SCALABLE HIERARCHICAL ENERGY DISTRIBUTION GRID UTILIZING HOMOGENEOUS CONTROL LOGIC

US 2020 52 492A1

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Techniques are disclosed for implementing a scalable hierarchical energy distribution grid utilizing homogeneous control logic are disclosed that provide distributed, autonomous control of a multitude of sites in an energy system using abstraction and aggregation techniques. A hierarchical energy distribution grid utilizing homogeneous control logic is provided that includes multiple control modules arranged in a hierarchy. Each control module can implement a same energy optimization scheme logic to directly control site energy resources and possibly energy resources of sites associated with control modules existing below it in the hierarchy. Each control module can act autonomously through use a similar set of input values to the common optimization scheme logic.

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Claims

1. A distributed electrical grid system, comprising:
a plurality of control modules arranged in a hierarchy and associated with a corresponding plurality of sites, the plurality of control modules implementing a same optimization scheme logic, having an energy goal, to control one or more devices at a site according to the energy goal, the plurality of control modules including at least:
a first control module configured as a child of a second control module in the hierarchy, the first control module being associated with a first site and configured to:
receive a desired power curve from the second control module, wherein the desired power curve indicates a desired first site energy characteristic,
determine, based upon the desired power curve, whether a current energy profile of the first site satisfies the desired first site energy characteristic, and
when the current energy profile of the first site does not satisfy the desired first site energy characteristic, change the current energy profile of the first site by signaling one or more devices at the first site to change one or more of an energy load used at the first site, an amount of energy being stored at the first site, and an amount of energy generated by the first site; and
the second control module configured as a parent to a set of child control modules including the first control module, the second control module being configured to:
determine an aggregate child energy profile of a set of sites associated with the set of child control modules by at least determining an aggregate energy generation value of the set of sites, an aggregate energy storage value of the set of sites, and an aggregate load value of the set of sites;
based upon the aggregate child energy profile of the set of sites, determine a set of desired power curves corresponding to the set of sites; and
transmit a corresponding desired power curve of the set of desired power curves to a corresponding one of the set of child control modules.

Show 17 dependent claims

Description

The present application is a divisional application of U.S. patent application Ser. No. 14/802,811, filed Jul. 17, 2015, and claims the benefit of U.S. Provisional Application No. 62/119,925, filed Feb. 24, 2015, each of which is hereby incorporated by reference in its entirety for all purposes.

FIELD

Embodiments of the invention relate to the field of energy systems; and more specifically, to a scalable hierarchical energy distribution grid utilizing homogeneous control logic.

BACKGROUND

Recently, the installation of energy generation systems (e.g., systems that generate energy using renewable resources such as solar, wind, hydropower, etc.) at residential and non-residential sites has proliferated for various climate, cost, stability, security, and political reasons. One large segment of installed energy generation systems involves solar photovoltaic (PV) systems.

Many installed PV systems can be directly or indirectly connected to utility-maintained electrical grids (e.g., via a site's main panel/main line), and may thus be referred to as grid-connected systems. Grid-connected energy generation systems beneficially allow site loads to be serviced in whole or in part from the grid at various times. For example, during evening hours when a PV system cannot generate substantial amounts of energy due to lack of sunlight, a site may draw some or all of its energy from the grid. In some configurations, grid-connected energy generation systems can also allow excess energy generated at the site to be fed back to the grid (and used/stored by others), such as when generated PV energy production may exceed a site's local energy load/use.

However, a limitation with many grid-connected energy generation systems is that, unlike a traditional power plant, the system power output may be intermittent and non-dispatchable. For example, a grid-connected PV system is typically limited in its ability to provide power capacity at times of peak grid loads, balance grid voltage and frequency variability, and/or supply energy when prices are most economic.

To address these and other limitations, some systems have been developed that integrate grid-connected energy generation (e.g., PV) components with an on-site energy storage subsystem, such as a battery device and a battery inverter/charger. In these integrated systems, the energy storage subsystem can be configured to store excess energy as it is generated by the PV components, and possibly dispatch stored energy to satisfy local (or external, grid-wide) loads as needed. Additionally, energy storage capability enables a number of other features that can provide benefits to the site owner or the service provider of the system, such as the ability to time shift energy usage to minimize the need to pull energy from the grid, and thus reduce energy costs.

However, energy generation and/or storage systems are largely unable to flexibly adapt to changing conditions specific to the site, nearby sites, region, grid, etc. Accordingly, currently site loads and generators are largely uncontrolled, and at best are operated according to a schedule to provide what is guessed or hoped to be an efficient scheme. However, in reality many use cases would require some sort of additional coordination in real-time between resourcesat a site, or perhaps among multiple sitesto actually achieve the desired results. For example, on a cloudy day, energy from a storage system could potentially be dispatched to avoid a demand spike placed upon the grid.

Despite the advantages associated with integrating grid-connected PV energy generation with on-site energy storage, there are a number of challenges that make it difficult to efficiently deploy and control such integrated systems, particularly on a large, distributed scale. For example, it is tremendously difficult to attempt to control large numbers of energy generation and/or storage systems installed at various sites (in various geographic locations) utilizing differing device types that may have different capabilities, differing grid requirements, differing weather conditions, differing energy pricing schemes, etc.

Accordingly, there is a need for efficient, intelligent, adaptive control systems for energy generation and/or storage systems.

SUMMARY

The present disclosure relates generally to energy systems, and more specifically, to a scalable hierarchical energy distribution grid utilizing homogeneous control logic. Techniques are disclosed that allow for distributed, autonomous control of a multitude of sites in an energy system using abstraction and aggregation techniques for simplified yet powerful system control to achieve energy goals.

Accordingly, in some embodiments, a scalable hierarchical energy distribution grid utilizing homogeneous control logic is provided. The energy distribution grid can include multiple control modules arranged in a hierarchy, each of which implements a same energy optimization scheme logic. Each control module in the hierarchy can autonomously implement the same energy optimization scheme logic, reducing implementation and configuration complexities and any need for large-scale communications or reporting. Some or all of the control modules can directly control energy resources (e.g., generation devices, storage devices, load devices, etc.) at an associated site according to the optimization scheme logic.

In some embodiments, each control module can act independently (or autonomously) through use a similar set of input values to the common optimization scheme logic to thereby control energy resources at an associated site.

In some embodiments, each control module interacts with one level of control modules above and/or below the control module, and may not need to see (or even be aware of) details of or data related to other control modules at further levels up or down in the hierarchy. For example, a child control module's optimization scheme logic can utilize its local EG value, load value, and storage value, while its parent can consider the aggregate EG value, aggregate load value, and aggregate storage value for all of its immediate child control modules in the hierarchy.

Moreover, some embodiments utilize abstraction techniques so that a control module need not be aware of details of other control modules (or sites) in the hierarchy, and can instead be aware of local, site-specific details and/or data from control modules directly connected to it in the hierarchy.

In some embodiments, the hierarchy of control modules can be scaled indefinitely, as each control module can be configured to only consider data from its corresponding site, one level up in the hierarchy (e.g., parent control module control signals), and/or one level downstream in the hierarchy (e.g., child control module data), allowing for the optimization scheme logic to be simplified, easily developed, and rapidly executed. This can allow the optimization scheme logic to react to changing conditions quickly and with minimal processing/networking overhead.

In some embodiments, each control module can implement a same optimization scheme logic configured to target a common goal. For example, the optimization scheme logic can be configured to, based upon the set of input values, control energy devices at the corresponding site to adjust a net load of the site (e.g., a power curve) such that it is as flat and low as possible. The optimization scheme logic can also be configured to only flatten the net load of the site, only target a low net load of the site, or place varying degrees of weight/importance upon these factors in determining how to control the site's energy devices.

The optimization scheme logic can also be configured to, based upon the set of input values, control energy devices at the corresponding site to adjust the energy profile (e.g., load/storage/generation) of the site based upon grid energy pricing data such that the net cost of energy taken from the grid at the site is minimized. Accordingly, the optimization scheme logic can be configured to cause energy to be taken from the grid, if needed, during relatively inexpensive or low-demand times, and/or dispatched to the grid during relatively in-demand times (from the perspective of the grid).

The foregoing, together with other features and embodiments will become more apparent upon referring to the following specification, claims, and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a simplified high-level hierarchy of control modules of an energy distribution grid utilizing homogeneous control logic according to some embodiments.

FIG. 2 shows a fractal nature of hierarchical control modules utilizing homogeneous control logic according to some embodiments.

FIG. 3 shows exemplary sites and hardware-based configurations for implementing hierarchical control modules utilizing homogeneous control logic according to some embodiments.

FIG. 4 shows an exemplary control module implemented using a site gateway and other site energy devices sites according to some embodiments.

FIG. 5 shows an exemplary optimal net flow power curve, combined load and generation curves with excess generation, and an aggregate power curve according to some embodiments.

FIG. 6 shows an exemplary price curve and a price-optimized net power curve according to some embodiments.

FIG. 7 shows exemplary communications between parent control modules and child control modules in an interactive hierarchical energy distribution grid configuration according to some embodiments.

FIG. 8 shows a flow in a control module configured as a parent in a hierarchy of control modules for energy optimization configuration according to some embodiments.

FIG. 9 shows a flow in a control module configured as an independent node in a mesh or hierarchy of control modules for energy optimization configuration according to some embodiments.

FIG. 10 shows a block diagram of an exemplary computer apparatus according to some embodiments.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of embodiments of the invention. However, it will be apparent that various embodiments may be practiced without these specific details. The figures and description are not intended to be restrictive.

One approach to implementing a control system for multiple energy resources at multiple sites may include configuring each site (e.g., each device at each site, for example) to communicate with a centralized control server. Such a control server could be configured to control the energy generation, storage, etc., at each individual site to manage resources in the entire grouping of energy generation/storage sites. However, as the network continues to expand, the control server would require higher and higher levels of bandwidth to track data from all connected devices at all connected sites, such as the real-time (RT) energy generation, RT load requirements, RT storage capabilities, etc., for each site in the grid.

Such a configuration, with a continued addition of controlled sites, may be computationally expensive and difficult to reliably manage. Moreover, the control server may suffer from latency problems created by huge numbers of communications with huge numbers of devices over a limited bandwidth channel.

Additionally, the control server may be required to be able to communicate using a variety of protocols to be able to connect with potentially multiple types of devices from potentially multiple providers. As a variety of different types of energy system devices (e.g., inverters, batteries, controllable loads, PV devices, etc. of various makes and models) may be present across the different sites. Such end devices implement numerous protocols and behaviors, offering heterogeneous interfaces for communications purposes.

Accordingly, in certain embodiments, a scalable hierarchical energy distribution grid utilizing homogeneous control logic is provided. The energy distribution grid includes multiple control modules arranged in a hierarchy, each of which implements a same energy optimization scheme logic. Each control module in the hierarchy can autonomously implement the same energy optimization scheme logic, reducing implementation and configuration complexities and any need for large-scale communications or reporting. Some or all of the control modules can directly control energy resources (e.g., generation devices, storage devices, load devices, etc.) at a site according to the optimization scheme logic.

In some embodiments, each control module can act independently (or autonomously) through use a similar set of input values to the common optimization scheme logic to thereby control energy resources at an associated site.

In some embodiments, an optimization scheme logic of a leaf control module in the hierarchy (e.g., a control module of a site including energy resources and not serving as a parent to any other control module) can utilize a total energy generation (EG) value of the site as a first input, a total load value as a second input, a total storage capacity as a third input. The optimization scheme logic can also utilize energy price data from a price signal as an input.

In some embodiments, a parent control module (e.g., a control module configured as a parent to one or more other control modules in the hierarchy) can use similar inputs to the same optimization scheme logic. However, these inputs can be based upon aggregate data from the sites of the corresponding child control modules. In some embodiments, a parent control module optimization scheme logic can use an aggregate EG value of the child control module sites as a first input, an aggregate load value of the child control module sites as a second input, an aggregate storage value of the child control module sites as a third input, etc. In some embodiments, the parent control module optimization scheme logic can use energy price data from a price signal as an input.

In some embodiments, each parent control module only sees (is aware of) one level of control modules above and/or below the parent control module, and may not need to see (or even be aware of) details of or data related to other control modules at further levels up or down in the hierarchy. For example, a child control module's optimization scheme logic can utilize its local EG value, load value, and storage value, while its parent can consider the aggregate EG value, aggregate load value, and aggregate storage value for all of its immediate child control modules in the hierarchy.

Moreover, some embodiments benefit from an application of abstraction, wherein a control module need not be aware of details of other control modules (or sites) in the hierarchy, and can instead focus upon local, site-specific details and/or data from control modules directly connected to it in the hierarchy.

Accordingly, embodiments can theoretically be scaled indefinitely, as each control module can be configured to only consider data from its corresponding site, one level up in the hierarchy (e.g., parent control module control signals), and/or one level downstream in the hierarchy (e.g., child control module data), allowing for the optimization scheme logic to be simplified, easily developed, and rapidly executed. Thus, the optimization scheme logic may react to changing conditions quickly and with minimal processing/networking overhead.

In some embodiments, each control module can implement a same optimization scheme logic configured to target a common goal. For example, the optimization scheme logic can be configured to, based upon the set of input values, control energy devices at the corresponding site to adjust a net load of the site (e.g., a power curve) such that it is as flat and low as possible. Flat energy loads are beneficial for the site and the grid as a whole, as flat loads are predictable and do not have energy spikes that can cause the grid (or site resources) to suffer from demand exceeding supply, which can lead to supply problems such as brownouts and/or hardware problems in the system. Moreover, low net loads are beneficial for the site and the grid as a whole, as it indicates a smaller amount of energy demand, thereby reducing the amount of energy required from the grid and the associated cost to the site. The optimization scheme logic can also be configured to only flatten the net load of the site, only target a low net load of the site, or place varying degrees of weight/importance upon these factors in determining how to control the site's energy devices.

As another example, the optimization scheme logic can be configured to, based upon the set of input values, control energy devices at the corresponding site to adjust the energy profile (e.g., load/storage/generation) of the site based upon grid energy pricing data such that the net cost of energy taken from the grid at the site is minimized. Accordingly, the optimization scheme logic can be configured to cause energy to be taken from the grid, if needed, during relatively inexpensive or low-demand times, and/or dispatched to the grid during relatively in-demand times (from the perspective of the grid).

For purposes of illustration, several of the examples and embodiments that follow are described in the context of energy generation, consumption and/or storage (EGS) systems that use solar PV technology for energy generation and battery technology for energy storage. However, it should be appreciated that embodiments of the present invention are not limited to such implementations. For example, in some embodiments, alternative types of energy generation technologies (e.g., wind turbine, solar-thermal, geothermal, biomass, hydropower, etc.) may be used. In other embodiments, alternative types of energy storage technologies (e.g., compressed air, flywheels, pumped hydro, superconducting magnetic energy storage (SMES), etc.) may be used. One of ordinary skill in the art will recognize many modifications, variations, and alternatives.

FIG. 1 shows a simplified high-level hierarchy 100 of control modules of an energy distribution grid utilizing homogeneous control logic according to some embodiments. The depicted hierarchy 100 of control modules depicts four levels of control modules 105A-105N. In various embodiments there can be more levels or fewer levels, as well as more control modules (e.g., tens, hundreds, thousands, tens of thousands, millions, etc.) or fewer control modules (e.g., two, three, etc.). Moreover, the use of the suffix N in 105N is not meant to indicate a particular number of control modules, only that there may be multiple such control modulese.g., two, five, ten, one hundred, one thousand, one million, etc.

Throughout this description, the concept of a hierarchy is presented with regard to configurations of relationships between and/or roles of control modules. Thus, the described control modules in a hierarchy, can be viewed as somewhat analogous to nodes of a tree as used in the fields of computer science and/or mathematics. Thus, the hierarchy may refer to a collection of control modules having levels or generations of control modules, where a parent control module might be associated with one or more child control modules. A control module can potentially be only a parent control module (e.g., a root control module in the hierarchy), both a parent and a child control module (e.g., a mid-level control module), or only a child control module (e.g., a leaf control module in the hierarchy not having any children).

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