Pub Date : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102749
Keaton Chia, Amy LeBar, Vardhan Agarwal, Man Kit Sam Lee, Joe Ikedo, Jesse Wolf, Kim Trenbath, J. Kleissl
The growth of and reliance on renewable energy necessitate a multi-pronged approach to achieve grid reliability and economics. As they represent a notable portion of U.S. energy consumption, commercial buildings must play an active role in this effort. Conserving energy and responding to grid conditions through demand flexibility can be achieved through the integration of major building systems. Integration of plug and process loads with lighting and heating, ventilation, and air conditioning systems maximizes the effectiveness of integrated building energy management. In this research, we demonstrate the integration of smart outlets into a building automation system. We cover the installation process as well as the architecture required for smart outlets to communicate data to the building automation system and to receive commands back. After recording power measurements for one week as a baseline, we configured the building automation system to turn the smart outlets on and off according to a set schedule. This resulted in energy savings of 66% during 1 week on 25 plug loads. This work demonstrates that grid-interactive efficient buildings are achievable through building system integration.
{"title":"Integration of a Smart Outlet-Based Plug Load Management System with a Building Automation System","authors":"Keaton Chia, Amy LeBar, Vardhan Agarwal, Man Kit Sam Lee, Joe Ikedo, Jesse Wolf, Kim Trenbath, J. Kleissl","doi":"10.1109/GridEdge54130.2023.10102749","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102749","url":null,"abstract":"The growth of and reliance on renewable energy necessitate a multi-pronged approach to achieve grid reliability and economics. As they represent a notable portion of U.S. energy consumption, commercial buildings must play an active role in this effort. Conserving energy and responding to grid conditions through demand flexibility can be achieved through the integration of major building systems. Integration of plug and process loads with lighting and heating, ventilation, and air conditioning systems maximizes the effectiveness of integrated building energy management. In this research, we demonstrate the integration of smart outlets into a building automation system. We cover the installation process as well as the architecture required for smart outlets to communicate data to the building automation system and to receive commands back. After recording power measurements for one week as a baseline, we configured the building automation system to turn the smart outlets on and off according to a set schedule. This resulted in energy savings of 66% during 1 week on 25 plug loads. This work demonstrates that grid-interactive efficient buildings are achievable through building system integration.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123001481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102733
Weixi Wang, Robert Flores, G. Razeghi, J. Brouwer
Building electrification, vehicle electrification, and renewable distributed energy resources (DER) are all viewed as key technologies for reducing greenhouse gas and pollutant emissions. However, the added electrification may stress, damage infrastructure, and result in early replacement of electrical distribution system components. Conversely, DER may alleviate infrastructure strain, resulting in lower overall costs through delayed infrastructure repairs and upgrades. Regardless, the effect of electrification and high use of renewable DER are generally addressed qualitatively, not quantitatively. This paper presents a method to quantify the effects of electrification, DER, and other emerging clean energy technologies on local electric distribution infrastructure. This is accomplished by predicting the degradation of distribution transformers and power cables, followed by the optimal resizing of electric components such that cost is minimized. The method is demonstrated for two scenarios where the buildings and vehicles across a small community are electrified, resulting in accelerated distribution infrastructure degradation and replacement.
{"title":"Quantifying Transformer and Cable Degradation in Highly Renewable Electric Distribution Circuits","authors":"Weixi Wang, Robert Flores, G. Razeghi, J. Brouwer","doi":"10.1109/GridEdge54130.2023.10102733","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102733","url":null,"abstract":"Building electrification, vehicle electrification, and renewable distributed energy resources (DER) are all viewed as key technologies for reducing greenhouse gas and pollutant emissions. However, the added electrification may stress, damage infrastructure, and result in early replacement of electrical distribution system components. Conversely, DER may alleviate infrastructure strain, resulting in lower overall costs through delayed infrastructure repairs and upgrades. Regardless, the effect of electrification and high use of renewable DER are generally addressed qualitatively, not quantitatively. This paper presents a method to quantify the effects of electrification, DER, and other emerging clean energy technologies on local electric distribution infrastructure. This is accomplished by predicting the degradation of distribution transformers and power cables, followed by the optimal resizing of electric components such that cost is minimized. The method is demonstrated for two scenarios where the buildings and vehicles across a small community are electrified, resulting in accelerated distribution infrastructure degradation and replacement.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121969799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102737
Wenzong Wang, A. Huque
Grid-forming (GFM) inverter, which can regulate voltage and frequency independently, is a key component in an inverter-based microgrid. However, an industry acceptable consistent and uniform method of defining the functions and performance requirements for GFM inverters in microgrid is presently lacking. As a result, utility planners constantly face the challenge of defining these requirements by themselves in contractual agreements with plant developers.This paper presents initial investigation results towards developing the performance requirements for a GFM inverter based power plant in a microgrid. Specifically, the requirements related to steady state voltage regulation are developed based on detailed simulation studies on a real microgrid. The need and benefits for a grid-forming inverter based power plant to regulate the voltage magnitude, balance the three-phase voltages and regulate voltage harmonics inside the microgrid are shown. The results are expected to assist distribution utility planners in developing detailed performance requirements for GFM inverter based power plants in microgrid projects.
{"title":"Steady State Voltage Regulation Requirements for Grid-Forming Inverter based Power Plant in Microgrid Applications","authors":"Wenzong Wang, A. Huque","doi":"10.1109/GridEdge54130.2023.10102737","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102737","url":null,"abstract":"Grid-forming (GFM) inverter, which can regulate voltage and frequency independently, is a key component in an inverter-based microgrid. However, an industry acceptable consistent and uniform method of defining the functions and performance requirements for GFM inverters in microgrid is presently lacking. As a result, utility planners constantly face the challenge of defining these requirements by themselves in contractual agreements with plant developers.This paper presents initial investigation results towards developing the performance requirements for a GFM inverter based power plant in a microgrid. Specifically, the requirements related to steady state voltage regulation are developed based on detailed simulation studies on a real microgrid. The need and benefits for a grid-forming inverter based power plant to regulate the voltage magnitude, balance the three-phase voltages and regulate voltage harmonics inside the microgrid are shown. The results are expected to assist distribution utility planners in developing detailed performance requirements for GFM inverter based power plants in microgrid projects.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114692815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102727
Kyle A. Skeen, G. Venayagamoorthy
Microgrids are a promising technology to achieve the sustainability goals set by the UN to fight against climate change, create affordable and clean energy, sustainable cities and communities, and economic growth by creating a reliable, resilient, green power infrastructure. There are limitations to the benefits that microgrids can provide. To overcome the limitations and bolster the benefits of individual microgrids, they can be interconnected, creating a network of microgrids (NoMs). NoMs have many benefits that individual microgrids cannot accomplish, such as participating in power interchange between connected microgrids and the utility grid. This will increase reliability and resiliency and create economic benefits for the participants of NoMs. Challenges exist in NoMs, including data analysis, communication, and cyber-security to operations and management of the NoMs. This paper will go over the benefits that NoMs can provide and the challenges currently being researched in academia.
{"title":"Network of Microgrids: Opportunities and Challenges","authors":"Kyle A. Skeen, G. Venayagamoorthy","doi":"10.1109/GridEdge54130.2023.10102727","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102727","url":null,"abstract":"Microgrids are a promising technology to achieve the sustainability goals set by the UN to fight against climate change, create affordable and clean energy, sustainable cities and communities, and economic growth by creating a reliable, resilient, green power infrastructure. There are limitations to the benefits that microgrids can provide. To overcome the limitations and bolster the benefits of individual microgrids, they can be interconnected, creating a network of microgrids (NoMs). NoMs have many benefits that individual microgrids cannot accomplish, such as participating in power interchange between connected microgrids and the utility grid. This will increase reliability and resiliency and create economic benefits for the participants of NoMs. Challenges exist in NoMs, including data analysis, communication, and cyber-security to operations and management of the NoMs. This paper will go over the benefits that NoMs can provide and the challenges currently being researched in academia.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128653017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102722
Shishir Shekhar, Shashwat Shekhar
Each year, millions of people and thousands of businesses are impacted by underground cable system failures. Underground cables are considered critical equipment within any power system, and typically one of the most expensive components of the system to repair. When they fail, the customer impact is immense and has the potential to cause severe collateral damage or worse, public safety concerns. Replacing underground power cables can be very expensive and time consuming and can take months or even years when associated with significant design, civil and construction work. Over 99% of solid dielectric (i.e.: XLPE or EPR) cable system failures are associated to Partial Discharge (PD). This paper characterizes the waveforms of Partial Discharge (PD) time domain signals utilizing a unique dataset of measured conditions of underground power cable systems. Machine Learning and Deep Learning models have been developed and evaluated for the purposes of providing the foundation for automated condition monitoring and predictive maintenance. The results demonstrate a step towards a predictive maintenance approach for underground cable systems.
{"title":"Improving Utility Cables Diagnostics and Prognostics using Machine Learning","authors":"Shishir Shekhar, Shashwat Shekhar","doi":"10.1109/GridEdge54130.2023.10102722","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102722","url":null,"abstract":"Each year, millions of people and thousands of businesses are impacted by underground cable system failures. Underground cables are considered critical equipment within any power system, and typically one of the most expensive components of the system to repair. When they fail, the customer impact is immense and has the potential to cause severe collateral damage or worse, public safety concerns. Replacing underground power cables can be very expensive and time consuming and can take months or even years when associated with significant design, civil and construction work. Over 99% of solid dielectric (i.e.: XLPE or EPR) cable system failures are associated to Partial Discharge (PD). This paper characterizes the waveforms of Partial Discharge (PD) time domain signals utilizing a unique dataset of measured conditions of underground power cable systems. Machine Learning and Deep Learning models have been developed and evaluated for the purposes of providing the foundation for automated condition monitoring and predictive maintenance. The results demonstrate a step towards a predictive maintenance approach for underground cable systems.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124331607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102745
J. Acken, Naresh Sehgal, D. Bansal, R. Bass
The present state of edge computing is an environment of different computing capabilities connected via a wide variety of communication paths. The energy grid is relying upon distributed energy devices connected at the edge of the internet. Consider the scenario where each edge device is customer-owned distributed energy resource (DER) that is connected via a trustworthy link to a grid service provider. Each DER keeps a local simple trust record of interactions. Information protection is provided by internet https standards, however, trust must be evaluated throughout operation. This paper presents a model for representing and evaluating trust in general and applied to the energy grid as a key example. Actors on the edge may interact with each other as well as with a central datacenter.
{"title":"Security and Trust Metrics for Edge Computing","authors":"J. Acken, Naresh Sehgal, D. Bansal, R. Bass","doi":"10.1109/GridEdge54130.2023.10102745","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102745","url":null,"abstract":"The present state of edge computing is an environment of different computing capabilities connected via a wide variety of communication paths. The energy grid is relying upon distributed energy devices connected at the edge of the internet. Consider the scenario where each edge device is customer-owned distributed energy resource (DER) that is connected via a trustworthy link to a grid service provider. Each DER keeps a local simple trust record of interactions. Information protection is provided by internet https standards, however, trust must be evaluated throughout operation. This paper presents a model for representing and evaluating trust in general and applied to the energy grid as a key example. Actors on the edge may interact with each other as well as with a central datacenter.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131880705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102712
Maitha Al Shimmari, D. Wallom
High-quality short-term load forecasting, particularly day-ahead, is essential to enable the demand response aggregator’s participation in the electricity market. The accuracy of load forecasting depends on many factors, including the size and quality of historical data, selection of the forecasting model, availability of weather data, and types of business sectors. This paper implements three state-of-the-art regression models, ridge regression (RR), random forests (RF), and gradient boosting (GB) to capture intricate variations in three UK cities (Newcastle, Peterborough, and Sheffield) in five business sectors (retail, entertainment, social, industrial, and other) from the UK non-domestic electricity load profiles and provide accurate day-ahead load forecasting. The models are implemented on a historical dataset that contains 7527 UK businesses with geographical postal codes, 30-min electricity consumption, and weather metrics. The performance is evaluated using the coefficient of determination R-squared. The presented results show that GB outperforms RF and RR as it provides the most accurate forecasting results, with limited improvement in forecasting results by including weather data. The aggregated business sectors’ forecasting accuracy is higher than individual business sectors’ forecasts.
{"title":"Short-term load forecasting using UK non-domestic businesses to enable demand response aggregators’ participation in electricity markets","authors":"Maitha Al Shimmari, D. Wallom","doi":"10.1109/GridEdge54130.2023.10102712","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102712","url":null,"abstract":"High-quality short-term load forecasting, particularly day-ahead, is essential to enable the demand response aggregator’s participation in the electricity market. The accuracy of load forecasting depends on many factors, including the size and quality of historical data, selection of the forecasting model, availability of weather data, and types of business sectors. This paper implements three state-of-the-art regression models, ridge regression (RR), random forests (RF), and gradient boosting (GB) to capture intricate variations in three UK cities (Newcastle, Peterborough, and Sheffield) in five business sectors (retail, entertainment, social, industrial, and other) from the UK non-domestic electricity load profiles and provide accurate day-ahead load forecasting. The models are implemented on a historical dataset that contains 7527 UK businesses with geographical postal codes, 30-min electricity consumption, and weather metrics. The performance is evaluated using the coefficient of determination R-squared. The presented results show that GB outperforms RF and RR as it provides the most accurate forecasting results, with limited improvement in forecasting results by including weather data. The aggregated business sectors’ forecasting accuracy is higher than individual business sectors’ forecasts.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132183831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102716
M. Arifujjaman, R. Salas, A. Johnson, J. Araiza, F. Elyasichamazkoti, A. Momeni, Shadi Chuangpishit, F. Katiraei
The significant growth in the integration of distributed energy sources (DERs) and the interactive behaviors between inverter controllers and protection system draws up considerable challenges. Their validation and adoption require careful assessment in modeling, simulation, and testing. The traditional approach focusing on a detailed model, while substantially simplifying the remainder of the system under test, is no longer sufficient. Real-time simulation and Power Hardware-in-the-Loop (PHIL) techniques emerge as indispensable tools for validating the behavior of Photovoltaic (PV) inverters and their impact/interaction on/with the feeder protection system. This paper aims to describe a detailed the development, demonstration, and validation of a PHIL testbed for Distributed Energy Resource (DER) integration that encompasses the test setup architecture, hardware components, software systems, communications, and integration. Ultimately, the result of performance validation of the developed testbed at the Sothern California Edison (SCE) test facility is presented for a test scenario as an example.
{"title":"Development, Demonstration, and Validation of Power Hardware-in-the-loop (PHIL) Testbed for DER Dynamics Integration in Southern California Edison (SCE)","authors":"M. Arifujjaman, R. Salas, A. Johnson, J. Araiza, F. Elyasichamazkoti, A. Momeni, Shadi Chuangpishit, F. Katiraei","doi":"10.1109/GridEdge54130.2023.10102716","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102716","url":null,"abstract":"The significant growth in the integration of distributed energy sources (DERs) and the interactive behaviors between inverter controllers and protection system draws up considerable challenges. Their validation and adoption require careful assessment in modeling, simulation, and testing. The traditional approach focusing on a detailed model, while substantially simplifying the remainder of the system under test, is no longer sufficient. Real-time simulation and Power Hardware-in-the-Loop (PHIL) techniques emerge as indispensable tools for validating the behavior of Photovoltaic (PV) inverters and their impact/interaction on/with the feeder protection system. This paper aims to describe a detailed the development, demonstration, and validation of a PHIL testbed for Distributed Energy Resource (DER) integration that encompasses the test setup architecture, hardware components, software systems, communications, and integration. Ultimately, the result of performance validation of the developed testbed at the Sothern California Edison (SCE) test facility is presented for a test scenario as an example.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129181031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102742
Conner Ozatalar, R. Ahmad, Phillip Pambuh, Harshil Shah
As more behind the meter solar farms are installed onto the power grid, the true load on the power grid becomes more hidden to the utility because the meters only read the net difference between the native load and the solar generation. This becomes problematic for grid planning since the grid needs to be ready to handle the full native load in the case of a hot and cloudy summer day when load is very high and solar generation is low. This study breaks down solar irradiance into the diffuse components from the ground and sky, and the direct (beam) irradiance. These components are then combined using the Liu-Jordan model to estimate the solar irradiance on a tilted surface. This method was then applied to data from a weather station in an area in ComEd’s service territory to estimate the solar panel output of a local 2MW metered solar farm. When comparing the predicted generation and measured generation from this solar farm, it was determined that their existed inconsistencies within the data set. After reducing the size of the data set to remove potentially poor data, this method estimated solar production with an R2 value of 0.900 with an average absolute value error of 148kW. Based on these findings, this methodology had produced efficient results and can also be used to determine when a solar farm is not producing as expected.
{"title":"Estimating the Output of Behind the Meter Solar Farms by Breaking Irradiance Data into its Diffuse and Direct Components","authors":"Conner Ozatalar, R. Ahmad, Phillip Pambuh, Harshil Shah","doi":"10.1109/GridEdge54130.2023.10102742","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102742","url":null,"abstract":"As more behind the meter solar farms are installed onto the power grid, the true load on the power grid becomes more hidden to the utility because the meters only read the net difference between the native load and the solar generation. This becomes problematic for grid planning since the grid needs to be ready to handle the full native load in the case of a hot and cloudy summer day when load is very high and solar generation is low. This study breaks down solar irradiance into the diffuse components from the ground and sky, and the direct (beam) irradiance. These components are then combined using the Liu-Jordan model to estimate the solar irradiance on a tilted surface. This method was then applied to data from a weather station in an area in ComEd’s service territory to estimate the solar panel output of a local 2MW metered solar farm. When comparing the predicted generation and measured generation from this solar farm, it was determined that their existed inconsistencies within the data set. After reducing the size of the data set to remove potentially poor data, this method estimated solar production with an R2 value of 0.900 with an average absolute value error of 148kW. Based on these findings, this methodology had produced efficient results and can also be used to determine when a solar farm is not producing as expected.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115826999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-10DOI: 10.1109/GridEdge54130.2023.10102711
Avik Ghosh, Cristian Cortes-Aguirre, Yi-An Chen, Adil Khurram, J. Kleissl
The recent increase in the intermittent variable renewable energy sources (VRES) results in mismatches between demand and supply that can cause grid instability. These issues can be mitigated with battery energy storage systems (BESS). However, BESS are generally dispatched conservatively to manage uncertainties in VRE forecast. Therefore, this paper proposes an online adaptive stochastic model predictive control (A-SMPC) based approach that minimizes electricity costs by expanding the BESS state of charge (SOC) limits beyond the nominal range of 20% – 80%. Allowing the SOC limits to expand, results in violation of the nominal SOC constraints. Chance constraints are implemented in the proposed A-SMPC method that guarantee that the probability of violating nominal SOC constraints remains below a desired value. Furthermore, the A-SMPC cost function includes time-of-use demand charges that have not been considered before in this type of model. Simulations based on historical load and PV generation data from the Port of San Diego for January 2019 shows that the proposed formulation outperforms the traditional MPC formulation, that does not include nominal SOC constraint violation, by reducing the monthly electricity costs by 7%. The proposed A-SMPC method results in 8% higher BESS utilization which translates to about 1 extra charging/discharging cycle during the analyzed month which is unlikely to have a significant impact on BESS lifetime.
{"title":"Adaptive Chance Constrained MPC under Load and PV Forecast Uncertainties","authors":"Avik Ghosh, Cristian Cortes-Aguirre, Yi-An Chen, Adil Khurram, J. Kleissl","doi":"10.1109/GridEdge54130.2023.10102711","DOIUrl":"https://doi.org/10.1109/GridEdge54130.2023.10102711","url":null,"abstract":"The recent increase in the intermittent variable renewable energy sources (VRES) results in mismatches between demand and supply that can cause grid instability. These issues can be mitigated with battery energy storage systems (BESS). However, BESS are generally dispatched conservatively to manage uncertainties in VRE forecast. Therefore, this paper proposes an online adaptive stochastic model predictive control (A-SMPC) based approach that minimizes electricity costs by expanding the BESS state of charge (SOC) limits beyond the nominal range of 20% – 80%. Allowing the SOC limits to expand, results in violation of the nominal SOC constraints. Chance constraints are implemented in the proposed A-SMPC method that guarantee that the probability of violating nominal SOC constraints remains below a desired value. Furthermore, the A-SMPC cost function includes time-of-use demand charges that have not been considered before in this type of model. Simulations based on historical load and PV generation data from the Port of San Diego for January 2019 shows that the proposed formulation outperforms the traditional MPC formulation, that does not include nominal SOC constraint violation, by reducing the monthly electricity costs by 7%. The proposed A-SMPC method results in 8% higher BESS utilization which translates to about 1 extra charging/discharging cycle during the analyzed month which is unlikely to have a significant impact on BESS lifetime.","PeriodicalId":377998,"journal":{"name":"2023 IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125184095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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