school.eecs.wsu.edu€¦ · Web viewUI-ASSIST. Texas A&M Statement of work. 2.3.2 . Storage sizing a. nd siting and optimization (TAMU-Singh, MIT, Panasonic) 2.3.2.1 Identification

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ASSISTTexas A&M Statement of work

2.3.2 Storage sizing and siting and optimization (TAMU-Singh, MIT, Panasonic) 2.3.2.1 Identification of storage types and uses: Storage can be used both for economic purposes as well as enhancing reliability in the presence of intermittent sources like wind and solar. It can provide economic benefits by storing energy during lower price periods and then using it during the more expensive period. In the presence of intermittent sources, it can help to improve reliability by compensating the intermittency. During this subtask, various types of centralized storage as well distributed storage like the electric vehicles and rooftop solar will be identified and studied for suitability. 2.3.2.2 Optimizing the size and location: Siting and size of storage is important from the point of view of economics and reliability. This is a complex problem. We have developed methods based on stochastic programing, robust programing as well as distributionally robust methods at the level of the transmission grid. We will modify these methods for their use at the distribution level. In developing these methods the role of the market will also be included. The interaction distribution level storage and transmission level will also be studied. Strategies for the optimum use of storage will also be developed. 2.3.2.3 Simulations to illustrate and test the methods: Simulations will be performed on test systems to illustrate the use of the algorithms as well operational strategies. Any deficiencies discovered will be remedied during this subtask.2.3.2.4 Deliverables: A report containing (a) various storage types that be usefully employed at the distribution grid and their characteristics, (b) algorithms for optimization of siting and sizing. (c) operation strategies for the optimal deployment of storage.

2.4.4 AC/DC Microgrid Protection Mechanism (IITK, IITBBS, TAMU-Begovic, IITD, NETRA)2.4.4.1 Characterization of Challenges for AC/DC Microgrid Protection Mechanisms: Various problems and potential solutions will be identified for AC microgrid protection (operational modes, effects of distributed generation, topology-induced issues, as well as compliance and standardization problems). The same exercise will be performed for DC microgrids (mainly grounding issues and schemes for interruption of DC current). Particular focus will be given to issues related to implementation of high penetration distributed generation, solar in particular, and other issues related to future evolution of microgrids. The analysis will take into account legacy protection infrastructure and needs for developing an outgrowth on its basis that would demonstrate flexibility and improve both reliability and resilience.2.4.4.2 Solutions for AC/DC Microgrid Protection Mechanisms: An overview of solutions will be proposed for different identified issues. It will be done in the context of Hierarchically Coordinated Protection (HCP). The proposed approach relies on the three protection layers: predictive protection (using high speed computational techniques to compare current conditions to past disturbances), adaptive/settingless protection (having tripping logic based on the select features obtained in real-time), and relay operation correction in case of unintended tripping (intended to be used as a verification toll in case of misoperations). Particular focus will be given to communication-assisted relaying schemes and communication infrastructure that they require.2.4.4.3 Development of Foundation for Simulation Experiment Design: Among the developed schemes, some will be selected to be tested in an embedded system environment, using real relays in conjunction with carefully designed simulation models providing a virtual interface to the relay under a variety of conditions of interest.

2.4.4.4 Deliverables: A report will be developed containing (a) the overview of the characterization of changes and challenges that a transition to modern microgrids would create, both on the AC and the DC side, both in islanded and the grid-connected modes; (b) the portfolio of solutions amounting to a three-layer Hierarchical Protection System (HPC), and (c) development of a series of simulated relay test environments suitable for subsequent implementation as a relay testing in embedded system environment.2.5.1 Cyber physical interdependence analysis (TAMU-Begovic, WSU) 2.5.1.1 Identification of types of interdependence: Types of interdependencies will be identified. The types can be characterized based on the impact or technology. The impacts can be characterized as catastrophic, inconvenience or localized. The catastrophic are the ones that can lead to widespread outage and are the most serious. The inconvenience is like losing access to the provider and localized would be effecting individual customers. The technology types could be soft or hard. The soft would be where there is malfunction and hard would be actual hardware failures. The categorization of these interdependencies would help us prioritize the selection of interdependencies for modeling.2.5.1.2 Developing methods for reliability including interdependence: Interdependence introduces dependent failures which makes the reliability analysis more challenging and sometimes computationally intractable especially when the dependence is wide spread. Most of the reliability literature has focused on independent failures assuming that the cyber part is perfectly reliable or includes only limited dependence like the common mode failures. At the bulk power system level, we have introduced an approach using the concept of cyber-physical interface (CPIM) and consequent event matrix (CEM). First we will investigate the applicability of this approach at the distribution grid including DERs. Then suitable approaches at the distribution level will be developed.2.5.1.3 Simulations to illustrate and test the methods: Simulations will be performed to illustrate and test the efficiency of the developed methods on test systems. These simulations will both illustrate the techniques developed as well as identify modifications to redress any deficiencies. Any issues identified will be addressed during this task.2.5.1.3 Deliverables: A report including the why and how of interdependencies of cyber and physical parts of the distribution grid. The concepts and algorithms developed will be described along with studies of test systems developed.2.7.8 Reliability Assessment and Improvement (IITK, TAMU-Singh, IITD, WSU) 2.7.8.1 Identification of the structure of the problem: The distribution grid is undergoing many changes leading to more complex network than it was ever before. The inclusion of DERs , storage both centralized as well distributed, demand response bringing load closer to the supply and interaction with the market are making the system very complex. In this subtask, the various elements of the problem will be identified and the structure of the problem defined. 2.7.8.2. Development of reliability analysis methods: In the past the reliability analysis at the distribution level has been limited mostly to the simple radial system. Some work has been done to include DERs but a comprehensive model is lacking. This subtask will develop methods for reliability analysis that include the storage, interactions with demand response and DERs. Both analytical methods and simulation methods will be investigated. 2.7.8.3. Reliability improvement: Studies will be made to show how the developed tools can be used to improve the reliability by using different operational strategies for storage and renewable sources.2.7.8.4 Deliverables: Deliverables a report describing the structure of the problem as well as algorithms developed for reliability studies and strategies to improvement. 3.2.3 Texas A&M Smart Grid Lab-Kezunovic3.2.3.1. Development and implementation of a time-domain simulation model for commercial microgrid using OPAL Real-time simulator: This effort will be focused on a microgrid that contain renewable PV generation, mobile storage (EVs), and fixed battery storage such as may be found in shopping centers, university campuses or hospital complexes.

3.2.3.2. Development and implementation of a time-domain simulation model for distribution grid using OPAL Real-time simulator: This effort will be focused on a typical distribution grid with feeders connected to commercial microgrids3.2.3.3. Use of the models to evaluate HIL responses of traditional protection under microgrid interfacing: This effort will be aimed at deciding which protection scheme may be used for the distribution and microgrid protection, and particular rely products will be selected for testing and evaluation3.2.3.4. Implementation of Use Cases for evaluation of improved protection using simulation models: This effort will produce specific evaluation cases where faults and various operating conditions between the distribution grid and microgrid will be generated through simulation and protection system performance will be evaluated by connecting relays in the HIL mode of operation. The final goal is to identify shortcomings of the traditional protection approaches. 3.2.3.5. Deliverables: a report that summarizes models for simulation, Use Cases for evaluation and test results, and recommends future developments of new protection algorithms. 4.2.3 US Semi-Urban Pilot 2 (TAMU-Kezunovic) 4.2.3.1. Development and implementation of a distribution network model using ABB suite of DMS software: This effort will be aimed at studying the distribution grid performance under the conditions of bidirectional power flows caused by distributed generation and the customer sites containing PV generation and battery storage. 4.2.3.2. Development and implementation of the building energy resource model that consists of rooftop PV generation, mobile (EV) battery storage and fixed large size community battery storage: This effort will evaluate performance of the flexible load comprising PV generation, mobile energy storage (EVs) and fixed energy storage in different modes of operation. 4.2.3.3. Interfacing the two models and evaluating impacts on the grid and customer support during outages: This effort will particularly focused on studies to evaluate requirements for customer PV generation and energy storage to stating grid outages. 4.2.3.4. Deliverables: a report that summarizes models for simulation, Use Cases for evaluation and test results, and recommends future developments of control strategies to optimize the use of microgrids to support operation of the distribution system, as well as to optimize its operation in a standalone mode. 6.2.0 Creating Next Generation of Power Professionals-Kezunovic, Singh, Begovic 6.2.0.1. Developing Course Syllabus for special topics course on the use of storage and distributed generation in smart grids and microgrids: This course will be a new addition for the senior undergraduate and graduate students interested in learning the future role and impact of energy storage and distributed generation in smart grids. This will be a special topic course.6.2.0.2. Developing lecture notes and Lab exercises: This effort will focus on development instructional materials to support such a course6.2.0.2. Developing and implementing lab exercises using simulation environment from the Smart grid Lab and Semi-Urban Pilot 2: this effort will create testbed simulation cases that may be used in the classroom and lab teaching. 6.2.0.4 Deliverables: The course will be taught before the end of the grant to evaluate usefulness of the instructional material and to obtain student feedback to the topics and hands-on lab exercises.

UI-ASSISTTexas A&M Statement of work

2.3.2 Storage sizing and siting and optimization (TAMU-Singh, MIT, Panasonic) 2.3.2.1 Identification of storage types and uses: Storage can be used both for economic purposes as well as enhancing reliability in the presence of intermittent sources like wind and solar. It can provide economic benefits by storing energy during lower price periods and then using it during the more expensive period. In the presence of intermittent sources, it can help to improve reliability by compensating the intermittency.

During this subtask, various types of centralized storage as well distributed storage like the electric vehicles and rooftop solar will be identified and studied for suitability. 2.3.2.2 Optimizing the size and location: Siting and size of storage is important from the point of view of economics and reliability. This is a complex problem. We have developed methods based on stochastic programing, robust programing as well as distributionally robust methods at the level of the transmission grid. We will modify these methods for their use at the distribution level. In developing these methods the role of the market will also be included. The interaction distribution level storage and transmission level will also be studied. Strategies for the optimum use of storage will also be developed. 2.3.2.3 Simulations to illustrate and test the methods: Simulations will be performed on test systems to illustrate the use of the algorithms as well operational strategies. Any deficiencies discovered will be remedied during this subtask.2.3.2.4 Deliverables: A report containing (a) various storage types that be usefully employed at the distribution grid and their characteristics, (b) algorithms for optimization of siting and sizing. (c) operation strategies for the optimal deployment of storage.

2.4.4 AC/DC Microgrid Protection Mechanism (IITK, IITBBS, TAMU-Begovic, IITD, NETRA)2.4.4.1 Characterization of Challenges for AC/DC Microgrid Protection Mechanisms: Various problems and potential solutions will be identified for AC microgrid protection (operational modes, effects of distributed generation, topology-induced issues, as well as compliance and standardization problems). The same exercise will be performed for DC microgrids (mainly grounding issues and schemes for interruption of DC current). Particular focus will be given to issues related to implementation of high penetration distributed generation, solar in particular, and other issues related to future evolution of microgrids. The analysis will take into account legacy protection infrastructure and needs for developing an outgrowth on its basis that would demonstrate flexibility and improve both reliability and resilience.2.4.4.2 Solutions for AC/DC Microgrid Protection Mechanisms: An overview of solutions will be proposed for different identified issues. It will be done in the context of Hierarchically Coordinated Protection (HCP). The proposed approach relies on the three protection layers: predictive protection (using high speed computational techniques to compare current conditions to past disturbances), adaptive/settingless protection (having tripping logic based on the select features obtained in real-time), and relay operation correction in case of unintended tripping (intended to be used as a verification toll in case of misoperations). Particular focus will be given to communication-assisted relaying schemes and communication infrastructure that they require.2.4.4.3 Development of Foundation for Simulation Experiment Design: Among the developed schemes, some will be selected to be tested in an embedded system environment, using real relays in conjunction with carefully designed simulation models providing a virtual interface to the relay under a variety of conditions of interest.2.4.4.4 Deliverables: A report will be developed containing (a) the overview of the characterization of changes and challenges that a transition to modern microgrids would create, both on the AC and the DC side, both in islanded and the grid-connected modes; (b) the portfolio of solutions amounting to a three-layer Hierarchical Protection System (HPC), and (c) development of a series of simulated relay test environments suitable for subsequent implementation as a relay testing in embedded system environment.2.5.1 Cyber physical interdependence analysis (TAMU-Begovic, WSU) 2.5.1.1 Identification of types of interdependence: Types of interdependencies will be identified. The types can be characterized based on the impact or technology. The impacts can be characterized as catastrophic, inconvenience or localized. The catastrophic are the ones that can lead to widespread outage and are the most serious. The inconvenience is like losing access to the provider and localized would be effecting individual customers. The technology types could be soft or hard. The soft would be where there is malfunction and hard would be actual hardware failures. The categorization of these interdependencies would help us prioritize the selection of interdependencies for modeling.

2.5.1.2 Developing methods for reliability including interdependence: Interdependence introduces dependent failures which makes the reliability analysis more challenging and sometimes computationally intractable especially when the dependence is wide spread. Most of the reliability literature has focused on independent failures assuming that the cyber part is perfectly reliable or includes only limited dependence like the common mode failures. At the bulk power system level, we have introduced an approach using the concept of cyber-physical interface (CPIM) and consequent event matrix (CEM). First we will investigate the applicability of this approach at the distribution grid including DERs. Then suitable approaches at the distribution level will be developed.2.5.1.3 Simulations to illustrate and test the methods: Simulations will be performed to illustrate and test the efficiency of the developed methods on test systems. These simulations will both illustrate the techniques developed as well as identify modifications to redress any deficiencies. Any issues identified will be addressed during this task.2.5.1.3 Deliverables: A report including the why and how of interdependencies of cyber and physical parts of the distribution grid. The concepts and algorithms developed will be described along with studies of test systems developed.2.7.8 Reliability Assessment and Improvement (IITK, TAMU-Singh, IITD, WSU) 2.7.8.1 Identification of the structure of the problem: The distribution grid is undergoing many changes leading to more complex network than it was ever before. The inclusion of DERs , storage both centralized as well distributed, demand response bringing load closer to the supply and interaction with the market are making the system very complex. In this subtask, the various elements of the problem will be identified and the structure of the problem defined. 2.7.8.2. Development of reliability analysis methods: In the past the reliability analysis at the distribution level has been limited mostly to the simple radial system. Some work has been done to include DERs but a comprehensive model is lacking. This subtask will develop methods for reliability analysis that include the storage, interactions with demand response and DERs. Both analytical methods and simulation methods will be investigated. 2.7.8.3. Reliability improvement: Studies will be made to show how the developed tools can be used to improve the reliability by using different operational strategies for storage and renewable sources.2.7.8.4 Deliverables: Deliverables a report describing the structure of the problem as well as algorithms developed for reliability studies and strategies to improvement. 3.2.3 Texas A&M Smart Grid Lab-Kezunovic3.2.3.1. Development and implementation of a time-domain simulation model for commercial microgrid using OPAL Real-time simulator: This effort will be focused on a microgrid that contain renewable PV generation, mobile storage (EVs), and fixed battery storage such as may be found in shopping centers, university campuses or hospital complexes. 3.2.3.2. Development and implementation of a time-domain simulation model for distribution grid using OPAL Real-time simulator: This effort will be focused on a typical distribution grid with feeders connected to commercial microgrids3.2.3.3. Use of the models to evaluate HIL responses of traditional protection under microgrid interfacing: This effort will be aimed at deciding which protection scheme may be used for the distribution and microgrid protection, and particular rely products will be selected for testing and evaluation3.2.3.4. Implementation of Use Cases for evaluation of improved protection using simulation models: This effort will produce specific evaluation cases where faults and various operating conditions between the distribution grid and microgrid will be generated through simulation and protection system performance will be evaluated by connecting relays in the HIL mode of operation. The final goal is to identify shortcomings of the traditional protection approaches. 3.2.3.5. Deliverables: a report that summarizes models for simulation, Use Cases for evaluation and test results, and recommends future developments of new protection algorithms. 4.2.3 US Semi-Urban Pilot 2 (TAMU-Kezunovic)

4.2.3.1. Development and implementation of a distribution network model using ABB suite of DMS software: This effort will be aimed at studying the distribution grid performance under the conditions of bidirectional power flows caused by distributed generation and the customer sites containing PV generation and battery storage. 4.2.3.2. Development and implementation of the building energy resource model that consists of rooftop PV generation, mobile (EV) battery storage and fixed large size community battery storage: This effort will evaluate performance of the flexible load comprising PV generation, mobile energy storage (EVs) and fixed energy storage in different modes of operation. 4.2.3.3. Interfacing the two models and evaluating impacts on the grid and customer support during outages: This effort will particularly focused on studies to evaluate requirements for customer PV generation and energy storage to stating grid outages. 4.2.3.4. Deliverables: a report that summarizes models for simulation, Use Cases for evaluation and test results, and recommends future developments of control strategies to optimize the use of microgrids to support operation of the distribution system, as well as to optimize its operation in a standalone mode. 6.2.0 Creating Next Generation of Power Professionals-Kezunovic, Singh, Begovic 6.2.0.1. Developing Course Syllabus for special topics course on the use of storage and distributed generation in smart grids and microgrids: This course will be a new addition for the senior undergraduate and graduate students interested in learning the future role and impact of energy storage and distributed generation in smart grids. This will be a special topic course.6.2.0.2. Developing lecture notes and Lab exercises: This effort will focus on development instructional materials to support such a course6.2.0.2. Developing and implementing lab exercises using simulation environment from the Smart grid Lab and Semi-Urban Pilot 2: this effort will create testbed simulation cases that may be used in the classroom and lab teaching. 6.2.0.4 Deliverables: The course will be taught before the end of the grant to evaluate usefulness of the instructional material and to obtain student feedback to the topics and hands-on lab exercises.

UI-ASSISTTexas A&M Statement of work

2.3.2 Storage sizing and siting and optimization (TAMU-Singh, MIT, Panasonic) 2.3.2.1 Identification of storage types and uses: Storage can be used both for economic purposes as well as enhancing reliability in the presence of intermittent sources like wind and solar. It can provide economic benefits by storing energy during lower price periods and then using it during the more expensive period. In the presence of intermittent sources, it can help to improve reliability by compensating the intermittency. During this subtask, various types of centralized storage as well distributed storage like the electric vehicles and rooftop solar will be identified and studied for suitability. 2.3.2.2 Optimizing the size and location: Siting and size of storage is important from the point of view of economics and reliability. This is a complex problem. We have developed methods based on stochastic programing, robust programing as well as distributionally robust methods at the level of the transmission grid. We will modify these methods for their use at the distribution level. In developing these methods the role of the market will also be included. The interaction distribution level storage and transmission level will also be studied. Strategies for the optimum use of storage will also be developed. 2.3.2.3 Simulations to illustrate and test the methods: Simulations will be performed on test systems to illustrate the use of the algorithms as well operational strategies. Any deficiencies discovered will be remedied during this subtask.2.3.2.4 Deliverables: A report containing (a) various storage types that be usefully employed at the distribution grid and their characteristics, (b) algorithms for optimization of siting and sizing. (c) operation strategies for the optimal deployment of storage.

2.4.4 AC/DC Microgrid Protection Mechanism (IITK, IITBBS, TAMU-Begovic, IITD, NETRA)2.4.4.1 Characterization of Challenges for AC/DC Microgrid Protection Mechanisms: Various problems and potential solutions will be identified for AC microgrid protection (operational modes, effects of distributed generation, topology-induced issues, as well as compliance and standardization problems). The same exercise will be performed for DC microgrids (mainly grounding issues and schemes for interruption of DC current). Particular focus will be given to issues related to implementation of high penetration distributed generation, solar in particular, and other issues related to future evolution of microgrids. The analysis will take into account legacy protection infrastructure and needs for developing an outgrowth on its basis that would demonstrate flexibility and improve both reliability and resilience.2.4.4.2 Solutions for AC/DC Microgrid Protection Mechanisms: An overview of solutions will be proposed for different identified issues. It will be done in the context of Hierarchically Coordinated Protection (HCP). The proposed approach relies on the three protection layers: predictive protection (using high speed computational techniques to compare current conditions to past disturbances), adaptive/settingless protection (having tripping logic based on the select features obtained in real-time), and relay operation correction in case of unintended tripping (intended to be used as a verification toll in case of misoperations). Particular focus will be given to communication-assisted relaying schemes and communication infrastructure that they require.2.4.4.3 Development of Foundation for Simulation Experiment Design: Among the developed schemes, some will be selected to be tested in an embedded system environment, using real relays in conjunction with carefully designed simulation models providing a virtual interface to the relay under a variety of conditions of interest.2.4.4.4 Deliverables: A report will be developed containing (a) the overview of the characterization of changes and challenges that a transition to modern microgrids would create, both on the AC and the DC side, both in islanded and the grid-connected modes; (b) the portfolio of solutions amounting to a three-layer Hierarchical Protection System (HPC), and (c) development of a series of simulated relay test environments suitable for subsequent implementation as a relay testing in embedded system environment.2.5.1 Cyber physical interdependence analysis (TAMU-Begovic, WSU) 2.5.1.1 Identification of types of interdependence: Types of interdependencies will be identified. The types can be characterized based on the impact or technology. The impacts can be characterized as catastrophic, inconvenience or localized. The catastrophic are the ones that can lead to widespread outage and are the most serious. The inconvenience is like losing access to the provider and localized would be effecting individual customers. The technology types could be soft or hard. The soft would be where there is malfunction and hard would be actual hardware failures. The categorization of these interdependencies would help us prioritize the selection of interdependencies for modeling.2.5.1.2 Developing methods for reliability including interdependence: Interdependence introduces dependent failures which makes the reliability analysis more challenging and sometimes computationally intractable especially when the dependence is wide spread. Most of the reliability literature has focused on independent failures assuming that the cyber part is perfectly reliable or includes only limited dependence like the common mode failures. At the bulk power system level, we have introduced an approach using the concept of cyber-physical interface (CPIM) and consequent event matrix (CEM). First we will investigate the applicability of this approach at the distribution grid including DERs. Then suitable approaches at the distribution level will be developed.2.5.1.3 Simulations to illustrate and test the methods: Simulations will be performed to illustrate and test the efficiency of the developed methods on test systems. These simulations will both illustrate the techniques developed as well as identify modifications to redress any deficiencies. Any issues identified will be addressed during this task.2.5.1.3 Deliverables: A report including the why and how of interdependencies of cyber and physical parts of the distribution grid. The concepts and algorithms developed will be described along with studies of test systems developed.2.7.8 Reliability Assessment and Improvement (IITK, TAMU-Singh, IITD, WSU)

2.7.8.1 Identification of the structure of the problem: The distribution grid is undergoing many changes leading to more complex network than it was ever before. The inclusion of DERs , storage both centralized as well distributed, demand response bringing load closer to the supply and interaction with the market are making the system very complex. In this subtask, the various elements of the problem will be identified and the structure of the problem defined. 2.7.8.2. Development of reliability analysis methods: In the past the reliability analysis at the distribution level has been limited mostly to the simple radial system. Some work has been done to include DERs but a comprehensive model is lacking. This subtask will develop methods for reliability analysis that include the storage, interactions with demand response and DERs. Both analytical methods and simulation methods will be investigated. 2.7.8.3. Reliability improvement: Studies will be made to show how the developed tools can be used to improve the reliability by using different operational strategies for storage and renewable sources.2.7.8.4 Deliverables: Deliverables a report describing the structure of the problem as well as algorithms developed for reliability studies and strategies to improvement. 3.2.3 Texas A&M Smart Grid Lab-Kezunovic3.2.3.1. Development and implementation of a time-domain simulation model for commercial microgrid using OPAL Real-time simulator: This effort will be focused on a microgrid that contain renewable PV generation, mobile storage (EVs), and fixed battery storage such as may be found in shopping centers, university campuses or hospital complexes. 3.2.3.2. Development and implementation of a time-domain simulation model for distribution grid using OPAL Real-time simulator: This effort will be focused on a typical distribution grid with feeders connected to commercial microgrids3.2.3.3. Use of the models to evaluate HIL responses of traditional protection under microgrid interfacing: This effort will be aimed at deciding which protection scheme may be used for the distribution and microgrid protection, and particular rely products will be selected for testing and evaluation3.2.3.4. Implementation of Use Cases for evaluation of improved protection using simulation models: This effort will produce specific evaluation cases where faults and various operating conditions between the distribution grid and microgrid will be generated through simulation and protection system performance will be evaluated by connecting relays in the HIL mode of operation. The final goal is to identify shortcomings of the traditional protection approaches. 3.2.3.5. Deliverables: a report that summarizes models for simulation, Use Cases for evaluation and test results, and recommends future developments of new protection algorithms. 4.2.3 US Semi-Urban Pilot 2 (TAMU-Kezunovic) 4.2.3.1. Development and implementation of a distribution network model using ABB suite of DMS software: This effort will be aimed at studying the distribution grid performance under the conditions of bidirectional power flows caused by distributed generation and the customer sites containing PV generation and battery storage. 4.2.3.2. Development and implementation of the building energy resource model that consists of rooftop PV generation, mobile (EV) battery storage and fixed large size community battery storage: This effort will evaluate performance of the flexible load comprising PV generation, mobile energy storage (EVs) and fixed energy storage in different modes of operation. 4.2.3.3. Interfacing the two models and evaluating impacts on the grid and customer support during outages: This effort will particularly focused on studies to evaluate requirements for customer PV generation and energy storage to stating grid outages. 4.2.3.4. Deliverables: a report that summarizes models for simulation, Use Cases for evaluation and test results, and recommends future developments of control strategies to optimize the use of microgrids to support operation of the distribution system, as well as to optimize its operation in a standalone mode. 6.2.0 Creating Next Generation of Power Professionals-Kezunovic, Singh, Begovic

6.2.0.1. Developing Course Syllabus for special topics course on the use of storage and distributed generation in smart grids and microgrids: This course will be a new addition for the senior undergraduate and graduate students interested in learning the future role and impact of energy storage and distributed generation in smart grids. This will be a special topic course.6.2.0.2. Developing lecture notes and Lab exercises: This effort will focus on development instructional materials to support such a course6.2.0.2. Developing and implementing lab exercises using simulation environment from the Smart grid Lab and Semi-Urban Pilot 2: this effort will create testbed simulation cases that may be used in the classroom and lab teaching. 6.2.0.4 Deliverables: The course will be taught before the end of the grant to evaluate usefulness of the instructional material and to obtain student feedback to the topics and hands-on lab exercises.

Preliminary Scope of Work for Lawrence Berkeley National LaboratoryJhi-Young Joo, 8/21/17

Background to DER-CAM for operation and planning with microgrid

The Distributed Energy Resources Customer Adoption Model (DER-CAM) is an economic and environmental model of customer DER adoption. This model has been in development at Berkeley Lab since 2000. The objective of the model is to minimize the cost of operating on-site generation and combined heat and power (CHP) systems, either for individual customer sites or a µGrid. In other words, the focus of this work is primarily economic.To achieve this objective, the following issues must be addressed: Which is the cost-optimal configuration of distributed generation technologies that a specific customer can install? What is the appropriate level of installed capacity of these technologies that minimizes cost? How should the installed capacity be operated so as to minimize the total customer energy bill?

It is assumed that the customer desires to install distributed generation to minimize the cost of energy consumed on site. Consequently, it should be possible to determine the technologies and capacity the customer is likely to install and to predict when the customer will be self-generating electricity and/or transacting with the power grid, and likewise when purchasing fuel or using recovered heat.

The DER-CAM model chooses which DG and/or CHP technologies a customer should adopt and how that technology should be operated based on specific site load and price information, and performance data for available equipment options. The inputs to and outputs from DER-CAM are illustrated below.Key Inputs Into The Model include customer's end-use load profiles (typically for space heat, hot water, gas only, cooling, and electricity only), customer's default electricity tariff, natural gas prices, and other relevant price data, capital, operating and maintenance (O&M), and fuel costs of the various available technologies, together with the interest rate on customer investment, basic physical characteristics of alternative generating, heat recovery and cooling technologies, including the thermal-electric ratio that determines how much residual heat is available as a function of generator electric outputOutputs which are determined by the optimization model are:1. capacities of DG and CHP technology or combination of technologies to be installed2. when and how much of the capacity installed will be running3. total cost of supplying the electric and heat loads.

Background: Applying Micro-PMU in distribution system operation with high DER including storage

There has been significant work, by LBNL to develop applications for new data streams from the microPMU product (developed by PSL through a project funded by ARPA-E). This work is moving into the application commercialization stage, along with continuing fundamental and novel research. The key value in this work is

development of novel applications of distribution data, which rely on or are enhanced by precisely synchronized measurement points.

Some of the key applications which will be utilized in this work include PV Disaggregation integration to forecasting, power flow monitoring for curtailment and control in high penetration of DER scenarios, voltage dependency, model validation and parameterization, incipient failure detection and operator notification – all enabled by the microPMU data streams. The μPMUs used in the LBNL test network were developed by Power Standards Laboratory. μPMUs can capture portions of the operating state of the system to provide actionable intelligence in real-time. Our research focuses on building a bottom-up view of the electric power distribution system using μPMU measurements in the context of limited availability of accurate system models and supplemental measurements (such as SCADA and AMI). To do so, we utilize μPMU measurements to parameterize approximations to system power flows.Each μPMU collects 512 samples of data per cycle, totalling 30720 samples per second, and downsamples/converts the data to the phasor domain with an output of 120 samples/second of three phases of voltage and current, magnitude and phase angle. For a given sample the dataset contains the magnitude and angle of the voltage for all three phases, the magnitude and angle of the current for all three phases and the time stamp. This data is available for a number of μPMUs across the test network, and LBNL will support collection of further data at partner utilities. PMUs and related analytics are the enabling technology which we can apply in this scenario – this work is in research development now, and will be presented at a later stage of the project. Some of the applications developed and in development by the team include:Transient event detection in real-time with arbitrary threshold levels: • Synchronized transient event time• rate of change of magnitudeDiagnosis of transient events and disturbances through cross-referencing and correlation of synchronized data at different locations:• detecting high-impedance faults• locating source of voltage sags (T versus D versus local)High-precision inputs for state estimation and model validation:• phase connectivity (ABC) and network topology• impedance change estimationEmpirical analysis of power quality impacts associated with distributed generation:• voltage variability assessment• reverse power flow detectionEquipment health diagnostics:• arcing faults relational equipment • tap changer/capacitor/switch misoperation.

The work completed in this project will focus on the visibility and situational awareness applications which include:

Visibility behind the substation to enhance situational awareness for transmission operators:• Real time PV disaggregation and visibility• Voltage Dependent Load modeling and load model validation • Early warning of potential instabilities originating from DG• Validation and parameterization of voltage and frequency ride-through behavior of inverters

Background to CyDER for demand response simulation and integration using standard interface and support transactive energy

CyDER (Cyber physical co-simulation platform for DER integration) is a modular, scalable, and interoperable tool for power system planning and operation that is being developed through funding from DOE SunShot, to work seamlessly with existing tools in the utilities (including CymDist (Eaton) and DigSilent Power Factory for distribution planning and network modeling) and will enable the high penetration of distributed energy resource. The tool enhances current utility tools by providing a computationally efficient platform, which will be capable of

quasi-static time series simulation, evaluating 1000’s of scenarios for PV, EV and Load analysis, and includes modules for model validation, and other related distribution PMU analytics. In this operations, smart PV inverter controls, and in-feed from real-time distribution sensor measurements including the microPMU will be developed and demonstrated. CyDER is designed to interface with commercial distribution planning tools, to enable transmission, distribution and behind the meter co-simulation, and synchronized analytics. The first prototype was developed around DigSilent PowerFactory and Modelica, utilizing a Functional Mockup Interface to tie the tools together and is now expanded to integrate CYMDIST, a commercial distribution planning simulation tool widely used by US utilities, and GridDyn, and open source transmission simulator developed by LLNL, along with data links to PV inverter, Electric Vehicles, μPMU and other custom modules developed by the team. This will allow users to make full use of the utility models developed in CYMDIST and CYMDIST’s simulation capabilities, along with building new state of the art links to solve key utility problems such as model validation and analytics in the planning environment. For example, the present evolution of the tool is linked through the FMI and custom API, to BTRDB- a custom time series data base developed by UC Berkeley through the ARPA-E project “micro-synchrophasors for distribution” (http://btrdb.io/) - to bring in distribution PMU analytics into the CymDist interface, including voltage, power flow, and topology information to validate and calibrate the models.

Background to V2GSim work at LBNL

The Vehicle-to-Grid Simulator (V2G-Sim), developed at LBNL provides systematic quantitative methods to address the uncertainties and barriers facing vehicle-grid integration (VGI). In the real world, each person drives a different vehicle, in different ways, with different trip distances, at different times. Predicting the adequacy of plug-in electric vehicles (PEVs) for the needs of drivers, and accurately predicting the impacts and opportunities to the electricity grid from increased PEV deployment requires models that can consider these differences at the individual vehicle level.

V2G-Sim models the driving and charging behavior of individual PEVs to generate temporally- and spatially-resolved predictions of grid impacts and opportunities from increased plug-in electric vehicle (PEV) deployment. V2G-Sim provides bottom up modeling from individual vehicle dynamics all the way up to aggregate grid impacts and opportunities. Any managed charging or discharging control approach can be modeled to predict the impacts on individual vehicles, or at any grid scale. Battery degradation from driving or vehicle-grid services can be modeled with battery degradation models integrated into V2G-Sim. The model is scalable to simulate impacts and opportunities for any number of vehicles (from 1 to 1 million or more PEVs).

Overview of Tasks

Please also include tasks that LBNL will be helping with in Phase I and other phases.

Task 1: DER-CAM Integration and Testing (DOE Task 2.3 Managing and Optimizing Energy Storage)

This project will demonstrate the use of day-ahead optimization and supervisory control to plan and implement charging/discharging schedules for a system comprised of electric storage and photovoltaic cells connected to the wider grid at a demonstration location specified at the beginning of this project. The software platform will use forecasters to predict building load and insolation and leverage the LBNL-developed tool Operations DER-CAM to generate charging/discharging schedules that maximize the economic value of the system by reducing on-peak consumption and managing tariff demand charges. The platform will be constructed to communicate with the building SCADA in an automated fashion, thereby providing seamless delivery of instructions. A supervisory control algorithm will be developed to ensure that the local balance of supply and demand of electricity is maintained. We will identify architectures and customer engagement strategies in dynamic pricing DR transactions to generate a feedback of load flexibility profiles, status, and schedules. The research outcome of this project will be tested at one site in India, by using data obtained from the site and running developed algorithms. If the project side wishes to acquire our technologies, following the demonstration, separate license agreements will be discussed.

http://btrdb.io/

Task 1.1: Identify optimal DER services to maximize the value for local utility Task 1.2: Identify and validated daily/hourly DER dispatch utilizing IEDs Task 1.3: Assess economic benefits from optimal dispatch by the developed algorithm at the

designated test site Task 1.4: Integrate system level solution Task 1.5: Technology transfer and licensing

Task 2:DER-CAM , V2G Sim and CyDER Integration with Operations(DOE Task 2.9 Integrating DMS and DER control)

This project will develop and use the CyDER platform to reduce the evaluate dispatch and control scenarios for high penetration of distributed resources. CyDER will be the backbone that integrates all the technologies, communications, and control algorithms. We will ensure that it fulfills necessary grid requirements (e.g., maintains a specified voltage setpoint, limit power infeed, or power consumption to a specified level), which will simplify and accelerate the interconnection process from the utility side.

For the optimal dimensioning of the system, including storage, PV, and load management, we will use DER-CAM, an advanced optimization tool developed at Berkeley Lab. We will subsequently use a co-simulated CyDER/DER-CAM interface, a state-of-the-art co-simulation tool developed at Berkeley Lab to conduct simulations for several different scenarios of dispatch and optimization while accounting for distribution constraints and scenarios with real data and integrated advanced sensor analytics.

The platform will communicate with numerous sources, including to and from the utility operator, from the system operator (for example CAISO), to and from distributed energy resource planning with DER-CAM, and to and from devices themselves

LBNL will expand, test and demonstrate the highly modular and scalable tool by integrating the different simulation modules based on the Functional Mockup Interface, an industry-driven open-source standard that is supported by more than 50 tools. A simulation model exported in accordance with the FMI standard is referred to as Functional Mock-up Unit (FMU). The expansion of the work presented in this scope is to utilize these standards for integration of CyDER with operational and microgrid tool DER-CAM, and implement integration modules for data analytics focused around distribution PMU’s, validation, building, grid and inverter modules. This work will enable economic and multi scenario dispatch of resources, in addition to planning and operations analysis for distribution system. The system will be tested in scenarios up to 50% of PV penetration on a distribution feeder utilizing validated utility models.

Task 2.1: Functional Specification for Operational Integration V2GSim, with DER-CAM and CyDER, scenario definition

Task 2.2: Platform Development & prototype integration Task 2.3: Platform validation and testing in DMS environment Task 2.4: Application of V2G Sim predictive charging schedules to distribution modeling and analytics for

impact analyses Task 2.5: identify optimal schedules for charging in varied high penetration scenarios including varied %

penetration levels, variability, and inverter types.

Task 3: μPMU based Analytics & Design for Integration with Operational/Distribution Management System Tools(DOE Task 2.7 DSO Functions for Optimal Operation and Management of DER)

Task 3 will develop and report on analytics, enabled by μPMU technology. The team will, in the first phase of work, utilize simulated and existing datasets, and in the second and third phase, apply analytics around the integrated data. The team will support the development of system and sub-system requirements for operational integration of μPMU analytics with the Operational Product, lead and participate in the design and

development of PV and Distribution Grid State Estimation, real-time monitoring, and support the procurement and integration of μPMU measurements with a distribution management system tool. We are developing advanced supervisory and distributed algorithms for DER coordination that rely on μPMU measurements, other grid sensors and prior knowledge on grid status. The team will utilize empirical data to investigate different coordination schemes among multiple distributed energy resource units, and the potential for multiple devices at electrically independent nodes. In future work, installation of μPMUs at multiple substation locations would provide an invaluable data source for further microgrid and grid design research in this area, while providing operational data to assist with existing operations of the site, i.e. voltage variability, frequency, and angle stability information could be provided to the device controller, or general electrical operations team for improving performance.

Support the development of field test requirements and utility distribution system planning and integration. Support the evaluation and reporting of the technical and economic performance of the solution. The co- simulation platform will be developed to integrate with the operational tool at the systems layer. Modules will be developed in FMI, to enable utility planning model communication with feeder reconfiguration, sensors and model validation including advancements in topology and phase identification. Co-simulation framework developed in the tool will be extended in this task to allow for fast operational analysis and validation of utility data and decision making with high penetration of PV.

Task 3.1: Guidance on purchase, installation and data collection from μPMUs Task 3.2: Data characterization and event detection application integration Task 3.3: Specific application data analysis and integration for component level models Task 3.4: Operational Integration of Analytics Task 3.5: Benefits Assessment

Task 4: Storage Selection Analysis(DOE Task 2.2 Modeling and Prototyping Energy Storage)Storage selection for the grid of the future is different than it is today, mainly because the supply of energy will be less predictable as more renewables are put online. And although, today, the load is fairly erratic, the variability is over seconds and within a certain band that is fairly constant day in and day out. The problem with the variability of renewables is that the change can be over seconds, hours, days, and months. These time constants require a new strategy for deciding on how to best supply a consistent and reliable source of energy while limiting the use of non-renewable carbon based fuels and while simultaneously maintaining present day costs. We propose to quantify the energy storage requirements for different levels of renewables brought online and provide guidance on the types of energy storage that might best fit those needs.

4.1Analyze the energy storage needs for a given set of applications.4.2Establish energy storage size requirements.4.3 Identify possible energy storage solutions.4.4Estimate the levelized cost of electricity based on different storage technologies.

Task 5: Lab Testing and Validation(DOE Task 3)

For this task, LBNL will communicate and work with the leads of the tasks who perform lab testing and validation. The goals of the LBNL’s tasks are to modify the models developed in the previous tasks during the lab testing period when necessary, and provide guidance in the overall lab testing procedure to ensure consistency in the models and functionalities developed in both software and lab environments.

Task 6: Impact Analysis and Policy Recommendation(DOE Task 5)

This task is to study various impacts the project will bring to the electric industry sector and society in general. The studies conducted will guide certain policy and regulatory changes to be brought by the concerned

government bodies in US and India for large-scale deployment of such systems. Some of the analysis identified by the lead of this task includes

1) Impact on system performance such as peak load reduction, energy saving.

2) Impact on system expansion and operation cost.

3) Environmental impact such as reduction in carbon oxide emission.

4) Societal impact to be established through extensive survey on India and US side.

5) Recommending ideal structure of microgrid for rural, urban and semi-urban areas in Indian context.

6) Suggesting regulatory changes required to promote and accelerate microgrid installations and their integrated operation with grid.

7) Suggesting suitable DSO model in the Indian as well as US context.

8) Technical due-diligence of energy storage at mini grids and microgrids. Eventually the proof-of-concept and benchmarking will be established.

LBNL will provide guidance and feedback on this task to ensure the policy and regulatory recommendations are consistent with the results of the analysis, testing, and validations performed by LBNL in the previous tasks.

Links to deliverables and timeline is needed following format.Also, LBNL will attend workshop organized by UI-ASSIST team and contribute towards report for each quarter as well as yearly report.

Recent Applicable References to Work Completed by LBNL

DER-CAMSTADLER Michael, Gonçalo CARDOSO, Salman MASHAYEKH, Thibault FORGET, Nicholas DEFOREST, Ankit AGARWAL, Anna SCHÖNBEIN, “Value streams in microgrids: A literature review,” Applied Energy Journal by Elsevier, Volume 162, 15 January 2016, page 980-989, ISSN: 0306-2619, http://dx.doi.org/10.1016/j.apenergy.2015.10.081, LBNL-1003608.

Ghatikar, Girish, Salman Mashayekh, Michael Stadler, Rongxin Yin, and Zhenhua Liu. "Distributed Energy Systems Integration and Demand Optimization for Autonomous Operations and Electric Grid Transactions." Applied Energy, no. Special Issue on Integrated Energy Systems (2015).Schittekatte, Tim, Michael Stadler, Gonçalo Cardoso, Salman Mashayekh, and Sankar Narayanan. "The Impact of Short-term Stochastic Variability in Solar Irradiance on Optimal Microgrid Design." IEEE Transactions on Smart Grid PP, no. 99 (2016).

VirGIL (predecessor to CyDER) S. Rotger-Griful, S. Chatzivasileiadis, R. H. Jacobsen, E. M. Stewart, J. M. Domingo and M. Wetter, "Hardware-in-the-Loop co-simulation of distribution Grid for demand response," 2016 Power Systems Computation Conference (PSCC), Genoa, 2016, pp. 1-7 S. Chatzivasileiadis et al., "Cyber–Physical Modeling of Distributed Resources for Distribution System Operations," in Proceedings of the IEEE, vol. 104, no. 4, pp. 789-806, April 2016

μPMU analytics C. Roberts, E. M. Stewart and F. Milano, "Validation of the Ornstein-Uhlenbeck process for load modeling based on µPMU measurements," 2016 Power Systems Computation Conference (PSCC), Genoa, 2016, pp. 1-7.

A. Liao., E. M. Stewart and E. C. Kara, "Micro-synchrophasor data for diagnosis of transmission and distribution level events," 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), Dallas, TX, USA, 2016, pp. 1-5 Jamei, Mahdi; Stewart, Emma; Peisert, Sean; Scaglione, Anna; McParland, Chuck; Roberts, Ciaran; et al.(2016). Micro Synchrophasor-Based Intrusion Detection in Automated Distribution Systems: Towards Critical Infrastructure Security. IEEE Internet Computing, 20(5)

Data Integration Stewart, E.M., D. Arnold, A. von Meier, R. Arghandeh, S. Kiliccote. 2015. Accuracy and Validation of Measured and Modeled Data for Distributed PV Interconnection and Control. IEEE PES General Meeting, Denver, CO. July McMorran, A., E. Stewart, C. Shand, S. Rudd, G. Taylor. 2012. Addressing the Challenge of Data Interoperability for Off-Line Analysis of Distribution Networks in the Smart Grid, IEEE PES Transmission and Distribution Conference and Exposition, Orlando, FL. May.

PV Analysis Provisional Patent Application 2016-077 “Contextually Supervised Generation Estimation”, E.c.Kara, E.M. Stewart, C. Roberts, M. Tabone Stewart, E., J. MacPherson, S. Vasilic, D. Nakafuji, and T. Aukai. 2012. Analysis of High-Penetration Levels of Photovoltaics into the Distribution Grid on Oahu, Hawaii: Detailed Analysis of HECO Feeder WF1. Golden CO: National Renewable Energy Laboratory subcontract report NREL/SR-5500-54494.

GE - STATEMENT OF WORK (SOW) FOR UI-ASSIST

UI-ASSIST: US-India collAborative for smart diStribution System wIth StorageGE Power (“GE”)

A. SCOPE OF WORKOverall project scope of work led by Washington State University (“WSU”) is to address challenges associated with DER integration and seamless control into evolving DSO functions by carefully integrated collection of tasks based on scientific approaches and building on the existing collaborative effort. The US-India team behind this proposal represents the strongest universities, national laboratories, electrical utilities and vendors in the field of power engineering and each member has an established track record of contributing to the significant changes already occurring in electric distribution system. The formation of this strong team was possible because these organizations have years of cooperation as well as across geographic borders.

The team’s approach is to solve this problem will be to deploy solutions from nonlinear optimization, transactive control, data analytics, resilient design methods, power engineering, sensing technology, advanced protection, material science, computer science, cyber-physical co-simulation, machine learning, and social science to bridge the gap between DSO operation and DER control. The integration of team members from utilities and vendors will provide collaboration on real-world integration challenges and requirements as well as leveraging commercial-off-the-shelf tools for solutions. The diversity and depth of testbed and pilot demonstration will challenge the developed solutions as utility engineers work side by side with researchers for the wide variety of distribution system challenges.

GE’s scope of work consists of following two tracks:

Track A: Subject Matter Expert Consulting Services – Assist the WSU team in model and methodology development for DER monitoring and control in the DSO/Microgrid operation framework as outlined above. Key tasks under this track are:

(a) Review/Comment the team’ work for model and methodology development related to a subset of tasks described later in this section from real-world challenges perspective

(b) In parallel to model and methodology development, develop the use case design for lab scale testing and field implementation

Track B: Lab Scale Test Support and Field Implementation – Assist the WSU team in model and methodology development for DER monitoring and control in the DSO/Microgrid operation framework as outlined above. Key tasks under this track are:

(a) Implementing the models and integrating the GE e-terra based Microgrid Energy Management System (MEMS) software and associated third-party software for lab scale test support and field implementation at Philadelphia Navy Yard

(b) Import of SCADA data and displays for use with the pilot system.

(c) Modeling the Distribution SCADA

(d) Import of the GIS or equivalent data for use with geographic displays.

(e) Modeling the pilot distribution electrical information and complete the model as necessary (from the GIS or equivalent extract) for two (2) substations at Philadelphia Navy Yard.

(f) Implement the model in test simulation as well as field implementation environment at Philadelphia Navy Yard as shown below

B. TASKS TO BE PERFORMED

PHASE I: Kickoff and Finalizing Overall Project Management Architecture:

This task for GE is to provide necessary support in order for WSU to finalize the overall project management plan as well as all necessary agreements for executing the finalized project plan. GE’s project manager / principal investigator will work with the WSU management team to execute the following tasks in line with the overall project management.

Tasks for Objective 1.1: Finalize project management planTasks for Objective 1.1 Deliverables Timeline / budget1.1.1 MAP Your task to correct phase,

objectives, task and GE - Project Management Plan

Y1 Q2

Budget – 8

Tasks for Objective 1.2 – Finalize agreements for project managementTasks for Objective 1.2 Deliverables Timeline / budget1.2.2 Finalize agreements and finalize

GE statement of project objectivesGE – Statement of Project Objectiv

Y1 Q2

Budget - 2%

PHASE II: Research and Development Activities: During the phase of R&D activities, GE ‘s SME will review the work of model and methodology development and will provide comments from real world challenges perspective. In parallel, use case design will be developed for lab testing ad field implementation at Philadelphia Navy Yard. Project Objective 2.1: Assist in Developing Benchmark Test Systems

Tasks for Objective 2.1 Deliverables Timeline / budget2.1.3

Determine modeling and testing criteria for DER interconnection projects

Review / Comments by GE SMEs on the work done by others

Y1Q3 – Y2Q4

Budget - 5%

2.1.4Incorporate updated criteria and DER models into feeder models

(a) Assist the team by providing subject matter expertise (SMEs)

Y1Q3 – Y2Q4

Budget - 7%

(b) Develop use case structure for TNY-MEMS potential pilot – Feeder Model for Commercial and Large Industrial Settings

2.1.5Integrate control devices and mechanism models in feeder models


(b) Develop use case structure for TNY-MEMS potential pilot

Y1Q3 – Y2Q4

Budget - 3%

Project Objective 2.4: Analyzing Microgrid and Active Distribution System Concepts of DER


Analyze secondary power controllers for power management

Distributed secondary controllers for proportional power haring

Communication layout/scheme for consensus control

Optimal utilization of renewable sources



Y1Q3 – Y2Q4

Budget - 5%

Project Objective 2.7: DSO Functions for Optimal Operation and Management of DER


Optimal Operation of DERs



Y1Q3 – Y3Q4

Budget - 5%

2.7.6Distributed Optimization for demand side management

Assist the team by providing subject matter expertise (SMEs)

Y1Q3 – Y3Q4

Budget - 5%

Project Objective 2.8: DSO Functions Considering Regulation and Market Design


Address market and regulatory issues

Assist the team by providing subject matter expertise (SMEs)

Y1Q3 – Y3Q4

Budget - 3%

2.8.2Address Distribution Market Development



Y1Q3 – Y3Q4

Budget - 7%

Project Objective 2.9: Integrating DMS and DER Control


Architecture definition, hierarchy design plan, transactive control plans


(b) Develop use case design for TNY-MEMS potential pilot

(c) Protocols and contribution to standards

Y1Q3 – Y3Q4

Budget - 5%

2.9.2Simulation, verification and validation with Transactive Control



Y1Q3 – Y3Q4

Budget - 5%

2.9.3Simulation, and validation of DER Control Interface with DSO using DER-CAM



Y1Q3 – Y3Q4

Budget - 5%

PHASE III: Lab testing and Validation:

During the phase of Lab Testing and Validation, GE will provide support services to WSU for development of lab-scale model for testing and validation purposes.

Tasks for Objective 3.3 Deliverables Timeline / budget3.3 Testing and Validation using Lab

Scale Validation System (a) Support WSU for

development of Lab Scale distribution system using Philadelphia Navy Yard System

(b) Testing and Validation of Use Case Design and functions defined in Task2 using GE MEMS application in integration with other sub-systems

Y2Q3 – Y3Q4

Budget - 10%

PHASE IV: Pilot Level Field Implementation: During the phase of Pilot level field implementation, GE will implement the Philadelphia Navy Yard model and use case design defined as part of the phase II. The implementation will be done on GE’s commercial off the shelf system called GE e-terra based Microgrid Energy Management System (MEMS) software

Tasks for Objective 4.3 Deliverables Timeline / budget4.3 Field Implementation for Urban

Feeders(a) Implementation of use

cases for Philadelphia Navy Yard System

(b) Testing and Validation of Use Case Design and functions defined in Task2 using GE MEMS application in integration with other sub-systems

Y2Q2 – Y4Q2

Budget - 25%

PHASE V: Impact Analysis and Policy RecommendationGE has no scope of work for this phase

PHASE VI: Capacity Building and Workforce TrainingGE has no scope of work for this phase

C. DELIVERABLES AND MILESTONE

STATEMENT OF WORK (SOW) FOR UI-ASSISTVenkata Consulting Solutions Inc.

UI-ASSIST: US-India collAborative for smart diStribution System wIth StorageA. SCOPE OF WORK

Scope of work for this proposal is to address challenges associated with DER integration and seamless control into evolving DSO functions and helping with demonstration tasks. Work scope also includes microgrid interface with ADMS and helping with test case modeling.

B. TASKS TO BE PERFORMED

PHASE I: Kickoff and Finalizing Overall Project Management Architecture:

Tasks for Objective 1.1: Finalize project management plan Deliverables Timeline / budget

1.1.3: Finalize project progress monitoring and project direction plan

Provide support and assistance to WSU

Review the document and provide feedback with comments

(Q1Y1-Q2Y1)

1.1.4: Finalize intellectual property and commercialization plan



(Q1Y1-Q2Y1)

1.1.5: Finalize knowledge distribution, promotional material, and workforce development plans;



(Q1Y1-Q2Y1)

1.1.6: Finalize project management plan



(Q1Y1-Q2Y1)

Tasks for Objective 1.2: Finalize agreements for project management

Deliverables Timeline / budget

Task 1.2.1

Task 1.2.2

Task 1.2.3

Task 1.2.4


Review the document and provide feedback with comments as needed

(Q1Y1-Q2Y1)

PHASE II: Research and Development Activities:

Tasks for Objective 2.1: Developing Benchmark Test Systems Deliverables Timeline / budget

2.1.1 Determine modeling/ testing criteria for feeders and Report (Q3Y1-Q3Y1)

pilot projects

2.1.2 Update feeders models based on 2.1.1 Test systems (Q3Y1-Q4Y1)

2.1.3 Determine modeling and testing requirements for varying penetration of different kind of DER and microgrids

Report (Q3Y1-Q4Y1)

2.1.4 Develop updated models working with utility partners

Test systems (Q3Y1-Q4Y1)

Tasks for Objective 2.3: Managing and Optimizing Energy Storage


2.3.3 Storage value maximization and use cases Report (Q3Y1-Q3Y2)

Tasks for Objective 2.4: Analyzing Microgrid and Active Distribution System Concepts for DER


2.4.2 Primary controller design and interface Report on requirement

(Q3Y1-Q3Y2)

2.4.3 Secondary controllers for power management Report on requirement

(Q3Y1-Q4Y2)

2.4.4 AC/DC Microgrid protection Mechanism Report and test systems

Q2Y2-Q4Y3

Tasks for Objective 2.7: DSO Functions for Optimal Operation and Management of DER


2.7.5 System reconfiguration and state estimation with DER and microgrid and enhanced sensor data

Report and Results with team

(Q1Y2-Q2Y3)

2.7.5 Optimal operation of DER for restoration, volt/ var control and other service

Report and results

2.7.7 Demand side management with DER and Storage and demand response

Report and results using test feeders

(Q1Y2-Q3Y3)

2.7.8 Reliability assessment with DER and Microgrid Report

Tasks for Objective 2.8: DSO Functions Considering Regulation and Market Design


2.8.2 Distribution market development including DSO role and interaction with TSO. Consider DER/ DR incentives for providing services like VVC

Report (Q2Y3-Q3Y4)

Tasks for Objective 2.9: Integrating DMS and DER Control Deliverables Timeline / budget

2.9.1 Architecture plan, design and control plans Report (Q2Y3-Q3Y4)

2.9.2 Simulation, verification and validation Assist team (Q2Y3-Q3Y4)

2.9.3 Simulation, verification and validation with enhanced DER and microgrid

Assist team (Q2Y3-Q3Y4)

PHASE III: Lab testing and Validation:

Tasks for Objective 3.3: Testing and Validation using Lab Scale Distribution System


3.3.3 Testing and validation of a large-scale utility system in Lab with ADMS

Assist in testing and validation

(Q1Y4-Q4Y4)

PHASE IV: Pilot Level Field Implementation: Objective 4.3: Field Implementation for Urban Feeders

Tasks for Objective 4.3: Field Implementation for Urban Feeders


4.3.4 Field implementation in TNY or its equivalent Field test data and analysis

(Q1Y5-Q4Y5)

4.3.4 Collect and analyze field test data Report

Total budget will be equally distributed for all the year.C. DELIVERABLES AND MILESTONE

All deliverables and timeline is marked in above table

WSU

WSU is participating in this deliverable.

Documents

school.eecs.wsu.edu€¦ · Web viewUI-ASSIST. Texas A&M Statement of work. 2.3.2 . Storage sizing a. nd siting and optimization (TAMU-Singh, MIT, Panasonic) 2.3.2.1 Identification