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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Integrated Distribution Planning

The contemporary distribution planning landscape is comprised of an increasing number of factors that require integration into the engineering of the modern electric grid. Expectations for electric utilities to accommodate heightened awareness of stakeholders' interest in things like decarbonization, resilience and equity are growing. As these interests are formed into objectives, many jurisdictions will experience increasing levels of load modifying technologies like DER, building and industrial electrification and electric vehicles which prove not only to challenge the capabilities of the grid; but the processes by which planning for it is traditionally done. Other related factors that strain the conventional distribution planning mold are the swelling amount and sources of data associated with these technologies and the need it creates for improved capabilities in the processes and tools that manage it. As the complexity of the distribution system expands, so will the distribution system's effects on the transmission and generation systems that it is a part of. Forecasting distribution system load and DER are examples of areas where this complexity will manifest, and harmonizing distribution forecasting with transmission and generation forecasting requires higher amounts of intentionality as these typically separate processes become a solitary one. Of course, core activities do not cease as a utility begins to integrate these other factors, and in this webinar we explore specifics of how distribution planning can be expected to evolve as progress towards Integrated Distribution System Planning is made.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Development of an Energy Efficient and Cost Effective Autonomous Vehicle Research Platform

Commercialization of autonomous vehicle technology is a major goal of the automotive industry, thus research in this space is rapidly expanding across the world. However, despite this high level of research activity, literature detailing a straightforward and cost-effective approach to the development of an AV research platform is sparse. To address this need, we present the methodology and results regarding the AV instrumentation and controls of a 2019 Kia Niro which was developed for a local AV pilot program. This platform includes a drive-by-wire actuation kit, Aptiv electronically scanning radar, stereo camera, MobilEye computer vision system, LiDAR, inertial measurement unit, two global positioning system receivers to provide heading information, and an in-vehicle computer for driving environment perception and path planning. Robotic Operating System software is used as the system middleware between the instruments and the autonomous application algorithms. After selection, installation, and integration of these components, our results show successful utilization of all sensors, drive-by-wire functionality, a total additional power* consumption of 242.8 Watts (*Typical), and an overall cost of $118,189 USD, which is a significant saving compared to other commercially available systems with similar functionality. This vehicle continues to serve as our primary AV research and development platform.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Program Review: Thermal Systems Pillar

Program Review: Thermal Systems Pillar Slides go over the 3 year plan for the integrated energy systems program DETAIL. The plan goes into connections between the DETAIL components and plans for future integration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Enhancing the Survivability of Power Systems With Grid-Edge DERs Against DoS Attacks

Power system survivability, defined as the ability of a system to maintain steady-state functionality under varying operational conditions, reflects its resilience against disturbances. While existing research primarily focuses on physical-layer disturbances, the increasing prevalence of grid-edge DERs, which are primarily used for integrating renewable energy, has significantly expanded the cyber attack surface. As a result, operational disruptions caused by cyber threats are posing significant challenges to system survivability and cannot be overlooked. To fill this gap, we redefine system survivability to incorporate the cyber layer’s status and propose a Distributionally Robust Optimization (DRO) approach to enhance power system survivability against potential cyber-physical threats. In this paper, we first analyze the operational guidelines of systems with a high penetration of DERs under various cyber network conditions and redefine survivability in this context. Next, we focus on the most common cyber threat, Denial-of-Service (DoS) attacks, and develop a corresponding attack model. This model allows for the creation of a kernel-based ambiguity set that captures attack uncertainties using historical data. Finally, we transform the proposed DRO model as a tractable optimization problem, with its solution providing an optimal cyber redundancy plan to enhance system survivability in DoS attack scenarios. Simulation results on the IEEE 13-node and 123-node test feeders demonstrate the effectiveness of our proposed model in improving system survivability. This model can also be expanded to include other types of common attacks and serve as a comprehensive planning tool to improve overall cyber physical survival of the system.

cybersecurity↗
DOE OSTI2025

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗
DOE OSTI2021

Design and Operation of Energy Systems with Large Amounts of Variable Generation: IEA Wind TCP Task 25 (Final Summary Report)

This report summarizes findings on wind integration from the 17 countries or sponsors participating in the International Energy Agency Wind Technology Collaboration Program (IEA Wind TCP) Task 25 from 2006-2020. Both real experience and studies are reported. Many wind integration studies incorporate solar energy, and most of the results discussed here are valid for other variable renewables in addition to wind. The national case studies address several impacts of wind power on electric power systems. In this report, they are grouped under long-term planning issues and short-term operational impacts. Long-term planning issues include grid planning and capacity adequacy. Short-term operational impacts include reliability, stability, reserves, and maximizing the value of wind in operational timescales (balancing related issues). The first section presents the variability and uncertainty of power system-wide wind power, and the last section presents recent studies toward 100% shares of renewables. The appendix provides a summary of ongoing research in the national projects contributing to Task 25 for 2021-2024. The design and operation of power and energy systems is an evolving field. As ambitious targets toward net-zero carbon energy systems are announced globally, many scenarios are being made regarding how to reach these future decarbonized energy systems, most of them involving large amounts of variable renewables, mainly wind and solar energy. The secure operation of power systems is increasingly challenging, and the impacts of variable renewables, new electrification loads together with increased distribution system resources will lead to somewhat different challenges for different systems. Tools and methods to study future power and energy systems also need to evolve, and both short-term operational aspects (such as power system stability) and long-term aspects (such as resource adequacy) will probably see new paradigms of operation and design. The experience of operating and planning systems with large amounts of variable generation is accumulating, and research to tackle the challenges of inverter-based, nonsynchronous generation is on the way. Energy transition and digitalization also bring new flexibility opportunities, both short and long term.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Next Generation System Analysis Model: Recently Added Features and Future Plans

To better enable informed decision-making regarding the back-end of the nuclear fuel cycle, the Integrated Waste Management System (IWMS) program within the U.S. Department of Energy, Office of Nuclear Energy (DOE-NE) has been sponsoring the development and application of system analysis tools capable of analyzing various system options for the management of spent nuclear fuel (SNF) and high-level radioactive waste (HLW).With these tools, IWMS architecture analyses are being conducted to support the future deployment of a comprehensive nuclear waste management system that considers all major back-end aspects of the nuclear fuel cycle (i.e., transportation, storage, and disposal). The Next Generation System Analysis Model (NGSAM) is an agent-based simulation software tool expressly designed to be capable of modeling features within various IWMS architectures. NGSAM imports data from Oak Ridge National Laboratory (ORNL)’s unified database (e.g., historic assembly information, thermal profiles for assembly heat, and at-reactor dry storage loadings) to ensure that each simulation initializes with a realistic representation of the state of commercial SNF in the U.S. Recent major enhancements implemented into NGSAM in the period since NGSAM was last presented at the WM2019 conference include: • Tracking of railroad escort car acquisition and buffer car acquisition • The addition of heavy haul truck (HHT) and barge routes for some sites, as well as support for user-defined inter-modal routes • Updates to the logic that checks the transportation cask thermal limit maps prior to package transport • An allocation method that predicts when reactor sites would pack assemblies from their spent fuel pools for dry storage, and prioritizes shipments directly from the pools of those reactor sites in the preceding periods (before the predicted loadings to dry storage), thus reducing the number of casks loaded into dry storage at reactor sites • The addition of “reactor site family” operational limits to restrict the number of loads of SNF taken from the pool and from dry storage at a given reactor site each year • Added support for multiple canister loading map options and packages with multiple compatible transportation overpacks • Updates to the handling of non-commercial SNF, including a new database that contains data to support the updates • Updates to allow analysis of hypothetical scenarios which include repackaging at reactor sites, e.g., for possible comparative analysis with other scenarios • Implementation of additional output reports, or modification of existing ones • The ability to generate and implement user edits via the NGSAM website • The ability to model loading SNF from pool storage at an interim storage facility (ISF) into dry storage at the ISF • The ability to model consolidating SNF from different existing storage containers at a DOE site into the same DOE standard canister • The ability to model transferring SNF casks from one transportation mode to another, e.g., from HHT to rail, referred to as transloading. These new features have improved NGSAM capabilities and users’ experience with the model. Preliminary NGSAM requirements for modeling advanced reactor fuels, reprocessing, treatment, and conditioning were considered, and this paper describes them at a high level. Other nuclear fuel cycle system analysis tools developed under sponsorship of DOE-NE, like the VISION code developed at Idaho National Laboratory, might be better suited for initial high-level analysis of those technologies and advanced fuel cycles. As technologies are developed and system concepts evolve, NGSAM could provide value by providing more detailed modeling of transport, storage, and disposal of spent fuel and wastes from advanced reactors and advanced fuel cycles at the fuel element and waste container level.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Cybersecurity Considerations for Hydrogen Infrastructure in Airport Environments

This report explores key cybersecurity concerns and best practices within environments that serve as reference points for the development of hydrogen fueling infrastructure for aviation. This cybersecurity analysis leverages prior NREL studies: 1) hydrogen fueling station component validation to identify vulnerabilities and failure events documented in physical equipment, and 2) electric aircraft charging infrastructure analysis to explore primary cybersecurity vulnerabilities. It reviews the criticality of digitized technologies in sustaining hydrogen fuel production, storage, and fueling systems, noting cybersecurity concerns that are universal to power systems and industrial control systems in general. In considering cybersecurity vulnerabilities within a future landscape of hydrogen energy for aviation applications, a reference architecture was intended to reveal the points of connection between assets and the potential sensors that are vulnerable to manipulation in the event of compromised access or communication within a SCADA system. A generalized reference architecture can help stakeholders, engineers, or strategists understand connections, criticalities, and standard practices when it comes to designing and planning for new systems. There are several gaps to account for in assessing the future of hydrogen production, storage, and fueling for aviation. Engaging stakeholders, including aircraft manufacturers, electric utilities, site property owners, and local communities, will inform decision-making around site structure, operations, and resources for future hydrogen fueling infrastructure to understand operational needs and cybersecurity awareness. Cybersecurity mitigation strategy must consider physical attack vectors that emerge with the integration of hydrogen systems into existing airport security requirements. The cybersecurity risk assessment contained in this report is an entry point into potential future granular-level analyses to be conducted as part of hazard and risk assessments for safe aviation hydrogen infrastructure, determining how the scale of hydrogen fuel infrastructure for aviation impacts the volume of cyber attack vectors, and what, if any, are the vulnerabilities associated with different types of on-board hydrogen systems. In this nascent development phase, assessing how best to integrate cybersecurity practices into an evolving U.S. aviation landscape provides critical insights into building increased awareness and stakeholder engagement to support a cyber-resilient infrastructure.

08 HYDROGEN↗
DOE OSTI2021

Resilience in an Age of Increasing Electrification

The purpose of this paper is to outline considerations of global electrification trends that include the resultant system-wide and nation-wide impacts. How we incorporate resilience into the electrification plans that transition a nation from conventional energy sources to secure clean energy sources can impact the success of those transitions. Electrification of energy systems has the potential to impact power system planning, emissions, and security; however, little research has been done on comprehensive solutions for incorporating resilience into the electrification process. Research has been conducted on aspects of the energy system and electrification, but not holistically across disciplines and related to the interdependencies of different components of the grid system. This gap in research provides an opportunity for more analysis and a collection of best practices to incorporate resilience into electrification plans. Although clean energy is the goal of an increasing number of nations worldwide, there are many considerations in designing clean energy transitions and increased electrification. Resilience must be factored into clean energy transitions from planning, market design, and policies to modernized grid structures and new, location-specific dispersed energy and energy storage technologies.

clean energy↗
DOE OSTI2024

Predictive model using artificial neural network to design phase change material-based ocean thermal energy harvesting systems for powering uncrewed underwater vehicles

Uncrewed Underwater Vehicles (UUVs) are a major beneficiary of the phase change material (PCM)-based ocean thermal energy harvesting technology for their mission needs. However, this technology relies on different parameters and energy conversion steps that could be critical to the general energy generation efficiency. Sea trials showed that the design performed lower than their laboratory design specifications. This underperformance results from different factors, mainly the UUV’s trajectory, travel time, underwater ocean currents, temperature fluctuations, and biofouling on the heat exchanger due to long term underwater operations. Therefore, there exists a need to continuously monitor the ambient energy harvesting system and predict system performance, for mission planning purposes. Two major parameters influencing the energy harvesting system include the final pressure inside the hydraulic energy storage vessel or accumulator, and the electrical load value. Here, this work focuses on the hydraulic to electric energy conversion system. Therefore, a combination of numerical model and experimental testing is used to develop a predictive model using artificial neural network using MATLAB. After validation with experimental testing, 1000 data samples obtained from the numerical model are used to train the ANN. Compared to the experimental results, the developed ANN model can predict in less than a second the designed benchtop system’s total efficiency with less than 15 percent maximum error range. This predictive model development represents a cost-effective way for optimization and a computational energy efficient mode aboard UUVs for mission planning for deployed UUVs using PCM-based ocean thermal energy harvesting technology.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

A Demonstration System for Geological Thermal Energy Storage of Concentrating Solar Thermal

Energy storage is increasingly necessary as variable energy technologies are deployed. Seasonal energy storage can shift energy generation from the summer to the winter, but these technologies must have extremely large energy capacities and low costs. Geological Thermal Energy Storage (GeoTES) is proposed as a solution for long-term energy storage [1]. Excess thermal energy can be stored in permeable reservoirs such as aquifers and depleted hydrocarbon reservoirs for several months. The energy capacity cost of GeoTES is very low which makes it suitable for both daily- and seasonal storage of Concentrating Solar Thermal (CST) energy, thus enabling CST to provide value to electricity markets and thermal energy off-takers. A CST-GeoTES demonstration system has been funded by the U.S. Department of Energy, Solar Energy Technology Office. In this article, we will describe this demonstration system and progress that has been made in its development. The demonstration system will comprise a 2 MWth parabolic trough with an 8m aperture developed by Gossamer Space Frames, seven wells, and a 100 kWe power cycle. The demonstration system will be deployed in Kern County, California by Premier Resource Management. A techno-economic model for CST-GeoTES systems has also been developed [3] and is applied to the demonstration system and its planned future expansion. The model integrates the output of specialist models of each subsystem, which enables the performance and cost of both the subsurface and surface systems to be captured. Off-design models enable the performance to be evaluated at each hour of the year, before being aggregated to evaluate the economic potential of CST-GeoTES. Initial analysis indicates that CST-GeoTES can provide long duration energy storage capabilities with low marginal costs of energy capacity - leading to a low value of Levelized Cost of Storage (LCOS) compared to alternative technologies, see Figure 2. In this article, we investigate the cost and performance of the specific demonstration site being developed by PRM and explore a range of operational profiles that can deliver different value streams, such as daily and seasonal storage, capacity, and resiliancy.

14 SOLAR ENERGY↗
DOE OSTI2024

Planning and Operations in Electricity Markets Under System Tansformation: Key Findings

This report summarizes a set of key findings that have been developed through a set of interconnected research activities performed by five institutions between January 2020 and December 2023. The project team, comprising Argonne National Laboratory, the National Renewable Energy Laboratory, Lawrence Berkeley National Laboratory, the Electric Power Research Institute, and Johns Hopkins University, collective engaged with the North American Independent System Operators and Regional Transmission Operators (ISO/RTOs) to identify the key challenges they are facing and opportunities for the project team to provide technical assistance in several prioritized challenge areas.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Autonomous Emergency Landing for Fixed-Wing Aircraft with Energy-Constrained Closed-Loop Prediction

Here this paper presents a new approach for autonomous motion planning for aircraft suffering from a loss-of-thrust emergency. Specifically, we show how modifications to the Closed-Loop Rapidly exploring Random Trees (CL-RRT) framework combined with controlled energy dissipation can enable rapid and effective kinodynamic motion planning. This CL-RRT Glide algorithm uses closed-loop prediction not only for node connections but also to estimate the remaining energy and prune infeasible paths. This greatly speeds up the search process, which is essential for emergency situations. In addition, we improve the ability of the gliding aircraft to reach a goal position and energy state. We do so by creating a Dissipative Total Energy Control Scheme (TECS). Dissipative TECS enables the glider to lose excess altitude in order to reach a desired energy level. Simulation results illustrate how the proposed methods enable faster motion planning. We also integrate the system into a small unmanned aerial vehicle system and experimentally demonstrate autonomous glide planning and execution during a motor-failure event. This type of algorithm can primarily benefit unmanned aircraft but can also serve to assist pilots in stressful emergency situations.

42 ENGINEERING↗
DOE OSTI2021

Software Tools for Energy Storage Valuation and Design

Abstract Purpose of Review As the application space for energy storage systems (ESS) grows, it is crucial to valuate the technical and economic benefits of ESS deployments. Since there are many analytical tools in this space, this paper provides a review of these tools to help the audience find the proper tools for their energy storage analyses. Recent Findings There are many software tools for valuating ESS. These tools can be classified into two groups: (1) power system simulation and planning tools for analyzing the technical contributions of ESSs, and (2) techno-economic analysis tools for valuating the economic benefits of ESS deployment and specifying the optimal design of energy systems that include ESSs. While many of the tools, developed by the national laboratories, are free to use, the commercial tools are also of great importance in meeting the customers’ specific needs. Summary This paper provides a review of software tools for ESS valuation and design. A review of analysis tools for evaluating the technical impacts of energy storage deployments is also provided, as well as a discussion of development trends for valuation and design tools.

Nguyen, Tu A. (ORCID:0000000181148063)↗
DOE OSTI2023

Joint optimization of electric bus charging infrastructure, vehicle scheduling, and charging management

High upfront costs of vehicles and charging infrastructure as well as the lack of knowledge related to infrastructure planning and electric bus system operation are major obstacles to the implementation of battery electric buses (BEBs). To tackle the obstacles and promote BEB adoption, a comprehensive optimization framework was developed to address the combined charging infrastructure planning, vehicle scheduling, and charging management problem for BEB systems, with the goal to minimize the total cost of ownership. The problem was formulated as a mixed-integer non-linear problem. A genetic algorithm-based approach was then proposed to solve the problem. Last, three alternative scenarios based on a sub-transit network in Salt Lake City, Utah, were analyzed and compared with the optimal scenario results in the numerical experiments. Our comparison results demonstrate the effectiveness of the proposed model and solution algorithm in determining a cost-efficient planning strategy for BEB systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

MOD-Plan: Multi-Objective Decision Planning Framework for Electric Grid Resilience, Equity, and Decarbonization [Slides]

Traditionally electric grid planning strives to maintain safe, reliable, efficient, and affordable service for current and future customers. As policies, social preferences, and the threat landscape evolve, additional considerations for power system planners are emerging, including decarbonization, resilience, and energy equity and justice. The MOD-Plan framework leverages and extends prior work to provide a framework for integrating incorporating resilience, equity, and decarbonization into integrated distribution system planning.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Flexible Nuclear Energy for Clean Energy Systems

As part of the Nuclear Innovation: Clean Energy Future initiative, this report describes flexibility in nuclear systems, the value it can bring, and international experiences surrounding flexible nuclear energy. Flexible nuclear energy for this report is defined as “The ability of nuclear energy generation to economically provide energy services at the time and location they are needed by end-users. These energy services can include both electric and non-electric applications utilizing both traditional nuclear power plants and advanced integrated systems.” Flexibility in nuclear systems is enabled by three main mechanisms: core ramping, integrated energy systems with multiple byproducts, and thermal storage. The value of this flexibility on a $\$$/MW and a $\$$/MWh basis can be estimated through a combination of physics and economics modeling. International agencies describe their experiences in operating flexible nuclear systems and describe their plans for increasing nuclear flexibility in a clean energy system, likely with a high penetration of variable renewable energy.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Coastal Acoustic Buoy for Offshore Wind: Project Synthesis

The population of North Atlantic right whales is critically endangered and their habitat overlaps with offshore windfarm leases. It is therefore imperative that effective mitigation strategies be used to avoid impacts on right whales during the construction of offshore windfarms. The Department of Energy issued FOA Number DE-FOA-0001924 to encourage the development of technology that could monitor large exclusion zones for right whales in order to mitigate potential impact of construction noise on right whales. This report summarizes the past two years of the development and evaluation of the Coastal Acoustic Buoy for Offshore Wind (CABOW) project which aimed to develop technology to monitor large exclusion zones for North Atlantic right whales. Over the course of the project SMRU Consulting have implemented a rigorous design process including comparison of different approaches (e.g., single sensor vs multiple sensors), as well as consideration of placement and timing of acoustic monitoring. We have evaluated critical components of the CABOW system including, reliability, detection range, and bearing accuracy in areas adjacent to offshore windfarm leases in Maryland by conducting 3,536 playbacks of simulated right whale upcalls. The maximum call detection range was 7.5 km when noise was 99 dB re 1µPa rms (50-225 Hz), but this reduced to < 1 km when ambient noise levels were high. Our detection probability in the field was measured as a function of range as well as the source-to-noise level ratio allowing us to build a model to predict the probability of detection under various scenarios (sample size: 3,536 calls x 5 buoys = 17,680). The median bearing error was -0.25° but this is likely an underestimate of error due to experimental design. Using the published recall and precision of the two detectors we implemented in the CABOW system, we estimate that at a recall of 80%, our precision was > 80%, within the range of what we were aiming for in this project. To estimate our exclusion zone false negative and false positive rates, we built a simulation model using the empirical data from our field trial. We modelled three to nine CABOW units placed on the 10 km exclusion zone and estimated our false negative rate to be 1% or less (which was our project goal) and our false positive rate to be between seven and nine percent, slightly above our goal of 5%. However, we also modelled an equivalent PAM system that does not have bearing capabilities and found the false positive rates for that system to be six to eight times higher than the CABOW rate. This higher false positive rate of PAM systems with low spatial information could have significant cost repercussions for offshore wind developers by adding work shutdowns or delays without providing additional protection for right whales. The model we built allows us to explore the placement of PAM systems under various scenarios and will thus help facilitate planning of PAM mitigation systems to meet NOAA Incidental Harassment Authorizations for specific windfarms. We achieved an average system uptime of 98.3%, just below our goal of 99%. The issues that caused these short losses of data have been identified and fixed. Right whale detections and audio clips were typically transferred via radio from the buoys to the base station in two to four seconds. We therefore believe we have developed a highly robust real-time PAM system. Based on the above, we feel we have achieved the stated funding goal of developing a cost-effective and robust real-time PAM system that enables the monitoring of large exclusion zones for right whales during the constructions of offshore windfarms. This should lead to decreased costs and risks for the offshore wind sector while providing robust mitigation for right whales. It is important to state that PAM mitigation will need to be implemented with other mitigation strategies (e.g., visual observers) to provide a complete mitigation strategy to ensure that any effects on right whales from offshore windfarm construction is minimized.

17 WIND ENERGY↗