned by the market participant C or the distribution system operator B as necessary. In this way, even if the connection state of the power distribution system changes due to the occurrence of a disaster, it is possible to avoid the occurrence of system constraints and perform a correct economic optimization system simulation.

[0086] <Fourth embodiment> Next, with reference to FIG. 7, a distributed resource market trading platform according to a fourth embodiment will be described in detail. The functional configuration of the distributed resource market trading platform according to the fourth embodiment is similar to that of the first embodiment (FIG. 1), and therefore a description thereof will be omitted.

[0087] In the first embodiment, it has been described that in the economic optimization simulation by the economic optimization system simulation unit 13, the DLMP is calculated by matching based on the constraint conditions (voltage violation, system congestion) of the power distribution system equipment (see FIG. 4, etc.). On the other hand, in this embodiment, the economic optimization system simulation unit 13 calculates the DLMP taking into consideration the environmental value and the resilience value in addition to the constraints of the power distribution system equipment. Specifically, the process flow shown in FIG. 7 is executed.

[0088] (Processing flow of distributed resource market trading platform) 7 is a diagram showing a processing flow of the distributed resource market trading platform according to the fourth embodiment. The processing flow shown in FIG.

[0089] First, the economic optimization system simulation unit 13 performs a calculation process of the DLMP based on the voltage violation and system congestion (step S60). This process is the same as that described in the first embodiment (FIG. 4).

[0090] Next, the economic optimization system simulation unit 13 performs a calculation process of the DLMP based on the environmental value (step S61). Here, the environmental value is added to the DLMP calculated in step S60.

[0091] That is, in the DLMP calculation in step S61, the income of the power generation facility with a small environmental load is increased. The environmental value portion is determined, for example, as follows.

[0092] (1) The smaller the amount of CO2 emissions per unit of electricity generated, the greater the environmental value. This maximizes the value of renewable energy sources and also increases the value of efficient power generation facilities. (2) By making the increase small for locations with a low DLMP before the environmental value is added and large for locations with a high DLMP, pricing will be set to promote the effect of DLMP in inducing the introduction of equipment to alleviate grid constraints.

[0093] Next, the economic optimization system simulation unit 13 performs a calculation process of the DLMP based on the resilience value (step S62). Here, the resilience value is further added to the DLMP calculated in step S61.

[0094] That is, in the DLMP calculation in step S61, the resilience value is added to the DLMP of the node connected to the power generation facility close to the node connected to the load facility that is greatly affected in the event of a power outage. An incentive is given to the power generation facility with a high possibility of being affected. The resilience value is determined, for example, as follows.

[0095] (1) Select the load equipment that will be most affected in the event of a power outage. For example, you may rank them as follows: Hospitals, police, fire stations, etc. > Street lights, schools > Densely populated residential areas (2) In the event of a disaster, if the area including the target load equipment can be operated as an independent grid (if the power supply route is secured), the power generation equipment within that area will be given a resilience value. (3) A higher resilience value may be set for power generation equipment located close to the target load equipment. (4) When locally connected distributed energy resources (DER) can operate independently for a long period of time, the resilience value is set high.

[0096] In the above-described embodiment, both the DLMP calculation based on the environmental value (step S61) and the DLMP calculation based on the resilience value (step S62) are described as being executed, but other embodiments are not limited to this aspect. The distributed resource market trading platform 1 according to another embodiment may perform only one of the DLMP calculation based on the environmental value (step S61) and the DLMP calculation based on the resilience value (step S62).

[0097] (Action, effect) As described above, the distributed resource market trading platform 1 according to the fourth embodiment calculates at least one of the DLMP based on the environmental value and the DLMP based on the resilience value. Calculating the DLMP based on environmental value can encourage the introduction of equipment with low environmental impact in the target power distribution system, while calculating the DLMP based on resilience value can encourage the supply of power to important load equipment in the event of a wide-area power outage.

[0098] In the above-described embodiment, the various processes executed by the distributed resource market trading platform 1 are stored in a computer-readable recording medium in the form of a program, and the above-described various processes are performed by the computer reading and executing the program. The computer-readable recording medium refers to a magnetic disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, a semiconductor memory, etc. The computer program may be distributed to a computer via a communication line, and the computer that receives the program may execute the program.

[0099] The program may be for implementing some of the above-mentioned functions, or may be a so-called differential file (differential program) that can implement the above-mentioned functions in combination with a program already recorded in the computer system.

[0100] In addition, in the above-mentioned embodiment, the distributed resource market trading platform 1 may have an internal CPU and the CPU may execute the above-mentioned processing according to a program, or may be realized by hardware alone or by a custom LSI such as an FPGA (Field Programmable Gate Array).

[0101] As described above, several embodiments according to the present disclosure have been described, but all of these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the gist of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents as described in the claims, as well as in the scope and gist of the invention.

[0102] <Additional Notes> The distributed resource market trading device, the distributed resource market trading method, and the program described in each embodiment can be understood, for example, as follows.

[0103] (1) In a first aspect, a distributed resource market trading platform 1 (distributed resource market trading device) includes a system model creation unit 10 that creates a distribution system model based on distribution system information including wiring information of the distribution system and specification information of equipment that constitutes the distribution system, a market trading unit 14 that acquires bidding information including desired operation schedules of the distributed resources from information management devices of multiple market participants that manage distributed resources connected to the distribution system, an economic optimization system simulation unit 13 (simulation unit) that applies the acquired bidding information to the distribution system model to perform time-zone and location-specific power flow and voltage calculations of the distribution system and derives an optimal operation schedule, which is the operation schedule that satisfies the constraints of the equipment that constitutes the distribution system, and a notification unit 15 that notifies the information management devices of the market participants of the optimal operation schedule.

[0104] (2) In a second aspect, the distributed resource market trading device described in (1) further includes an electricity supply and demand forecasting unit 12 that forecasts future electricity supply and demand, and an economic optimization system simulation unit 13 derives an operation schedule that is adaptable to the electricity supply and demand forecast as the optimal operation schedule.

[0105] (3) In a third aspect, the economic optimization system simulation unit 13 of the distributed resource market trading device described in (1) or (2) further derives, as the optimal operation schedule, an operation schedule that minimizes the electricity price.

[0106] (4) In a fourth aspect, in the distributed resource market trading platform 1 described in any one of (1) to (3), the bidding information acquired by the market trading unit includes desired prices in the bidding for the distributed resources obtained from information management devices of multiple market participants managing the distributed resources, and if there is no solution that satisfies the constraint conditions, the economic optimization system simulation unit 13 formulates a power generation suppression / demand suppression plan based on the desired prices. [Explanation of symbols]

[0107] 1. Distributed resource market trading platform 10 System Model Creation Department 11 Registration processing section 12 Electricity Supply and Demand Forecasting Department 13 Economic Optimization System Simulation Department 14 Market Trading Department 15 Notification Department 2 Market Participant Terminal Device 3. DSO System 4 DERMS 5 Day-ahead market & real-time market 6 ADMS 7 Payment Systems

Claims

1. a system model creation unit that creates a distribution system model based on distribution system information including wiring information of the distribution system and specification information of equipment that constitutes the distribution system; a market trading unit that acquires bidding information including desired operation schedules of distributed resources connected to the power distribution system from information management devices of a plurality of market participants that manage the distributed resources; a simulation unit that applies the acquired bidding information to the power distribution system model to perform time-zone and location-specific power flow and voltage calculations in the power distribution system, and derives an optimal operation schedule that satisfies constraints on facilities that constitute the power distribution system; and A notification unit that notifies the optimal operation schedule to an information management device of a market participant; Equipped with the bidding information acquired by the market trading unit includes desired prices in bidding for the distributed resources acquired from information management devices of a plurality of market participants managing the distributed resources; When there is no solution that satisfies the constraint condition, the simulation unit formulates a power generation suppression / demand suppression plan based on the desired price. A distributed resource market trading device.

2. Further comprising an electric power supply and demand prediction unit for predicting future electric power supply and demand, The simulation unit derives, as the optimal operation schedule, an operation schedule that is adaptable to the power supply and demand forecast.

2. The distributed resource market trading apparatus of claim 1.

3. The simulation unit further derives, as the optimal operation schedule, an operation schedule that minimizes the electricity price.

3. A distributed resource market trading device according to claim 1 or 2.

4. A distributed resource market trading device creates a distribution system model based on distribution system information including wiring information of the distribution system and specification information of facilities constituting the distribution system; a step of the distributed resource market trading device acquiring bidding information including desired operation schedules of the distributed resources from information management devices of a plurality of market participants who manage the distributed resources connected to the power distribution system; The distributed resource market trading device applies the acquired bidding information to the power distribution system model to calculate power flow and voltage by time zone and location in the power distribution system, and derives an optimal operation schedule which is an operation schedule that satisfies constraints of facilities constituting the power distribution system; The distributed resource market trading device notifies the optimal operation schedule to an information management device of a market participant; having The bidding information acquired in the step of acquiring the bidding information includes a desired price in a bid for the distributed resource acquired from an information management device of a plurality of market participants managing the distributed resource, In the step of deriving the optimal operation schedule, if there is no solution that satisfies the constraint condition, the distributed resource market trading device formulates a power generation suppression / demand suppression plan based on the desired price. Decentralized resource market trading method.

5. On the computer, creating a distribution system model based on distribution system information including wiring information of the distribution system and specification information of equipment constituting the distribution system; acquiring bidding information including desired operation schedules of distributed resources connected to the power distribution system from information management devices of a plurality of market participants who manage the distributed resources; A step of applying the acquired bidding information to the power distribution system model to perform time-zone and location-specific power flow and voltage calculations in the power distribution system, and deriving an optimal operation schedule which is an operation schedule that satisfies constraints of facilities constituting the power distribution system; notifying an information management device of a market participant of the optimal operation schedule; A program for executing The bidding information acquired in the step of acquiring the bidding information includes a desired price in a bid for the distributed resource acquired from an information management device of a plurality of market participants managing the distributed resource, In the step of deriving the optimal operation schedule, if there is no solution that satisfies the constraint condition, a power generation suppression / demand suppression plan is formulated based on the desired price. program.

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