Roaming credit system for efficient compliance renewable energy powered charging stations

Abstract

Embodiments include an electric vehicle charging station having a bi-directional charger electrically coupled to an electrical grid and one or more renewable energy sources, an energy storage device electrically connected to the bi-directional charger, and a processing system configured to control operation of the bi-directional charger. The processing system is configured to monitor a state of charge of the energy storage device, calculate an estimated power demand for the electric vehicle charging station over a period of time, calculate an estimated power generation of the one or more renewable energy sources over the period of time, and responsively control the bi-directional charger.

Description

Technical Field

[0001] The present disclosure relates to charging stations. More specifically, the present disclosure relates to charging stations that are at least partially powered by renewable energy sources (wind energy, solar energy, and battery energy storage systems (BESS)). Background Art

[0002] As the electric vehicle (EV) infrastructure continues to develop, particularly in regions of North America (the United States and Canada), it has become increasingly important to effectively utilize renewable energy sources (RES) in EV charging stations. Renewable energy sources improve the eco-friendliness of charging stations and also reduce the dependence on grid power generation. The strategic implementation and management of renewable energy sources have become increasingly important because the usage of charging stations and the integration of renewable energy vary significantly from station to station.

[0003] Typically, a charging station is part of a charging station network that includes multiple charging stations operated by a single entity. In a charging station network, some charging stations face limited usage (low utilization) despite having ample opportunities to utilize renewable energy for power generation. In contrast, other charging stations with high utilization may lack significant renewable energy production. Summary of the Invention

[0004] In one exemplary embodiment, an electric vehicle charging station is provided. The electric vehicle charging station includes a bidirectional charger electrically coupled to a power grid and one or more renewable energy sources, an energy storage device electrically connected to the bidirectional charger, and a processing system configured to control the operation of the bidirectional charger. The processing system is configured to monitor the charge state of the energy storage device, calculate an estimated power demand for the electric vehicle charging station over a period of time, and calculate an estimated power generation of one or more renewable energy sources during that period. Based on determining that the estimated power generation of one or more renewable energy sources during that period is greater than the estimated power demand during that period and the charge state of the energy storage device is less than the maximum charge state, the processing system is configured to instruct the bidirectional charger to charge the energy storage device. Based on determining that the estimated power generation of one or more renewable energy sources during that period is greater than the estimated power demand during that period and the charge state of the energy storage device is equal to the maximum charge state, the processing system is configured to instruct the bidirectional charger to transmit the power generated by one or more renewable energy sources to the power grid.

[0005] In addition to one or more features described herein, the processing system is further configured to update a renewable energy credit balance based on the amount of power delivered to the power grid generated by one or more renewable energy sources.

[0006] In addition to one or more features described herein, the processing system is further configured to: based on determining that the estimated power generation of one or more renewable energy sources during the time period is less than the estimated power demand during the time period and the charge state of the energy storage device is greater than the minimum charge state, instruct the bi-directional charger to discharge the energy storage device to meet the estimated power demand.

[0007] In addition to one or more features described herein, the processing system is further configured to: based on determining that the estimated power generation of one or more renewable energy sources during the time period is less than the estimated power demand during the time period and the charge state of the energy storage device is equal to the minimum charge state, instruct the bi-directional charger to obtain power from the power grid to meet the estimated power demand.

[0008] In addition to one or more features described herein, the processing system is further configured to update the renewable energy credit balance based on the amount of power obtained from the power grid.

[0009] In addition to one or more features described herein, the processing system is further configured to monitor and record the percentage of power provided to one or more vehicles by the electric vehicle charging station from one or more renewable energy sources during the time period.

[0010] In addition to one or more features described herein, based on an analysis of the historical power demand of the electric vehicle charging station, calculate the estimated power demand for the electric vehicle charging station over a time period.

[0011] In addition to one or more features described herein, based on an analysis of the historical power generation of one or more renewable energy sources, the corresponding historical weather conditions, and the weather forecast for the time period, calculate the estimated power generation of one or more renewable energy sources during the time period.

[0012] In addition to one or more features described herein, the electric vehicle charging station is the first electric vehicle charging station among a plurality of electric vehicle charging stations in a charging station network.

[0013] In an exemplary embodiment, an electric vehicle charging network is provided. The electric vehicle charging network includes a charging station network management system and a plurality of electric vehicle charging stations in communication with the charging station network management system. Each of the plurality of electric vehicle charging stations includes a bi-directional charger electrically coupled to a power grid and one or more renewable energy sources, an energy storage device electrically connected to the bi-directional charger, and a processing system configured to control the operation of the bi-directional charger. The processing system is configured to monitor the charge state of the energy storage device, calculate an estimated power demand for the electric vehicle charging station over a period of time, and calculate an estimated power generation of one or more renewable energy sources during that period. Based on determining that the estimated power generation of one or more renewable energy sources during the period is greater than the estimated power demand during the period and the charge state of the energy storage device is less than the maximum charge state, the processing system is configured to instruct the bi-directional charger to charge the energy storage device. Based on determining that the estimated power generation of one or more renewable energy sources during the period is greater than the estimated power demand during the period and the charge state of the energy storage device is equal to the maximum charge state, the processing system is configured to instruct the bi-directional charger to transmit the power generated by one or more renewable energy sources to the power grid.

[0014] In addition to one or more features described herein, the processing system is further configured to transmit a renewable energy credit to the charging station network management system, wherein the value of the renewable energy credit is based on the amount of electricity delivered to the power grid generated by one or more renewable energy sources.

[0015] In addition to one or more features described herein, the processing system is further configured to: based on determining that the estimated power generation of one or more renewable energy sources during the period is less than the estimated power demand during the period and the charge state of the energy storage device is greater than the minimum charge state, instruct the bi-directional charger to discharge the energy storage device to meet the estimated power demand.

[0016] In addition to one or more features described herein, the processing system is further configured to: based on determining that the estimated power generation of one or more renewable energy sources during the period is less than the estimated power demand during the period and the charge state of the energy storage device is equal to the minimum charge state, instruct the bi-directional charger to obtain power from the power grid to meet the estimated power demand.

[0017] In addition to one or more features described herein, the processing system is further configured to obtain a renewable energy credit from the charging station network management system, wherein the value of the renewable energy credit is based on the amount of electricity obtained from the power grid.

[0018] In addition to one or more features described herein, a processing system of an electric vehicle charging station is configured to monitor and record the percentage of power obtained from one or more renewable energy sources that is provided by the electric vehicle charging station to one or more vehicles during the time period.

[0019] In addition to one or more features described herein, a charging station network management system is configured to monitor and record the percentage of power obtained from one or more renewable energy sources that is provided by a plurality of electric vehicle charging stations to one or more vehicles during the time period.

[0020] In addition to one or more features described herein, an estimated power demand for an electric vehicle charging station is calculated based on an analysis of historical power demands of the electric vehicle charging station over a time period.

[0021] In addition to one or more features described herein, an estimated power generation of one or more renewable energy sources during the time period is calculated based on an analysis of historical power generation of the one or more renewable energy sources, corresponding historical weather conditions, and a weather forecast for the time period.

[0022] In addition to one or more features described herein, a charging station network management system is configured to control one or more operating characteristics of a plurality of electric vehicle charging stations based on a contextual multi-armed bandit analysis of shared context data of the plurality of electric vehicle charging stations, where the shared context data includes power generation forecasts and power demand forecasts.

[0023] In an exemplary embodiment, a method for operating a charging station is provided. The method includes monitoring a charge state of an energy storage device of the charging station, calculating an estimated power demand for the charging station over a time period, and calculating an estimated power generation of one or more renewable energy sources during the time period. Based on determining that the estimated power generation of the one or more renewable energy sources during the time period is greater than the estimated power demand during the time period and the charge state of the energy storage device is less than a maximum charge state, the method includes charging the energy storage device with power generated by the one or more renewable energy sources. Based on determining that the estimated power generation of the one or more renewable energy sources during the time period is greater than the estimated power demand during the time period and the charge state of the energy storage device is equal to the maximum charge state, the method includes transmitting power generated by the one or more renewable energy sources to the power grid. Based on determining that the estimated power generation of the one or more renewable energy sources during the time period is less than the estimated power demand during the time period and the charge state of the energy storage device is equal to a minimum charge state, the method includes obtaining power from the power grid to meet the estimated power demand.

[0024] When taken in conjunction with the accompanying drawings, the above and other features and advantages of the present disclosure will become apparent from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Other features, advantages, and details appear only by way of example in the following detailed description, which refers to the accompanying drawings, in which:

[0026] Figure 1 is a block diagram of a system including a charging station powered by renewable energy according to an exemplary embodiment;

[0027] Figure 2 is a flowchart showing a method for operating a charging station powered by renewable energy according to an exemplary embodiment;

[0028] Figure 3 is a flowchart showing another method for operating a charging station powered by renewable energy according to an exemplary embodiment;

[0029] Figure 4 is a flowchart showing yet another method for operating a charging station powered by renewable energy according to an exemplary embodiment;

[0030] Figure 5 is a flowchart showing a contextual multi-armed bandit method for determining the optimal operating characteristics of a charging station operated by a charging station operator; and

[0031] Figure 6 is a block diagram of a system including a charging station powered by renewable energy according to an exemplary embodiment. DETAILED DESCRIPTION

[0032] The following description is merely exemplary in nature and is not intended to limit the present disclosure, its application, or its uses. Various embodiments of the present disclosure are described herein with reference to the related drawings. Alternative embodiments of the present disclosure may be designed without departing from the scope of the claims. Various connections and positional relationships (such as above, below, adjacent, etc.) are set forth between the elements in the following description and the drawings. Unless otherwise stated, these connections and / or positional relationships may be direct or indirect, and the present disclosure is not intended to be limited in this regard. Thus, the coupling of entities may refer to direct or indirect coupling, and the positional relationship between entities may be direct or indirect positional relationship.

[0033] As described herein, a charging station configured to receive power from renewable energy and the power grid is provided. In an exemplary embodiment, the charging station includes a bidirectional charger electrically coupled to the power grid and one or more renewable energy sources. The one or more renewable energy sources may include solar energy, wind energy, etc. The charging station further includes an energy storage device, such as a battery pack, electrically connected to the bidirectional charger. The charging station further includes a processing system configured to control the operation of the bidirectional charger.

[0034] In an exemplary embodiment, a processing system is configured to monitor a state of charge of an energy storage device, calculate an estimated power demand for a charging station over a period of time, and calculate an estimated power generation of one or more renewable energy sources during the period. Based on the state of charge of the energy storage device, the estimated power demand for the charging station, and the estimated power generation of one or more renewable energy sources during the period, the processing system responsively instructs a bi-directional charger to charge or discharge the energy storage device to maximize utilization of the energy generated by one or more renewable energy sources while meeting the power demand for the charging station.

[0035] Referring now to Figure 1 , a block diagram of a system 100 including a charging station 110 powered by a renewable energy source 106 according to an exemplary embodiment is shown. The charging station 110 includes an energy storage device 112, a processing system 114, and a bi-directional charger 116. In an exemplary embodiment, the processing system 114 includes one or more processors and a memory that includes computer program instructions configured to control the operation of the charging station 110. In an exemplary embodiment, the charging station 110 is configured to charge an energy storage device 122 of an electric vehicle 120, the energy storage device 122 being removably coupled to the charging station 110.

[0036] In an exemplary embodiment, the charging station 110 is electrically connected to the power grid 102 via a transmission network 104. The power grid 102 is operated by a processing system 109 of one or more power market operators 108. In an exemplary embodiment, the processing system 109 of one or more power market operators 108 and the processing system 114 of the charging station 110 communicate with a communication network 130 (such as the Internet). In an exemplary embodiment, the processing system 109 includes one or more processors and a memory that includes computer program instructions configured to control the operation of the power grid 102 under the control of the power market operator 108. The system 100 may also include one or more of a processing system 135 of a regulatory or state agency 134, a weather forecasting system 132, and a processing system 136 of a charging station network 140, the charging station network 140 being communicable with the communication network 130.

[0037] In an exemplary embodiment, the charging station 110 is part of a charging station network 140 that includes a plurality of charging stations 110 that are co-owned and operated. In one embodiment, the processing system of each of the plurality of charging stations 110 is configured to communicate with a processing system 136 of the charging station network 140, the processing system 136 coordinating the control and operation of each of the plurality of charging stations 110. In an exemplary embodiment, the processing system 136 includes one or more processors and a memory that includes computer program instructions configured to control the operation of the charging station network 140.

[0038] In an exemplary embodiment, the processing system 114 is configured to monitor the charge state of the energy storage device 112, calculate the estimated power demand for the charging station 110 over a period of time, and calculate the estimated power generation of one or more renewable energy sources 106 during that period.

[0039] In one embodiment, the estimated power demand for an electric vehicle charging station over a period of time is calculated based on an analysis of the historical power demand of the electric vehicle charging station. For example, the estimated power demand of the charging station can be estimated by the processing system 114 based on historical power consumption. The processing system 114 can collect historical power consumption data from the charging station. This data should include timestamps, power consumption values, and any relevant contextual information (such as time of day, day of the week, weather conditions). Next, the processing system 114 can clean and preprocess the collected data to handle missing values, outliers, and inconsistencies. The preprocessing can also include converting the timestamps to a consistent format and extracting relevant features, such as time of day, day of the week, and holidays. The processing system 114 can create additional features that may affect power demand, such as special events, promotions, or any external factors that influence usage patterns. Once the data has been preprocessed, the processing system 114 divides the dataset into a training set and a test set. The training set is used to train a prediction model, while the test set is used to evaluate its performance. In an exemplary embodiment, various prediction models can be evaluated, such as autoregressive integrated moving average (ARIMA), long short-term memory (LSTM) neural networks, gradient boosting trees (such as XGBoost), and support vector machines (SVM). Once the prediction model has been selected and trained, the trained model is deployed to generate real-time power demand predictions based on the latest data.

[0040] In one embodiment, the estimated power generation of one or more renewable energy sources during the period is calculated based on an analysis of the historical power generation of the one or more renewable energy sources, the corresponding historical weather conditions, and the weather forecast for the period. In an exemplary embodiment, the processing system 114 can collect historical power generation data and historical weather data from the charging station. This data should include timestamps, power consumption values, and any relevant contextual information (such as time of day, day of the week). Next, the processing system 114 can clean and preprocess the collected data to handle missing values, outliers, and inconsistencies. Once the data has been preprocessed, the processing system 114 divides the dataset into a training set and a test set. The training set is used to train a prediction model, while the test set is used to evaluate its performance. In an exemplary embodiment, various prediction models can be evaluated, such as autoregressive integrated moving average (ARIMA), long short-term memory (LSTM) neural networks, gradient boosting trees (such as XGBoost), and support vector machines (SVM). Once the prediction model has been selected and trained, the trained model is deployed to generate power generation predictions for one or more renewable energy sources based on the latest data.

[0041] In an exemplary embodiment, the charging station network 140 includes charging stations disposed in a geographical area that includes a plurality of electricity market operators 108 that supply power to the power grid 102. In an exemplary embodiment, a charging station operator manages a plurality of charging stations 110 that may be dispersed throughout a geographical area in districts managed by different electricity market operators 108.

[0042] In an exemplary embodiment, the electricity market operator 108 is configured to communicate with one or more regulatory authorities that regulate the operation of the power grid 102. In one embodiment, the regulatory authority 134 may require a charging station operator within a geographical area to obtain a minimum percentage of the power sold from renewable energy for its charging stations. In one embodiment, the processing system 109 of the electricity market operator 108 monitors the compliance of the charging station operator with the regulations provided by the regulatory authority 134.

[0043] In an exemplary embodiment, a credit system is used to address the imbalance between the renewable energy production and the electricity demand of different charging stations 110 of the charging station network 140 served by different electricity market operators 108. By promoting more efficient energy use and enabling the exchange of renewable energy credits (RECs) between the charging stations 110 within the charging station network 140, the utilization rate of renewable resources and the compliance with the current Renewable Portfolio Standard (RPS) can be improved.

[0044] Now referring to Figure 2 , a flowchart illustrating a method 200 for operating a charging station powered by renewable energy according to an exemplary embodiment is shown. In an exemplary embodiment, the method 200 is executed by the processing system 114 of the charging station 110, as Figure 1 shown. At block 202, the method 200 includes monitoring the state of charge (SOC) of the energy storage device of the charging station. Next, at block 204, the method 200 includes calculating the estimated power demand (P D ) for the electric vehicle charging station over a period of time. At block 206, the method 200 includes calculating the estimated power generation (P G ) of one or more renewable energy sources during the period.

[0045] At decision block 208, the method 200 includes determining whether P G is greater than P D . If P G is greater than P D , the method 200 proceeds to decision block 210 and determines whether the SOC of the energy storage device is less than the maximum SOC. Based on determining that P G is not greater than P D, Method 200 proceeds to decision block 218 and determines whether the state of charge (SOC) of the energy storage device is greater than the minimum SOC. Based on determining that the SOC of the energy storage device is less than the maximum SOC, Method 200 proceeds to block 216, and the energy storage device is charged using one or more renewable energy sources. Based on determining that the SOC of the energy storage device is not less than the maximum SOC, Method 200 proceeds to block 212, and the electricity generated by one or more renewable energy sources is transmitted to the power grid. At block 214, Method 200 includes increasing the renewable energy credit balance based on the amount of electricity transmitted to the power grid generated by one or more renewable energy sources.

[0046] Based on determining that the SOC of the energy storage device is greater than the minimum SOC, Method 200 proceeds to block 220, and the energy storage device discharges to meet the estimated power demand. Based on determining that the SOC of the energy storage device is not greater than the minimum SOC, Method 200 proceeds to block 222, and power is obtained from the power grid to meet the estimated power demand. The obtained power can be stored in the energy storage device and / or directly provided to an electric vehicle connected to the charging station. At block 224, Method 200 includes reducing the renewable energy credit balance based on the amount of electricity obtained from the power grid.

[0047] As used herein, is the total power output of renewable energy at each charging station of the charging station network at time t, is the power output of the wind turbine at time t, is the power output of the solar panel at t, where In an exemplary embodiment, the state of charge (SOC) of the battery of the charging station at time t can be expressed as where the SOC is subject to the following constraints:

[0048]

[0049]

[0050]

[0051]

[0052]

[0053] where, is the state of charge of the battery connected to the charging station at time t, and are the maximum and minimum states of charge of the battery connected to the charging station, and are the charging and discharging electricity received and given by the battery at time t, and are the minimum and maximum charge amounts received by the battery, and are the minimum and maximum discharge amounts given by the battery, η ch and η Dch are the charge and discharge efficiencies of the battery, B cap is the battery capacity, and Δt is the time slot or time period.

[0054] An additional constraint is where is the amount of power credit generated for exchange at time t, is the minimum credit that each charging station operator (CSO) can have, is the maximum credit that each CSO can have. In an exemplary embodiment, will include the following: trading time, power rate from the grid during the trade, trading location, identification of the grid operator or name of the utility company to which the power is purchased or sold.

[0055] Another constraint is where is the amount of charge that each CS needs to provide to the EV during each hour of operation, is the total amount of charge that each CS can provide to its customers. In an exemplary embodiment, is the amount of power transmitted (credited or sold) from the charging station to the grid at time t, while is the amount of power transmitted (credited or sold) from the grid to the charging station.

[0056] Now referring to Figure 3 , a flowchart illustrating another method 300 for operating a charging station powered by renewable energy according to an exemplary embodiment is shown. In an exemplary embodiment, method 300 is executed by the processing system 114 of the charging station 110, as Figure 1 shown. As shown in block 302, method 300 includes obtaining and Next, at block 304, the value for the time period t is set to 1. At decision block 306, method 300 includes determining whether Based on the determination method 300 proceeds to decision block 312, otherwise method 300 proceeds to decision block 308. At decision block 308, method 300 includes determining whether Based on the determination method 300 proceeds to block 316, otherwise method 300 proceeds to block 310. At decision block 312, method 300 includes determining whether Based on the determination method 300 proceeds to block 314, otherwise method 300 proceeds to block 318.

[0057] Continue to refer to Figure 3 , in block 310, method 300 includes setting to be equal to In block 314, set to be equal to In block 316, method 300 includes setting to be equal to In block 318, method 300 includes setting to be equal to In decision block 320, method 300 includes determining whether Based on the determination Method 300 proceeds to block 326, otherwise method 300 proceeds to block 334.

[0058] In decision block 322, method 300 includes determining whether Based on the determination Method 300 proceeds to block 330, otherwise method 300 proceeds to block 340. In decision block 324, method 300 includes determining whether Based on the determination Method 300 proceeds to block 332, otherwise method 300 proceeds to block 340. In decision block 326, method 300 includes determining whether Based on the determination Method 300 proceeds to block 328, otherwise method 300 proceeds to block 336.

[0059] In block 328, method 300 includes setting to be equal to In block 330, method 300 includes setting to be equal to In block 332, method 300 includes setting to be equal to In block 334, method 300 includes setting to be equal to In block 336, method 300 includes setting to be equal to In block 338, method 300 includes setting to be equal to In block 342, method 300 includes updating the value. In decision block 340, method 300 determines whether t + 1 > H, where H is the end of the time period for prediction.

[0060] Now refer to Figure 4, which shows a flowchart illustrating a method 400 for operating a charging station powered by renewable energy. In an exemplary embodiment, the method 400 is executed by a processing system 114 of the charging station 110, as Figure 1 shown.

[0061] As shown in block 402, the method 400 includes obtaining and Next, at block 404, a value for time period t is set to 1. At decision block 406, the method 400 includes determining whether Based on the determination the method 400 proceeds to decision block 412, otherwise the method 400 proceeds to decision block 408. At decision block 408, the method 400 includes determining whether Based on the determination the method 400 proceeds to block 416, otherwise the method 400 proceeds to block 410. At decision block 412, the method 400 includes determining whether Based on the determination the method 400 proceeds to block 414, otherwise the method 400 proceeds to block 418.

[0062] Continuing to refer to Figure 4 , at block 410, the method 400 includes selling electricity from the charging station to the power grid. At block 414, the method 400 includes the charging station purchasing electricity from the power grid. At block 416, the method 400 includes charging the battery of the charging station with electricity obtained from renewable energy. At block 418, the method 400 includes discharging the battery of the charging station to meet the power demand of the charging station. At decision block 420, the method 400 includes determining whether Based on the determination the method 400 proceeds to block 436, otherwise the method 400 proceeds to block 434.

[0063] At decision block 422, the method 400 includes determining whether Based on the determination the method 400 proceeds to block 430, otherwise the method 400 proceeds to block 440. At decision block 424, the method 400 includes determining whether Based on the method 400 proceeds to block 432, otherwise the method 400 proceeds to block 440.

[0064] At block 430, method 400 includes selling electricity generated from renewable energy to the power grid after the battery at the charging station is fully charged. At block 438, method 400 includes increasing the renewable energy credit balance associated with the charging station based on the electricity sold to the power grid. At block 432, method 400 includes purchasing electricity from the power grid after the battery at the charging station is completely depleted. At block 434, method 400 includes reducing the renewable energy credit balance associated with the charging station based on the electricity purchased from the power grid. At block 436, method 400 includes further reducing the negative renewable energy credit balance associated with the charging station based on the electricity purchased from the power grid. At block 442, method 400 includes updating the renewable energy credit value associated with the charging station and / or the charging station network including the charging station. At decision block 440, method 400 determines whether t+1>H, where H is the end of the time period for prediction.

[0065] A discussion will now be made with reference to Figure 6 an example, Figure 6 illustrates a system 600 including a charging station (CS) powered by renewable energy according to an exemplary embodiment. In one example, Company X is a charging station operator (SCO) 604 that operates a network of electric vehicle (EV) charging stations in various districts (illustrated as the state of California) of a geographical area 602. Each district is managed by a different power market operator 606. The power market operator 606 is configured to communicate with a regulatory agency 611 and a state agency 612 to ensure proper use and compliance with regulations regarding the creation and use of renewable energy credits. The power market operator 606 is configured to provide their generation capacity, transmission status, regulatory information, energy pricing, unit capacity, power transactions, etc. to a system operator 610. The power market operator 606 is configured to provide their market prices to an energy production and energy demand forecasting model 608.

[0066] The charging station operator (CSO) 604 is configured to provide the charging demand, CSs’ and regulatory information, generation capacity, demand forecasting, power transactions, etc. for each charging station (SC) of the charging station network to the system operation 610. In the exemplary embodiment, the energy production and energy demand forecasting model 608 is configured to estimate the electricity generated from renewable energy associated with each charging station and to estimate the predicted power demand of each charging station. In the exemplary embodiment, the energy production and energy demand forecasting model 608 is created at least in part based on context information 605. The system 600 further includes a roaming credit calculation unit 609 that is configured to calculate and maintain a renewable energy credit balance associated with each charging station operator 604.

[0067] Both charging stations (CS) CS1 and CS2 are partially powered by renewable energy sources (RES), such as wind energy, solar energy, and battery energy storage systems (BESS) located within the charging station facilities. In one embodiment, CS1 is located in Region A and is managed by Utility Company A, while CS2 is located in Region B and is managed by Utility Company B. CS1 is equipped with a relatively large capacity for generating renewable energy but faces the challenge of low utilization rate (UR), estimated to be below 2%. In contrast, CS2 has a more limited renewable energy production capacity but benefits from a high utilization rate ranging between 15 - 20%. Both CS1 and CS2 are equipped with facilities to sell or credit excess renewable energy production to their respective utility companies. In an exemplary embodiment, system operator 610 is configured to enable CS1 and CS2 to transfer excess renewable energy to their respective utility companies, allowing for credit requirements within their own regions or across regions. For example, CS1 can provide 5 kWh of energy to Utility Company A, and CS2, owned by Company X, can redeem an equal amount of 5 kWh from Utility Company A or Utility Company B. This process is subject to time-of-use (ToU) rates and other related fees or charges.

[0068] In an exemplary embodiment, renewable energy certificates (RECs) generated by charging stations can be traded among utility companies within a specific district or state / province. For example, in the state of California, all six major utility companies - PacifiCorp, PG&E, Liberty Utilities, SCE, Bear Valley Electric Services, and SDG&E - have reached an agreement to exchange generated RECs among themselves. When generating and trading renewable energy certificates (RECs), specific rates and tariffs will apply. For example, RECs created during peak demand periods will have a higher value, reflecting the time-of-use electricity rates, real-time pricing, or locational marginal prices in effect at that time.

[0069] In an exemplary embodiment, adopting the described renewable credit system provides several potential benefits for charging station operators, such as financial efficiency, transaction cost savings, enhanced regulatory compliance, and reduced environmental impact. In an exemplary embodiment, the renewable credit system provides a significant financial advantage for Company X, especially if the price of selling excess renewable energy in Region A is lower than the cost of purchasing additional energy in Region A or Region B. By leveraging the credit system, Company X can effectively: (i) "virtually store" or "systematically store" surplus renewable energy, allowing it to recover that energy from Utility Company A as needed, thus circumventing the lower selling price. (ii) "transfer" excess energy from Region A to Region B, avoiding the financial inefficiency of selling at a lower price and then buying at a higher price.

[0070] In an exemplary embodiment, the renewable credit system can also significantly save transaction costs. This is especially true when considering time-of-use (ToU) rates and various associated fees. By crediting energy instead of participating in traditional buying and selling processes, Company X can bypass the costs associated with these transactions. Additionally, the company can take advantage of the energy rate differences between the two utilities to further improve its cost-effectiveness.

[0071] In an exemplary embodiment, the renewable credit system enhances Company X's ability to comply with existing renewable energy standards or mandates that may be in effect in Region A, Region B, or both. By leveraging credits in Region A or transferring them from Region A (characterized by excess renewable energy production) to Region B (characterized by high utilization but lower renewable energy production), Company X can effectively increase its share of energy from renewable sources. This strategic approach enables the company to not only comply with regulatory requirements but also demonstrate its commitment to sustainable energy practices.

[0072] In an exemplary embodiment, Company X's implementation of the renewable credit system maximizes the utilization of its renewable energy production, ensuring that energy is not wasted regardless of its geographical consumption point. This approach greatly contributes to reducing the company's overall environmental footprint and supporting renewable energy generation. The environmental benefits include ensuring the full utilization of the generated renewable electricity, which aligns with the broader goal of encouraging the use of renewable energy and reducing greenhouse gas emissions. Under the renewable credit system, Company X effectively generates its own renewable energy certificates (RECs) at CS1 where renewable energy production is dominant. Instead of selling these RECs, Company X uses them to balance the energy consumption at CS2 where the demand is high but renewable energy generation is limited. This represents an "internal" or "self-supply" REC trading model where the company uses RECs generated at one site to compensate for the energy use at another site within the same region. This approach provides an efficient solution for companies with multiple facilities and different renewable energy production and consumption patterns to achieve their renewable energy goals.

[0073] In an exemplary embodiment, the renewable credit system provides Company X with enhanced adaptability in managing its energy consumption across different sites. This flexibility becomes particularly important when predicting future changes in the company's energy demand. For example, if the utilization at CS1 increases or the renewable energy production at CS2 improves, Company X can seamlessly modify the amount of credits transferred between and within the corresponding zones.

[0074] In an exemplary embodiment, a renewable credit system promotes the use of renewable energy. For example, by crediting surplus renewable energy, Company X actively advocates the adoption of green energy practices. This strategy not only helps to achieve a more sustainable energy landscape but also enhances the company's position as a leader in environmental management and sustainability.

[0075] Now returning to Figure 6 , in an exemplary embodiment, the system operator 610 is configured to control the exchange of renewable energy credits between the charging stations of the charging station operator 604. In one embodiment, the system operator 610 of the charging station network is configured to ensure that the exchange of renewable energy credits between the charging stations of the charging station operator 604 complies with one or more objective functions and operating constraints.

[0076] In one embodiment, the objective function of the charging station operator 604 is to maximize the profit of the charging station network, which can be expressed as: Where ESG i is the energy sold to the power grid at charging station i, EPG i is the energy purchased from the power grid at charging station i, OC i is the operating cost of charging station i, α is the price or cost coefficient of the energy sold to the power grid, β is the price or cost coefficient of the energy purchased from the power grid, and N is the number of charging stations (CS) in the charging station network. In another embodiment, the objective function of the charging station network 140 is to minimize the carbon emissions of the charging station network, which can be expressed as: Where γ is the emission volume coefficient of the energy purchased from the power grid.

[0077] In one embodiment, one operating constraint of the charging station operator 604 is the energy balance constraint, which can be expressed as: Where REP i is the renewable energy generated at charging station i, EC i is the energy consumed for charging EVs at charging station i, CTA i,j is the amount of credit transferred from charging station i to charging station j, CTA k,i is the amount of credit transferred from charging station k to charging station i, is the total credit transferred from charging station i to all other charging stations, and is the total credit transfer received by charging station i from all other charging stations.

[0078] In one embodiment, another operating constraint of the charging station operator 604 is the transmission line capacity constraint, which can be expressed as: Where is the lower limit of the transmission line limit connecting station i to j, The upper limit of the transmission line connecting stations i to j is restricted. In one embodiment, another operational constraint of the charging station network 140 is a regulatory compliance constraint, which can be expressed as: where RPS i is the minimum amount of renewable energy that charging station i is required to use or produce, as required by regulations, y i is the amount of renewable energy generated at charging station i, is the amount of renewable energy credits (RECs) transferred from station j to station i, is the amount of renewable energy credits (RECs) transferred from station i to station j.

[0079] In one embodiment, another operational constraint of the charging station operator 604 is a preferred credit transfer constraint, which can be expressed as: λ∑ i,j PCT i,j ×CTA i,j , where λ is a weighting factor that balances the importance of profit maximization and preferred credit transfer (PCT), and PCT i,j is the preference or priority for transferring credits from station i to station j, which is a function of various factors such as power demand, renewable energy production capacity, etc. In one embodiment, another operational constraint of the charging station network 140 is a local consumption constraint, which can be expressed as: α(y i -∑ j CTA i,j +∑ j CTA j,i ), where α is a weighting factor that balances the importance of profit maximization and maximizing local consumption of renewable energy (RES), y i is the amount of renewable energy generated at charging station i, ∑ j CTA i,j is the total credit transferred from charging station i to all other charging stations (j), and ∑ j CTA j,i is the total credit transferred from all other charging stations j to charging station i.

[0080] In an exemplary embodiment, the system operator 610 of the charging station network is configured to ensure that the exchange of renewable energy credits between the charging stations of the charging station operator 604 complies with one or more objective functions and operational constraints, and to determine the optimal operational characteristics of the charging stations operated by the charging station operator 604 to optimize the objective function provided between the charging stations. In one embodiment, the system operator 610 of the charging station network is configured to perform a multi-objective constrained optimization process, such as Pareto optimization, the scalarization method, or the contextual multi-armed bandit method, to optimize the objective function provided between the charging stations operated by the charging station operator 604.

[0081] In one embodiment, the system operator 610 of the charging station network is configured to perform a multi-objective constrained optimization process using three main components. The first component is the decision vector, which includes decision variables such as the credit transfer amount (CTA), the energy purchased from the grid (EPG), and the energy sold to the grid (ESG). The second component is the objective function, in which two conflicting objectives are addressed: maximizing economic profit and minimizing gas emissions. The third component relates to constraints, which are divided into hard constraints and soft constraints. The hard constraints include energy balance, transmission line limits, and regulatory compliance, while the soft constraints include preferential credit transfer and maximizing local consumption. Using an optimizer, perhaps a derivative-free optimizer, the process aims to determine the optimal values of the decision variables.

[0082] In another embodiment, the system operator 610 of the charging station network is configured to perform a Pareto optimization process that includes two steps. The first step, the optimization process, involves solving the problem by adhering to two sets of constraints: hard constraints and soft cost constraints. The solution uses a multi-objective optimizer, such as multi-objective particle swarm optimization or non-dominated sorting genetic algorithm (NSGA-II). This phase produces multiple trade-off solutions that address the initial conflicting objectives: F1X aimed at maximizing economic profit and F2X focused on minimizing gas emissions. The second step, the decision-making process, involves applying a preference or weighting scheme, which can be selected automatically or manually. Finally, this step culminates in the establishment of a Pareto front. This front represents compromise solutions that are suitable for various criteria set by the system operator, effectively balancing the conflicting objectives.

[0083] In another embodiment, the system operator 610 of the charging station network is configured to perform a scalar method that converts a multi-objective optimization problem into a single-objective optimization. The objective function f(X) = ω1·f1(X) - ω2·f2(X) - P sc is maximized, where X = {CTA, EPG, ESG} T is the decision variable vector, CTA is the credit transfer amount, EPG is the energy purchased from the grid, ESG is the energy sold to the grid, f1(X) is the economic profit to be maximized, f2(X) is the carbon emission to be minimized, and P sc is a penalty added to the overall objective function to simulate soft constraints, and ω i is called the weight, which is normalized without loss of generality such that ∑ω i = 1.

[0084] In another embodiment, the system operator 610 of the charging station network is configured to execute an optimization strategy for multi-criteria construction to determine the optimal values of decision variables, such as the amount of transfer created, the energy purchased from the large grid, the energy sold to the grid, the operating cost, the emissions produced, and the preferred credit transfer, which maximizes the joint utility function, subject to hard and soft constraints or the best compromise Pareto solution, and allows the customer to manually or automatically select based on a predefined weighting scheme or preference information set by the customer.

[0085] In FIG. 500, as Figure 5 shown, the contextual multi-armed bandit agent 502 is configured to determine the optimal operating characteristics of the charging station 506 operated by the charging station operator. Generally speaking, the contextual multi-armed bandit method is a decision-making strategy for determining the optimal values of decision variables. The situation simulated by the contextual multi-armed bandit is one in which, in a series of independent trials, the presentation engine automatically selects an action 504 from a set of possible actions based on a given context 510 (auxiliary information) to maximize the reward function 508. In an exemplary embodiment, the shared context 510 is incorporated to find the optimal operating characteristics of the charging stations 506 in the charging station network based on context information 510 such as vector conditions, demand forecasts, or charging prices. These contexts 510 are used to calculate the rewards 508 associated with each arm / action 504. The contextual multi-armed bandit agent 502 is developed by defining the state / context 510 and the possible actions 504, defining the reward function 508, and training a neural network that takes an encoded state / context and outputs an estimated reward for each possible action, and finally selects the action for each context according to the learned strategy. Different strategies can be used to learn the optimal strategy, such as ε-greedy, upper confidence bound (UCB), or meta-learning, such as LinRel or LinUCB.

[0086] The terms "a" and "an" do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items. The term "or" means "and / or" unless the context clearly dictates otherwise. References throughout the specification to "one aspect" mean that a particular element (e.g., a feature, a structure, a step, or a property) described in connection with that aspect is included in at least one aspect described herein and may or may not be present in other aspects. Additionally, it should be understood that the described elements may be combined in any suitable manner in the various aspects.

[0087] When an element such as a layer, a film, a region, or a substrate is referred to as being "on" another element, it can be directly on the other element or there can also be intervening elements. In contrast, when an element is referred to as being "directly on" another element, there are no intervening elements.

[0088] Unless otherwise stated herein, all test standards are the latest valid standards as of the filing date of this application, or, if priority is claimed, the filing date of the earliest priority application in which the test standard appears.

[0089] Unless otherwise defined, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0090] Although the foregoing disclosure has been described with reference to exemplary embodiments, those skilled in the art will understand that various changes may be made and equivalents may be substituted for its elements without departing from its scope. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the disclosure without departing from the essential scope of the disclosure. Therefore, it is intended that the disclosure not be limited to the particular embodiments disclosed, but that it will include all embodiments falling within its scope.

Claims

1. An electric vehicle charging station, comprising: A bi-directional charger electrically coupled to the power grid and one or more renewable energy sources; An energy storage device electrically connected to the bi-directional charger; And A processing system configured to control the operation of the bi-directional charger, wherein the processing system is configured to: Monitor the charging state of the energy storage device; Calculate the estimated power demand for the electric vehicle charging station over a period of time; Calculate the estimated power generation of one or more renewable energy sources during the period; Based on determining that the estimated power generation of one or more renewable energy sources during the period is greater than the estimated power demand during the period and the charging state of the energy storage device is less than the maximum charging state, instruct the bi-directional charger to charge the energy storage device; and Based on determining that the estimated power generation of one or more renewable energy sources during the period is greater than the estimated power demand during the period and the charging state of the energy storage device is equal to the maximum charging state, instruct the bi-directional charger to transmit the power generated by one or more renewable energy sources to the power grid.

2. The electric vehicle charging station according to claim 1, wherein, The processing system is further configured to update the renewable energy credit balance based on the amount of power delivered to the power grid generated by the one or more renewable energy sources.

3. The electric vehicle charging station according to claim 1, wherein, The processing system is further configured to: based on determining that the estimated power generation of one or more renewable energy sources during the period is less than the estimated power demand during the period and the charging state of the energy storage device is greater than the minimum charging state, instruct the bi-directional charger to discharge the energy storage device to meet the estimated power demand.

4. The electric vehicle charging station according to claim 1, wherein, The processing system is further configured to: based on determining that the estimated power generation of one or more renewable energy sources during the period is less than the estimated power demand during the period and the charging state of the energy storage device is equal to the minimum charging state, instruct the bi-directional charger to obtain power from the power grid to meet the estimated power demand.

5. The electric vehicle charging station according to claim 4, wherein, The processing system is further configured to update the renewable energy credit balance based on the amount of power obtained from the power grid.

6. The electric vehicle charging station according to claim 1, wherein, The processing system is further configured to monitor and record the percentage of power provided by the electric vehicle charging station to one or more vehicles obtained from the one or more renewable energy sources during the period.

7. The electric vehicle charging station according to claim 1, wherein, Calculate the estimated power demand for the electric vehicle charging station over a period of time based on an analysis of the historical power demand of the electric vehicle charging station.

8. The electric vehicle charging station according to claim 1, wherein, Calculate the estimated power generation of one or more renewable energy sources during the period based on an analysis of the historical power generation of the one or more renewable energy sources, the corresponding historical weather conditions, and the weather forecast during the period.

9. The electric vehicle charging station according to claim 1, wherein, The electric vehicle charging station is the first electric vehicle charging station among a plurality of electric vehicle charging stations in a charging station network.

10. An electric vehicle charging network, comprising: A charging station network management system; And A plurality of electric vehicle charging stations in communication with the charging station network management system, wherein each of the plurality of electric vehicle charging stations includes: A bi-directional charger electrically coupled to the power grid and one or more renewable energy sources; An energy storage device electrically connected to the bi-directional charger; and A processing system configured to control the operation of a bi-directional charger, wherein the processing system is configured to: Monitor the charging state of an energy storage device; Calculate an estimated power demand for an electric vehicle charging station over a period of time; Calculate an estimated power generation of one or more renewable energy sources during the period; Based on determining that the estimated power generation of one or more renewable energy sources during the period is greater than the estimated power demand during the period and the charging state of the energy storage device is less than the maximum charging state, instruct the bi-directional charger to charge the energy storage device; and Based on determining that the estimated power generation of one or more renewable energy sources during the period is greater than the estimated power demand during the period and the charging state of the energy storage device is equal to the maximum charging state, instruct the bi-directional charger to transmit the power generated by one or more renewable energy sources to the power grid.

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