ENERGY DISPATCH OPTIMIZATION USING A FLEET OF DISTRIBUTED ENERGY RESOURCES

A dispatch optimization system and virtual power plant can be utilized and controlled in order to support the operations of a power distribution system. For example, upon determining an electrical need, the dispatch optimization system and/or virtual power plant may make an energy adjustment by allocating the energy adjustment among distributed energy resources of a fleet of distributed energy resources in order to achieve the energy adjustment. The dispatch optimization system and/or virtual power plant may determine the allocation among the distributed energy resources based on the economic costs and storage costs to use each distributed energy resource.

BACKGROUND

Power grids are commonly connected to distributed energy resources that can be located anywhere on the grid. The distributed energy resources can be used to consume or produce power on the grid. For example, a distributed energy resource may consume or produce power to respond to an event on the grid requiring additional power production or consumption. When a distributed energy resource is used to consume or produce power, there is an economic cost of controlling and using the distributed energy resource.

Each distributed energy resource may have an ideal storage level. For example, a distributed energy resource's ideal storage level may be the maximum storage level of the distributed energy resource. The distributed energy resource at its maximum storage level will be able to discharge the stored power in response to future events on the power grid such as a peak time of power consumption. In another example, the distributed energy resource's ideal storage level is half of the maximum storage level of the distributed energy resource. A distributed energy resource at half of its maximum storage level will be able to discharge or consume power in response to future power consumption and power generation events on the grid. When a distributed energy resource consumes or produces power, the distributed energy resource may move away from its ideal storage level. A storage cost is the deviation from the distributed energy resource's ideal power level when the distributed energy resource is used to produce or consume power.

SUMMARY

In general terms, this disclosure is directed to optimizing the use of distributed energy resources by blending the economic cost and storage cost to use each distributed energy resource. Using the blended cost, the selection of distributed energy resources to respond to an energy dispatch request is optimized.

One aspect is a method for optimizing the dispatch of energy, the method comprising: determining an energy need; determining a first subgroup of a fleet of distributed energy resources to use to meet the energy need based on an economic cost to use each distributed energy resource to meet a portion of the energy need; determining a second subgroup of the fleet of distributed energy resources to use to meet the energy need based on a storage cost to use each distributed energy resource to meet the portion of the energy need; and causing the first subgroup of the fleet of distributed energy resources and the second subgroup of the fleet of distributed energy resources to make energy adjustments to meet the energy need.

Another aspect is a dispatch optimization system comprising at least one processing device and at least one non-transitory computer-readable medium storing data instructions that, when executed by the at least one processing device, cause the dispatch optimization system to: determine an energy need; select a first distributed energy resource in a fleet of distributed energy resources to meet at least a portion of the energy need, selecting the first distributed energy resource including: determine a first economic cost to use the first distributed energy resource to dispatch energy to meet at least the portion of the energy need; determine a second economic cost to use a second distributed energy resource to dispatch energy to meet at least the portion of the energy need; determine the first economic cost and the second economic cost are within a range; determine a first storage cost to use the first distributed energy resource to dispatch energy to meet at least the portion of the energy need, the first storage cost comprising a level of deviation from an ideal storage level of the first distributed energy resource; determine a second storage cost to use the second distributed energy resource to dispatch energy to meet at least the portion of the energy need, the second storage cost comprising the level of deviation from the ideal storage level of the second distributed energy resource; and determine the first storage cost is less than the second storage cost; and instruct the first distributed energy resource to make an energy adjustment to meet the at least the portion of the energy need.

A further aspect is a method for optimizing the dispatch of energy, the method comprising: determining an energy need; determining an energy adjustment needed across a fleet of distributed energy resources to meet the energy need; determining an allocation of the energy adjustment among the fleet of distributed energy resources, determining the allocation comprising: determining an economic cost to use each distributed energy resource of the fleet of distributed energy resources to dispatch energy to meet the allocation of the energy adjustment; determining a first subgroup of the fleet of distributed energy resources to use to meet the energy need based on the economic cost to use each distributed energy resource; determining a second subgroup of distributed energy resources distinct from the first subgroup having economic costs within a threshold; determining a difference between a final level of deviation from an ideal storage level of each distributed energy resource of the second subgroup and a final storage level of each distributed energy resource of the second subgroup when used to dispatch energy to meet the allocation of the energy adjustment and a current level of deviation from the ideal storage level of each distributed energy resource of the second subgroup and a current storage level of each distributed energy resource of the second subgroup; determining a third subgroup of the fleet of distributed energy resources from the second subgroup to use to meet the energy need based on the difference between the final level of deviation and the current level of deviation for each distributed energy resource of the second subgroup; and determining the allocation of the energy adjustment among the first subgroup and the third subgroup; and instructing the distributed energy resources of the first subgroup and the third subgroup to make energy adjustments according to the allocation to meet the energy need.

Another aspect is a method for optimizing the dispatch of energy, the method comprising: determining an energy need; determining a subgroup of a fleet of distributed energy resources based on an ideal storage deviation value to use each distributed energy resources to meet a portion of the energy need; and causing the subgroup of the distributed energy resources to make energy adjustments to meet the energy need.

An additional aspect is a method for optimizing the dispatch of energy, the method comprising: determining an energy need; determining a subgroup of a fleet of distributed energy resources based on a difference between: a final level of deviation from an ideal storage level of each distributed energy resource and a final storage level of distributed energy resource when used to dispatch energy to meet the energy need; and a current level of deviation from the ideal storage level of each distributed energy resource and a current storage level of each distributed energy resource; and causing the subgroup of the distributed energy resources to make energy adjustments to meet the energy need.

DETAILED DESCRIPTION

Various examples will be described in detail with reference to the drawings, wherein like reference numerals represent like parts and assemblies throughout the several views. Reference to various example does not limit the scope of the claims attached hereto. Additionally, any examples set forth in this specification are not intended to be limiting and merely set forth some of the many possible examples for the appended claims.

The present disclosure describes an example dispatch optimization method and system involving a fleet of distributed energy resources (“DERs”), a dispatch optimization system, and a virtual power plant (“VPP”). The dispatch optimization system and virtual power plant can be utilized and controlled in order to support the operations of a power distribution system. For example, upon determining an electrical need, the dispatch optimization system and/or virtual power plant may make an energy adjustment. The energy adjustment can include charging and/or discharging. The dispatch optimization system and/or virtual power plant allocates the energy adjustment among the distributed energy resources of its fleet in order to achieve the energy adjustment.

A dispatch optimization system may determine which distributed energy resources of the fleet should be used to meet the energy need based on an economic cost and a storage cost to use each distributed energy resource. In examples, multiple DERs will be used to meet the energy need. The economic cost is the monetary cost to use the distributed energy resource, such as by receiving energy or discharging energy. A distributed energy resource's storage cost is the difference in the deviation from an ideal storage level of each distributed energy resource that will result from use for each distributed energy resource. For example, a distributed energy resource may have an ideal storage level of one hundred percent of its capacity. The distributed energy resource may have a current storage level of ninety percent of its capacity, and the storage level will drop to eighty percent if the distributed energy resource is used to meet a portion of the energy need. The storage cost would be higher than a distributed energy resource also having an ideal storage level of one hundred of its capacity and will drop from a state of one hundred percent to ninety percent. In an example, the storage cost may be a negative value or set to a minimum value such as zero for a distributed energy resource, indicating that it would be beneficial to use the distributed energy resource to meet a portion of the energy need. For example, the ideal storage level of the distributed energy resource may be fifty percent, the current storage level sixty percent, and the resulting storage level if used fifty-five percent. The storage level of the distributed energy resource is moving closer to the ideal storage level when used to meet the energy need.

In some examples, a DER may not be capable of storing energy. The storage cost to use a DER with no storage capacity is zero or another minimum cost value. The minimum storage cost value indicates that it is beneficial to use the DER with no storage capacity to meet the energy need when evaluating DERs based on storage cost.

In some examples, the economic cost and/or storage cost to use multiple DERs may be identical or nearly identical (e.g., within a range). A dispatch optimization system may consider a health value of the DERs to determine which DERs to use that have the same or similar economic costs and/or storage costs to use to meet the energy need. In an example, the health value is based on a number of activations that each DER has experienced. The DER(s) with less activations will be chosen over DER(s) with more activations. The health value may be based on other characteristics of the DER such as DER age, operating efficiency, current capacity compared to the capacity when the DER was new (e.g., manufacturing date, installation date), etc.

The dispatch optimization system may assign the economic costs or the storage costs a higher priority. For example, if the economic costs have a higher priority, the dispatch optimization system determines a first subgroup of the fleet of energy resources to use based on the economic costs of the distributed energy resources. The dispatch optimization system selects the distributed energy resources that are the cheapest to use first. If the selected distributed energy resources are sufficient to meet the energy need, the dispatch optimization system will not compare the storage costs of the distributed energy resources. Otherwise, once the first subgroup is determined, the dispatch optimization system selects a second subgroup of distributed energy resources based on the storage costs of the remaining distributed energy resources.

In an example, only the distributed energy resources that are not in the first subgroup and that have an economic cost within a threshold value are considered. For example, the first subgroup may have an economic cost of thirteen cents per kilowatt hour (kWh) and lower. Of the remaining distributed energy resources, only distributed energy resources having an economic cost below 15 cents per kWh may be considered when the dispatch optimization system selects the second subgroup of distributed energy resources based on storage cost. The threshold may be determined based on the remaining number of distributed energy resources needed to meet the energy need with the first subgroup of distributed energy resources.

The dispatch optimization system may alternatively assign the storage costs a higher priority than the economic costs. The first subgroup described above would be determined by comparing the storage costs of each distributed energy resource. The second subgroup described above would be determined by comparing the economic costs of each distributed energy resource.

Once the dispatch optimization system determines which distributed energy resources to use, the dispatch optimization system can directly instruct the selected distributed energy resources to make energy adjustments to meet the energy need. Alternatively, the dispatch optimization system can instruct the virtual power plant to cause the selected distributed energy resources to make energy adjustments to meet the energy need.

FIG.1is a flow chart illustrating an example method100of controlling a fleet of distributed energy resources to meet an energy need. Method100begins at operation102, and an energy need is determined. For example, a dispatch optimization system and/or virtual power plant may monitor a power distribution system to determine an energy need. The energy need may be a need for energy generation or energy consumption. For example, there may be an insufficient amount of energy produced for the current level of consumption on the power distribution system and/or will require costly start-up procedure of a power plant, resulting in an energy need of generation. In another example, there may be an excess amount of energy being produced that is straining the grid and/or will require costly shut down procedure of a power plant, resulting in an energy need of consumption to consume or otherwise reduce the excess energy.

Once the energy need is determined, a first subgroup of a fleet of distributed energy resources is determined to be used to meet the energy need based on an economic cost to use each distributed energy resource to meet a portion of the need at operation104. For example, the first subgroup of distributed energy resources may include all distributed energy resources that have an economic cost of ten cents per kWh or less. In an example, the dispatch optimization system may determine which distributed energy resources to include in the first subgroup by comparing the economic costs of each distributed energy resource to determine a cut off value (e.g., ten cents per kWh).

The dispatch optimization system may exclude from the first subgroup any distributed energy resources having economic costs within a range that indicates the difference between economic costs may be outweighed by the differences in storage costs. The range can be a predetermined value or a dynamic value that changes based on factors of the power distribution system. For example, there may be a group of distributed energy resources that have economic costs of either 9.1 cents per kWh, 9.2 cents per kWh, or 9.3 cents per kWh, and the threshold range may be set to economics costs within 0.5 cents per kWh for example. Because the economic costs to use any of the group of distributed energy resources are within the range, the dispatch optimization system may not include the group of distributed energy resources in the first subgroup. The dispatch optimization system may wait to compare the storage costs of the group of distributed energy resources in operation106and place one or more of the distributed energy resources in a second subgroup to be used to meet the energy need.

Next, a second subgroup of distributed energy resources is determined based on a storage cost to use each distributed energy resource to meet the energy need in operation106. The storage cost is a deviation from the ideal storage level for each distributed energy resource. In an example, a dispatch optimization system may determine which distributed energy resource to include in the second subgroup based on the storage costs. As used herein, a storage cost is equivalent to a difference in a storage cost value if the DER is used to meet the energy need and a current storage cost value. For example, the dispatch optimization system may determine the difference between the storage cost for each DER to reach its ideal storage level if it is used to meet the energy need and the current storage cost for each DER to reach its ideal storage level. If the DER's storage level will move away from the ideal storage level if used, the difference will be a positive value. If the DER's storage level will move toward the ideal storage level if used, the difference will be a negative value. Therefore, the DERs having negative values are preferred over DERs with positive values. Calculating the storage cost for each DER will be explained in further detail herein with reference toFIG.8.

The dispatch optimization system may determine which distributed energy resources to use based on storage cost in order based on the distributed energy resources that have the lowest economic cost that are not included in the first subgroup. For example, if the distributed energy resources in the first subgroup all have an economic cost of ten cents per kWh or less, the dispatch optimization system will evaluate the distributed energy resources having an economic cost of 10.1 cents per kWh first, then 10.2 cents and so on. The dispatch optimization system will then select the distributed energy resources having a combination of the relative lowest storage costs and economic costs to include in the second subgroup.

Distributed energy resources that will move closer to its ideal storage level have a negative or minimum value storage cost, which is preferred when evaluating DERs based on storage cost. For example, a distributed energy resource having an ideal storage level of eighty percent of its capacity and will move from a storage level of fifty percent to forty percent if used will have a positive storage cost, while a distributed energy resource having an ideal storage level of eighty percent of its capacity and will move from a storage level of thirty percent to forty percent if used will have a negative or minimum value storage cost. The distributed energy resource with a negative storage cost would be chosen so the resulting storage level for both distributed energy resources is closer to the ideal storage level.

Finally, in operation108, the first and second subgroups of distributed energy resources are caused to make energy adjustments to meet the energy need. For example, a dispatch optimization system and/or a virtual power plant may cause or instruct the first and second subgroups to make energy adjustments to meet the energy need. For example, the total energy need may be an additional 50 kWh, or an energy generation need. The first and second subgroups will discharge a total of 50 kWh. For example, there may be three distributed energy resources in each subgroup. The distributed energy resources may discharge 3 kWh, 5 kWh, 7 kWh, 8 kWh, 12 kWh, and 15 kWh respectively to meet the energy need. The distributed energy resources typically have different properties such as storage capacity, rate of discharge, rate of charge, current storage level, and ideal storage level. Each distributed energy resource may discharge a different amount of energy base on the properties. For example, each distributed energy resource may discharge energy to make each distributed energy resource as close to its ideal storage level as possible. In another example, the distributed energy resources having the fastest rate of discharge will discharge more energy, so the energy need is met as quickly as possible. The dispatch optimization system can determine the amount of energy each distributed energy resource will discharge and/or consume based on one or more properties of the distributed energy resources.

In an example, some distributed energy resources may discharge energy and some distributed energy resources may consume energy. By having specific distributed energy resources discharge or consume energy, more distributed energy resources' storage levels may move closer to the ideal storage level for each distributed energy resource. For example, the dispatch optimization system may determine that the increased total economic cost to discharge certain distributed energy resources and charge others is worth making more distributed energy resources have an ideal or closer to ideal storage level.

FIG.2is a schematic block diagram illustrating an example power distribution system200. In this example, the power distribution system200includes an electric utility202and a virtual power plant204. The example electric utility202includes a power plant210, a distribution grid212, and a grid operation control center214having a utility computing device216operated by a platform operator O. The example virtual power plant204includes a dispatch optimization system220, and a fleet of distributed energy resources222across a plurality of sites. In the illustrated example, site1includes DER1, site2includes DER2, and site3includes DER3. The example dispatch optimization system220includes a computing device230, a VPP status monitor232, and an energy control objective engine234. The energy control objective engine234includes economic cost analyzer236and storage cost analyzer238. Portions of the power distribution system200can communicate across a data communication network240.

A reliable power distribution system200is critical for modern societies, which rely on the power distribution system200to supply a consistent source of electricity for an endless variety of electrical needs. Several examples of systems that utilize electrical power include refrigeration systems, lighting, heating and air conditioning systems, computers and portable electronics, and electric motors.

An electric utility202is one possible source of electrical power, and traditionally has been the primary source. A power plant210generates electricity and distributes the energy across the distribution grid212. The distribution grid212can include many different components, but at its core, the distribution grid212includes transmission lines that conduct electricity from the power plant210to the consumer sites.

In the illustrated example, the utility202also includes the grid operation control center214, including a platform operator O, that is tasked with monitoring and controlling the grid operations in an effort to maintain a stable and reliable supply of electricity on the distribution grid212. However, the virtual power plant204and/or the dispatch optimization system220may autonomously monitor and control the gird operations.

The grid operation control center214can utilize a variety of tools to not only monitor the real-time status of the distribution grid212, but also to forecast both supply and demand in the future. For example, the grid operation control center214may have one or more computerized models that take as inputs current operating conditions as well as various other data, such as weather forecasts, in order to make predictions about consumer demand in the future. By comparing the power plant210production capabilities and the predicted demand, the grid operation control center214can try to identify possible problems before they occur, in an effort to avoid power outages, voltage drops, or frequency variations.

As one example, one of the biggest consumers of electrical energy is air conditioning systems. On a particularly hot day, the utility202can see a significant increase in peak demand due to the simultaneous operation of numerous home and commercial air conditioning systems. Therefore, if the utility202is not prepared or is not able to supply sufficient electrical energy at a time of peak demand, the utility202may need to shut down portions of the grid or start up backup power generators in order to maintain adequate quality on the rest of the grid.

But it is not only meeting peak demand that the grid operation control center214must worry about. The growth of renewable energy sources (solar, wind, and the like) also presents challenges to the utility202. Solar and wind generators provide variable amounts of energy depending on the conditions. During a clear bright day, a solar generator can supply maximum energy to the grid, but cloud cover or dark of night reduce or eliminate solar production. Similarly, wind speeds can vary the amount of production from wind generators. Therefore, utilities connected to substantial renewable energy sources may also have the opposite problem of having too much energy at times of peak production. Additionally, it can be costly to power down a power generator such as a power plant at times of excess energy production, so it can be advantageous to consume the excess power rather than power down one or more generators.

One of the advantages of the power distribution system200including the virtual power plant204is that the virtual power plant204can help to support the operations of the utility202. It can do this by supplying additional power to the grid during times of peak consumption and can also do this by drawing excess power from the grid during times of peak production.

The example virtual power plant204shown inFIG.2includes an example dispatch optimization system220, and a fleet of distributed energy resources222.

The distributed energy resources can include a variety of resources including electrical generators (e.g., distributed generation systems) and storage systems (e.g., distributed energy storage systems). Examples of electrical generators include renewable energy sources, such as solar power (e.g., photovoltaics), wind power, geothermal power, small hydro, biomass, biogas, and the like. Examples of storage systems include battery, pumped hydro, compressed air, and thermal energy storage systems.

In the illustrated example, the virtual power plant204includes DERs that are distributed across a plurality of sites. Each site can have one or more DERs. For example, site1includes DER1, site2includes DER2, and site3includes DER3. An example of the DER1is a solar generator250and a first battery storage system252. An example of the DER2is a wind power generator254and a second battery storage system256. An example of the DER3is a gas generator258and a third battery storage system260. DERs do not have to include a battery storage system, but each of the examples shown inFIG.2includes a similar battery storage system for ease of explanation.

The dispatch optimization system220provides centralized control of the fleet of distributed energy resources of the virtual power plant204. In this example, the dispatch optimization system220includes the computing device230, which operates the VPP status monitor232, and the energy control objective engine234. The VPP status monitor232monitors the status and operation of the fleet of DERs222. For example, the dispatch optimization system220can determine that an energy need exists via the VPP status monitor. In some examples, the VPP status monitor232maintains a virtual model of the fleet of DERs222, as illustrated and described in more detail herein with reference toFIG.3.

The energy control objective engine234includes an economic cost analyzer236and a storage cost analyzer238. The economic cost analyzer236can analyze the fleet of DERs to select a subgroup of DERs to be used to meet an existing energy need based on the economic cost to use each DER in the fleet. The storage cost analyzer238can analyze the fleet of DERs to select a subgroup of DERs to be used to meet an existing energy need based on the storage cost to use each DER in the fleet.

Portions of the power distribution system200, such as the computing devices described herein, and the distributed energy resources, can communicate with one another across a data communication network240. The data communication network240can include one or more data communication networks, such as the Internet, cellular data communication networks, local area networks, and the like.

FIG.3is a schematic block diagram illustrating an example of the dispatch optimization system220, shown inFIG.2. In this example, the VPP status monitor232includes a distributed energy resource communication engine310and a distributed energy resource modelling engine312. The energy control objective engine234ofFIG.2includes an economic cost analyzer236, a storage cost analyzer238, a utility communication engine320, allocation engine322, distributed energy resource communication engine324, and an objective monitor326.

As explained above with reference toFIG.2, the VPP status monitor232monitors the status and operation of the fleet of DERs222inFIG.2. The VPP status monitor232utilizes the distributed energy resource communication engine310to receive and monitor the status and operation of the fleet of DERs222. The distributed energy resource communication engine310can receive information about each distributed energy resource in the fleet of DERs222such as the current storage level, storage capacity, rate of charge, rate of discharge, the economic cost DER, and the storage cost to use the DER. The economic cost may be produced by the economic cost analyzer236, and the storage cost may be produced by the storage cost analyzer238.

The distributed energy resource modelling engine312creates and maintains a virtual model of the fleet. For example, it can create a virtual model of the distributed energy resources in the fleet of DERs222inFIG.2, as illustrated and described in more detail herein with reference toFIG.4. The model can contain any desired information about the distributed energy resources in the fleet, including the current storage level, storage capacity, rate of charge, rate of discharge, the economic cost, and the storage cost. The distributed energy resource modelling engine312can communicate with the energy control objective engine234to receive information about the distributed energy resources, including the economic cost and storage cost to use each DER to meet an energy need.

As explained above with reference toFIG.2, the energy control objective engine234can determine the necessary adjustment needed to reach the energy target. The utility communication engine320enables the energy control objective engine234to communicate with a utility, such as utility202inFIG.2. The utility communication engine320can receive instructions from the utility which includes determining a group of DERs to use to meet an energy need.

The allocation engine322enables the energy control objective engine234to determine subgroups of DERs and allocate energy adjustments to the subgroups of DERs to meet an energy need. For example, the allocation engine322can determine a subgroup of DERs and allocate energy adjustments between the distributed energy resources in the fleet of DERs222inFIG.2. The allocation engine322, for instance, may select DER1and DER2to meet the energy need and determine that DER1should charge to ninety percent of its capacity and DER2should charge to sixty percent of its capacity to meet the energy need. The allocation engine322may select every distributed energy resource in the fleet or select a subgroup of the distributed energy resources. For example, the allocation engine selects a subgroup to use to meet the energy need based on the economic cost to use each DER. The economic cost may first be determined by economic cost analyzer236and sent to the allocation engine322. The allocation may select another subgroup to use to meet the energy need based on the storage cost to use each DER. The storage cost may first be determined by storage cost analyzer238and sent to the allocation engine322. Alternatively, the allocation engine may determine the subgroup of DERs to use based a combination of the economic cost and storage cost of each DER. The allocation engine322can determine a specific energy adjustment or a uniform energy adjustment for each DER in the selected subgroup(s) to meet the energy need.

The allocation engine322can communicate with the VPP status monitor232to obtain any information needed to allocate the energy adjustment. For example, the allocation engine322may receive the current storage level, storage capacity, rate of charge, and the rate of discharge for each distributed energy resource in the fleet. The allocation engine322can receive this information from the virtual model created by the distributed energy resource modelling engine312. Alternatively, the allocation engine322can communicate with distributed energy resource communication engine324to receive the same information. This allows the energy control objective engine234to allocate the energy adjustment without communicating with the VPP status monitor232.

The distributed energy resource communication engine324operates like the distributed energy resource communication engine310described above. The distributed energy resource communication engine324allows the energy control objective engine234to communicate with the fleet of distributed energy resources without communicating with the VPP status monitor232.

The objective monitor326tracks the status of the energy adjustment and can monitor the fleet of distributed energy resources in real-time to ensure that the energy target will be met. The objective monitor can communicate with the distributed energy resource communication engine324or the VPP status monitor232to communicate with the fleet of distributed energy resources and receive information on the current energy adjustments made by the distributed energy resources. In some examples, the objective monitor326receives this information from the virtual model created by the distributed energy resource modelling engine312. The objective monitor326may indicate that the current allocation is insufficient to meet the energy need. The allocation engine322can then reallocate the energy adjustment for each selected distributed energy resource if necessary. The economic cost analyzer236can determine the economic cost to use each DER and the storage cost analyzer238can determine the storage cost to use each DER to remedy the insufficient allocation and meet the energy need. The allocation engine322may then determine a subgroup of DERs to use to meet the energy need based on the economic cost and/or the storage cost to use each DER. The distributed energy resource communication engine324can then instruct the distributed energy resources of the fleet to make energy adjustments according to the revised allocations. This will cause the fleet of distributed energy resource to meet the energy need.

FIG.4is a schematic block diagram illustrating an example of the fleet of distributed energy resources222, shown inFIG.2, and corresponding virtual model400of the fleet. In some examples the virtual model400is part of the VPP status monitor232, shown inFIG.2. The dispatch optimization system220can create and/or update the virtual model400. For example, the VPP status monitor232may include a distributed energy resource modelling engine312as shown inFIG.3to create and/or update the virtual model400. In the illustrated example, the fleet of distributed energy resources222includes DER1, DER2, and DER3ofFIG.1. The first battery storage system model452is the virtual representation of the first battery storage system252. The second battery storage system model456is the virtual representation of the second battery storage system256. The third battery storage system model460is the virtual representation of the third battery storage system260. In an example, the first battery storage system model452, the second battery storage system model456, and the third battery storage system model460model the entire distributed energy resource. The distributed energy resources may be modelled as battery storage system models to simplify the virtual model400.

The virtual model400stores any information related to the fleet of distributed energy resources222. This information can include DER properties such as capacity, current storage level, rate of charge, rate of discharge, the economic cost to use the DER, the ideal storage level, the storage cost to use the DER, and other operating points of each distributed energy resource. For example, the DER1model includes virtual battery storage system352, a capacity of 13.5 kWh, a rate of charge of 3.3 kW, a rate of discharge of 5 kW, and an economic cost of 6.2 cents per kWh. In some examples, the information in the virtual model can be updated by communicating with the distributed energy resource communication engine310, allocation engine322, distributed energy resource communication engine324, and objective monitor326as shown inFIG.3.

The virtual model400can update in real-time to effectively model the current state of the fleet of distributed energy resources222. For example, the distributed energy resource modelling engine312monitors and updates the virtual model400continuously. The virtual model400can be used to determine an energy need. For example, the energy control objective engine234may communicate with the distributed energy resource modelling engine312as shown inFIG.3to access the virtual model400.

The virtual model400can also be provided to a utility, such as utility202inFIG.2. The virtual model400can be leveraged to determine the existence of an energy need. For example, a utility can use the virtual model400to determine that an energy need exists and that discharging DER1and DER2have the lowest economic cost, storage cost, and/or a combination of economic and storage costs to provide energy to meet the energy need. The virtual model400can also be used to forecast the state of the power distribution system200, including energy levels of the distributed energy levels at future times.

FIG.5is a schematic block diagram illustrating an example fleet of distributed energy resources modeled as virtual representations of battery systems as shown in the virtual model400ofFIG.4. The modeled DERs all have a current storage level, a new storage level if the DER is used to meet an energy need, a change in storage level if the DER is used to meet the energy need, and an ideal storage level. For example, DER1has a current storage level of thirty percent, a new storage level510, and an ideal storage level502. DER2has a current storage level of sixty percent, a new storage level512, and an ideal storage level504. DER3has a current storage level of 95 percent, a new storage level514, and an ideal storage level506. DER4has a current storage level of ten percent, a new storage level516, and an ideal storage level508.

As described above, DER1has a negative or minimum value storage cost because the new storage level510is closer to the ideal storage level502than the current storage level. For example, if DER1is used to meet the energy need, the storage level will increase from thirty percent to new storage level510. For example, new storage level510may be 45 percent storage level, closer to ideal storage level502, which may be eighty percent.

DER2, DER3, and DER4have a positive storage cost if used to meet the energy need because new storage level512is further from the ideal storage level504, new storage level514is further from the ideal storage level506, and new storage level516is further from the ideal storage level508compared to the DERs current states of charge. DER3will experience a larger change than the other two DERs, so the storage cost is higher. As a result, DER2and DER3will typically be selected before DER4if the subgroup of DERs is being determined based on storage cost. DER4has the smallest change in storage level, so it may be selected before DER2is selected. Alternatively, DER4has a higher difference between the new storage level516and ideal storage level508, so it may be selected after DER2, which has a smaller difference between new storage level512and ideal storage level504. For example, the storage cost analyzer238may determine the storage cost giving the magnitude of change a higher priority or the resulting difference in storage level a higher priority.

FIG.6is a flow chart illustrating an example method600of controlling a fleet of distributed energy resources to meet an energy need. For example, the dispatch optimization system220may perform one or more operations of the method600to control a fleet of DERs to meet the energy need. The method600begins by determining an energy need in operation602. For example, the VPP status monitor232or energy control objective engine234illustrated inFIG.2may monitor the power distribution system200to determine that there is an energy need.

An energy adjustment needed across a fleet of DERs to achieve the energy need is determined in operation604. For example, the allocation engine322may determine an energy adjustment that is needed across the fleet of distributed energy resources222to meet the energy need.

Next, it is determined which two DERs of the fleet of DERs have the lowest economic costs in operation606. For example, the economic cost analyzer236may determine the two DERs having the lowest economic cost to use to meet the energy need. In one example, more than two DERs may have the lowest economic cost, and more than two DERs may be selected in operation606.

The DERs selected in operation in608have the economic costs of each DER compared to determine if the economic costs are within a threshold value. For example, the economic cost analyzer236and/or the allocation engine322may compare the economic costs to determine whether the economic costs are within a threshold value. The threshold value may be a predetermined value or a dynamic value. The threshold value can be set to make the selection of DERs primarily based on the economic cost or primarily based on different factors such as a storage cost. For example, the threshold value can be set to require the economic costs to be identical for flow to proceed to operation610. In another example, the threshold value can be set to allow any value for economic costs, allowing the method600to always flow to operation610. In another example, the threshold value is set in between the two threshold values described above so that there is a range of economic cost differences that will allow flow to proceed to operation610.

If the economic cost of the DERs is within the threshold value, flow proceeds to operation610. In operation610, the storage costs to use each selected DER to meet the energy need is determined. For example, the storage cost analyzer238may determine the storage cost to use the two or more DERs that were selected in operation606. The determination of the storage cost to use the DERs is described in more detail herein with respect toFIG.8

The DER with the lowest storage cost is then selected to be used to meet the energy need in operation612. For example, the storage cost analyzer238and/or the allocation engine322selects the DER with the lowest storage cost to be used to meet the energy need. In some examples, the DERs have identical storage costs. In these examples, the DER may be chosen based on a health value of each DER. For example, the health value may be based on a number of activations, the age, and/or the operating efficiency of each DER.

Moving back to operation608, if the economic costs are not within the threshold, flow proceeds to operation614. In operation614, the DER with the lowest economic cost is selected. For example, the economic cost analyzer236and/or the allocation engine322selects the DER with the lowest economic cost to be used to meet the energy need. In some examples, the DERs have identical economic costs. In these examples, the DER may be chosen based on a health value of each DER.

Flow proceeds from operations612and614to operation616. In operation616, it is determined whether the energy need will be met by the currently selected DER(s). For example, the allocation engine322will determine whether the DERs selected in operations612and614will meet the energy need.

If the energy need will not be met, flow proceeds back to606so one or more DERs can also be selected to meet the energy need.

If the energy need will be met, flow proceeds to operation618, and an allocation of the energy adjustment among the selected DERs is determined. For example, the allocation engine322may determine an energy allocation for each DER to meet the energy need. The energy allocation for each DER may be specific to each DER or uniform.

In operation620, the DERs are then instructed to make the energy adjustments according to the allocations determined in operation618to cause the energy need to be met. For example, the allocation engine322may cause the selected DERs to make energy adjustments according to the determined allocations via the distributed energy resource communication engine324.

FIG.7is a flow chart illustrating an example method700of controlling a fleet of distributed energy resources to meet an energy need. For example, the dispatch optimization system220may perform one or more operations of the method700to control a fleet of DERs to meet the energy need. The method600begins by determining an energy need in operation702. For example, the VPP status monitor232or energy control objective engine234illustrated inFIG.2may monitor the power distribution system200to determine that there is an energy need.

An energy adjustment needed across a fleet of DERs to achieve the energy need is determined in operation704. For example, the allocation engine322may determine an energy adjustment that is needed across the fleet of distributed energy resources222to meet the energy need.

Next, an economic cost to use each DER to meet the energy need is determined in operation706. For example, the economic cost analyzer236may determine the economic cost to use each DER in the fleet of DERs.

The economic cost is then used to determine a first subgroup of a fleet of distributed energy resources to be used to meet the energy need at operation708. For example, the first subgroup of distributed energy resources may include all distributed energy resources that have an economic cost of eight cents per kWh or less.

In operation710, a second subgroup of DERs of the fleet of DERs is determined that have an economic cost within a threshold. For example, there may be multiple DERs having economic costs close to the cut off value of eight cents per kWh to be included in the first subgroup determined in operation708. The threshold may be half a cent, so any DERs having economic costs of 8.1 cents to 8.6 cents will be included in the subgroup. In an example, the threshold is determined to ensure that there are enough DERs in the second subgroup to meet the total energy need with the first subgroup of DERs.

The storage costs for each DER of the second subgroup is determined in operation712. For example, the storage cost analyzer238may determine the storage cost to use each DER in the second subgroup. The determination of the storage cost to use the DER is described in more detail herein with respect toFIG.8.

A third subgroup of DERs is determined from the second subgroup of DERs based on the storage costs of each DER in the second subgroup in operation714. For example the allocation engine322may determine the third subgroup of DERs based on the storage cost.

In operation716, an allocation of the energy adjustment among the first and third subgroups of DERs is determined. For example, the allocation engine322may determine an energy allocation for each DER in the first and third subgroup to meet the energy need. The energy allocation for each DER may be specific to each DER or uniform. In an example, the allocation for each DER is based on the properties of each DER such as capacity, rate of charge, and rate of discharge.

Finally, the DERs in the first and third subgroups of DERs are instructed to make energy adjustments according to the determined allocation to achieve the energy need. For example the energy control objective engine234may instruct the first and third subgroups of DERs to make energy adjustments via the distributed energy resource communication engine324.

FIG.8is a graphical representation of an example storage cost function. The normalized storage cost function illustrated inFIG.8is an example function that may be used to determine the storage cost to use a DER to meet the energy need. For example, the storage cost analyzer238may use the storage cost function when evaluating DERs to determine the storage cost to use each DER.

The example storage cost function is defined as a second order polynomial where the x-axis is normalized storage level scaled to [−50,50] centered about the ideal storage level for the asset and the y-axis is normalized storage cost scaled to [0,100]. In this example, the ideal storage level for the DER is fifty percent storage level. The storage cost function can be altered to allow the storage cost analyzer238to use the function for any ideal storage level.

The storage cost analyzer238determines the starting position on the storage cost function based on the DER's current storage level versus the nominal energy. The normalization function may include constraints such as capacity, ideal storage level, operational storage level limits, and/or user or owner defined storage level limits. The constraints can be used to map the storage level energy values into the scale that is bounded from −50 to 50.

Once the starting position of the DER is determined, the storage cost analyzer238calculates the current storage cost by calling the storage cost function. The current storage cost is the storage cost for the DER to reach the ideal storage level from the current storage level. The example storage cost function is StorageCost(x)=0.04x2, where the input x is the normalized current storage level. Storage cost802is the current storage cost in the illustrated example.

Once the current storage cost is determined, the storage cost analyzer238determines the final storage cost, or the storage cost if the DER is used to meet the energy need. For example, the storage cost analyzer238will use the resulting storage level with storage cost function to determine the storage cost if the DER is used. The resulting storage level can be determined by adding the requested energy associated with energy need to the DER's current storage level, normalizing the storage level on the x-axis, and using the function to calculate the storage cost. Storage cost804is the final storage cost in the illustrated example.

Next, the storage cost analyzer238calculates the change in storage cost by subtracting the current storage cost from the storage cost of the DER if it is used to meet the need. In the illustrated example, the change in storage cost is storage cost804minus storage cost802, which is 15−55=−40. The negative value indicates that DER's storage level will be closer to the ideal storage level if the DER is used to meet the energy need. Therefore, the DER will be chosen to be used to meet the energy need over a DER having a change in storage cost value of −35, −12, 0, 13, 25, etc., when storage cost is being used to evaluate the DERs.

One or more aspects described herein can be implemented with a computing environment. A computing environment is a set of one or more virtual or physical computers that cause output based on input. Example computers include desktop computers, servers, mobile computing devices, wearable computing devices, virtualized computing devices, other computers, or combinations thereof. Many example computers include one or more processors, memory, and one or more interfaces.

The one or more processors are collections of one or more virtual or physical components that are configured to provide output in response to input. In many examples, the one or more processors are so configured by obtaining and executing instructions (e.g., stored in the memory) and using data (e.g., also stored in the memory). The one or more processors can take any of a variety of forms, such as central processing units, graphics processing units, coprocessors, tensor processing units, artificial intelligence accelerators, microcontrollers, microprocessors, other forms, or combinations thereof. In some examples, the one or more processors are so configured through specifically designed hardware. Examples of such processors include application-specific integrated circuits, field programmable gate arrays, other processors, or combinations thereof.

The memory is a collection of one or more virtual or physical components configured to store instructions or data for later retrieval and use. In many examples, the memory is a non-transitory computer readable medium, though in certain circumstances the memory can be transitory. Examples of transitory memory include data encoded into transient signals. Examples of non-transitory memory include random access memory, cache memory (e.g., which may be incorporated into the one or more processors), read only memory, optical memory, magnetic memory, solid state memory, other memory, or combinations thereof. In some examples, the memory can be configured to be portable, such as enclosed hard drives, thumb drives, CD-ROM disks, DVDs, BLU-RAY disks, other media, or combinations thereof. In some examples, the memory can be incorporated with the one or more processors (e.g., via registers or cache memory).

The one or more interfaces are one or more virtual or physical components by which the computing environment can receive input or provide output. Example interfaces for providing output include one or more visual output components (e.g., displays or lights), auditory output components (e.g., speakers), haptic output components (e.g., vibratory components), other output components, or combinations thereof. Example interfaces for receiving input include one or more visual input components (e.g., still cameras, video cameras, optical sensors), auditory input components (e.g., microphones), haptic input components (e.g., touch or vibration sensitive components), motion input components (e.g., mice, gesture input controllers, or movement sensors), buttons (e.g., keyboards or mouse buttons), position sensors, other input components, or combinations thereof. The one or more interfaces can include components for sending or receiving data from other computing environments or devices, such as one or more wired connections or wireless connections.

One or more of the one or more interfaces can facilitate connection of the computing environment to a network. The network can be a set of one or more other computing devices or environments. Example networks include local area networks, wide area networks, or the Internet.

The environment and its one or more physical computers can include any of a variety of other components to facilitate performance of operations described herein. Example components include one or more power units (e.g., batteries, capacitors, or power harvesters) that provide operational power, one or more busses to provide intra-device communication, one or more cases or housings to encase one or more components, other components, or combinations thereof.

In some instances, the computing device or the environment can be a general-purpose computing device or environment. They may be constructed from one or more consumer or off-the-shelf components. In some instances, via hardware or software configuration, the computing device or the environment can be a special purpose computing device. The one or more computing devices or computing environments can individually or in cooperation to perform operations described herein.

A person of skill in the art, having benefit of this disclosure, may recognize various ways for implementing technology described herein. The person of skill in the art may use any of a variety of programming languages and libraries (e.g., libraries that provide functions for obtaining, processing, and presenting data). Operating systems may provide their own libraries or application programming interfaces useful for implementing aspects described herein. A person of skill in the art, with the benefit of the disclosure herein, can use programming tools to assist in the creation of software or hardware to achieve techniques described herein. Such tools can include intelligent code completion tools, artificial intelligence tools.

A person of skill in the art with the benefit of disclosures herein can use any of a variety of known techniques to implement aspects described herein.

Various modifications and additions can be made to the exemplary examples discussed without departing from the scope of the present invention. For example, while the examples described above refer to particular features, the scope of this invention also includes examples having different combinations of features and examples that do not include all of the described features. Accordingly, the scope of the present invention is intended to embrace all such alternatives, modifications, and variations together with all equivalents thereof.

Reference to “one example” or “an example” means that a particular feature, structure, or characteristic described in connection with the example is included in at least one example of the disclosure. The appearances of the phrase “in one example” in various places in the specification are not necessarily all referring to the same example, nor are separate or alternative examples mutually exclusive of other examples. Moreover, various features are described which may be exhibited by some examples and not by others.

The following are additional clauses relative to the present disclosure, which can be combined and/or otherwise integrated with any of the embodiments described above, the clauses listed herein, and/or the claims listed below.

Clause 1. A method for optimizing the dispatch of energy, the method comprising:determining an energy need;determining a first subgroup of a fleet of distributed energy resources to use to meet the energy need based on an economic cost to use each distributed energy resource to meet a portion of the energy need;determining a second subgroup of the fleet of distributed energy resources to use to meet the energy need based on a storage cost to use each distributed energy resource to meet the portion of the energy need; andcausing the first subgroup of the fleet of distributed energy resources and the second subgroup of the fleet of distributed energy resources to make energy adjustments to meet the energy need.

Clause 2. The method of Clause 1, wherein the first subgroup is determined by:ranking the distributed energy resources from a lowest economic cost to a highest economic cost; andselecting the distributed energy resources in order of the ranking starting from the lowest economic cost.

Clause 3. The method of Clause 1, wherein the second subgroup is determined by:ranking the distributed energy resources from a lowest storage cost to a highest storage cost; andselecting the distributed energy resources in order of the ranking starting from the lowest storage cost.

Clause 4. The method of Clause 1, wherein determining the first subgroup of the fleet of distributed energy resources is further based on a health value of each distributed energy resource.

Clause 5. The method of Clause 4, wherein determining the first subgroup comprises:determining the economic costs of a group of distributed energy resources of the fleet of distributed energy resources are within a range; andselecting distributed energy resources from the group to include in the first subgroup based on the health value of each distributed energy resource of the group.

Clause 6. The method of Clause 1, wherein determining the second subgroup of the fleet of distributed energy resources is based on a health value of each distributed energy resource.

Clause 7. The method of Clause 6, wherein determining the second subgroup comprises:determining the storage costs of a group of distributed energy resources of the fleet of distributed energy resources are within a range; andselecting distributed energy resources from the group to include in the first subgroup based on the health value of each distributed energy resource of the group.

Clause 8. The method of Clause 1, wherein the storage cost comprises a difference between a final storage cost and a current storage cost of the distributed energy resource.

Clause 9. The method of Clause 8, wherein the current storage cost is a level of deviation from an ideal storage level and a current storage level of the distributed energy resource.

Clause 10. The method of Clause 8, wherein the final storage cost is a level of deviation from an ideal storage level and a final storage level when the distributed energy resource is used to meet the portion of the energy need.

Clause 11. The method of Clause 8, wherein determining the second subgroup comprises:ranking the distributed energy resources from a lowest storage cost to a highest storage cost; andselecting the distributed energy resources in order of the ranking from the lowest storage cost until the first subgroup and the second subgroup of distributed energy resources are sufficient to meet the energy need.

Clause 12. A dispatch optimization system comprising at least one processing device and at least one non-transitory computer-readable medium storing data instructions which, when executed by the at least one processing device, cause the dispatch optimization system to:determine an energy need;determine a first subgroup of a fleet of distributed energy resources to use to meet the energy need based on an economic cost to use each distributed energy resource to meet a portion of the energy need;determine a second subgroup of the fleet of distributed energy resources to use to meet the energy need based on a storage cost to use each distributed energy resource to meet the portion of the energy need; andcause the first subgroup of the fleet of distributed energy resources and the second subgroup of the fleet of distributed energy resources to make energy adjustments to meet the energy need.

Clause 13. The dispatch optimization system of Clause 12, wherein to determine the first subgroup comprises to:rank the distributed energy resources from a lowest economic cost to a highest economic cost; andselect the distributed energy resources in order of the ranking starting from the lowest economic cost.

Clause 14. The dispatch optimization system of Clause 12, wherein to determine the second subgroup comprises to:rank the distributed energy resources from a lowest storage cost to a highest storage cost; andselect the distributed energy resources in order of the ranking starting from the lowest storage cost.

Clause 15. The dispatch optimization system of Clause 12, wherein to determine the first subgroup of the fleet of distributed energy resources is further based on a health value of each distributed energy resource.

Clause 16. The dispatch optimization system of Clause 15, wherein to determine the first subgroup comprises to:determine the economic costs of a group of distributed energy resources of the fleet of distributed energy resources are within a range; andselect distributed energy resources from the group to include in the first subgroup based on the health value of each distributed energy resource of the group.

Clause 17. The dispatch optimization system of Clause 12, wherein to determine the second subgroup of the fleet of distributed energy resources is based on a health value of each distributed energy resource.

Clause 18. The dispatch optimization system of Clause 17, wherein to determining the second subgroup comprises to:determine the storage costs of a group of distributed energy resources of the fleet of distributed energy resources are within a range; andselect distributed energy resources from the group to include in the first subgroup based on the health value of each distributed energy resource of the group.

Clause 19. The dispatch optimization system of Clause 12, wherein the storage cost comprises a difference between a final storage cost and a current storage cost of the distributed energy resource.

Clause 20. A non-transitory computer-readable medium that stores a set of instructions which when executed perform a method executed by the set of instructions comprising:determining an energy need;determining a first subgroup of a fleet of distributed energy resources to use to meet the energy need based on an economic cost to use each distributed energy resource to meet a portion of the energy need;determining a second subgroup of the fleet of distributed energy resources to use to meet the energy need based on a storage cost to use each distributed energy resource to meet the portion of the energy need; andcausing the first subgroup of the fleet of distributed energy resources and the second subgroup of the fleet of distributed energy resources to make energy adjustments to meet the energy need.

Clause 21. A dispatch optimization system comprising at least one processing device and at least one non-transitory computer-readable medium storing data instructions that, when executed by the at least one processing device, cause the dispatch optimization system to:determine an energy need;select a first distributed energy resource in a fleet of distributed energy resources to meet at least a portion of the energy need, selecting the first distributed energy resource including to:determine a first economic cost to use the first distributed energy resource to dispatch energy to meet at least the portion of the energy need;determine a second economic cost to use a second distributed energy resource to dispatch energy to meet at least the portion of the energy need;determine the first economic cost and the second economic cost are within a range;determine a first storage cost to use the first distributed energy resource to dispatch energy to meet at least the portion of the energy need, the first storage cost comprising a level of deviation from an ideal storage level of the first distributed energy resource;determine a second storage cost to use the second distributed energy resource to dispatch energy to meet at least the portion of the energy need, the second storage cost comprising the level of deviation from the ideal storage level of the second distributed energy resource; anddetermine the first storage cost is less than the second storage cost; and instruct the first distributed energy resource to make an energy adjustment to meet the at least the portion of the energy need.

Clause 22. The dispatch optimization system of Clause 21, wherein selecting the first distributed energy resource further comprises:receiving a first distributed energy resource health value associated with the first distributed energy resource; andreceiving a second distributed energy resource health value associated with the second distributed energy resource.

Clause 23. The dispatch optimization system of Clause 22, wherein:the first distributed energy resource health value comprises a number of activations of the first distributed energy resource; andthe second distributed energy resource health value comprises the number of activations of the second distributed energy resource.

Clause 24. A method for optimizing the dispatch of energy, the method comprising:determining an energy need;determining an energy adjustment needed across a fleet of distributed energy resources to meet the energy need;determining an allocation of the energy adjustment among the fleet of distributed energy resources, determining the allocation comprising:determining an economic cost to use each distributed energy resource of the fleet of distributed energy resources to dispatch energy to meet the allocation of the energy adjustment;determining a first subgroup of the fleet of distributed energy resources to use to meet the energy need based on the economic cost to use each distributed energy resource;determining a second subgroup of distributed energy resources distinct from the first subgroup having economic costs within a threshold;determining a current storage level of each distributed energy resource in the second subgroup;determining an ideal storage level of each distributed energy resource in the second subgroup;determining a final storage level of each distributed energy resource in the second subgroup, the final storage level being the storage level of the distributed energy resource if it is used to dispatch energy to meet the allocation of the energy adjustment;determining a current deviation for each distributed energy resource of the second subgroup, the current deviation being the difference between the current storage level and the ideal storage level;determining a final deviation for each distributed energy resource of the second subgroup, the final deviation being the difference between the final storage level and the ideal storage level;determining a difference between the final deviation and the current deviation for each distributed energy resource of the second subgroup;determining a third subgroup of the fleet of distributed energy resources from the second subgroup to use to meet the energy need based on the difference between the final deviation and the current deviation for each distributed energy resource of the second subgroup; anddetermining the allocation of the energy adjustment among the first subgroup and the third subgroup; andinstructing the distributed energy resources of the first subgroup and the third subgroup to make energy adjustments according to the allocation to meet the energy need.

Clause 25. A method for optimizing the dispatch of energy, the method comprising:determining an energy need;determining a subgroup of a fleet of distributed energy resources based on an ideal storage deviation value to use each distributed energy resources to meet a portion of the energy need; andcausing the subgroup of the distributed energy resources to make energy adjustments to meet the energy need.

Clause 26. A method for optimizing the dispatch of energy, the method comprising:determining an energy need;determining a subgroup of a fleet of distributed energy resources based on a difference between:a final level of deviation from an ideal storage level of each distributed energy resource and a final storage level of distributed energy resource when used to dispatch energy to meet the energy need; anda current level of deviation from the ideal storage level of each distributed energy resource and a current storage level of each distributed energy resource; andcausing the subgroup of the distributed energy resources to make energy adjustments to meet the energy need.

Clause 27. A method for optimizing the dispatch of energy, the method comprising:determining an energy need;determining a subgroup of a fleet of distributed energy resources by:determining a current storage level of each distributed energy resource;determining an ideal storage level of each distributed energy resource in the second subgroup;determining a final storage level of each distributed energy resource, the final storage level being the storage level of the distributed energy resource if it is used to dispatch energy to meet the allocation of the energy adjustment;determining a current deviation for each distributed energy resource, the current deviation being the difference between the current storage level and the ideal storage level;determining a final deviation for each distributed energy resource, the final deviation being the difference between the final storage level and the ideal storage level;determining a difference between the final deviation and the current deviation for each distributed energy resource; anddetermining the subgroup of the fleet of distributed energy resources to use to meet the energy need based on the difference between the final deviation and the current deviation for each distributed energy resource; andcausing the subgroup of the distributed energy resources to make energy adjustments to meet the energy need.