Abstract: A controller for equipment that operate to provide heating or cooling to a building or campus includes a processing circuit configured to obtain utility rate data indicating a price of resources consumed by the equipment to serve energy loads of the building or campus, obtain an objective function that expresses a total monetary cost of operating the equipment over an optimization period as a function of the utility rate data and an amount of the resources consumed by the equipment, determine a relationship between resource consumption and load production of the equipment, optimize the objective function over the optimization subject to a constraint based on the relationship between the resource consumption and the load production of the equipment to determine a distribution of the load production across the equipment, and operate the equipment to achieve the distribution.

Abstract: A computer system that includes a processor device and a storage device is configured to determine a base load at a utility customer site using power usage data for the utility customer site, to determine a variable load at the utility customer site during a range of temperature independent days using the power usage data, and to determine a temperature dependent load at the utility customer site that exceeds the variable load and the base load using the power usage data. The computer system is further configured to assign a flag to each time interval that the temperature dependent load exceeds a power usage threshold and to determine if the utility customer site has an equipment malfunction based on a number of the flags assigned within a time period.

Abstract: Devices, systems and methods for predicting future consumption values of load(s) in power distribution systems are provided. The present disclosure provides for receiving a request for a load prediction for at least one meter; extracting time series data relating to the at least meter; retrieving future weather conditions for a particular location based on the at least one meter; and providing the extracted data and the future weather conditions to a prediction model that predicts load usage for the at least one meter. Additionally, the present disclosure provides for performing at least one action based on the prediction, wherein the action includes outputting at least one of a communication signal and/or at least one control signal to at least one client or at least one meter.

Abstract: Methods, systems, and apparatus, including computer-readable media, for environment monitoring and management. In some implementations, information indicating a planned usage level for usage of cloud computing services is accessed by a group of multiple computing environments over a period of time. Usage of cloud computing services is monitored for the group of multiple computing environments. A usage measure indicating an amount of usage of cloud computing services by the group of multiple computing environments is generated over the period of time. A cloud computing usage notification is generated based on the planned usage level and the usage measure. The cloud computing usage notification is provided for presentation by an electronic device.

Abstract: Embodiments include systems and methods for mass data optimization. Embodiments include receiving user data from a user server which is being continuously collected locally by the user server and storing the user data on a storage device of a storage module, deduplicating the user data on the storage device, performed by the storage module, compressing the user data on the storage device, performed by the storage module, transparently intercepting the user data by a data intercept module, rerouting the transparently intercepted data to a data communication optimization module for optimization and intelligent routing, optimizing communication by the data communication optimization module so that the intercepted user data is configured differently for data communication to a remote centralized datacenter or server, and transmitting the differently configured data to a centralized datacenter or server.

Abstract: A deep learning process receives desired process values associated with the one or more process stations. The deep learning processor receives desired target values for one or more key performance indicators of the manufacturing process. The deep learning processor simulates the manufacturing process to generate expected process values and expected target values for the one or more key performance indicators to optimize the one or more key performance indicators. The simulating includes generating a proposed state change of at least one processing parameter of the initial set of processing parameters. The deep learning processor determines that expected process values and the expected target values are within an acceptable limit of the desired process values and the desired target values. Based on the determining, the deep learning processes causes a change to the initial set of processing parameters based on the proposed state change.

Abstract: Disclosed are a power grid user classification method and device, and a computer-readable storage medium. The method includes: determining user power consumption data of each time period within a time interval, the user power consumption data of each time period including user power consumption data of each time granularity within the time period; generating a power consumption pattern image of a user within the time interval based upon the user power consumption data of each time period within the time interval; and classifying the user based upon an image recognition result of the power consumption pattern image. A user is classified by performing image recognition on a power consumption pattern image, and file information does not need to be manually inputted, thereby reducing a manual workload. In addition, a completion defect caused by incomplete file information is avoided, thereby improving the accuracy of classification.

Abstract: A method, system and apparatus are provided for optimized load shaping for optimizing production and consumption of energy. Information signals indicative of a first load shape signal are obtained corresponding to a total load, a renewable energy load of one or more renewable energy sources and a non-renewable energy load of one or more non-renewable energy sources. The first load shape signal corresponding to renewable energy load is removed from a non-renewable energy load to obtain a resulting load shape signal. The resulting load shape is flattened signal by apportioning the resulting load shape signal across time intervals to obtain a flattened load shape signal. At least a portion of the first component corresponding to the renewable energy load is added to the flattened load shape signal to create an optimized load shape signal. The optimized load shape signal is provided to modulate electric loads of energy-consuming devices.

Abstract: A method performed in which a forecast, a generation information, and a regulation information are obtained. A control information is determined in the event that the regulation information is indicative of over- or underproduction. The control information is indicative of a control of the amount of electrical energy generated by power generating plants. The control information is determined based on the obtained regulation and generation information. Based on the time at which the regulation information is obtained, the remaining time of a predetermined interval is determined. Operating points for the plants are determined such that, for the remaining time, the amount of energy generated by the plants for the predetermined interval saves or generates at least one determined difference between the amount of energy generated according to the forecast and generation information. The operating points are based on the control information. The determined control information is output.

Abstract: A refuse vehicle includes a lift assembly configured to engage and lift a refuse receptacle, an electrical energy system configured to store power and supply power to the refuse vehicle, and a controller configured to determine an amount of power required to move the refuse vehicle to a charging location, measure one or more electrical attributes of the refuse vehicle to determine an amount of length of time the refuse vehicle can operate based on a remaining power of the electrical energy system, and limit operations of one or more components of the refuse vehicle based on a comparison of the amount of power required to move the refuse vehicle to the charging location and the amount of length of time the refuse vehicle can operate.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a thermostat-based demand response event. In one aspect, a method includes accessing, for sites, historical readings of HVAC activity, indoor temperature, and outdoor temperature and building a model for each of the sites using the historical readings of HVAC activity, indoor temperature, and outdoor temperature. The method also includes using a simulation engine to achieve a target load shed and load reduction shape for a thermostat-based demand response event, and performing the thermostat-based demand response event based on results of the simulation engine.

Abstract: Methods, systems, and apparatus, for handling applications in an ambient computing system. One of the methods includes determining, by a low-power processing component, that particular sensor signals have a particular property. In response, a machine learning engine performs an inference pass over a machine learning model using the sensor signals to generate a model output. If the model output of the machine learning engine matches an application-specific condition, one or more of the other processing components are activated to execute an particular application corresponding to the application-specific condition.

Abstract: A building energy system includes HVAC equipment, green energy generation, a battery, and a predictive controller. The HVAC equipment provide heating or cooling for a building. The green energy generation collect green energy from a green energy source. The battery stores electric energy including at least a portion of the green energy provided by the green energy generation and grid energy purchased from an energy grid and discharges the stored electric energy for use in powering the HVAC equipment. The predictive controller generates a constraint that defines a total energy consumption of the HVAC equipment at each time step of an optimization period as a summation of multiple source-specific energy components and optimizes the predictive cost function subject to the constraint to determine values for each of the source-specific energy components at each time step of the optimization period.

Abstract: An example system includes an aggregator configured to receive a service collaboration request and iteratively determine, based on minimum and maximum power values for DERs under its management, an optimized operation schedule. The aggregator may also be configured to iteratively determine, based on the optimized operation schedule, an estimated flexibility range for devices under its management and output an indication thereof. The system may also include a power management unit (PMU) configured to iteratively receive the indication and determine, based on a network model that includes the estimated flexibility range, a reconfiguration plan and an overall optimized operation schedule for the network. The PMU may also be configured to iteratively cause reconfiguration of the network based on the plan. The PMU and aggregator may also be configured to iteratively, at a fast timescale, cause energy resources under their management to modify operation based on the overall optimized operation schedule.

Abstract: The present disclosure provides systems and methods for an operation of an electric power plant comprising a renewable energy resource and an energy storage device. The method may comprise determining, at a first time, a forecast of predicted energy production by the electric power plant over a time period subsequent to the first time based on a forecast for the time period; detecting a current state of charge of the energy storage device; calculating a range of automatic generation controls the electric power plant is capable of satisfying for the time period based on the forecast of predicted energy production and the detected current state of charge of the energy storage device; and signaling, from the electric power plant to a central utility controlling a power grid, the range of automatic generation controls the electric power plant is capable of satisfying for the time period.

Abstract: A system for machine-learning for prediction grid carbon emissions includes a computing device configured to receive, from a first local grid monitoring device monitoring a first local grid, a plurality of first power output quantities of a plurality of power generators in the first local grid, train an emission projection machine-learning process, wherein training further includes compiling a plurality of training data entries, each training data entry correlating a plurality of past power output quantities with at least a reported carbon emission datum and training the emission projection machine-learning process using the plurality of training data entries, and generate, using the emission projection machine-learning process, a projected carbon emission rate as a function of the plurality of power output quantities.

Abstract: A method and a system for dispatching an independent energy storage power station is provided.

Abstract: Various techniques and circuit implementations for power reduction management in integrated circuits are disclosed. Certain techniques include the implementation of rate control circuits to control a clock rate for circuits associated with a communication fabric in an integrated circuit. The clock rate may be reduced based trigger signals received from power delivery trigger circuits coupled to the integrated circuit and voltage regulators providing power to the integrated circuit. Additional techniques may include the use of rate limiter circuits in a memory pipeline.

Abstract: Methods and systems for optimizing performance of a cyber-physical system include training a machine learning model, according to sensor data from the cyber-physical system, to generate one or more parameters for controllable sensors in the cyber-physical system that optimize a performance indicator. New sensor data is collected from the cyber-physical system. One or more parameters for the controllable sensors are generated using the trained machine learning module and the new sensor data. The one or more parameters are applied to the controllable sensors to optimize the performance of the cyber-physical system.

Abstract: A bed system includes microclimate control capabilities for providing quality sleep experience. The bed system can include a microclimate control subsystem configured to supply conditioned air (e.g., heated or cooled air) to a mattress, or draw ambient air from the mattress, to achieve a desired temperature at the top of the mattress. Utilizing supply of conditioned air to provide air at desired temperature to the mattress system, or utilizing air suction to drain heat away from the mattress system, can provide precise microclimate control at the mattress, thereby permitting conformable sleep.

Abstract: A model is generating for predicting energy workloads to adjust electrical energy supply to meet varying short-term energy demands at a microcosm level. A model is developed, using a computer, to facilitate predicting energy workloads for adjusting energy supplies to meet an energy demand. The model includes receiving, at the computer, input parameters of dynamic values of workloads as historical data, and generating a predictive model by analyzing the input parameters. The model further includes predicting short-term energy demands based on the predictive model, the predicted short-term energy demands include identifying a predicted peak value. Also, the model includes initiating short term energy output in an electrical grid to a transformer level component in the electrical grid based on the predicted short term energy demands.

Abstract: Various embodiments include a method for controlling an exchange of energy in an energy system having multiple energy subsystems each connected to one another for the purpose of exchanging energy, the method comprising: receiving respective supply data at a control center from the energy subsystems, wherein the supply data comprise respective remuneration conditions of the applicable energy subsystem for receiving and/or providing energy; determining optimum conditions on the basis of the supply data of the energy subsystems; and controlling the exchange of energy between the energy subsystems on the basis of the optimum conditions with the control center.

Abstract: A system for integrating electric loads into the utility electric grid in a cost-effective, emissions-minimizing way. The system generates and implements a computer-readable series of instructions for one or more of starting and stopping electrical device charging or operation powering of an electrical device's duty cycle.

Abstract: In an embodiment of the present disclosure, a prior probability is used by an approach of Bayesian statistics, for all nodes stored in fault handling history data or for a target node for which a fault cause has been identified and all nodes of the same type as the target node, restoration probabilities of handling methods for an identical fault cause are calculated, priorities are set for the handling methods based on the calculated restoration probabilities, and a handling method to be presented is determined in accordance with the priorities.

Abstract: An energy storage system for a building includes a battery asset configured to store electricity and discharge the stored electricity for use in satisfying a building electric load. The system includes a planning tool configured to identify one or more selected functionalities of the energy storage system and generate a cost function defining a cost of operating the energy storage system over an optimization period. The cost function includes a term for each of the selected functionalities. The planning tool is configured to generate optimization constraints based on the selected functionalities, attributes of the battery asset, and the electric energy load to be satisfied. The planning tool is configured to optimize the cost function to determine optimal power setpoints for the battery asset at each of a plurality of time steps of the optimization period.

Abstract: A method for operating one or more processors to control access of a plurality of pieces of rechargeable electrical equipment, optionally including one or more electric vehicles, to one or more charging resources. An access queue is maintained for each of the charging resources. Each piece of equipment is assigned a position in an access queue when it is determined to have a state of charge (SOC) less than the first level. Each access queue is updated based on charger information such as its availability. A piece of equipment is assigned access to a charging resource when its queue position reaches a first priority position and a charging resource is available. For each piece of equipment assigned access to a charging resource, an instruction to end access to the charging resource is issued when an elapsed charging time period is equal to or greater than a break time or the SOC is greater than a second level representative of an operational charge.

Abstract: A hybrid machine-learning and simulation-based system provides forecasting for an energy system. The system predicts day-ahead and real-time supply and demand, and prices of energy, and generates inputs to an optimization algorithm performed by an Independent System Operator (ISO) that affects behavior of electricity generators and electricity consumers to improve the economic efficiency of electricity grids, and reduce harmful emissions.

Abstract: The present disclosure provides systems and methods for managing a temperature of a battery energy storage system (“BESS”). A method may comprise identifying operating temperature limitations of the BESS; obtaining a forecast horizon comprising a forecast of external environmental conditions for a time period; identifying a charging/discharging schedule of the BESS; simulating operation of the BESS for the time period for each of a plurality of sequences of thermal management modes according to the charging/discharging schedule and the forecast horizon, the simulating generating an energy consumption and an operating temperature forecast of for each of the plurality of sequences of thermal management modes; selecting a sequence of thermal management modes of the plurality of sequences; and operating the equipment according to the selected sequence of thermal management modes.

Abstract: IR drop predictions are obtained using a maximum convolutional neural network. A circuit structure is partitioned into a grid. For cells of the circuit structure in sub-intervals of a clock period, power consumption of the cell is amortized into a set of grid tiles that include portions of the cell, thus forming a set of power maps. The power maps are applied to a neural network to generate IR drop predictions for the circuit structure.

Abstract: Techniques are presented for scheduling the charging of electric vehicles (EVs) that protect the resources of local low voltage distribution networks. From utilities, data on local low voltage distribution networks, such as the rating of a distribution transformer through which a group of EVs are supplied, is provided to a load manager application. Telematics information on vehicle usage is provided from the EVs, such as by way of the original equipment manufacturer. From these data, the load manager application determines schedules for charging the group of EVs through a shared low voltage distribution network so that the capabilities of the local low voltage distribution network are not exceeded while meeting the needs of the EV user. Charging schedules are then transmitted to the on-board control systems of the EVs for implementation.

Abstract: A method for operating an electric island power network having a renewable energy generation plant, a conventional energy generation plant, an energy store, and an energy consumer, includes: defining first operating parameters for the network for when a frequency and/or voltage of the network is outside defined limits; operating the network using the first operating parameters causing the frequency and voltage of the network to both be within the defined limits; defining second operating parameters for the network after the expiration of a defined time span over which the frequency and voltage have remained within the defined limits, the second operating parameters being defined such that operating the network using the second operating parameters causes the network to operate cost-optimally. If verified that the second operating parameters ensure that the frequency and voltage remain within the defined limits, operation using the second operating parameters is maintained. Otherwise, it is discontinued.

Abstract: According to an embodiment, disclosed are a DC-DC converter for compensating for a ripple, in a solar linked energy storage system, and a control method thereof. In particular, disclosed is a DC-DC converter for compensating for a ripple generated in a DC link where a single phase inverter and a converter are connected. The DC-DC converter may obtain a frequency of a grid to compensate for the ripple.

Abstract: Physical and/or financial instruments may optimally hedge the cash flow of one or more renewable energy generators based on a desired risk and return profile of renewable infrastructure investors. Baseline revenues may be determined based on forward-looking electricity market price scenarios corresponding to qualified market products intended for sale from the renewable energy generators. Risk and return metrics of cash flows of the renewable energy generators may be determined. At least one physical hedge and/or financial hedge may be added. The size and operation of the renewable energy generators along with any physical hedges, or financial hedges, or both physical and financial hedges, may be optimized across multiple market price scenarios of qualified market products to optimize investor-tailored risk and return utility functions.

Abstract: A method for designing a battery storage at a connection point of a load to an energy supply network. In the method, at least two different load types are determined. A load profile of the load before a connection and an operation of the battery storage is recorded. The load profile is evaluated and the load is assigned to one of the load types depending on the evaluation. The battery storage is designed so that the load is assigned to a different load type after the battery storage has been connected and operated.

Abstract: A method of managing energy by use of processing logic that comprises a load processor as a cloud service is provided. The method includes receiving power load information from a data collection system located at a building and using a cloud analysis layer that employs machine-learning and artificial intelligence for optimization control, analyzing the received power load information to disaggregate load waveform signals and identify device-based power loads by use of a neural network to perform historical device demand and performance analysis to generate device-based demand forecasting, generating demand forecasts for the building to mitigate peak demand based on analysis of a power draw signal and the generated device-based demand forecasting, and determining whether the generated demand forecast for the building is to peak in a near future, based on threshold values of at least one of generated device-based demand forecasting, power price or cost information, and user behavior analysis.

Abstract: A method for controlling production of one or more refined resources by an energy provider includes predicting a demand for the refined resources by one or more consumers of the refined resources as a function of an incentive offered by the energy provider. The method further includes performing an optimization of an objective function subject to a constraint based on the predicted demand for the refined resources to determine an amount of the refined resources for the energy provider to produce and a value of the incentive at multiple times within a time period. The method also includes providing setpoints for equipment of the energy provider that cause the equipment to produce the amount of the refined resources determined by performing the optimization.

Abstract: An approach and system incorporating obtaining a semantic model for a device, identifying properties from the semantic model, processing the properties, obtaining a property role, identifying a property role filter that correlates to the property role, and generating parameter values associated with the property role filter.

Abstract: Systems, methods, and devices may enable management of a renewable power asset. A control device may generate a Day-Ahead (DA) pricing model, a Real-Time (RT) pricing model and a renewable generation model for the renewable power asset. Optimal DA commitments may be determined, and an optimal RT schedule estimated. A DA power delivery strategy and an RT power delivery strategy may be determined. The determined DA and RT power delivery strategies may be evaluated based on obtained real power prices. The DA and RT power delivery strategies may be redetermined, and the renewable power asset may be controlled to deliver power the DA and RT power delivery strategies. The value of the renewable power asset may be maximized while bounding financial risks and returns associated with scheduling the renewable power asset as tailored to risk preferences of the renewable power asset owner or operator.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing thermal modeling. In one aspect, a method includes receiving monitoring data comprising temperature data measured inside a site, mode data, and state data, receiving weather data descriptive of weather at the site, and aligning the received temperature data, mode data, and state data with the received weather data. The method also includes determining an internal heat gain representing an amount of heat generated at the site irrespective of the heating or cooling system, determining at least one of a thermal product for the site or a thermal potential for the heating or cooling system, generating, based on the internal gain and the thermal product or the thermal potential, a thermal model for the site, and providing, as output, the generated thermal model.

Abstract: An autonomous electric vehicle comprises a battery electrically connected to the electric motor for powering the electric motor and a battery status prediction module to predict, based on event data from a mobile device of a user of the vehicle, whether the vehicle will be parked at a destination and a time period when the vehicle will be parked. The battery status prediction module predicts a predicted battery status at the end of the time period based on a temperature profile for the time period obtained from a remote weather server. The battery status prediction module determines if the predicted battery status at the end of the time period will have at least a minimum battery capacity to travel a distance to a charging station determined by the battery status prediction module from the destination and a location of the charging station.

Abstract: The present disclosure provides systems and methods for an operation of an electric power plant comprising a renewable energy resource and an energy storage device. The method may comprise determining, at a first time, a forecast of predicted energy production by the electric power plant over a time period subsequent to the first time based on a forecast for the time period; detecting a current state of charge of the energy storage device; calculating a range of automatic generation controls the electric power plant is capable of satisfying for the time period based on the forecast of predicted energy production and the detected current state of charge of the energy storage device; and signaling, from the electric power plant to a central utility controlling a power grid, the range of automatic generation controls the electric power plant is capable of satisfying for the time period.

Abstract: A content consumption monitor (CCM) generates intent data that identifies topics of real-time interest to users. The CCM uses the intent data to direct information to more interested audiences and reduce the information noise/overload that may prevent users from viewing information. This targeted information may increase user conversion rates for seminars, advertising, documents downloads, or any other activity associated with published information. In one example, the CCM generates the intent data from third party content. Using a wide variety of third party content enables the CCM to better identify current user interests. The CCM may aggregate the intent data for different demographics, such as for companies, job positions, age, gender, or geographic locations.

Abstract: A rated power supply system powered by PoE receives power from external power sources and supplies power to PDs. The system connects with PDs through PoE output interfaces. The system has a PoE analog controller to turn on/off of the output power of all the PoE output interfaces. The PoE analog controller also detects the output current of all the PoE output interfaces. The system has a packet switch controller, a power state detecting circuit and a voltage conversion circuit. The voltage conversion circuit merges the power received from external power sources to generate a first voltage. The CPU calculates an output current upper limit based on the first voltage. The CPU gets a total output current from the PoE analog controller. If the total output current exceeds the output current upper limit, the CPU will turn off the PoE output interfaces according to power output priorities of the PoE output interfaces.

Abstract: A storage device includes a non-volatile memory including a plurality of non-volatile memory cells, a buffer memory configured to temporarily store write data to be written to the non-volatile memory or read data read from the non-volatile memory, and a controller configured to receive a sleep mode signal from an external host. When the sleep mode signal is received by the controller, the controller is configured to block a first power supplied to the non-volatile memory and set the buffer memory to one of a first mode in which a second power is blocked from being supplied to the buffer memory and a second mode in which the buffer memory operates with low power. The write data stored in the buffer memory is written to the non-volatile memory when the buffer memory is set to the first mode.

Abstract: The present is directed to systems, methods, and devices for Zero Touch Provisioning (“ZTP”). The system can include a data center computing network, and at least one server coupled with the data center computing network. The server can receive a trigger signal from a triggering device coupled to a data center hardware network, transmit an executable script to the triggering device, which executable script instructs the triggering device to generate a definition file including a unique identifier of the triggering device, receive the definition file from the triggering device, identify a configuration file associated with the triggering device based at least in part on the unique identifier, and provision the triggering device by delivering the configuration file to the triggering device.

Abstract: Data samples are selected for power consumption management. This includes receiving samples associated with respective times, distributed in a sliding time window as current samples and in a past period as past samples. Selected past samples are determined by keeping a first share of the past samples, including most recent ones, and a second share through eliminating among the past samples deprived from the first share, called a complementary share, part of the past samples in function of at least some of the current samples and of elimination conditions depending on similarity criteria applied to at least the first and complementary shares. The selected past samples are provided with the current samples for power consumption management. Applications to power failure detection and power consumption dynamic adaptation.

Abstract: A method and system are provided to calculate a future behavioral data and identify a relative causal impact of external factors affecting the data. Behavioral data and data for one or more external factors are harvested for a first time period. New behavioral data is harvested for a second time period. New data for the second time period is harvested. Based on a second training algorithm, a forecast time series value of a future behavioral data for a third time period that is after the second time period is calculated. A relative causal impact between each external factor and the predicted time series value of the behavioral data, for the third time period, is identified.

Abstract: A distributed energy resource (DER) device is coupled to a utility meter in a “behind-the-meter” configuration. The utility meter analyzes a commitment generated by the DER device to determine a specific operation performed by the DER device at a particular time. The utility meter analyzes metrology data to identify an “event” associated with the particular time and then attempts to map the identified event back to the DER device based on a library of events associated with different DER devices. The utility meter also attempts to map the identified event to the specific operation set forth in the commitment. If the utility meter can successfully map the identified event to both the DER device and to the specific operation set forth in the commitment, then the utility meter generates a validated commitment. The validated commitment can be used to facilitate an energy market settlement process.

Abstract: The present invention belongs to the field of information technology, involving the techniques of fuzzy modeling, reinforcement learning, parallel computing, etc. It is a method combining granular computing and reinforcement learning for construction of long-term prediction interval and determination of its structure. Adopting real industrial data, the present invention constructs multi-layer structure for assigning information granularity in unequal length and establishes corresponding optimization model at first. Then considering the importance of the structure on prediction accuracy, Monte-Carlo method is deployed to learn the structural parameters. Based on the optimal multi-layer granular computing structure along with implementing parallel computing strategy, the long-term prediction intervals of gaseous generation and consumption are finally obtained. The proposed method exhibits superiority on accuracy and computing efficiency which satisfies the demand of real-world application.

Abstract: Systems and methods for identifying optimal siting locations for an energy storage system. Siting locations are identified based on a value index derived from pricing data associated with a plurality of nodes on an electrical grid. An index is derived for each selected node of the plurality of nodes to produce a siting recommendation.