Patent Application
20240127369
Generally, the present disclosure relates to charging loads on a power system operation for electric vehicles (EVs) and, more specifically, to a method and device for generation of charging price signals for electric charging sites.
With the fast adoption of electric vehicles (EVs) in our society, utilities have growing concerns over the impact of charging loads on power system operations. Utilities themselves are also increasingly operating as EV charging point operators at small or larger scales. There is a growing need to incentivize EV charging point operators (CPO) or operation algorithms/agents to control their charging loads intelligently to avoid adverse impacts of these loads on power system operations. One of the many approaches is to use a charging price signal to make EV charging point operators aware of any pending power system stresses, so the EV charging point operators can take appropriate actions to control charging loads during stressful system operations. This process is called price responsive load management. However, some traditional approaches are complex and costly to implement. Some approaches under development are too challenging to put into practice any time soon.
Therefore, there is a need to have an alternative approach to generate the price signals sensibly to allow CPOs to manage charging loads at their electric charging stations.
Exemplary embodiments of the present disclosure provide a method for a device to determine a charging price for an electric charging site. The device includes one or more processors configured to perform the method. The method includes:
An off-line process to generate the weather and time dependent load profiles of a power delivery distribution feeder (the feeder), and an on-line process to derive the real-time price signal representing the risk of voltage violations for the charging site.
The method further includes data analytical considerations to use utility automated metering infrastructure (AMI) data to derive feeder load profiles, and a digital simulation model (digital twin) of the feeder to derive the feeder load, charging site voltage, load, dynamic hosting capacity and the price signal curve.
In another exemplary embodiment, the present disclosure provides a method for a device to determine a charging price for an electric charging site. The device includes one or more processors configured to perform the method. The method includes receiving time tags and data for at least one electric charging site, which are obtained based on automated metering infrastructure (AMI) data for the at least one electric charging site, and temperature and/or weather related information for a service area of the at least one electric charging site; clustering power consumption of the at least one electric charging site with similar temperature and/or weather related information and time tags; calculating a center of mass and a distribution confidence parameter for the clustered power consumption of the at least one electric charging site to obtain a look-up table; retrieving historical information of voltage VPCC and injection power Psite pairs that is measured at a point of common coupling (PCC) of the at least one electric charging site and stored on a memory of the device; receiving weather forecast information for the service area of the at least one electric charging site; and calculating a dynamic hosting capacity (DHC) curve and a price curve for the at least one electric charging site based on the center of mass and the distribution confidence parameter for the clustered power consumption, the historical information of voltage VPCC and injection power Psite pairs, and the weather forecast information.
The first three method steps are off-line and the last three method steps are on-line.
The method further includes identifying a maximum DHC line and a minimum DHC line of the DHC-time plane; identifying an upper premium price boundary (UPPB) and a lower premium price boundary (LPPB) of the DHC-time plane; and dividing a region between the maximum DHC line and the minimum DHC line of the DHC-time plane into three sub-regions. The three sub-regions include a first premium price region that is between the maximum DHC line and the UPPB of the DHC-time plane, a second premium price region that is between the LPPB and the minimum DHC line of the DHC-time plane, and a DHC based price region that is between the UPPB and LPPB of the DHC-time plane.
The method further includes dividing the DHC based price region into a region for load and a region for price p generation according to the following equations:
p=p
nr_scr
,P
site∈[0,DHCuppb-P
p=p
nr_scr
−p
nr_scr
max[(Psite−DHCuppb-P
p=p
nr_ld
,P
site
ε[DHC
lppb-P
,0]
p=p
nr_ld
+p
nr_ld
max[(Psite−DHCuppb-P
where pnr_scr represents a normal compensation price for distributed energy resource (DER) injection power Psite (positive) into a grid from the PCC of the electric charging site; pnr_ld represents a normal billing price for electric charging site load power Psite (negative) drawn from the grid at the PCC of the electric charging site. pnr_scrmax and pnr_ldmax represent maximum penalty prices the electric charging site needs to pay at its power injection or sink operation modes relative to the grid, respectively.
The identifying the maximum DHC line and the minimum DHC line of the DHC-time plane includes deriving per unit voltage deviations δV from a nominal value based on the historical information of voltage VPCC and injection power Psite pairs according to the following equation:
δV=VPCC/VBase−1
where VBase is a rated voltage at the PCC of the electric charging site, plotting the derived points of the per unit voltage deviations δV and the injection power Psite on a corresponding graph to obtain a δV-Psite curve; and deriving the maximum DHC line and the minimum DHC line of the DHC-time plane by extrapolating the δV-Psite curve to intercept with a δVmax threshold and a δVmin threshold based on a curve fitting method or a machine learning process.
The δVmax threshold and the δVmin threshold are 0.05 and −0.05, respectively, according to local regulatory voltage limits.
The identifying the UPPB and the LPPB of the DHC-time plane includes determining two corresponding voltage deviation thresholds δVuppb and δVlppb, respectively, based on the δV-Psite curve; and determining the UPPB and the LPPB of the DHC-time plane by the two corresponding voltage deviation thresholds δVuppb and δVlppb. The two corresponding voltage deviation thresholds δVuppb and δVlppb are configuration parameters determined by how sensitive the voltage VPCC is to an injection power Psite change.
The two corresponding voltage derivation thresholds δVuppb and δVlppb are at least one of: chosen arbitrarily; according to voltage sensitivity study of the at least one electric charging site; or 0.04 and −0.04, respectively, when statutory limits for the δVmax threshold and the δVmin threshold are 0.05 and −0.05, respectively.
The method further includes updating the look-up table with the information of weather forecast for the service area of the at least one electric charging site, the DHC curve and the price curve for the at least one electric charging site.
The method further includes training the device with the updated look-up table for calculating DHC curves and price curves for electric charging sites.
In another exemplary embodiment, the present disclosure provides a device for determining multiple price regions based on the dynamic hosting capacity concept, where the boundary of the regions is determined by curve fitting method.
The device further includes algorithm to calculate charging site price from dynamic hosting capacity based on a set of linear or quadratic equations.
In another exemplary embodiment, the present disclosure provides a device for determining a charging price for an electric charging site, the device including one or more processors configured to receive time tags and data for at least one electric charging site, which are obtained based on automated metering infrastructure (AMI) data for the at least one electric charging site, and temperature and/or weather related information for a service area of the at least one electric charging site; cluster power consumption of the at least one electric charging site with similar temperature and/or weather related information and time tags; calculate a center of mass and a distribution confidence parameter for the clustered power consumption of the at least one electric charging site to obtain a look-up table; retrieve historical information of voltage VPCC and injection power Psite pairs that is measured at a point of common coupling (PCC) of the at least one electric charging site and stored on a memory of the device; receive weather forecast information for the service area of the at least one electric charging site; and calculate a dynamic hosting capacity (DHC) curve and a price curve for the at least one electric charging site based on the center of mass and the distribution confidence parameter for the clustered power consumption, the historical information of voltage VPCC and injection power Psite pairs, and the weather forecast information.
In another exemplary embodiment, the present disclosure provides a non-transitory computer-readable medium having computer-executable instructions stored thereon which, when executed by one or more processors, cause a device to receive time tags and data for at least one electric charging site, which are obtained based on automated metering infrastructure (AMI) data for the at least one electric charging site, and temperature and/or weather related information for a service area of the at least one electric charging site; cluster power consumption of the at least one electric charging site with similar temperature and/or weather related information and time tags; calculate a center of mass and a distribution confidence parameter for the clustered power consumption of the at least one electric charging site to obtain a look-up table; retrieve historical information of voltage VPCC and injection power Psite pairs that is measured at a point of common coupling (PCC) of the at least one electric charging site and stored on a memory of the device; receive weather forecast information for the service area of the at least one electric charging site; and calculate a dynamic hosting capacity (DHC) curve and a price curve for the at least one electric charging site based on the center of mass and the distribution confidence parameter for the clustered power consumption, the historical information of voltage VPCC and injection power Psite pairs, and the weather forecast information.
Exemplary embodiments of the present disclosure provide a method for determining a representative energy delivery price (charging price) for an electric charging site using an electric vehicle supply equipment (EVSE) device, the charger. The method includes: (1) receiving load profile and time tags dataset for at least one electric charging site, which are obtained based on automated metering infrastructure (AMI) data for the at least one electric charging site, and weather data (e.g., an ambient temperature) for a service area of the at least one electric charging site; (2) clustering power consumption of the at least one electric charging site with similar ambient temperature and time tags; (3) calculating a center of mass and a distribution confidence parameter for the clustered power consumption of the at least one electric charging site to obtain a look-up table; (4) retrieving historical information of voltage VPCC and injection power Psite pairs that is measured at a point of common coupling (PCC) of the at least one electric charging site and stored on a memory of the device; (5) receiving weather forecast information for the service area of the at least one electric charging site; and (6) calculating a dynamic hosting capacity (DHC) curve and a price curve for the at least one electric charging site based on the center of mass and the distribution confidence parameter for the clustered power consumption, the historical information of voltage VPCC and injection power Psite pairs, and the weather forecast information.
With the growing use of electric vehicles (EVs), electric charging sites for charging EVs have grown rapidly. EV charging point operators or operation algorithms/agents for electric charging sites wish to control charging loads of their electric charging sites intelligently to avoid adverse impacts of these loads on power system operations, and/or to reduce their risk of paying heavy electricity peak demand charges to the utility serving them. In general, dynamic hosting capacity (DHC) parameters at an electric charging site's point of common coupling (PCC) is derived for subject time. These DHC parameters are used in turn to derive a charging price as a function of PCC power, namely, Psite, to discourage charging demands by EV drivers that could cause voltage violations at the PCC. For example, the charging price may be represented by f (PCC power) or f (Psite). That is, various PCC power results in different charging prices for an electric charging site.
One approach is to use a charging price signal to make EV charging point operators or operation algorithms/agents for electric charging sites aware of possible pending power system stresses. With that awareness, the EV charging point operators or operation algorithms/agents can take appropriate actions to control the charging loads of their electric charging sites, and thus, avoiding potential voltage violations at PCCs of their electric charging sites. This process is called a price responsive load management.
A traditional way of generating a charging price signal for a power system is to use the optimal power flow (OPF) based locational marginal price (LMP) calculation method. This method requires a detailed electrical system model for the feeder and adjacent territories of the utility service area, detailed information about load demands, e.g., charging demands from EVs, and generator model of EVs, and fuel costs at an electric charging site. For those electric charging stations that are connected to an electric power distribution network, this approach is too complex and costly to implement.
In addition, the LMP based price generation method typically only applies in a deregulated energy market for a wholesale market, which is traditionally oriented for transmission systems. In such a wholesale market, network models and methodologies are usually mature and validated. On the other hand, the energy market in distribution systems is still in its infancy. There are many more stakeholders to be satisfied. Further, the energy market in distribution systems is expected to take a longer time to develop. Therefore, it remains challenging to put an OPF based distribution LMP calculation method into practice.
As such, an alternative method, which can preferably leverage data analytics, is highly desirable. This alternative method may be also described as a data-based solution. According to exemplary embodiments of the present disclosure, the methods and devices provided herein do not need an enterprise level OPF based algorithm to determine a charging price at an electric charging site. Thus, they can be implemented easily for each electric charging site using the disclosed process and implemented architecture. Further, a curve fitting method to determine DHC parameters in real time is introduced. Furthermore, the methods and devices provided herein introduce a premium price scheme to discourage load demands/charging demands at an electric charging site that can cause network constraint violations, for example, an overvoltage situation or an under-voltage situation.
In general, the methods and devices provided herein enable charging site operators to inform the consumers of a charging price at an electric charging site. For example, a message may be, the consumers have to pay more to charge their EVs at certain point in time due to the current strained state of the grid. For instance, charging high prices is to discourage the consumers from charging their EVs, and thus, to relive the strain on the grid. The methods and devices may be also applied to other types of load managements. For example, charging high prices at a café of an electric charging site is to discourage running heating, ventilation, and air conditioning (HVAC) in the café.
The feeder 102 acts as a central circuit that controls and distributes electricity to outgoing circuits downstream, for example, serving neighborhood or even a town through power lines. Through the power lines, the feeder 102 distributes electricity to the electric charging site 100, which in turn distributes electricity to EVs 104 as shown in
As shown in
Generally, voltage rise by the DER should be kept under a maximum permissible voltage rise, which is defined as the voltage rise that brings the maximum voltage magnitude exactly to the regulatory overvoltage limit. A hosting capacity is used to indicate a maximum amount of generation by the DER. For example, within the maximum amount of generation, there should be no compromising of power quality indexes, and at the same time, an assurance of the system reliability. Therefore, dynamic behaviors of the hosting capacity, namely the hosting capacity in relation to an integrated impact of harmonic voltage distortion and voltage rise, throughout a period of time is observed and analyzed. The period of time may include, for example, daily, weekly, monthly, or even yearly periods. The dynamic behaviors of the hosting capacity is known as dynamic hosting capacity (DHC). Conversely, voltage dip cannot be lower than a permissible value (typically 0.95 per unit), and the site 100's net load should be limited to not cause the voltage dip violation. We can consider the net load requirement as negative dynamic hosting capacity (DHC) for site 100.
Therefore, the generation of the charging price signals takes into account feeder's loading conditions over time, namely feeder load profiles. Furthermore, the charging price signals is a function of the voltage VPCC and net power injection Psite, f (Psite), from the electric charging site's point of common coupling (PCC) or point of interaction (POI). As shown in
The process 200 for generating charging price signals for an electric charging site includes an off-line process 202 and an on-line process 204, as shown in
In the off-line process 202 as shown in
Further, information such as ambient temperature for a service area of the at least one electric charging site will be also retrieved. As shown at step 208 of
In general, the off-line process 202 uses a large amount of data to build a prediction model based on historical data. Due to the volume of the data, this off-line process 202 may be time-consuming. However, once the prediction model is established, it can be used to make predictions based on current and future data, e.g., weather forecast, very quickly and with low computational power. For example, this prediction model may be used in a price generation algorithm.
According to an exemplary embodiment of the present disclosure, at step 210 of
As such, the look-up table 212 is derived using data analytics methods based on the information of related data and ambient temperature of the at least one electric charging site, and based on the information of time tags as well. The look-up table 212 contains information such as Tfd, Pfd and TimeTag. Tfd represents outdoor temperature for the area, e.g., the neighborhood, where the feeder is located, for example, the feeder 102 of the electric charging site 100 as shown in
As shown in
The on-line process 204, as shown in
The term “real time” refers to at least one of the time of occurrence of the associated events, the time of measurement and collection of predetermined data, the time to process the data, and the time of a system response to the events and the environment. In the embodiments described herein, these activities and events occur substantially instantaneously.
If, for example, the historian 214 contains site PCC voltage and net load over a period of a year, the historian 214 may be regarded as having enough representation of site 100's operation conditions.
According to an exemplary embodiment of the present disclosure, real time weather forecast information 216 is provided to the on-line process 204. The real time weather forecast information 216 is up-to-date weather forecast information when running the on-line process 204, compared to the historical weather data for the service area of the at least one electric charging site at 208. The real time weather forecast information 216 may include ambient temperature of the service area of the electric charging site. Additionally and/or alternatively, the real time weather forecast information 216 may include additional information of humidity and other parameters of that service area when necessary.
During the on-line process 204, the look-up table 212 derived through the off-line process 202 is used to derive Pffl, as shown at 218 in
According to an exemplary embodiment of the present disclosure, the above-described parameters generated through the on-line process 204 are used to update the off-line process 202. Further, the above-described parameters generated through the on-line process 204 are also used to further train the on-line process 204 itself. For example, the on-line process 204 may be trained based on the above-described parameters according to a machine-learning algorithm. Additionally and/or alternatively, other training methods may be also used for training the on-line process 204 based on the above-described parameters.
According to an exemplary embodiment of the present disclosure, a calculated DHC during the on-line process 204 represents the maximum permissible amount of generation or loading of an electric charging site, for example, the electric charging site 100 as shown in
According to an exemplary embodiment of the present disclosure, the region between the maximum dynamic hosting capacity line 302 and the minimum dynamic hosting capacity line 304 is further divided into three regions. These three regions include two premium price regions and a normal price region. For example, as shown in
The definition of these two premium price regions 312 and 314 allows a penalty to be applied to a DER's charging price, namely, a charging price of the electric charging site 100 as shown in
As described above, based on Pffl derived from the look-up table 212 through the off-line process 202 and the historical (VPCC, PRO measurements stored in the historian 214 for the on-line process 204, as shown in
δV=VPCC/VBase−1 Equation 1
where VBase is a rated voltage at the PCC of the electric charging site.
According to an exemplary embodiment of the present disclosure, as a next step of the on-line process 204 as shown in
According to an exemplary embodiment of the present disclosure, the two corresponding voltage deviation thresholds 520 and 522 may be determined by how sensitive the voltage VPCC of the PCC of the electric charging site 100 as shown in
As such, four DHC values, which include the maximum DHC 402 and minimum DHC 404 as shown in
According to an exemplary embodiment of the present disclosure, the normal price region 310 of
p=p
nr_scr
,P
site∈[0,DHCuppb-P
p=p
nr_scr
−p
nr_scr
max[(Psite−DHCuppb-P
p=p
nr_ld
,P
site
∈[DHC
lppb-P
,0] Equation 4
p=p
nr_ld
+p
nr_ld
max[(Psite−DHCuppb-P
where pnr_scr represents a normal compensation price for distributed energy resource (DER) injection power Psite (positive) into a grid from the PCC of the electric charging site 100, as shown in
where pnr_scrmax and pnr_ldmax represent maximum penalty prices the electric charging site 100 of
According to an exemplary embodiment of the present disclosure, the electric charging site power Psite may be controlled to be within the range of (DHCmin-P
As such, an establishment of a relationship between price (p) and net power injection Psite from the electric charging site's PCC can be made based on the calculated regions for load and for generation according to the piecewise function with different sub-functions as described above.
According to some other exemplary embodiments of the present disclosure, the functions for the upper premium price region 612 and the lower premium price region 614 may be linear rather than quadratic. That is, various other functions may be used. However, those functions generally require a price increase with increasing power in a premium region for a load situation, and further, requires a compensation reduction with increasing power in a premium region for a generation situation.
According to some other exemplary embodiments of the present disclosure, variations of the functions applied within a normal price region, for example, the normal price region 610 as shown in
According to some exemplary embodiments of the present disclosure, a variation of the off-line process 202 described according to
According to some exemplary embodiments of the present disclosure, user satisfaction ratings could be requested after charging users at the electric charging site to provide test criteria for additional training of the off-line process 202 and the on-line process 204, as shown in
According to some exemplary embodiments of the present disclosure, electric charging site price signal receivers could provide hard or soft limits for key parameters to charging and management controllers at the electric charging site that set additional constraints. For example, a method to check validity of settings would be implemented to ensure solvable conditions.
According to some exemplary embodiments of the present disclosure, the methods introduced herein could be used to run a distribution energy market without the need for OPF or a full network model. For example, economic dispatch is performed to get the nodal generation/load and baseline system price, and then, charging prices are adjusted at each node as a function of the DHC limits.
At step 702, the device receives time tags and data for at least one electric charging site, which are obtained based on automated metering infrastructure (AMI) data for the at least one electric charging site, and temperature and/or weather related information for a service area of the at least one electric charging site.
At step 704, the device clusters power consumption of the at least one electric charging site with similar temperature and/or weather related information and time tags.
At step 706, the device calculates a center of mass and a distribution confidence parameter for the clustered power consumption of the at least one electric charging site to obtain a look-up table.
At step 708, the device retrieves historical information of voltage VPCC and injection power Psite pairs that is measured at a point of common coupling (PCC) of the at least one electric charging site and stored on a memory of the device.
At step 710, the device receives weather forecast information for the service area of the at least one electric charging site.
At step 712, the device calculates a dynamic hosting capacity (DHC) curve and a price curve for the at least one electric charging site based on the center of mass and the distribution confidence parameter for the clustered power consumption, the historical information of voltage VPCC and injection power Psite pairs, and the weather forecast information.
At step 802, the device identifies a maximum DHC line and a minimum DHC line of the DHC-time plane.
At step 804, the device identifies an upper premium price boundary (UPPB) and a lower premium price boundary (LPPB) of the DHC-time plane.
At step 806, the device divides a region between the maximum DHC line and the minimum DHC line of the DHC-time plane into three sub-regions. The three sub-regions comprise a first premium price region that is between the maximum DHC line and the UPPB of the DHC-time plane, a second premium price region that is between the LPPB and the minimum DHC line of the DHC-time plane, and a DHC based price region that is between the UPPB and LPPB of the DHC-time plane.
At step 902, the device divides the DHC based price region into a region for load and a region for generation according to the following equations;
p=p
nr_scr
,P
site∈[0,DHCuppb-P
p=p
nr_scr
−p
nr_scr
max[(Psite−DHCuppb-P
p=p
nr_ld
,P
site
∈[DHC
lppb-P
,0]
p=p
nr_ld
+p
nr_ld
max[(Psite−DHCuppb-P
where pnr_scr represents a normal compensation price for distributed energy resource (DER) injection power Psite (positive) into a grid from the PCC of the electric charging site; pnr_ld represents a normal billing price for electric charging site load power Psite(negative) drawn from the grid at the PCC of the electric charging site; pnr_scrmax and pnr_ldmax represent maximum penalty prices the electric charging site needs to pay at its power injection or sink operation modes relative to the grid, respectively.
At step 1002, the device derives per unit voltage deviations δV from a nominal value based on the historical information of voltage VPCC and injection power Psite pairs according to the following equation:
δV=VPCC/VBase−1
where VBase is a rated voltage at the PCC of the electric charging site.
At step 1004, the device plots the derived points of the per unit voltage deviations δV and the injection power Psite on a corresponding graph to obtain a δV-Psite curve.
At step 1006, the device derives the maximum DHC line and the minimum DHC line of the DHC-time plane by extrapolating the δV-Psite curve to intercept with a δVmax threshold and a δVmin threshold based on a curve fitting method or a machine learning process.
The δVmax threshold and the δVmin threshold are 0.05 and −0.05, respectively, according to local regulatory voltage limits.
At step 1102, the device determines two corresponding voltage deviation thresholds δV, lppb and δVlppb, respectively, based on the δV-Psite curve.
At step 1104, the device determines the UPPB and the LPPB of the DHC-time plane by the two corresponding voltage deviation thresholds δV, lppb and 0Vlppb. The two corresponding voltage deviation thresholds δV, lppb and δVlppb are configuration parameters determined by how sensitive the voltage VPCC is to an injection power Psite change.
The two corresponding voltage derivation thresholds δV, lppb and δVlppb are at least one of: chosen arbitrarily; according to voltage sensitivity study of the at least one electric charging site; or 0.04 and −0.04, respectively, when statutory limits for the δVmax threshold and the δVmin threshold are 0.05 and −0.05, respectively.
At step 1202, the device updates the look-up table with the information of weather forecast for the service area of the at least one electric charging site, the DHC curve and the price curve for the at least one electric charging site.
At step 1204, the device trains itself with the updated look-up table for calculating DHC curves and price curves for electric charging sites.
The processor 1302 may include one or more general-purpose processors, such as a central processing unit (CPU), or a combination of a CPU and a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), generic array logic (GAL), or any combination thereof.
The memory 1306 may include a volatile memory, for example, a random access memory (RAM). The memory 1306 may further include a non-volatile memory (NVM), for example, a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD). The memory 1306 may further include a combination of the foregoing types.
The memory 1306 may have computer-readable program codes stored thereon. The processor 1302 may read the computer-readable program codes stored on the memory 1306 to implement the methods 700-1200 shown in
The processor 1302 may further communicate with another computing device through the communication interface 1304. For example, the processor 1302 may further communicate with an external physical memory or an external memory on a cloud to obtain necessary data for further data analysis, such as for calculating a center of mass and a distribution confidence parameter for clustered power consumption of at least one electric charging site to obtain a look-up table. Additionally and/or alternatively, the processor 1302 may further communicate with an automated metering infrastructure (AMI) to obtain relevant data for the at least one electric charging site. Additionally and/or alternatively, the processor 1302 may further communicate with a local or a remote server to obtain up-to-date information of weather forecast for a service area of the at least one electric charging site in real time.
The processor 1302 may further trigger the display 1308 to display information to a user. For example, the processor 1302 may trigger the display 1308 to display data that is related to the at least one electric charging site, time tags of the data, ambient temperature for a service area of the at least one electric charging site, historical information of voltage VPCC and injection power Psite pairs that is measured at a point of common coupling (PCC) of the electric charging site, and information of weather forecast for the service area of the at least one electric charging site.
For example, the processor 1302 may trigger the display 1308 to display results of analyzing the data, including the clustered power consumption of the at least one electric charging site, the calculated center of mass and distribution confidence parameter for the clustered power consumption, and the calculated dynamic hosting capacity (DHC) curve and price curve for the at least one electric charging site based on the center of mass and the distribution confidence parameter for the clustered power consumption, the historical information of voltage VPCC and injection power Psite pairs, and the information of weather forecast.
For example, the processor 1302 may trigger the display 1308 to display the look-up table that contains the results based on the analysis of the data. For example, the processor 1302 may trigger the display 1308 to display a real time status of the process of the determination of a charging price for an electric charging site so to generate a charging price signal to present to an EV driver at the electric charging site, as shown in
A person of ordinary skill in the art will appreciate that the device 1300 as shown in
All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the disclosure and does not pose a limitation on the scope of the disclosure unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the disclosure.
Exemplary embodiments of the present disclosure are described herein, including the best mode known to the inventors for carrying out the disclosure. Variations of those exemplary embodiments may become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventors expect skilled artisans to employ such variations as appropriate, and the inventors intend for the disclosure to be practiced otherwise than as specifically described herein. Accordingly, this disclosure includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the disclosure unless otherwise indicated herein or otherwise clearly contradicted by context.
