Patent Application
20240086934
The present invention generally relates to analyzing parcels of land for the development of renewable energy projects, namely wind farms, solar farms, and energy storage, and, more particularly, relates to providing a computer-implemented method and system to automatically position a delineation over a combination of parcels of land for the identification of individual parcels of land, when aggregated, meet the goals for the development of renewable energy projects.
According to the U.S. Department of Energy, more wind energy was installed in the year 2020 than any other energy source, accounting for 42% of new U.S. capacity. In addition, utility-scale solar farm value is projected to quadruple by the year 2027.
Developing utility-scale renewable energy farms take time. There are two distinct phases the development phase and the construction phase. Together they typically take six or more years to complete. The development stage currently takes about two-thirds of this six-year time period. The development stage includes planning and site acquisition, transmission studies and interconnect agreement with the utility, negotiation of the power purchase agreement with a prospective off-taker, transmission permitting, generating permitting and approval, and financing. The construction phase includes the construction of transmission upgrades and site improvement, plant construction, and testing.
To produce renewable energy projects for specific utility-scale generation capacity, typically in Megawatts (MW), parcels of land must review. Identifying the best renewable energy sites across a large geographical area (e.g., the United States) is difficult because many factors, including resource considerations, land considerations, transmission considerations, and market considerations, may render any site uneconomic.
Historically, the development phase planning activities have been manually intensive. In order to determine the final development timeline, a planner models various scenarios in a spreadsheet to ensure operational constraints are respected. At the end of the planning process, there is no way to determine if the final schedule is optimal because of a large number of combinatorial factors are not solvable by a human with a spreadsheet.
The present invention provides a novel method and system for identifying various combinations of parcels of land with sufficient transmission, resources, market demand, and available land to build a wind farm, solar farm, or energy storage. Different criteria associated with each parcel of land include land characteristics including size, ownership, tree coverage, elevations, terrain, buildable land, transmission characteristics including substation hardware costs, network upgrades, general tie-in costs, market characteristics including historical locational marginal pricing (LMPs), and resource characteristics including net capacity factor (NCF) of wind or solar. In one example, the present invention identifies clusters of land parcels that minimize the number of land owners, maximize buildable land, minimize transmission cost, maximize NCF, and maximize nodal LMPs.
More specifically, disclosed is a system and method for identifying parcels of land to construct a renewable energy generation facility to generate electricity from wind or solar, or batteries or to construct a data center or other uses. A plurality of projections are performed. The projections begin with receiving an electricity requirement for a new renewable energy generation facility. This electricity requirement is typically expressed in megawatts of power. Next, the process accesses of data elements from a variety of data sources. Each of the data elements is associated with criteria. The criteria is used to project expected electricity output from new renewable energy sources. The criteria include a portfolio of a plurality of parcels of land each with i) land characteristics, ii) electricity transmission characteristics, and iii) market demand characteristics.
The data elements may be converted into a uniform data format within the each of the criteria.
In one example, the criteria of land characteristics for each parcel of land in the portfolio may be any combination of electricity transmission characteristics of the parcel, or market demand characteristics of the parcel. Further, these criteria may include any combination of the size of the parcel, ownership of the parcel, tree coverage in the parcel and/or tree clearing costs, the elevation of the parcel, terrain of the parcel, buildable land area of the parcel, location of the parcel to nearby renewable projects, or a land owner's willingness to sell rights to the parcel. Other criteria for each parcel of land in the portfolio may include the plurality of criteria further includes at least one resource score that is based on the strength of the wind or solar in each of the plurality of parcels of land.
In another example, the criteria of transmission characteristics for each parcel of land in the portfolio may include the size of substation hardware costs, network upgrade costs, or grid tie-in costs.
A total number of simulations (M) are executed in parallel up to the total number of jobs or until a time period expires by evaluating each of the plurality of parcels of land in the portfolio, including the land characteristics, the electricity transmission characteristics, and the market demand associated with the parcels of land in the portfolio. Next, a clustering algorithm is executed to produce results, wherein the results include a subset of the plurality of parcels of land in the portfolio;
The results are ranked from the total number of simulations (M) that meet the electricity requirement combined with a highest combined score of a cumulative size of the subset of the plurality of parcels of land in the portfolio, the electricity transmission characteristics, and the market demand.
The results may be displayed in various formats with various color overlays on maps illustrating the combination of the parcels of land with the highest ranking for development based on the criteria.
The accompanying figures, where like reference numerals, refer to identical or functionally similar elements throughout the separate views, and which, together with the detailed description below, are incorporated in and form part of the specification, serve to further illustrate various embodiments and to explain various principles and advantages all in accordance with the present disclosure, in which:
As required, detailed embodiments are disclosed herein; however, it is to be understood that the disclosed embodiments are merely examples and that the systems and methods described below are embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the disclosed subject matter in virtually any appropriately detailed structure and function. Further, the terms and phrases used herein are not intended to be limiting but rather to provide an understandable description.
Generally, the terms “a” or “an”, as used herein, are defined as one or more than one. The term plurality, as used herein, is defined as two or more than two.
The term “adapted to” describes the hardware, software, or a combination of hardware and software that is capable of, able to accommodate, to make, or that is suitable to carry out a given function.
The term “another”, as used herein, is defined as at least a second or more.
The term “configured to” describes the hardware, software, or a combination of hardware and software that is adapted to, set up, arranged, built, composed, constructed, designed, or that has any combination of these characteristics to carry out a given function.
The term “coupled,” as used herein, is defined as “connected,” although not necessarily directly, and not necessarily mechanically.
The term “fatal flaw” or “low score escalators” means that one of the land characteristics for a given parcel of land makes it entirely undesirable for development, even if the other land characteristics score high. For example, if the land owner is listed as a U.S. National Park, this parcel of land, in general, is not feasible for development.
The term “independent system operator” or “ISO” is an organization formed at the recommendation of the Federal Energy Regulatory Commission. In the areas where an ISO is established, it coordinates, controls, and monitors the operation of the electrical power system, usually within a single U.S. state but sometimes encompassing multiple states. Regional Transmission Organizations (RTOs) typically perform the same functions as ISOs but cover a larger geographic area.
The terms “including” and “having,” as used herein, are defined as comprising (i.e., open language).
The term “land characteristics” includes size, ownership, tree coverage, elevations, terrain, buildable land, location of nearby renewable projects, and the owner's willingness or sentiment to sell rights.
The term “locational marginal pricing” or “LMP” is adapting wholesale electric energy prices to reflect the value of electric energy at different locations, accounting for the patterns of load, generation, and the physical limits of the transmission system.
The term “net capacity factor” or “NCF” is the ratio of actual electrical energy output over a given period of time divided by the theoretical continuous maximum electrical energy output over that period.
The term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. In addition, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form.
The term “resource characteristics” includes the net capacity factor (NCF) of wind or solar, which describes the fraction of total capacity that is produced over the course of a typical year. Typical total capacities include 20, 25, 50, 75, 100, 150, 200, 250, and 400 Megawatts. Note solar capacities are typically on the lower end, and wind capacities are typically on the higher end of these typical capacities.
The term “simultaneous” means computations are carried out at the same time, which for larger data sets with various constraints, is not possible to be carried out completed by a group of humans and must be performed by a computer. For example, one human could not compute one simulation with all the constraints for thousands of various clusters of parcels of land across multiple counties and across multiple states with all the various criteria. It is infeasible for a human to calculate one simulation loop with one constraint, let alone perform it in parallel to a sort of global optimum.
The term “transmission characteristics” includes substation hardware costs, network upgrades, and grid tie-in costs, such as those to be compatible with Federal Energy Regulatory Commission Order 845.
The term “uniform data format” means data in a given format, whether date format, time format, currency format, scientific format, text format, or fractional format, so that all values of data are presented in a single consistent format for a given category or criteria.
It should be understood that the steps of the methods set forth herein are not necessarily required to be performed in the order described, and the order of the steps of such methods should be understood to be merely exemplary. Likewise, additional steps may be included in such methods, and certain steps may be omitted or combined in methods consistent with various embodiments of the present device.
Although the elements in the following method claims, if any, are recited in a particular sequence with corresponding labeling unless the claim recitations otherwise imply a particular sequence for implementing some or all of those elements, those elements are not necessarily intended to be limited to being implemented in that particular sequence.
Disclosed is a system for identifying a broad range of utility-scale renewable energy sites that are likely to be profitable and successful, across the whole country, based on a ranking algorithm that is displayed within a user interface (UI). The system provides developers with actionable information that increases the likelihood of project success while reducing the time to conclude recommended clusters of land parcels and a streamlined user interface that provides information not readily available.
Turning to
A high-level overview of one example of the present invention is shown in
In one example, the system includes a prospecting tool that ranks every land parcel in the country based on the high-level characteristics that influence project viability. The goal is to improve the odds of a prospect getting built and to reach a quicker conclusion.
The system combines all of the high-level factors that go into evaluating a successful prospect. Optional features may include: i) land parcel ownership, ii) evaluations extend nationwide in land parcel review, and iii) displaying relevant data layers that identify nearby features of significance.
The total score combines sub-scores, representing how feasible different components are, and can be adjusted for different types of prospects. For example, this prospecting tool is used for data centers and battery energy storage site selections, and additional types of prospects beyond renewable energy, data centers, and batteries for a cluster of land are planned.
Scores are combined. The combined score means that a very low score in one category allows the tool to avoid “fatal flaws” or “low score escalators” with projects. That is, the identification of a potential “fatal flaw” is such that it ranks a group of land parcels much lower, eliminating them from consideration. For example, the fatal flaws may include very high transmission costs, very long gen-tie lines, and too many landowners in an area.
For each cluster of land parcels, the system provides an overall score, which is a weighted value based on individual scores for resource, land, transmission, and market characteristics. Users can initiate a utility-scale wind or solar prospect within the user interface based on recommended clusters of land parcels, drawing their own candidate, or uploading external geospatial files.
The system quantifies the tradeoffs between resource, transmission, market, and land constraints that influence whether wind and/or solar project is successful. The ranking system identifies the best clusters of land parcels sufficient to build a utility-scale wind and solar farm and identifies those with the best combination of resource, transmission, and market characteristics while maximizing buildable land and minimizing land owners. In one example, the system evaluates enough clusters of land parcels to build 29 Terawatts of solar capacity spread across 1.1 million virtual solar farms and 5 Terawatts of wind capacity across 100,000 virtual wind farms across the contiguous United States. The virtual or potential wind and solar farms being ranked could provide 25 times the U.S. total electricity capacity, giving developers a cache of actionable intelligence that can improve the wind and solar development process to significantly reduce carbon emissions of America's electric power generation critical infrastructure.
Utility-Scale Wind/Solar Algorithm
The overall goal of land parcel clustering is to recommend clusters of parcels that have sufficient transmission, resources, market demand, and available land to build a wind or solar farm. Considers land characteristics (Land Score), transmission characteristics (substation hardware costs, network upgrades, gen-tie cost), market characteristics (historical LMPs), and resources (wind/solar NCF) in producing clusters of land parcels that minimize the number of land owners, maximize buildable land, minimize transmission cost, maximize NCF, and maximize nodal LMPs.
Standalone Storage Algorithm
In one example, the present invention brings together the factors, such as, the proximity to the substation, battery arbitrage opportunities, and land parcel characteristics (buildable land and building footprints) that influence the viability of standalone storage prospects, ultimately recommending the best properties to pursue these projects. The ultimate goal is to provide battery prospectors with recommendations of land parcels that have sufficient open land near substations that have good arbitrage opportunities. The system is also designed to generally highlight land and transmission characteristics more than market/arbitrage characteristics (until additional and more comprehensive arbitrage/load data), so users can pan on the user interface map based on displayed arbitrage values and then zoom into the appropriate scale for prospecting.
Data Center Algorithm
In one example, the present invention brings together the factors, including the proximity to fiber/population center/substation, land parcel characteristics, and more that influence the viability of data center prospects, ultimately recommending the best properties to pursue for these projects. Parcels within a small radius (typically 50 miles) of the center of major metro areas (top 100) are filtered and scored based on their proximity to fiber and transmission substations and land characteristics (number of buildings in parcel, the concentration of buildable land for a data center, the concentration of buildable land for a 25 MW solar plant).
Technical Document
The overall goal of the land parcel clustering algorithm is to codify all of the major influences on wind and solar prospect viability for every land parcel in the country, based on interviews with developers, historical analyses, and financial/physical relationships. The present invention produces an algorithm recommending clusters of parcels with sufficient transmission, resources, market demand, available land, and positive land owner sentiment to build a wind or solar farm. Developers see the clusters of parcels in a given area that have the best chance of culminating in a constructed wind/solar farm without showing any areas with insufficient land for construction. In designing the clustering algorithm, the main goals are to 1) provide reasonable compact clusters of parcels with minimal land ownership and 2) score those clusters with the appropriate tradeoffs between transmission, resource, market demand, suitable/advantageous land, and land owner sentiment. Here, details about the clustering and considerations as development continues are considered.
Overall, the system filters and ranks all land parcels throughout the country based on a combination of transmission/land/market/resource scores and then clusters them together to form enough buildable land to build wind or solar farms of five different capacities. All clusters of land parcels are then scored based on their transmission/land/market/resource scores. Developers in the Discover User Interface (discover.nexteraanalytics.com) can then view the top ten clusters of land parcels for wind or solar farms of a given capacity within the geographic area displayed in the user interface.
Documentation of Algorithm
The land parcel clustering, the process starts with reasonable objective assumptions that inform the weighting of score components and filtering of parcels. The process then continues by modifying the weighting of score components (resource/transmission/market/land as well as the components of the land score) to force the most appropriately shaped and ranked clusters.
Development of Clustering Algorithm
Process
In one example, the process begins with identifying the technology type, such as solar farm, wind farm, energy storage, or data center, and the capacity desired.
Load nationwide data.
Turning to
Loop Over Different Multi-County Regions Across the Continental USA (CONUS).
Load Regional Data, Subset Region Data, and Merged Together
Score Land Parcels
Build all Possible Clusters Centered on Every Single Filtered Land Parcel
Rank all Possible Land Parcel Clusters
National Aggregation and Normalization
Filtering
Filtering allows objective removal of non-buildable areas and tries to consider edge cases where appropriate without being too restrictive. Areas that provide marginal or atypical development potential are kept in the system but are generally scored lower.
Wind Filters
Solar Filters
Assumptions
Wind
Solar
Score Components and Weights
Resource Score
Transmission Score
Market Score
Land Score
Land Score Components
In one example, different land scores are added that form appropriate cluster shapes and give a proper ranking of clusters. The system uses scoring to pick the best parcels within a cluster and rank the clusters based on land/transmission/resource/market.
Turning to
Parcel Size Score: Wind Weight=0.42, Solar Weight=0.25 (for calculation of Land Score)
Owner Count Score: Wind Weight=0.37, Solar Weight=0.35 (for calculation of Land Score)
Buildable Land Score: Wind Weight=0.21, Solar Weight=0.20 (for calculation of Land Score)
Land cost score: Weight=0.2 for solar, 0 for wind
Land value score: Weight=0 to 0.05
Low Score Escalators
Sentiment Score Boost
The addition of third-party (LandGate in this case) advertisements from land owners about their desire to lease their land for wind, solar, or other mineral rights can help address another potential hurdle to renewable development: land owner sentiment. Combining this with Discover's core system gives users further insights into the main drivers of prospect viability.
LandGate is a website where landowners advertise their land for mineral/renewables leases, providing a powerful avenue to identify willing landowners. The system uses sentiment scoring, to prove a conditional score boost to land parcels (and clusters) that have land owners advertising their land for renewable (or other) leases on third-party websites like LandGate via a Sentiment Score Boost. The system also gives a partial Sentiment Score Boost to any additional land parcels nearby that the system identifies are owned by a land owner that advertised their land for renewable energy leases.
The Sentiment Score Boost is applied to each parcel (100 if the listing is in the same technology being considered in the algorithm, 75 if it is another renewable technology, 50 if it is oil/gas/mining or other properties owned by a “lister”), apply a Sentiment Score Boost based on the total score of the parcel 1200, cluster parcels together based on parcel scores and methods described previously, recalculate total scores, and then apply a sentiment boost to the cluster based on a weighted area average of the sentiment scores for parcels in the clusters.
Discover Score
Flow
Turning now to
In step 1806, data elements are accessed from a variety of data sources. Each of the data elements is associated with criteria. The criteria is used to project expected electricity output from new renewable energy sources. The criteria include a portfolio of a plurality of parcels of land, each with i) land characteristics, ii) electricity transmission characteristics, and iii) market demand characteristics.
In one example, the criteria of land characteristics for each parcel of land in the portfolio may be any combination of electricity transmission characteristics of the parcel, and market demand characteristics of the parcel. Further, these criteria may include any combination of the size of the parcel, ownership of the parcel, tree coverage in the parcel and/or tree clearing costs, the elevation of the parcel, terrain of the parcel, buildable land area of the parcel, location of the parcel to nearby renewable projects, or a land owner's willingness to grant rights to the parcel, i.e., sentiment score boost. Other criteria for each parcel of land in the portfolio may include the plurality of criteria further includes at least one resource score that is based on the strength of the wind or solar in each of the plurality of parcels of land.
In another example, the criteria of transmission characteristics for each parcel of land in the portfolio may include the size of substation hardware costs, network upgrade costs, or grid tie-in costs. The process continues to step 1808.
In step 1808, each of the plurality of data elements is converted into a uniform data format within each of the criteria. The process continues to step 1810.
In step 1810, the process begins a loop in which a total number of simulations (M) are executed in parallel up to the total number of jobs or until a time period expires by step 1812 and step 1814.
In step 1812, by evaluating each of the plurality of parcels of land in the portfolio, including the land characteristics, the electricity transmission characteristics, and the market demand associated with the parcels of land in the portfolio. Next, in step 1814, a clustering algorithm is executed to produce results, wherein the results include a subset of the plurality of parcels of land in the portfolio. The process continues to step 1816. If the number of simulations is complete or the time period expires, the process continues to step 1818. Otherwise, the process returns to step 1810.
In step 1818, the results are ranked from the total number of simulations (M) that meet the electricity requirement combined with a highest combined score of a cumulative size of the subset of the plurality of parcels of land in the portfolio, the electricity transmission characteristics, and the market demand. The process continues to step 1820.
In step 1820, the results may be displayed in various formats with various color overlays on maps illustrating the combination of the parcels of land with the highest ranking for development based on the criteria. The process ends in step 1822.
General Computer for Implementing Algorithm
The present invention can be implemented on a standalone computer system, a server, a web-server, a cloud computing system or a hybrid cloud system, or other on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user.
The processor 1900 in this example includes a hardware processor or CPU 1904 that is communicatively connected to a main memory 1906 (e.g., volatile memory), a non-volatile memory 1912 to support processing machine instruction and operations. The CPU is further communicatively coupled to a network adapter hardware 1916 to support input and output communications with external computing systems such as through the illustrated network 1930.
The processor 1900 further includes a data input/output (I/O) processor 1914 that is able to be adapted to communicate with any type of equipment, such as the illustrated system components 1928. The data input/output (I/O) processor, in various examples, is able to be configured to support any type of data communications connections, including present-day analog and/or digital techniques or via a future communications mechanism. A system bus 1918 interconnects these system components.
The present subject matter can be realized in hardware, software, or a combination of hardware and software. A system can be realized in a centralized fashion in one computer system or in a distributed fashion where different elements are spread across several interconnected computer systems. Any kind of computer system—or other apparatus adapted for carrying out the methods described herein—is suitable. A typical combination of hardware and software could be a general-purpose computer system with a computer program that, when being loaded and executed, controls the computer system such that it carries out the methods described herein.
The present subject matter can also be embedded in a computer program product, which comprises all the features enabling the implementation of the methods described herein, and which—when loaded in a computer system—is able to carry out these methods. The computer program in the present context means any expression, in any language, code, or notation, of a set of instructions intended to cause a system having an information processing capability to perform a particular function either directly or after either or both of the following a) conversion to another language, code or, notation; and b) reproduction in a different material form.
Each computer system may include, inter alia, one or more computers and at least a computer readable medium allowing a computer to read data, instructions, messages or message packets, and other computer readable information from the computer readable medium. The computer readable medium may include computer readable storage medium embodying non-volatile memory, such as read-only memory (ROM), flash memory, disk drive memory. CD-ROM, and other permanent storage. Additionally, a computer medium may include volatile storage such as RAM, buffers, cache memory, and network circuits. Furthermore, the computer readable medium may comprise computer readable information in a transitory state medium such as a network link and/or a network interface, including a wired network or a wireless network, that allow a computer to read such computer readable information. In general, the computer readable medium embodies a computer program product as a computer readable storage medium that embodies computer readable program code with instructions to control a machine to perform the above-described methods and realize the above-described systems.
Although specific embodiments of the subject matter have been disclosed, those having ordinary skill in the art will understand that changes are made to the specific embodiments without departing from the spirit and scope of the disclosed subject matter. The scope of the disclosure is not to be restricted, therefore, to the specific embodiments, and it is intended that the appended claims cover any and all such applications, modifications, and embodiments within the scope of the present disclosure.
