Operating parameter optimization of single color and four-color spatially separated QWIP focal plane array
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QWIP four color MWIR LWIR focal plane array infrar↗
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Results for “optimal operation”
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A mechanically compensated sapphire oscillator optimized for operation at 40 Kelvin
We present features for a second-generation thermomechanically compensated sapphire resonator. The new design shares the short thermal time constants characteristic of previously developed 10K and 77K CSO resonators. This, together with a thermal ballast methodology, allows effective compensation of temperature fluctuations over a wide range of time scales.
sapphire oscillator↗
Control architecture for predictive and optimal vehicle operations in a single vehicle environment
There is disclosed a cloud computing control system for vehicle speed control and also for control of a vehicle in a platoon. The cloud computing control system determines a speed trajectory and neutral coasting command for a first vehicle of the platoon and a vehicle controller determines a reference speed for the first vehicle in response to the speed trajectory and the neutral coasting command, and is response to one or more vehicle specific factors associated with the first vehicle.
Borhan, Hoseinali↗
Case Study: Hybridizing Nuclear Energy Systems in the U.S.
This case study was presented at the ICTP-IAEA VIRTUAL Course on Nuclear-Renewable Integrated Energy Systems: Phenomenology, Research and Development. It provides preliminary results on an analysis of hybridizing the two nuclear power plants owned by Xcel Energy and located in Minnesota. The analysis extends previous analyses that provided only price-taker results. Those assume adjusting the generation sold to the grid does not impact locational marginal electricity prices nor do they estimate impacts on the total net cost to serve the load. This presentation summarizes the new technique that was developed to both optimize the size and operations of the hybridized system and the resulting impacts on the grid when it is operated optimally.
ENERGY PLANNING, POLICY, AND ECONOMY↗
Calculation of Operations Efficiency Factors for Mars Surface Missions
For planning of Mars surface missions, to be operated on a sol-by-sol basis by a team on Earth (where a "sol" is a Martian day), activities are described in terms of "sol types" that are strung together to build a surface mission scenario. Some sol types require ground decisions based on a previous sol's results to feed into the activity planning ("ground in the loop"), while others do not. Due to the differences in duration between Earth days and Mars sols, for a given Mars local solar time, the corresponding Earth time "walks" relative to the corresponding times on the prior sol/day. In particular, even if a communication window has a fixed Mars local solar time, the Earth time for that window will be approximately 40 minutes later each succeeding day. Further complexity is added for non-Mars synchronous communication relay assets, and when there are multiple control centers in different Earth time zones. The solution is the development of "ops efficiency factors" that reflect the efficiency of a given operations configuration (how many and location of control centers, types of communication windows, synchronous or non-synchronous nature of relay assets, sol types, more-or-less sustainable operations schedule choices) against a theoretical "optimal" operations configuration for the mission being studied. These factors are then incorporated into scenario models in order to determine the surface duration (and therefore minimum spacecraft surface lifetime) required to fulfill scenario objectives. The resulting model is used to perform "what-if" analyses for variations in scenario objectives. The ops efficiency factor is the ratio of the figure of merit for a given operations factor to the figure of merit for the theoretical optimal configuration. The current implementation is a pair of models in Excel. The first represents a ground operations schedule for 500 sols in each operations configuration for the mission being studied (500 sols was chosen as being a long enough time to capture variations in relay asset interactions, Earth/Mars time phasing, and seasonal variations in holidays). This model is used to estimate the ops efficiency factor for each operations configuration. The second model in a separate Excel spreadsheet is a scenario model, which uses the sol types to rack up the total number of "scenario sols" for that scenario (in other words, the ideal number of sols it would take to perform the scenario objectives). Then, the number of sols requiring ground in the loop is calculated based on the soil types contained in the given scenario. Next, the scenario contains a description of what sequence of operations configurations is used, for how many days each, and this is used with the corresponding ops efficiency factors for each configuration to calculate the "ops duration" corresponding to that scenario. Finally, a margin is applied to determine the minimum surface lifetime required for that scenario. Typically, this level of analysis has not been performed until much later in the mission, and has not been able to influence mission design. Further, the notion of moving to sustainable operations during Prime Mission - and the effect that that move would have on surface mission productivity and mission objective choices - has not been encountered until the most recent rover missions (MSL and Mars 2018).
Layback, Sharon L.↗
A trilevel model against false gas-supply information attacks in electricity systems
The interdependence between natural gas and electricity systems is increasing rapidly due to the growing reliance on natural gas-fired generating units. Availability of natural gas for gas-fired generating units can impact the secure operation of electricity systems. Fuel supply shortage for gas-fired units can be caused by uncertain interruptible supply contracts and incorrect supply information. This article proposes a trilevel min-max-min defender-attacker-operator optimization problem to provide the power system operator a screening methodology that allocates a limited budget for best protecting critical fuel supply information, and also the strategies to sign firm supply contract to reduce natural gas supply uncertainties. We utilize a column and constraint generation (C&CG) algorithm to solve the proposed problem. We illustrate the effectiveness of this trilevel formulation using a case study based on IEEE 24-node test system.
42 ENGINEERING↗
Networked Microgrids Optimization
This project is mainly about the operation optimization of three networked microgrids (MG), including centralized optimization and distributed optimization. The alternating direction method of multipliers (ADMM) algorithm is used for distributed optimization. In the distribution network considered here, there is a Distribution Management system (DMS) as the system coordinator and several networked microgrids. In grid-connected mode, power could be imported or exported at the distribution substation bus according to the utility rate, and the exchanged power at point of common coupling (PCC) of any microgrid has a limitation. In islanded mode, the power imports/exports at the distribution substation are zero. In both grid-connected and islanded mode, the distribution substation is taken as a slack bus with fixed voltage magnitude.
Chen, Yang [Oak Ridge National Laboratory (ORNL), ↗
GOOML - Real World Applications of Machine Learning in Geothermal Operations
GOOML (Geothermal Operational Optimization with Machine Learning) is a machine-learning based framework that enables geothermal power plant operators to explore optimization opportunities for their assets in an efficient and robust digital environment. Backed by real-world data sources, thermodynamic constraints and steamfield intelligence, the GOOML environment provides new tools to explore how to best operate steamfields as well as test new scenarios and configurations prior to implementation in the field. To prove the effectiveness of GOOML, we have undertaken optimization experiments using reinforcement learning (RL) to generate operational suggestions using a balance of mass-take targets, sustainability considerations and net generation. Our experiments use the GOOML construct to explore different field parameters and perform multiple reinforcement learning experiments. Like a comprehensive laboratory workbench, we can change out components of a steamfield to perform testing under a variety of conditions (restrict mass, increase pressure, reroute steam, etc.). This flexibility allows us to explore conditions that would require significant infrastructure changes in a real-world setting at a fraction of the cost and time in a digital environment. The results highlight the benefits of using digital twins and advanced data analytics for the geothermal industry.
forecasting↗
Hylia: Time-Series Library for Network Operations (Hylia) v1
The ability to analyze time-series data in network operations is critical to ensure optimal operations and management in wide area networks, sensor networks, and cloud networks. Hylia is a collection of multiple time-series prediction algorithms designed specifically to work with network monitoring data sets such as SNMP, NETFLOW and other monitoring data. It is designed to recognize these features and perform the extrapolation of the data.
Kiran, Mariam↗
Optimal toolpath generation system and method for additively manufactured composite materials
The present disclosure relates to a system for optimizing an additive manufacturing (AM) process. The system may use a printing component for using a material to form a component in a layer-by-layer process. An electronic controller may control movement of one of the printing component or a substrate on which the component is being formed, in a manner to optimize a toolpath for the printing component as each layer of the component is formed. Optimization operations are performed using an optimization subsystem which enables manufacturability constraints, as well as optimized toolpaths for each layer of the component, to be defined using contours of a level-set function. The level-set function may be used to define the optimized toolpaths within a fixed, predetermined grid, and the optimized toolpaths then used to generate suitable code for controlling movement of the printing component relative to the substrate to create the part or structure in a layer-by-layer process, using the optimized toolpaths.
Lewicki, James↗
RODeO (Revenue Operation and Device Optimization Model) [SWR 20-67]
The Revenue, Operation, and Device Optimization (RODeO) model explores optimal system design and operation considering different levels of grid integration, equipment cost, operating limitations, financing, and credits and incentives. RODeO is a price-taker model formulated as a mixed-integer linear programming (MILP) model in the GAMS modeling platform. The objective is to maximizes the net revenue for a collection of equipment at a given site. The equipment includes generators (e.g., gas turbine, steam turbine, solar, wind, hydro, fuel cells, etc.), storage systems (batteries, pumped hydro, gas-fired compressed air energy storage, long-duration systems, hydrogen), and flexible loads (e.g., electric vehicles, electrolyzers, flexible building loads). The input data required by RODeO can be classified into three bins: 1) utility service data, which refers to retail utility rate information (meter cost, energy and demand charges), 2) electricity market data, which include energy and reserve prices, 3) other inputs, which refer to additional electrical demand, product output demand, technological assumptions, financial properties, and operational parameters.
Guerra Fernandez, Omar Jose↗
Learning Provably Stable Local Volt/Var Controllers for Efficient Network Operation
Here this paper develops a data-driven framework to synthesize local Volt/Var control strategies for distributed energy resources (DERs) in power distribution grids (DGs). Aiming to improve DG operational efficiency, as quantified by a generic optimal reactive power flow (ORPF) problem, we propose a two-stage approach. The first stage involves learning the manifold of optimal operating points determined by an ORPF instance. To synthesize local Volt/Var controllers, the learning task is partitioned into learning local surrogates (one per DER) of the optimal manifold with voltage input and reactive power output. Since these surrogates characterize efficient DG operating points, in the second stage, we develop local control schemes that steer the DG to these operating points. We identify the conditions on the surrogates and control parameters to ensure that the locally acting controllers collectively converge, in a global asymptotic sense, to a DG operating point agreeing with the local surrogates. We use neural networks to model the surrogates and enforce the identified conditions in the training phase. AC power flow simulations on the IEEE 37-bus network empirically bolster the theoretical stability guarantees obtained under linearized power flow assumptions. The tests further highlight the optimality improvement compared to prevalent benchmark methods.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Renewable energy integration and system operation challenge: control and optimization of millions of devices
The electric power infrastructure, originally designed and built on large-scale power plants, is evolving into a more resilient power generation and delivery system in which millions of smaller units of distributed energy generation resources units will be installed in sub-transmission and distribution networks. In order to control, manage and optimize the future grid, a hierarchical design is presented in this chapter which enables the distributed control on grid edge while inheriting the existing centralized control structure. This layered design of large-scale power system operation and control uses the following principle: reactive power control is treated as a primary control for voltage stability, and the real power control is primarily a grid-level control but can also be a supplementary control for voltage support in the case of insufficient reactive power control capacity. For the purpose of active control and operation at the distribution level, a recursive power network model is derived from nodal injection and branch power flow models. Based on the model, the proposed algorithms of hierarchical control, grid-edge inference and dynamic hosting allowance are developed and presented for multi-level controlled operation. And, a co-simulation architecture of integrated T&D system is presented to validate and demonstrate the feasibility and scalability of proposed algorithms.
Xu, Ying↗
Economic NMPC for a Reversible Solid Oxide Cell
Reversible solid oxide fuel cells (rSOCs) offer the flexibility to operate in tandem with the electric grid by switching between fuel cell and electrolysis modes based on real-time electricity prices. However, their complex, tightly coupled dynamic behavior poses significant challenges in determining optimal operating strategies. In this work, we present an economic nonlinear model predictive control (E-NMPC) framework to optimize the operation of rSOCs. The proposed E-NMPC is applied to a detailed rSOC flowsheet model that includes a utility scale rSOC module as well as balance-of-plant equipment necessary for thermal management. Our results demonstrate that in fuel cell mode, the E-NMPC strategy reduces hydrogen consumption compared to conventional set-point tracking NMPC, while maintaining the same level of electricity output. Also, in electrolysis mode, the E-NMPC yields a marginal improvement in hydrogen production. In addition, we explore the integration of a battery with the rSOC system to enhance flexibility in meeting electricity production and consumption targets.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Optimization Formulations for Storage Devices with Disjoint Operating Modes
Optimizing State of Charge for Storage Resources Storage of generated energy has existed as a contributing resource within the power sector for more than a century in the case of pumped hydro storage. Even though most regional transmission organizations/independent system operators do not currently optimize state of charge (SOC) for storage, the SOC is an essential aspect of the operating characteristics of storage. This paper investigates the impact on computational performance of optimizing storage with explicit representation of SOC. To investigate the model, the paper considers two contexts, stand-alone and large-scale. Analysis of the stand-alone context helps to explain why the combination of features in the storage model results in a difficult problem. Numerical results for large-scale test cases verify that new tighter valid inequalities can improve the computation compared with the standard model in the literature.
Business & Economics↗
AC Power Flow Based DLMP Calculation and Decomposition Method to Smooth Power Fluctuation of Distributed Renewable Energy Sources
As the penetration of renewable energy sources increases, the growing renewable power variability brings ramping issues to power systems. Meanwhile, the development of distributed energy resources (DERs) makes the distribution systems to provide both energy and ancillary services. To incentivise individual resources and customers to alleviate ramping issues on the demand side, a two-stage distribution locational marginal price (DLMP) calculation and decomposition method is developed to formulate the marginal power ramping price for DERs. In the first stage of the proposed method, a distribution system operator market scheduling model based on AC optimal power flow is designed to estimate the optimal operating point of the distribution system. Subsequently, the voltage and power flow constraints are linearised in stage two to calculate DLMP. Finally, based on the Lagrange function and sensitivity factors, DLMP is decomposed to the marginal costs for active/reactive power, voltage management, power loss and power variability. Case studies demonstrate that the proposed model can effectively smooth the power fluctuation and reduce the ramping flexibility requirements of distribution systems.
AC optimal power flow↗
Repurposing Inactive Oil and Gas Wells for Energy Storage: Maximizing the Potential Via Optimal Drivetrain Control: Preprint
In recent years, there has been a growing emphasis on utilizing energy storage to enhance grid resilience against disruptive events. While renewable energy supply continues to expand, gravity-based solutions like pumped hydro remain dominant in the commercial space. However, their geographical limitations constrain availability, scalability, and increase costs for co-locating solar and wind energy. An alternative approach proposes repurposing idle oil and gas wells located closer to existing grid infrastructure, offering a promising and cost-effective solution. This paper addresses the optimization and control of a regenerative drive system coupled to an interior permanent magnet synchronous machine in a 300-meter well with a 100-Newton weight. The study employs a dynamic MATLAB/Simulink model to simulate the operation of the electric drivetrain system during storage and discharge operations. The results demonstrate an initial round-trip efficiency of 85.9% for the electrical system alone and identify crucial factors for maximizing efficiency. The optimized operation and control of the electromechanical drivetrain system hold great potential for minimizing the levelized cost of storage while maximizing efficiency and revenue generation.
ENERGY STORAGE,ENGINEERING↗