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113 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Transferable Reinforcement Learning for Smart Homes: Preprint

To harness the great amount of untapped resources at the demand side, smart home technology plays a vital role in solving the "last mile" problem in smart grid. Reinforcement learning (RL), which has demonstrated an outstanding performance in solving many sequential decision-making problems, can be a great candidate to be used in smart home control. For instance, many studies have started investigating the load scheduling problem under dynamic pricing scheme. Based on those, this study aims at providing an affordable solution to encourage a higher smart home adoption rate. Specifically, we investigate combining transfer learning (TL) with RL to reduce the training cost of an optimal RL control policy. Given an optimal policy for a benchmark home, TL can jump-start the RL training of a policy for a new home, which has different appliances and user preferences. Simulation results show that by leveraging TL, RL training converges faster and requires much less computing time for new homes that are similar to the benchmark home. In all, this study proposes a cost-effective approach for training RL control policies for homes at scale, which ultimately reduces the controller's implementation costs, increases the adoption rate of RL controllers, and makes more homes grid-interactive.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗
DOE OSTI2024

A Tool to Incorporate Non-Energy Impacts in Energy Efficiency Investment Decision Making for Firms

Energy efficiency is a key demand-side strategy for sustainability, recently identified by the United States Department of Energy as a pillar of industrial decarbonization. The increased focus on decarbonization and the requirement for efficiency to enable electrification, another decarbonization pillar, due to the spark spread between natural gas and electricity prices, make energy efficiency increasingly relevant. Still, industries face challenges in adopting energy efficiency measures. Researchers have long found a gap in adoption of even those measures with a profitable net present value, attributed to lack of strategic value among other barriers (see for rigorous exploration and taxonomy). One solution to facilitate energy efficiency projects is the inclusion of non-energy impacts, as this has been shown to double potential deployment of such projects at system level. Energy efficiency can provide valuable benefits outside of simple operating cost reductions, from decreased pollution to enhanced productivity. The inclusion of these benefits in decision making assessments faces hurdles due to inconsistency of ancillary benefits across projects, difficulties in quantifying impacts and the need for additional measurement to quantify them. The decision-making tools to support this have been designed primarily for the European context. We begin with a stakeholder engagement process to better characterize the U.S. decision making process surrounding adoption of energy efficiency investments. Characterization of non-energy impacts has developed substantially over recent decades. Cagno et al. provided a framework for studying the applicability of these impacts to energy efficiency projects, listing 120 key performance indicators focused mainly on reductions of costs/harms. Other researchers have included impacts on the strategic and revenue side that can be merged into this framework as well. We seek a tractable set of impacts that can be included in a decision-making tool in the US, and as such are well suited to US industry, management and decision making processes. We also seek to understand how to best quantify or characterize these impacts. This work will demonstrate the results of a survey conducted among US manufacturing industry decision makers to assess the decision making landscape of stakeholders as well as the most relevant performance indicators for energy efficiency projects.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗
DOE OSTI2021

Distribution System State Estimation With High Penetration of Demand Response Enabled Loads

Demand-side operations incentivize utility customers to take part in various grid services. A demand response enabled load (DREL) is a flexible grid asset that schedules electricity consumption in response to a time-of-use (TOU) energy price. Consequently, its energy profile differs from that of a conventional load that is insensitive to price. This difference may cause new challenges for distribution system state estimation (DSSE). It is well known that DSSE often needs to use pseudo-measurements based on historic load profiles to increase system observability. However, historic profiles of conventional loads are not representative of DREL behaviors. Additionally, the inaccuracy impacts DSSE results and other DSSE-dependent operations. In this paper, we propose an online pseudo-measurement generation approach for DSSE with DRELs. We formulate an optimization model to represent DRELs self-adjusting actions. Sampling-based stochastic optimization techniques are proposed to account for uncertainties in DRELs. A set of representative DREL behavior data corresponding to the samples are used to characterize DREL pseudo-measurements. Case studies with modified IEEE 123-bus test system verify the validity of the proposed work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

AGGREGATE: dAta-driven modelinG preservinG contRollable dEr for outaGe mAnagemenT and rEsiliency (Final Report)

The AGGREGATE project team successfully developed and validated various modules for outage management. Brief summaries of each module are provided to showcase their strength for outage management and restoration for a distribution system with a high penetration of connected distribution energy resources (DERs). In recent years, inverter-based DERs have been widely deployed in distribution system. A most of behind-the-meter (BTM) solar power generation is not visible to the utility. The data-driven DER and load estimation modules are using machine learning (ML) and artificial intelligence (AI) to manage this issue, which provides an opportunity for distribution system operators (DSOs) to operate systems and make decisions in real-time for a distribution system with a high penetration of DERs deployed. Also, the estimated DER and true load can be further leveraged in network aggregation and cold-load pick up estimation for reducing the computing complexity and providing for fast restoration. After load demand and DER power generations have been estimated, the information will support topology and state estimation (SE). The topology estimation module demonstrated the viability of mixed integer linear programming (MILP) formulation to estimate the most likely operational radial topology and outage sections using power flow measurements, historical/estimated load and DERs data and smart meter ping measurements. Formulation includes continuous (power flow, load and DERs data) and binary measurements (smart meter ping measurements) in a single formulation. Errors in continuous data and binary data are modeled as normal distribution and Bernoulli distribution, respectively. In the future distribution grid, the power injection from controllable DERs will be essential for efficient and resilient grid operation. However, determining the optimal DER injections and restoration actions is dependent on knowledge of the system states. State estimation (SE), already the cornerstone of transmission energy management systems, will become commonplace in distribution management systems as more measurements become available from deployment of automated metering infrastructure (AMI). Observability analysis is the first step in SE, as it determines the sufficiency of the available measurements for accurately estimating the current system states. A new type of pseudo-measurement called a Correlational Measurement (CM) is introduced in this module, to enhance the observability of the system to enable more accurate SE. CMs encapsulate knowledge of correlation between demand patterns for similar classes of loads as well as injection patterns for same-technology renewable DERs. During grid contingency scenarios, DERs have been traditionally disconnected, without any fault ride-through capabilities. However, with new regulations and better technology, it is feasible for these resources to contribute to the grid’s restoration after an adverse event and hence enhance resilience. The controllability module proposes a two-step restoration scheme for the power system restoration process by leveraging additional degrees of freedom in power electronics interfaced DERs for mitigating voltage problems. In a resilience mode without the utility system, the distribution grid relies on DERs to serve critical load. In such a severe event with multiple faults on the distribution feeders, actuation of various protective devices (PDs) divides the distribution system into electrical islands. The undetected actuated PDs due to fault current contributions from DERs can delay the restoration process, thereby reducing the system resilience. The Advanced Outage Management (AOM) and the Advanced Feeder Restoration (AFR) modules developed in this project provide improved system resilience with multiple DERs. AOM identifies the faulted sections and actuated PDs in a distribution system with DERs by incorporating smart meter data. The most credible outage scenario including fault locations, PD actuations, and fault indicator (FI) failures is identified by a set of binary integer linear programming incorporating hypotheses. The AFR module serves to restore a distribution system with available energy resources taking into consideration the availability of utility sources and DERs. By partitioning the system into islands, critical load will be served with the available generation resources within islands based on the solution of a MILP. When the utility systems become available, the optimal path will be determined by a spanning tree search algorithm that reconnects these islands back to substations and restores the remaining load. The transmission and distribution (T&D) co-simulation module was used to validate the effect of a control action performed on the distribution side assets as it propagates to the transmission side. This ensures that the control action performed results in a feasible operating point on both the transmission and the distribution system. In addition to validation, the team used the T&D co-simulation module to demonstrate how distribution system assets can be used to mitigate issues on the transmission system. Specifically, the team demonstrated that appropriate switching operations on the distribution side can alleviate the line overload condition on the transmission side without causing new operational constraint violations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

WIPP Safety Significant Confinement Ventilation System (SSCVS) HEPA Filtration System - 20165

Construction and major equipment fabrication are underway for a new ventilation exhaust system at the Waste Isolation Pilot Plant (WIPP). The new system will provide continuous High Efficiency Particulate Air (HEPA) filtration of the exhaust from the WIPP underground while meeting the ventilation demands for expanded waste emplacement and mining operations over the next 40 years. The integrated system design provides for reducing the salt, clay and diesel particulates from the exhaust airflow from the mine, prior to full filtration using Nuclear Safety Significant HEPA filters. The SSCVS design and equipment provides for continuous HEPA filtration over a wide range of operating and environmental conditions. The new exhaust system connects to the existing underground exhaust shaft, and the exhaust air flows from the underground through Salt Reduction Units (SRUs) to protect the final filter banks from excessive moisture and rapid salt/dust buildup. Each of the six (6) SRUs consists of a demister to remove entrained moisture, a dry salt remover unit to filter approximately 90% of the entrained salt/dust, and an automated water treatment system to periodically clean the demisters and to remove salt from the SRU filters. At the maximum expected exhaust flowrate of 540,000 Cubic Feet per Minute (CFM) / 255 Cubic Meters per Second (m{sup 3}/s), five (5) salt removal units will be in operation with one unit in standby or maintenance. An in-line variable frequency drive booster fan will offset pressure losses through each SRU and reduce the relative humidity of the air exiting the SRUs. In the event of a high radiation alarm from the underground Continuous Air Monitors (CAMs), the SRUs will be automatically bypassed, which places the SRS and building outside of the Safety Significant confinement boundary. From the SRUs, the exhaust is directed to the HEPA filter banks located in the New Filter Building. The exhaust system is designed to provide continuous HEPA filtration prior to release to the exhaust stack. There are 22 HEPA filter banks rated at 27,000 CFM / 12.75 m{sup 3}/s each. Each filter bank has two stages of pre-filters and two stages of HEPA filters. The filtered air from each of the 22 filter banks discharges into a common concrete plenum, which is maintained under a negative pressure by the main exhaust fans. Six 1,000 HP variable frequency drive exhaust fans are provided on the south side of the plenum. At the maximum expected exhaust flow of 540,000 CFM, four exhaust fans and twenty filter banks will be in operation. The fans discharge vertically to a separate concrete exhaust plenum. The filtered exhaust is routed from the exhaust plenum to a 125-foot-tall exhaust stack via a 13-foot diameter duct. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗