Three-Stage Optimization Model to Inform Risk-Averse Investment in Power System Resilience to Winter Storms
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An in-depth study was conducted to determine what, if any, improvements could be made on the oxidant supply system for combined cycle MHD power plants which could be reflected in higher thermal efficiency and a reduction in the cost of electricity, COE. A systematic analysis of air separation process varitions which showed that the specific energy consumption could be minimized when the product stream oxygen concentration is about 70 mole percent was conducted. The use of advanced air compressors, having variable speed and guide vane position control, results in additional power savings. The study also led to the conceptual design of a new air separation process, sized for a 500 MW sub e MHD plant, referred to a internal compression is discussed. In addition to its lower overall energy consumption, potential capital cost savings were identified for air separation plants using this process when constructed in a single large air separation train rather than multiple parallel trains, typical of conventional practice.
Electrical actuator systems are being pursued as alternatives to hydraulic systems to reduce maintenance time, weight and costs while increasing reliability. Additionally, safety and environmental hazards associated with the hydraulic fluids can be eliminated. For most actuation systems, the actuation process is typically pulsed with high peak power requirements but with relatively modest average power levels. The power-time requirements for electrical actuators are characteristic of pulsed power technologies where the source can be sized for the average power levels while providing the capability to achieve the peak requirements. Among the options for the power source are battery systems, capacitor systems or battery-capacitor hybrid systems. Battery technologies are energy dense but deficient in power density; capacitor technologies are power dense but limited by energy density. The battery-capacitor hybrid system uses the battery to supply the average power and the capacitor to meet the peak demands. It has been demonstrated in previous work that the hybrid electrical power source can potentially provide a weight savings of approximately 59% over a battery-only source. Electrochemical capacitors have many properties that make them well-suited for electrical actuator applications. They have the highest demonstrated energy density for capacitive storage (up to 100 J/g), have power densities much greater than most battery technologies (greater than 30kW/kg), are capable of greater than one million charge-discharge cycles, can be charged at extremely high rates, and have non-explosive failure modes. Thus, electrochemical capacitors exhibit a combination of desirable battery and capacitor characteristics.
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A common solution to deal with the stochasticity introduced by fast-ramping wind power integration is to equip wind farms (WFs) with energy storage systems (ESSs) to formulate hybrid WF-ESSs. In addition to leveling off wind power fluctuations during normal operations, a hybrid WF-ESS can be a flexible power source to accumulate the power system restoration. In this paper, we propose a rolling optimization model for transmission network recovery and load restoration considering the contributions of WF-ESSs. The proposed model is formulated as a mixed integer linear programming problem that simultaneously optimizes the amount and location of restorable load blocks as well as the restoration lines. The cold load pickup features of interrupted loads considering the outage duration are modeled in detail. A chance-constrained method is employed to deal with the uncertainty of wind power, and a rolling horizon-based framework is adopted to reduce the influence of forecast error. Case studies are conducted on both New England 39-bus system and part of a provincial power system in China. The results show that the load restoration process can be significantly accelerated by employing the proposed method and contributions of hybrid WF-ESSs to power system restoration are validated.
PGBA, a plant growth facility developed for commercial space biotechnology research, successfully grew a total of 30 plants (6 species) for 10 days on board the Space Shuttle Endeavour (STS-77) and is scheduled for reflight on board MSL-1 (STS-83) for a 16 day flight. The PGBA life support systems provide atmospheric, thermal, and humidity control as well as lighting and nutrient supply in a 23.6 liter chamber. Atmosphere treatment includes ethylene and other hydrocarbon removal, CO2 replenishment, and O2 control. The normally closed system uses controlled CO2 replenishment from the crew cabin as required by the plants. Temperature is controlled (1 C) at user-specified setpoints between 20-32 C, using water-filled coolant loops, solid state Peltier thermoelectric devices, and liquid heat exchangers. The thermoelectric cooling systems were optimized for low power consumption and high cooling efficiencies. Relative humidity is maintained between 60-100% using a cooled porous metal plate to remove water vapor from the air stream without cooling the bulk air below the dew point. The lighting system utilizes three compact fluorescent bi-axial lights with variable lighting control and light intensity (PAR) between 220 and 330 micromol/sq m/s at a distance of 20 cm in spaceflight configuration (on orbit power limited to 230 Watt for entire payload). A ground, up to 550 micromol/sq m/s light intensity can be achieved with 330 Watt payload power consumption. Plant water and nutrient support is sustained via the 'Nutrient Pack' system including the passive or active 'Water Replenishable Nutrient Pack.' The root matrix material (soil or Agar) and nutrient formulation of each pack is prepared according to plant species and experimental requirements. These systems were designed by NASA Ames personnel. Data acquisition and control systems provide 32 channels of environmental data as well as digitized or analog video signals for downlink.
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Successive linear programming (SLP) is a practical approach for solving large-scale nonlinear optimization problems. Alternating current optimal power flow (ACOPF) is no exception, particularly the large size of real-world networks. However, in order to achieve tractability, it is essential to tune the SLP algorithm presented in the literature. This paper presents a modified SLP algorithm to solve the ACOPF problem, specified by the U.S. Department of Energy’s (DOE) Grid Optimization (GO) Competition Challenge 1, within strict time limits. The algorithm first finds a near-optimal solution for the relaxed problem (i.e., Stage 1). Then, it finds a feasible solution in the proximity of the near-optimal solution (i.e., Stage 2 and Stage 3). The numerical experiments on test cases ranging from 500-bus to 30,000-bus systems show that the algorithm is tractable. Here the results show that our proposed algorithm is tractable and can solve more than 80% of test cases faster than the well-known Interior Point Method while significantly reduce the number of iterations required to solve ACOPF. The number of iterations is considered an important factor in the examination of tractability which can drastically reduce the computational time required within each iteration.
The limitations of centralized optimization methods in managing electric power distribution systems operations have led to the distributed paradigm of computing and decision-making. Unfortunately, the existing distributed optimization algorithms are limited in their applicability to managing fast varying phenomena such as those resulting from highly variable Distributed Energy Resource (DER) generation patterns. They require a large number of communication rounds (in the order of 10 2 to 10 3 ) among the computing agents to solve one instance of the optimization problem. Related real-time distributed control methods are equally limited in their applications to power distribution systems with fast-changing DER generation; they require hundreds of rounds of communication and thus are slow in tracking the network-level optimal solutions. In this paper, we propose a novel distributed voltage controller that provides a fast-tracking of rapidly varying DER generation profiles while simultaneously converging to network-level optimal solutions within a few communication rounds. The proposed control algorithm leverages the radial topology of the system, which reduces the required communication rounds to reach the network-level optimum solution by order of magnitude. The novelty lies in carefully reducing the electrical network model from the perspective of each distributed controller and enabling appropriate data sharing among upstream and downstream nodes to achieve fast convergence. The simulation results demonstrate the effectiveness of the proposed approach in minimizing the feeder losses while maintaining the node voltage within the pre-specified limits.
We embellish a mixed-integer program that prescribes a set of renewable energy, conventional generation, and storage technologies to procure, along with a corresponding dispatch strategy. Specifically, we add combined heat and power to this set. The model minimizes fixed and operational costs less incentives for the use of various technologies, subject to a series of component interoperability and system-wide constraints. The resulting mixed-integer linear program contains hundreds of thousands of variables and constraints. We demonstrate how to efficiently formulate and solve the corresponding instances such that we produce near-optimal solutions in minutes. A previous rendition of the model required hours of solution time for the same instances.
In this project, we developed new, tighter Mixed-Integer Programming (MIP) formulations for the combined Alternating Current (AC) Security-Constrained Unit Commitment (SCUC) and Security-Constrained Optimal Power Flow (SCOPF). The work addresses a critical challenge in power system operations: efficiently determining which generation units to commit and how to optimally dispatch them while maintaining network reliability constraints for both normal and contingency scenarios. Our efforts: 1. Advance the Understanding of SCUC/SCOPF Modeling: By introducing tighter MIP formulations and leveraging cutting-edge optimization tools (Julia/JuMP, PowerModels.jl), this project has pushed forward the state of the art in efficient power systems scheduling. 2. Enhance Technical and Economic Feasibility: The methods developed provide more accurate and potentially faster solutions to large-scale, realistic scheduling and dispatch problems in electric power systems, which can translate into improved reliability and potentially lower costs for grid operations. 3. Benefit to the Public: Greater efficiency in power system operations leads to cost savings for utilities and end-users. Improved reliability and integration of advanced modeling approaches can facilitate the adoption of clean energy resources and better accommodate uncertainties in renewable generation. Because this technology could impact bulk power markets and reliability, these innovations have far-reaching public benefits in terms of cost savings, reliability, and sustainability.
An energy storage (ES) dispatch optimization was implemented to test lithium-ion battery ES, supercapacitor ES, and compressed air ES on two different industrial facilities – one intermittent process facility and one continuous process facility. The model first shows the capability of optimizing the size of a single technology on a single industrial facility to maximize the return on investment. Then, for the same profile, the model identifies significant differences in the optimal size for different technologies based on their performance parameters. Third, the model identifies differences in the optimal size for each technology based on the facilities profile. Here the results show compressed air ES yields the highest return on investment for both facilities and provides insight for future development of ES systems.