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At least 955 records · Page 53

Optimal Energy Dispatch Controller for Fuel Cell-Integrated Multi-Zone Building

Stationary fuel cells provide potential opportunities for energy savings when integrated with buildings. Through smart dispatch of both electrical power and heat generated by the fuel cells and managing the building loads, the buildings can achieve more efficient operation. In this paper, we develop an optimal energy dispatch controller to operate a fuel cell-integrated building. The controller leverages the inherent thermal storage and the dispatchable fuel cell to reduce its operating cost and to allow the building to participate in grid services. The proposed controller is implemented on two types of commercial buildings, a large office building and a large hotel, and the effectiveness of the controller is demonstrated through simulations. The results also indicate that the potential saving varies significantly with different system parameters, including season, fuel prices, and equipment sizing, which provide helpful insights for building operators and other stake holders.

39 EE - Hydrogen and Fuel Cell Technologies (EE-3F↗

EVI-EnSitePy (Electric Vehicle Infrastructure – Energy Estimation and Site Optimization Tool in Python) [EVI-X Modeling Suite] [SWR-25-07]

EVI-EnSitePy is a comprehensive agent-based tool designed for the analysis and design of high-power charging sites, encompassing a wide array of site agents including Electric Vehicles (EVs), chargers, energy storage units (ESS), renewable energy resources (DER), and loads. This versatile tool offers diverse functionalities and a modular modeling approach, allowing detailed configuration of agents based on power ratings, port numbers, energy capacities, demand requirements, charger interfaces, and flexibility to customize the tool for project specific goals. By simulating agent interactions and employing various metrics, EVI-EnSitePy enables the assessment of site performance, exploration of energy management systems (EMS), and implementation of innovative EV charging policies. Utilizing EV charge schedules and arrival states, the tool performs thorough charging site simulations, with outputs consisting of agent and site-level power profiles and statistical metrics. Employing a tree graph structure, EVI-EnSitePy supports nested site structures and power distribution modeling. The tool's ability to generate charging schedules deterministically or via stochastic analysis further enhances its versatility. Through its features and capabilities, EVI-EnSitePy offers a powerful platform for informed decision-making in the realm of high-power charging site design and operation.

Jackson, Derek [National Renewable Energy Laborato↗

Cryogenic C-band wavelength division multiplexing system using an AIM Photonics Foundry process design kit

Cryogenic environments make superconducting computing possible by reducing thermal noise, electrical resistance and heat dissipation. Heat generated by the electronics and thermal conductivity of electrical transmission lines to the outside world constitute two main sources of thermal load in such systems. As a result, higher data rates require additional transmission lines which come at an increasingly higher cooling power cost. Hybrid or monolithic integration of silicon photonics with the electronics can be the key to higher data rates and lower power costs in these systems. We present a 4-channel wavelength division multiplexing photonic integrated circuit (PIC) built from modulators in the AIM Photonics process development kit (PDK) that operate at 25 Gbps at room temperature and 10 Gbps at 40 K. We further demonstrate 2-channel operation for 20 Gbps aggregate data rate at 40 K using two different modulators/wavelengths, with the potential for higher aggregate bit rates by utilizing additional channels.

42 ENGINEERING↗

Cyber-Physical Dynamic System (CPDS) Modeling for Frequency Regulation and AGC Services of Distributed Energy Resources

The substantial integration of renewable energy brings significant challenges to balance the system in real time because of the variability and intermittency of renewable power. For the reliable system operation, the frequency regulation service is used to stabilize the system frequency through automatically balancing the system generation and load. On one hand, with the substantially increasing deployment of renewable energy in electricity system, the requirement of frequency regulation (FR) services increases significantly. On the other hand, the current main resource of FR services, the controllable conventional generation, is continuously decreasing in the system generation mix. This means that in the future high renewable penetration power system, additional and alternative reliable FR services providers such as distributed energy storage (DES) resources should be explored. Although the capability of utility-scale energy storage to provide FR services has been demonstrated, the integrated control and dynamic modeling of distributed energy resource (DER) providing frequency regulation grid services has been rarely explored. There are several challenges to adopt DERs to provide reliable grid services as illustrated in FERC 755. First, the distributed installation of DES requires a comprehensive cyber-physical dynamic system (CPDS) modeling to fully consider the impacts of the communication latency variability on its real time FR provision capability. Unlike the conventional generators, there are two-layer communication between DERs and system operators which increase the communication delay. Second, unlike the conventional generators whose dynamic models have been comprehensively studied, the difference among individual DER components' power-dynamic characteristic brings challenges in the accurate dynamic AGC modeling of its power-frequency relationship. Third, the temporal dependent state of charge uncertainty of DERs challenges DERs' power and frequency regulation capacities scheduling in the look-ahead generation scheduling. Therefore, the aggregator should optimize the frequency regulation provision from individual DER in real time. To overcome these challenges, this project proposes a cyber-physical dynamic system (CPDS) model to handle the uncertainty of DERs two-layer communication latency and power dynamics. The variability of DERs' communication delay and dynamic constraints will be comprehensively modeled. The DERs' AGC model with communication delay is designed to validate DESs' frequency regulation services. Like current performance-based frequency regulation evaluation, the delivery of DERs' frequency regulation will be assessed through post-analysis of the actual AGC response with respect to the AGC control signal from system operators. Consequently, the reliability improvement with DERs providing reliable frequency regulation services can be evaluated from a comprehensive perspective considering all the dynamics of communication and power.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Real-time disaggregation of renewable energy generation on an electricity distribution system

Techniques are described for disaggregation of renewable energy generation on an electricity distribution system. Aggregate power measurements are identified a distribution substation. Active power load of the distribution substation and active power generated by renewable energy sites can be disaggregated from the aggregate power measurements.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Paper #50 Preliminary Sizing of Active Magnetic Bearings for sCO2 Waste-Heat Recovery Application

The team of Southwest Research Institute (SwRI) and GE Vernova (GE) is executing a project developing conceptual designs of high-temperature active magnetic bearings (AMBs) for sCO2 machinery applications. The goals of the project are to develop conceptual designs for radial and thrust AMBs capable of operating in sCO2 environments up to 1000°F. A waste-heat recovery (WHR) application with hermetic machinery is used as a reference case to apply AMBs. This application was previously investigated to compare hermetic machinery enabled by process-lubricated bearings and high-speed motors/generators with traditional machinery configurations using oil-lubricated bearings, gearboxes, and grid-frequency electric machines. In this paper, AMBs are sized for the WHR application machinery. Load capacity estimates are made using first-principle-derived formulas that consider operation at elevated temperatures. Estimates of linearized AMB system coefficients are also made to aid future controller development. Rotor models are developed that account for AMB rotor geometry and housing space requirements. Rotordynamics studies are performed to identify reasonable target values for closed-loop AMB stiffness and closed-loop AMB damping. These target values will be used in future control studies.

Lipham, Robert↗

Comparison of Thermal Management Approaches for Integrated Traction Drives in Electric Vehicles

The continuous push to increase power densities of electric vehicle (EV) traction drive systems necessitates combining electric motor and power electronics into one unit. A single, compact traction drive unit with fewer interconnecting components also facilitates fast, automated assembly of electric vehicles, driving production costs down and enabling wider adoption of EVs. There are a number of challenges associated with the integration of power electronics with the electric machine, including thermal management of the combined traction drive system. However, one important benefit of integration from the thermal management system perspective is the potential for using a single fluid loop instead of two separate cooling systems for the electric machine and the power electronics/inverter. This paper reviews several integration approaches and, employing finite element analysis (FEA), compares thermal management solutions for the combined electric machine and power electronics systems. Namely, three different scenarios are modeled: (1) independent component (motor and power electronics) cooling, which is compared to the combined cooling system approach for (2) radially and (3) axially integrated power electronics modules into the motor enclosure. Temperature distributions for selected thermal loads and thermal resistances from the key heat-generating components to the cooling fluid are compared for each scenario.

47 OTHER INSTRUMENTATION↗

MBARI-WEC September and October 2022 Field Data

This data is needed to simulate a model of the MBARI-WEC (Monterey Bay Aquarium Research Institute, Wave Energy Converter device) in a simulation environment (e.g. Gazebo) for 56 observation dates in the time between September and October 2022, and to compare the simulation outputs to the corresponding field data of the physical MBARI-WEC. There were 50 observations chosen in Sept and 6 observations in Oct. To help understand terms below, a summary of the system can be found at the github link in the downloads section below. The Gazebo MBARI-WEC model is also provided, should users wish to simulate using this platform. There are 4 *mat files included. ................................................................................................................................................................................................................................... Spectrum and Simulation Inputs: September2022_spectrum_siminputs.mat and October2022_spectrum_siminputs.mat has data needed for simulation inputs in table format. These include the ocean spectrum for an observation and operating parameters of the MBARI-WEC during that observation. They are organized as rows representing an observation and columns representing data. For example, for the September *mat there are 50 rows. The first 7 columns are Datetime, sig_waveheight, peak_period, mean_period, heaveconedoor_status, pistonpos_mean, and scale_factor: - Datetime is the date and time the observation occurred in PST - sig_waveheight is the significant wave height of the ocean spectrum during that observation in meters - peak_period is the peak period of the ocean spectrum during that observation in seconds - mean_period is the mean period of the ocean spectrum during that observation in seconds - heaveconedoor_status is the status of the heave cone doors where 0 represents the doors are open and 1 represents they are closed - pistonpos_mean is the mean position of the PTO ram (piston) in meters - scale_factor is an additional factor of 0.5 --1.4 applied to a default damping relationship The next columns are data needed to represent the ocean spectrum. First are the frequencies [Hz] labeled as "f0-f38", then the variance density [m2/Hz] labeled as "vardens0-vardens38". October2022_spectrum_siminputs.mat follows as a similar format as above, but includes a larger amount of ocean spectrum frequencies and variance density elements. ................................................................................................................................................................................................................................... Field data: The field data is found in MBARIWEC_septdata.mat and MBARIWEC_octdata.mat for the observations of September and October, respectively. These contain data in a struct format. The struct contains the following fields for each observation: PC_BattCurr, PC_LoadCurr, PC_RPM, PC_Voltage, SC_Range, SC_Velocity, DateTime, where: - PC_BattCurr is the current flowing to or from the onboard batteries in Amps - PC_LoadCurr is the current flowing to the load dump in Amps - PC_RPM is the electric/hydraulic motor shaft speed (directly coupled) in RPM - PC_Voltage is the bus voltage at the power converter in Volts - SC_Range is the PTO ram (piston) position in meters where 0 is fully retracted and 2.03 is fully extended - SC_Velocity is the PTO ram (piston) velocity in meters/sec - DateTime is the date and time of the sampled field data in each observation in PST - Electric Power is equal to: PC_Voltage*(PC_BattCurr + PC_LoadCurr) in Watts For example, upon loading MBARIWEC_octdata.mat, the aforementioned fields would be loaded, each with {6x1} cells for the 6 observations chosen in October. Within the first cell of e.g. SC_Range would be sampled data representing the field data of the MBARIWEC PTO piston position for say, one hour, of the first October observation. The corresponding field DateTime would...

16 TIDAL AND WAVE POWER↗

Electrification Futures Study: Methodological Approaches for Assessing Long-Term Power System Impacts of End-Use Electrification

By its nature, electrification enhances the coupling between the electric sector and end-use sectors. Assessing the impacts of electrification requires both an examination of the complex interactions between sectors and a broader assessment of multiple parts of the energy system. The Electrification Futures Study (EFS) uses several complementary modeling and analysis tools to analyze the impacts of electrification on the U.S. energy system. In particular, the EFS relies on an overarching scenario analysis approach, but through the use of separate modeling approaches designed to assess various electricity demand- and supply-side futures. The primary model employed to generate the supply-side scenarios is the Regional Energy Deployment System (ReEDS) model, which is a capacity expansion model for the U.S. electricity system through 2050. Traditionally, the model has been primarily exercised in scenario analysis that implicitly assumed limited electrification. Because of this assumption, resulting future load profiles are approximated by historical ones, load growth is driven primarily by population and economic growth only, and changes in direct end-use natural gas consumption do not effect natural gas costs for electricity generation. In this report, we (1) reflect the potential for resource sharing between regions given these changes in demand, (2) represent how changes in natural gas consumption in end-use sectors could impact the economics of natural gas-fired generation, and (3) document a new model representation of demand-side flexibility used for the EFS. These improvements to ReEDS are employed for the EFS supply-wide analysis, which is summarized in a companion EFS report titled Electrification Futures Study: Scenarios of Power System Evolution and Infrastructure Development for the United States (Murphy et al. 2019). The data and methods documented in this report could also be adapted for other models with similar scope and limitations as ReEDS, and these data and methods could be used to assess future electric system scenarios.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Data Challenges for Structural Health Monitoring of Electrical Machines [Slides]

Induction motors (ID) consist of a stator and a rotor. Induction motor slip decreases with decreasing load. Induction motor slip decreases with decreasing load. This talk will focus on removing closely coupled power grid frequencies in an effort to observe the true dynamic response signature of induction motors. Using the time difference between the zeros, we found the frequency as it changes over time for each of the 3 phases. The frequencies of the 3 phases were averaged to reduce the effects of sidebands on the instantaneous frequency. The frequencies of the 3 phases were averaged to reduce the effects of sidebands on the instantaneous frequency. The Kalman filter rapidly synchronizes with the expected output. The sidebands were recreated using spectral subtraction. The frequency domain shows the reduction in magnitude of the power grid frequency the Kalman output was subtracted out. Kalman filtering also reveals important data about the sidebands in healthy motors. The Continuous Wavelet Transform is an alternative to a Bandpass IIR filter used in the analysis of the acceleration data. Analysis of the acceleration data reveals 4 sidebands around the slip frequency.

42 ENGINEERING↗

Grid Capacity – What is it, what determines it, does one number work, and how does it relate to electric vehicles?

Grid capacity is effectively how much power the system can reliably deliver, whether that is to serve loads (load service capacity) or accept generation (hosting capacity). Grid capacity can also mean different things at different scales. On the whole power system, grid capacity may be the maximum amount of power generation available. For a specific region, grid capacity may be limited by how much power the transmission and distribution lines can safely carry to that region. At the feeder level, it may be how much photovoltaic generation can be included before reliability or operations are impacted. At the end-use or residential level, grid capacity may be the size of the service breaker for that house.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022

Simulated hourly electric vehicle charging profiles for light-duty household passenger vehicles in the contiguous United States, 2018-2050. Profiles are differentiated by scenario, county, household and vehicle types, and charging type. Data was produced in 2022 using the Transportation Energy & Mobility Pathway Options (TEMPO) model and published in demand-side grid (dsgrid) toolkit format. Data are available for three adoption scenarios: "AEO Reference Case", which is aligned with the U.S. EIA Annual Energy Outlook 2018 (linked below), "EFS High Electrification", which is aligned with the High Electrification scenario of the Electrification Futures Study (linked below), and "All EV Sales by 2035", which assumes that average passenger light-duty EV sales reach 50% in 2030 and 100% in 2035. The charging shapes are derived from two key assumptions of which data users should be aware: "ubiquitous charger access", meaning that drivers of vehicles are assumed to have access to a charger whenever a trip is not in progress, and "immediate charging", meaning that immediately after trip completion, vehicles are plugged in and charge until they are either fully recharged or taken on another trip. These assumptions result in a bounding case in which vehicles' state of charge is maximized at all times. This bounding case would minimize range anxiety, but is unrealistic from the point of view of both electric vehicle service equipment (EVSE) (i.e., charger) access, and plug-in behavior as it can result in dozens of charging sessions per week for battery electric vehicles (BEVs) that in reality are often only plugged in a few times per week.

Array↗

Object-Oriented Controllable High-Resolution Residential Energy (OCHRE) (TM) Model

This presentation describes the OCHRE model as part of NREL's Powered By webinar series. OCHRE is a building energy modeling tool designed to model flexible loads in residential buildings. OCHRE includes detailed models and controls for flexible devices including HVAC equipment, water heaters, electric vehicles, solar PV, and batteries. It is designed to run in co-simulation with custom controllers, aggregators, and grid models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

County-level assessment of behind-the-meter solar and storage to mitigate long duration power interruptions for residential customers

Customer concerns over electric system resilience could drive early adoption of behind-the-meter solar-plus-storage (BTM PVESS), especially as wildfire, hurricane, and other climate-driven risks to electric grids become more pronounced. However, the resilience benefits of BTM PVESS are poorly understood, especially for residential customers, owing to lack of data and methodological challenges, making it difficult to forecast adoption trends. In this paper, we develop a methodology to model the performance of BTM PVESS in providing backup power across a wide range of customer types, geography / climate conditions, and long duration power interruption scenarios, considering both whole-building backup and backup of specific critical loads. We combine novel, disaggregated end-use load profiles across the continental United States with temporally and geospatially aligned solar generation estimates. We then implement a PVESS dispatch algorithm to calculate the amount of load served during interruptions. We find that PVESS with 10 kWh of storage can meet a limited set of critical loads in most United States counties during any month of the year, though this capability drops to meeting only 86% of critical load, averaged across all counties and months, when heating and cooling are considered critical. Backup performance is lowest in winter months where electric heat is common (southeast and northwest U.S.) and in summer months in places with large cooling loads (southwest and southeast U.S.). Winter backup performance varies by roughly 20% depending on infiltration rates, while summer performance varies by close to 15% depending on the efficiency of the central air-conditioning system. Differences in temperature set-points in Harris County correspond to a 40% range in winter backup performance and a 20% range in summer performance. Economic calculations show that a customer’s resilience value of PVESS must be high to motivate adoption of these systems.

14 SOLAR ENERGY↗

Policy Innovation and Governance for Irrigation Sustainability in the Arid, Saline San Joaquin River Basin

This paper provides a chronology and overview of events and policy initiatives aimed at addressing irrigation sustainability issues in the San Joaquin River Basin (SJRB) of California. Although the SJRB was selected in this case study, many of the same resource management issues are being played out in arid, agricultural regions around the world. The first part of this paper provides an introduction to some of the early issues impacting the expansion of irrigated agriculture primarily on the west side of the San Joaquin Valley and the policy and capital investments that were used to address salinity impairments to the use of the San Joaquin River (SJR) as an irrigation water supply. Irrigated agriculture requires large quantities of water if it is to be sustained, as well as supply water of adequate quality for the crop being grown. The second part of the paper addresses these supply issues and a period of excessive groundwater pumping that resulted in widespread land subsidence. A joint federal and state policy response that resulted in the facilities to import Delta water provided a remedy that lasted almost 50 years until the Sustainable Groundwater Management Act of 2014 was passed in the legislature to address a recurrence of the same issue. The paper describes the current state of basin-scale simulation modeling that many areas, including California, are using to craft a future sustainable groundwater resource management policy. The third section of the paper deals with unique water quality issues that arose in connection with the selenium crisis at Kesterson Reservoir and the significant threats to irrigation sustainability on the west side of the San Joaquin Valley that followed. The eventual policy response to this crisis was incremental, spanning two decades of University of California-led research programs focused on finding permanent solutions to the salt and selenium contamination problems constraining irrigated agriculture, primarily on the west side. Arid-zone agricultural drainage-induced water quality problems are becoming more ubiquitous worldwide. One policy approach that found traction in California is an innovative variant on the traditional Total Maximum Daily Load (TMDL) approach to salinity regulation, which has features in common with a scheme in Australia’s Hunter River Basin. The paper describes the real-time salinity management (RTSM) concept, which is geared to improving coordination of west side agricultural and wetland exports of salt load with east side tributary reservoir release flows to improve compliance with river salinity objectives. RTSM is a concept that requires access to continuous flow and electrical conductivity data from sensor networks located along the San Joaquin River and its major tributaries and a simulation model-based decision support designed to make salt load assimilative capacity forecasts. Web-based information dissemination and data sharing innovations are described with an emphasis on experience with stakeholder engagement and participation. The last decade has seen wide-scale, global deployment of similar technologies for enhancing irrigation agriculture productivity and protecting environmental resources.

54 ENVIRONMENTAL SCIENCES↗

DSO+T: Expanded Study Results DSO+T Study: Volume 5

The Distribution System Operator with Transactive (DSO+T) study investigates the engineering and economic performance of a transactive energy retail market coordinating a high penetration of customer-side flexible energy assets. The study seeks to answer whether such an implementation is cost effective for customers, recovers sufficient revenue for DSOs, and is equally applicable and beneficial to a range of flexible asset types, renewable generation scenarios, and market assumptions. This report volume provides a detailed set of results for the DSO+T study extending results presented in Volumes 1, 2, and 4. The engineering and economic performance of the transactive energy scheme is presented for two separate flexible asset deployments: flexible loads (HVAC units and residential water heaters) and behind-the-meter batteries. The results of each transactive case are compared to a business-as-usual case. These cases are subject to two different renewable generation scenarios, a moderate renewable generation scenario, representative of current levels of renewable generation deployment, and a future high renewables scenario, including the increased deployment of rooftop solar photovoltaic and electric vehicles. The transactive coordination scheme is shown to produce effective and stable control and decrease peak loads 9–15%. The resulting annual demand flexibility provides net economic savings of $3.3–5.0B per year for a region the size of Texas. Detailed analysis shows that net benefits were seen for a range of distribution system operator, customer, and flexible asset types. Both participating customer (with transactive flexible assets) and nonparticipating customers (with nonflexible assets) see reductions in annual utility bills and net annual energy expenses in the range of 10–16%.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Insights Into Seismicity Associated With Flexibly Operating Enhanced Geothermal System From Real‐Time Distributed Acoustic Sensing

Enhanced Geothermal Systems (EGS) have the capacity to broaden the accessible resource pool for geothermal power generation. Traditionally viewed as a “baseload” resource, their flexible operation might also enable dispatchable load‐following generation and long‐term energy storage, aligning them with the evolving landscape of decarbonized electricity systems. However, increasing permeability and extracting energy during EGS operations can induce microseismic events; for many prior EGS efforts, some associated seismicity has been observed. While energetically beneficial, the flexibility of EGS operations prompts our inquiry into whether new types of operations will yield previously unseen seismicity patterns. We demonstrate the use of distributed acoustic sensing (DAS) with real‐time edge computing to monitor seismicity during a pilot test of a cyclically operated EGS facility at the Blue Mountain geothermal field. Our focus lies in uncovering seismicity insights from the real‐time microseismic catalog, particularly during load‐following dispatchability tests simulating flexible EGS operation. Here, we find that variations in pore pressure consistently correlate with seismicity, and that controlling pressure cycles during flexible operations appears to constrain microseismic activity during subsequent cycles. The spatio‐temporal evolution of microseismic clouds recorded during cyclic injection cycles fits diffusive models over our available observation period. Additionally, seismicity elevation lags behind pore pressure increases, likely due to pressure diffusion to the fracture system boundary. Through real‐time monitoring, we offer novel insights into seismicity associated with flexibly operating EGS. Our findings suggest that leveraging DAS and edge computing can inform EGS operations and help mitigate induced seismicity.

Chamarczuk, Michal [Rice Univ., Houston, TX (Unite↗

Integrating AI Data Centers with the Power Grid

The rapid expansion of artificial intelligence (AI) has triggered an unprecedented surge in electricity demand, with US data center energy use projected to double or triple 2023 levels by 2028. This exponential growth places strain on grid infrastructure, which can hinder timely construction of desired computing capacity. To bridge this supply-demand gap, utilities and AI developers are increasingly turning to demand flexibility, a strategy that incentivizes shifting or reducing power use during peak periods of grid stress. Data centers are uniquely equipped for flexible operations due to their digital workloads, built-in redundancy, and onsite energy assets. This article outlines four primary mechanisms to enable data center flexibility: computational load flexibility (shifting tasks temporally or geographically), flexible use of core facility infrastructure adjustments, energy storage utilization, and onsite electricity generation. To encourage adoption, utilities are deploying new tariff designs, including voluntary interruptible service riders, mandated flexibility requirements, and streamlined interconnection processes for flexible loads. For the highly capitalized and rapidly growing AI industry, the primary motivators for embracing these strategies are expediting facility interconnection, satisfying emerging regulatory mandates, and mitigating community resistance. While demand flexibility cannot substitute the long-term need for new bulk power generation, it serves as an essential, immediate solution for enabling near-term deployment. By transforming data centers from grid stressors into stabilizing assets, flexible operations can ensure reliable grid integration, ease market pressures, and support a resilient power system.

24 POWER TRANSMISSION AND DISTRIBUTION↗