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NASA NTRS1993

A parameter optimization approach to controller partitioning for integrated flight/propulsion control application

A parameter optimization framework is presented to solve the problem of partitioning a centralized controller into a decentralized hierarchical structure suitable for integrated flight/propulsion control implementation. The controller partitioning problem is briefly discussed and a cost function to be minimized is formulated, such that the resulting 'optimal' partitioned subsystem controllers will closely match the performance (including robustness) properties of the closed-loop system with the centralized controller while maintaining the desired controller partitioning structure. The cost function is written in terms of parameters in a state-space representation of the partitioned sub-controllers. Analytical expressions are obtained for the gradient of this cost function with respect to parameters, and an optimization algorithm is developed using modern computer-aided control design and analysis software. The capabilities of the algorithm are demonstrated by application to partitioned integrated flight/propulsion control design for a modern fighter aircraft in the short approach to landing task. The partitioning optimization is shown to lead to reduced-order subcontrollers that match the closed-loop command tracking and decoupling performance achieved by a high-order centralized controller.

Schmidt, Phillip H.↗

DOE OSTI2022

Decentralized Distribution System Restoration with Grid-Forming/Following Inverter-Based Resources

The high penetration of distributed energy resources (DERs) in active distribution systems has posed challenges to the centralized distribution system restoration (DSR) strategies in current practice. On the other hand, the advancement in smart inverter technologies enables the bottom-up restoration capability. This paper is motivated to develop a 3-layered hierarchical framework for decentralized DSR, based on the grid-forming (GFM) and grid-following (GFL) grid-edge inverters. The first layer presents the tertiary control, which determines the load pickup schedule and generation dispatch of DERs, using the alternating direction method of the multipliers algorithm. The second layer consists of two control functions: GFM control, which regulates voltage and frequency, establishing a stable grid for GFL inverters to follow; and GFL control, which regulates the real and reactive power. In the third layer, the primary control is proposed to regulate the inverter voltage and current, which is developed based on the virtual oscillator control (VOC). Furthermore, the developed framework is tested in the modified IEEE 13-node test feeder. Two scenarios of grid-connected and islanded operating modes are designed, and simulation results demonstrate the effectiveness of decentralized DSR strategies for controlling grid-edge inverters to enhance the distribution system resilience.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

On the Need for Communication for Voltage Regulation of Power Distribution Grids

We consider the problem of regulating the voltage profile of a power distribution grid by controlling the reactive power injection of distributed microgenerators. We define a very general class of purely local feedback controllers in which reactive power injection is adjusted based on the local voltage measurements. This class includes most of the solutions proposed in the literature and in the latest grid codes. We show that these strategies do not guarantee the desired regulation, as each of them can have an equilibrium that is not feasible with respect to the desired voltage and power constraints. We then propose a networked feedback law to show that, by adding short-range communication between microgenerators, it is possible to design control strategies that provably converge to the feasible set. This fundamental performance gap between local and networked strategies is finally illustrated via simulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Traffic Signal Optimization by Integrating Reinforcement Learning and Digital Twins

Machine learning (ML) methods, especially reinforcement learning (RL), have been widely considered for traffic signal optimization in intelligent transportation systems. Most of these ML methods are centralized, lacking in scalability and adaptability in large traffic networks. Further, it is challenging to train such ML models due to the lack of training platforms and/or the cost of deploying and training in a real traffic networks. This paper presents an approach for the integration of decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin (DT) to optimize traffic signals for the reduction of traffic congestion and network-wide fuel consumption related to stopping. Specifically, the DGMARL agents learn traffic state patterns and make decisions regarding traffic signal control with assistance from a Digital Twin module, which simulates and replicates the traffic behaviors of a real traffic network. The proposed approach was evaluated using PTV-Vissim [1], a microscopic traffic simulation platform. PTV-Vissim is also the simulation engine of the DT, enabling emulation and optimization of the traffic signals on the MLK Smart Corridor in Chattanooga, Tennessee. Compared to an actuated signal control baseline approach, experiment results show that Eco_PI, a developed performance measure capturing the impact of stops on fuel consumption, was reduced by 44.27% in a 24-hour and an average of 29.88% in a PM-peak-hour scenario.

Multi-Agent Reinforcement Learning, Digital Twin, ↗

DOE OSTI2022

Roadmap for Deployment of Modularized Hydrothermal Liquefaction: Understanding the Impacts of Industry Learning, Optimal Plant Scale, and Delivery Costs on Biofuel Pricing

Hydrothermal liquefaction (HTL) is a promising technology for converting abundant organic wastes into fuels. Previous techno-economic analyses (TEAs) of HTL have been used to estimate the minimum fuel selling price (MFSP) of biofuel products, but these analyses often assume a bespoke plant design where each plant operates under unique process conditions and neglect transportation costs. However, transportation costs must be included in realistic TEAs, and further, a mass-produced fixed-scale modular plant design approach may be more effective than case-by-case plant design, provided that there is sufficient market capacity to benefit from modularization. This study estimates fuel price behavior in the presence of transportation costs and benefits stemming from modular plant design. This analysis indicates that a modular process capable of handling 60 dry tons per day (DTPD) is optimal, resulting in a ~25% reduction in MFSP (from $4.70/GGE, fully upgraded) at complete market feedstock utilization compared with case-by-case design. The associated cost reductions are attributable to learning benefits and modularization. Several HTL deployment “roadmaps” are then explored, with each roadmap consisting of different periods of case-by-case design followed by adoption of a modularized approach. A period of nonmodular industry growth up to market saturation of ~7% followed by implementation of modular plant design strikes a balance between the investment risk and learned cost reductions associated with modular plant design. However, if bespoke plants built during this period of nonmodular growth saturate more than 23% of available feedstock, learned cost reductions are significantly diminished. Here, this study points to the potential benefits of modularized and decentralized waste-to-energy processes when the modularization follows an optimal deployment strategy.

09 BIOMASS FUELS↗

DOE OSTI2022

DLMP of Competitive Markets in Active Distribution Networks: Models, Solutions, Applications, and Visions

Traditionally, the electric distribution system operates with uniform energy prices across all system nodes. However, as the adoption of distributed energy resources (DERs) propels a shift from passive to active distribution network (ADN) operation, a distribution-level electricity market has been proposed to manage new complexities efficiently. In addition, distribution locational marginal price (DLMP) has been established in the literature as the primary pricing mechanism. The DLMP inherits the LMP concept in the transmission-level wholesale market but incorporates characteristics of the distribution system, such as high $R/X$ ratios and power losses, system imbalance, and voltage regulation needs. The DLMP provides a solution that can be essential for competitive market operation in future distribution systems. This article first provides an overview of the current distribution-level market architectures and their early implementations. Next, the general clearing model, model relaxations, and DLMP formulation are comprehensively reviewed. The state-of-the-art solution methods for distribution market clearing are summarized and categorized into centralized, distributed, and decentralized methods. Then, DLMP applications for the operation and planning of DERs and distribution system operators (DSOs) are discussed in detail. Finally, visions of future research directions and possible barriers and challenges are presented.

42 ENGINEERING↗

DOE OSTI2018

Resilient Cyber Infrastructure for the Minimum Wind Curtailment Remedial Control Scheme

Integration of Distributed Energy Resources (DER) has been significantly increasing in the electric power grid. One of the predominant renewable energy sources, wind energy, possess a significant challenge for grid operations, due to its intermittency, uncertainty and variability. In changing operating conditions with wind uncertainty, part of wind may need to be curtailed for the safe utilization of the transmission system. In order to minimize the wind power curtailment and to keep the power flow on a transmission line under limits, a cyber resilient Distributed Remedial Action Scheme (DRAS) is developed in this paper. The RAS is formulated as an optimization problem, considering the real power flow constraints of the transmission lines and given wind variability. In this paper, DRAS is developed to operate even with failures in the computing nodes to make it cyber-resilient. Distributed simplex algorithm is used for optimization in the distributed RAS. The DRAS is implemented in a decentralized platform called Resilient Information Architecture Platform for Smart Grid (RIAPS) to enable cyber resiliency of DRAS. The effectiveness of the proposed approach is validated through offline simulations of the New England 39 bus system and online simulations of IEEE 14 bus test system. In conclusion, a cyber physical test bed utilizing Real Time Digital Simulator (RTDS), Phasor Measurement Units (PMUs), and BeagleBones has been used for the online simulation and validation of the developed cyber-resilient RAS.

17 WIND ENERGY↗

NASA NTRS2004

A Decision-Theoretic Approach to Autonomous Planetary Rover Control

The report discusses the: Decentralized Control of Markov Decision Processes. Study the complexity of decentralized control of Markov decision processes, and develop algorithms for finding optimal control policies. Scheduling Contract Algorithms. Develop an optimal method for scheduling runs of a contract anytime algorithm (one that takes the deadline as input) in situations where the deadline is unknown, multiple problem instances must be solved, and a multi-processor machine is available. Planetary Rover Control as a Markov Decision Process.Use the Markov decision process framework to formalize and solve problems in planetary rover control. Adaptive Peer Selection. Use reinforcement learning to maximize the expected down-load speed for a client in a peer-to-peer file sharing system.

Zilberstein, Shlomo↗

NASA NTRS2004

Masked Proportional Routing

Masked proportional routing is an improved procedure for choosing links between adjacent nodes of a network for the purpose of transporting an entity from a source node ("A") to a destination node ("B"). The entity could be, for example, a physical object to be shipped, in which case the nodes would represent waypoints and the links would represent roads or other paths between waypoints. For another example, the entity could be a message or packet of data to be transmitted from A to B, in which case the nodes could be computer-controlled switching stations and the links could be communication channels between the stations. In yet another example, an entity could represent a workpiece while links and nodes could represent, respectively, manufacturing processes and stages in the progress of the workpiece towards a finished product. More generally, the nodes could represent states of an entity and the links could represent allowed transitions of the entity. The purpose of masked proportional routing and of related prior routing procedures is to schedule transitions of entities from their initial states ("A") to their final states ("B") in such a manner as to minimize a cost or to attain some other measure of optimality or efficiency. Masked proportional routing follows a distributed (in the sense of decentralized) approach to probabilistically or deterministically choosing the links. It was developed to satisfy a need for a routing procedure that 1. Does not always choose the same link(s), even for two instances characterized by identical estimated values of associated cost functions; 2. Enables a graceful transition from one set of links to another set of links as the circumstances of operation of the network change over time; 3. Is preferably amenable to separate optimization of different portions of the network; 4. Is preferably usable in a network in which some of the routing decisions are made by one or more other procedure(s); 5. Preferably does not cause an entity to visit the same node twice; and 6. Preferably can be modified so that separate entities moving from A to B do not arrive out of order.

Wolpert, David↗

DOE OSTI2019

Economical and Engineering Aspects of Proactive Demand Participation: Centralized versus Bilateral Control Structure

The electric grid of the U.S. is evolving from a system characterized by large and centralized fossil-fueled power plants and passive customers to a network with high penetration of distributed energy resources and proactive customers. This project facilitated the evolution of the U.S. electric power grid by enabling proactive participation of demands in electricity market at the power distribution system level. This project has three major achievements. First, we designed a centralized distribution system operator managed electricity market and developed a three-phase alternating current optimal power flow algorithm to coordinate the operation of large-scale flexible loads and distributed energy resources. Second, we designed a decentralized bilateral coordinate framework for retail electricity market, where each participating customer is capable of making decisions regarding energy production and consumption based on information received from transactive neighbors. Third, the impacts of centralized and decentralized demand-side participation scheme on power system operations were evaluated with both large-scale simulations and hardware-in-the-loop demonstrations. The major activities and specific accomplishments of the technical tasks are summarized in Section II. The detailed design of centralized/hierarchical and decentralized/bilateral retail electricity markets and the comparison between the two markets are described in Section III. The accomplishments and conclusions of the project are presented in Section IV. The major findings of the project falls into three areas, hierarchical distribution system operator managed electricity market, bilateral electricity market and decentralized market coordination, and comparison of centralized and decentralized market coordination mechanisms. In the area of hierarchical distribution system operator managed electricity market design, an integrated wholesale and retail electricity market was shown to be highly effective in extracting the operational flexibility from distributed energy resources to provide services to the wholesale market. A chordal conversion based convex iteration algorithm is not only computationally efficiency but also achieves global optimal solution in coordinating the operations of a large number of heterogeneous distributed energy resources.In the area of decentralized market coordination approach, we showed that bilateral transactions among proactive customers and decentralized transaction clearing mechanism based on simultaneous second-price sealed-bid auction can encourage truthful bidding strategies from the market participations. In addition, the individual customers can rely on a portfolio optimization approach to maximize their returns while minimizing risks. In the area of comparison of centralized and decentralized market coordination mechanisms, we found that the centralized market design results in higher social welfare and lower wholesale market clearing price for electricity. In general, centralized market design favors distributed energy resources and fixed loads. The decentralized market design mostly benefits flexible loads and conventional generators.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2018

Robust Decentralized Secondary Frequency Control in Power Systems: Merits and Trade-Offs

Frequency restoration in power systems is conven- tionally performed by broadcasting a centralized signal to local controllers. As a result of the energy transition, technological advances, and the scientific interest in distributed control and optimization methods, a plethora of distributed frequency control strategies have been proposed recently that rely on communication amongst local controllers. In this paper we propose a fully decentralized leaky integral controller for frequency restoration that is derived from a classic lag element. We study steady-state, asymptotic optimality, nominal stability, input-to-state stability, noise rejection, transient performance, and robustness properties of this controller in closed loop with a nonlinear and multivariable power system model. Here, we demonstrate that the leaky integral controller can strike an acceptable trade-off between performance and robustness as well as between asymptotic disturbance rejection and transient convergence rate by tuning its DC gain and time constant. We compare our findings to conventional decentralized integral control and distributed- averaging-based integral control in theory and simulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Signal Whisperers: Enhancing Wireless Reception Using DRL-Guided Reflector Arrays

This paper presents a multi-agent reinforcement learning (MARL) approach for controlling adjustable metallic reflector arrays to enhance wireless signal reception in non-line-of-sight (NLOS) scenarios. Unlike conventional reconfigurable intelligent surfaces (RIS) that require complex channel estimation, our system employs a centralized training with decentralized execution (CTDE) paradigm where individual agents corresponding to reflector segments autonomously optimize reflector element orientation in three-dimensional space using spatial intelligence based on user location information. Through extensive ray-tracing simulations with dynamic user mobility, the proposed multi-agent beam-focusing framework demonstrates substantial performance improvements over single-agent reinforcement learning baselines, while maintaining rapid adaptation to user movement within one simulation step. Comprehensive evaluation across varying user densities and reflector configurations validates system scalability and robustness. The results demonstrate the potential of learning-based approaches for adaptive wireless propagation control.

deep reinforcement learning↗

NASA NTRS1996

Use of the Collaborative Optimization Architecture for Launch Vehicle Design

Collaborative optimization is a new design architecture specifically created for large-scale distributed-analysis applications. In this approach, problem is decomposed into a user-defined number of subspace optimization problems that are driven towards interdisciplinary compatibility and the appropriate solution by a system-level coordination process. This decentralized design strategy allows domain-specific issues to be accommodated by disciplinary analysts, while requiring interdisciplinary decisions to be reached by consensus. The present investigation focuses on application of the collaborative optimization architecture to the multidisciplinary design of a single-stage-to-orbit launch vehicle. Vehicle design, trajectory, and cost issues are directly modeled. Posed to suit the collaborative architecture, the design problem is characterized by 5 design variables and 16 constraints. Numerous collaborative solutions are obtained. Comparison of these solutions demonstrates the influence which an priori ascent-abort criterion has on development cost. Similarly, objective-function selection is discussed, demonstrating the difference between minimum weight and minimum cost concepts. The operational advantages of the collaborative optimization

Braun, R. D.↗

DOE OSTI2021

A stochastic biomass blending problem in decentralized supply chains

Blending biomass materials of different physical or chemical properties provides an opportunity to adjust the quality of the feedstock to meet the specifications of the conversion platform. We propose a model which identifies the right mix of biomass to optimize the performance of the thermochemical conversion process at the mini-mum cost. This is a chance-constraint programming (CCP) model which takes into account the stochastic nature of biomass quality. The proposed CCP model ensures that process requirements, which are impacted by physical and chemical properties of biomass, are met most of the time. We consider two problem settings, a centralized and a decentralized supply chain. We propose a mixed-integer linear program to model the blending problem in the centralized setting and a bilevel program to model the blending problem in the decentralized setting. We use the sample average approximation method to approximate the chance constraints, and propose solution algorithms to solve this approximation. We develop a case study for South Carolina using data provided by the Billion Ton Study. Based on our results, the blends identified consist mainly of pine and softwood residues. The blends identified and the suppliers selected by both models are different. The cost of the centralized supply chain is 2%–6% lower. The implications of these results are twofold. First, these results could lead to improved collaborations in the supply chain. Second, these results provide an estimate of the approximation error from assuming centralized decision making in the supply chain.

09 BIOMASS FUELS↗

NASA NTRS1994

A mission planning concept and mission planning system for future manned space missions

The international character of future manned space missions will compel the involvement of several international space agencies in mission planning tasks. Additionally, the community of users requires a higher degree of freedom for experiment planning. Both of these problems can be solved by a decentralized mission planning concept using the so-called 'envelope method,' by which resources are allocated to users by distributing resource profiles ('envelopes') which define resource availabilities at specified times. The users are essentially free to plan their activities independently of each other, provided that they stay within their envelopes. The new developments were aimed at refining the existing vague envelope concept into a practical method for decentralized planning. Selected critical functions were exercised by planning an example, founded on experience acquired by the MSCC during the Spacelab missions D-1 and D-2. The main activity regarding future mission planning tasks was to improve the existing MSCC mission planning system, using new techniques. An electronic interface was developed to collect all formalized user inputs more effectively, along with an 'envelope generator' for generation and manipulation of the resource envelopes. The existing scheduler and its data base were successfully replaced by an artificial intelligence scheduler. This scheduler is not only capable of handling resource envelopes, but also uses a new technology based on neuronal networks. Therefore, it is very well suited to solve the future scheduling problems more efficiently. This prototype mission planning system was used to gain new practical experience with decentralized mission planning, using the envelope method. In future steps, software tools will be optimized, and all data management planning activities will be embedded into the scheduler.

Wickler, Martin↗

DOE OSTI2021

Combined Techno-Economic Analysis and Life Cycle Assessment of an Integrated Direct Air Capture System with Advanced Algal Biofuel Production

The continuous increase in carbon dioxide (CO2) concentration in the atmosphere since the First Industrial Revolution correlates convincingly with the ongoing rise of the Earth's global average temperature contributing to climate change. As a response, carbon capture and sequestration (CCS) technologies are being implemented to mitigate anthropogenic CO2 emissions by capturing CO2 from high emitting point sources, such as power plants, refineries, and cement factories, and subsequently buried in geological formations underground for long term storage. Recently direct air capture (DAC) has emerged as a promising alternative technology that captures CO2 directly from the atmosphere for use or sequestration. This study investigates the potential synergistic benefits of integrating a solid amine-based DAC system with advanced algal biofuel production in photobioreactors (PBRs). DAC utilization allows the removal of atmospheric CO2 while also decoupling algae production facilities from anthropogenic point CO2 sources and avoiding the cost and logistics challenges of transporting CO2 long distances to remote facilities. Techno-economic analysis and life cycle assessment are performed to assess the economic and environmental benefits of heat and mass integration between the DAC and the PBR for biofuel production. The DAC-PBR system integration also considers on-site flue gas handling options, DAC capital utilization, the tradeoff between centralization or decentralization of key unit operations, and the PBR array. This presentation will discuss how optimizing DAC-PBR process integration can enhance the algal biofuel's economic and environmental sustainability and the prospect of DAC enabling the circular carbon economy.

algal biofuel production↗

DOE OSTI2021

Multilane Automated Driving With Optimal Control and Mixed-Integer Programming

Road vehicle lane changes often initiate traffic disturbances and can therefore impact road networks’ energy and time efficiency. Furthermore, unexpected changes in traffic conditions may also render lane changes counterproductive for the lane-changing vehicle. Vehicle-to-vehicle connectivity combined with anticipative control could address these challenges via improved lane change decisions by automated vehicles. In a move toward this objective, receding horizon control cast as a mixed-integer quadratic program is used to plan lane changing and acceleration in a coupled optimization. A long-term pacing module, based on Pontryagin’s minimum principle from optimal control theory, sets terminal and input references for receding horizon control to target a user’s expected travel time. To remove nonlinear vehicle dynamics from the receding horizon controller, lane change commands are passed to a pure pursuit steering module whose response is approximated by a second-order linear model. Here, comparison against a rule-based reactive algorithm in arterial and highway scenarios shows an 8.9%–13.7% reduction in energy consumption and a 5.2%–10.3% reduction in the travel time, along with navigational improvements.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Economies of Numbers Formulations for Optimal Process Family Design of Carbon Capture Systems

Enabling rapid, widespread deployment of many process variants across a range of design requirements is key to ensuring the widespread availability of critical decentralized technologies such as carbon capture systems. Unfortunately, traditional process design methods are not efficient for designing many similar variations of a process rapidly across a wide range of system requirements. Rather than considering each process variant as a separate design task, we have proposed viewing the entire set of variants as a “family.” In our work, we present rigorous optimization formulations that simultaneously design each variant within the family from a platform of available unit modules, largely inspired by product family and platform design.

Stinchfield, Georgia↗