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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Optimal Iterative Method for Network Utility Maximization with Intertemporal Constraints

Network utility maximization (NUM) has been widely used to solve the optimal resource allocation problem in wireless network and power systems. The network planner aims to allocate generation and demand over producers and consumers such that social welfare is maximized while supply and demand are balanced, and network constraints such as branch capacity constraints are satisfied. However, when taking into account dynamics of agents, such as ramp rate constraints on power output for suppliers and impact of cumulative prior consumption on current demand for consumers, a single-period formulation is not sufficient. We propose an iterative method for the multi-period NUM with intertemporal constraints based on dual decomposition. It features a price iteration scheme that achieves optimal social welfare, and the algorithm is guaranteed to converge if proper concavity condition holds. We illustrate the iterative algorithm by power system examples. It is shown that similar to locational marginal price (LMP) in power systems, time-varying congestion prices arise when there exists binding branch constraints. Simulation results also demonstrate that there is increase in social welfare compared to a single-period formulation.

Ma, Ke↗
DOE OSTI2025

Future Industrial Demand: Large, Elastic, and Concentrated [Guest Editorial]

Here, the future industrial demand on the grid is expected to be large, price sensitive, and concentrated. Grid industrial demand increasingly encompasses a new customer type defined as a large flexible load (LFL). This LFL type defines customers with consumption that responds quickly to price or other incentives without interrupting their core business process, such cryptocurrency mining. This customer type can often ramp up and down quickly, which across an entire industry can mean gigawatts of power within a few minutes. In addition to these price-sensitive loads, significant growth is also anticipated in sectors like manufacturing, data centers, and electrification of transportation and buildings.

Mukherjee, Srijib [Oak Ridge National Laboratory (↗
DOE OSTI2023

The Effect of Prosumer Duality on Power Market: The Effect of Market Regulation

Electricity prosumers are energy subsystems that not only consume, but also produce electricity. They are present in distribution level networks as traditional utility customers who have installed distributed energy resources. They are also present in transmission level networks as large conglomerates own both generation assets and large industrial loads. Previous work on the economics of prosumers has demonstrated that prosumers in a market have incentives to behave more competitively compared to producers and consumers in traditional markets. This paper further explores the behavior of prosumers and their response to market policies including the allocation of network losses and the impact of net metering. We extend a Cournot model of a dual prosumer and find that prosumers respond with higher supply quantities if network losses are allocated to demand base. They respond with lower supply quantities if network losses are allocated to supply base. Allocating network losses to demand base also causes equilibrium prices to decline. Markets where prosumers first satisfy their own load and then sell the balance of electricity to the grid have lower quantities and higher prices compared to markets where prosumers buy and sell at locational marginal price.

Tsybina, Eve↗
DOE OSTI2021

Modeling the Strategic Behavior of an Active Distribution Network in the ISO Markets

With increasing integration of distributed energy resources (DERs), active distribution networks (ADNs) can actively participate in the electricity markets by dispatching their DERs, which can change the existing electricity market paradigm. It is essential to investigate the strategic behaviors of ADNs and their DER dispatch when they participate in the wholesale market as price-makers. This paper proposes a bi-level optimization model to study the strategic behavior of an ADN in both energy and reserve markets. The optimal scheduling of DERs in the ADN is modeled as the upper level problem and the joint energy and reserve market-clearing of the ISO is modeled as the lower-level problem. The two-level optimization models exchange bidding information and energy/reserve prices with each other. The proposed bi-Ievel optimization problem is converted to a mathematical programming with equilibrium constraints (MPEC) by using Karush-Kuhn Tucker (KKT) conditions and strong duality theory. Further, the MPEC problem is reformulated as a computationally-solvable mixed integer second order cone programming (MISOCP) model. The simulation results on an illustrative case demonstrate the impact of the strategic bidding of the ADN on the day-ahead energy and reserve market prices.

active distribution network↗
DOE OSTI2024

Flexible Ramping Product Procurement in Day-Ahead Markets

Flexible ramping products (FRPs) emerge as a promising instrument for addressing steep and uncertain ramping needs through market mechanisms. Initial implementations of FRPs in North American electricity markets, however, revealed several shortcomings in existing FRP designs. Here, in many instances, FRP prices failed to signal the true value of ramping capacity, most notably evident in zero FRP prices observed in a myriad of periods during which the system was in acute need for rampable capacity. These periods were marked by scheduled but undeliverable FRPs, often calling for operator out-of-market actions. On top of that, the methods used for procuring FRPs have been primarily rule-based, lacking explicit economic underpinnings. In this paper, we put forth an alternative framework for FRP procurement, which seeks to set FRP requirements and schedule FRP awards such that the expected system operation cost is minimized. Using real-world data from U.S. ISOs, we showcase the relative merits of the framework in (i) reducing the total system operation cost, (ii) improving price formation, (iii) enhancing the the deliverability of FRP awards, and (iv) reducing the need for out-of-market actions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

A Transactive Network Template for Decentralized Coordination of Electricity Provision and Value

Transactive energy systems facilitate and plan for an allocation of electricity based on distributed, value-based negotiations. They are currently extending the usage of market-based principles and transactions from wholesale electricity markets where they exist today downward into distribution systems. While many interesting transactive energy systems have been proposed, few fully decentralize price discovery and price-responsive control. Even fewer plan for extensibility that will be needed for the ultimate growth of transactive networks and for the inclusion of additional and new objectives and new flexible, responsive assets. This paper introduces a transactive network template from which an agent may be configured at its network node to negotiate for electricity with other neighboring network agents, manage a set of locally-owned supply and demand assets, and induce local power balance through price discovery. A set of base object classes defines the template and may be extended. Three important basic computational responsibilities are allocated among the base object classes—scheduling, balancing, and network coordination. These several basic classes and responsibilities may be used to represent the perspective of large and small devices and regions anywhere in the electric power grid because they are based on the self-similarity of these objects and responsibilities. Agents in a transactive network are not at all unique in these basic responsibilities. So far, these following dynamic values have been addressed by extension of the base classes: energy value, demand charges, transport losses, and impacts of distributed renewable resources. The template is designed to be further extended to address and monetize still other valuable objectives.

Transactive, Transactive network, Transactive netw↗
DOE OSTI2018

Peer-to-Peer Energy Trading in a Prosumer-Based Community Microgrid: A Game-Theoretic Model

This paper proposes a novel game-theoretic model for peer-to-peer (P2P) energy trading among the prosumers in a community. The buyers can adjust the energy consumption behavior based on the price and quantity of the energy offered by the sellers. There exist two separate competitions during the trading process: 1) price competition among the sellers; and 2) seller selection competition among the buyers. The price competition among the sellers is modeled as a noncooperative game. The evolutionary game theory is used to model the dynamics of the buyers for selecting sellers. Moreover, an M-leader and N-follower Stackelberg game approach is used to model the interaction between buyers and sellers. Two iterative algorithms are proposed for the implementation of the games such that an equilibrium state exists in each of the games. The proposed method is applied to a small community microgrid with photo-voltaic and energy storage systems. Simulation results show the convergence of the algorithms and the effectiveness of the proposed model to handle P2P energy trading. The results also show that P2P energy trading provides significant financial and technical benefits to the community, and it is emerging as an alternative to cost-intensive energy storage systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Exploiting Redundancy and Application Scalability for Cost-Effective, Time-Constrained Execution of HPC Applications on Amazon EC2

The use of clouds to execute high-performance computing (HPC) applications has greatly increased recently. Clouds provide several potential advantages over traditional supercomputers and in-house clusters. The most popular cloud is currently Amazon EC2, which provides fixed-cost and variable-cost, auction-based options. The auction market trades lower cost for potential interruptions that necessitate checkpointing; if the market price exceeds the bid price, a node is taken away from the user without warning. We explore techniques to maximize performance per dollar given a time constraint within which an application must complete. Specifically, we design and implement multiple techniques to reduce expected cost by exploiting redundancy in the EC2 auction market. We then design an adaptive algorithm that selects a scheduling algorithm and determines the bid price. We show that our adaptive algorithm executes programs up to seven times cheaper than using the on-demand market and up to 44 percent cheaper than the best non-redundant, auction-market algorithm. We extend our adaptive algorithm to incorporate application scalability characteristics for further cost savings. In conclusion, we show that the adaptive algorithm informed with scalability characteristics of applications achieves up to 56 percent cost savings compared to the expected cost for the base adaptive algorithm run at a fixed, user-defined scale.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Optimal Coordination of Building Loads and Energy Storage for Power Grid and End User Services

Demand response and energy storage play a profound role in the smart grid. The focus of this paper is to evaluate benefits of coordination of flexible loads and energy storage for power grid and end user services. We present a Generalized Battery Model (GBM) to describe the flexibility of building loads and energy storage. A Model Predictive Control (MPC) approach is proposed to characterize the GBM parameters (power and energy limits) for flexible building loads. Moreover, we develop optimal coordination algorithms for them to provide ancillary and end user services such as energy arbitrage, frequency regulation, spinning reserve, as well as energy and demand charge reduction. Several case studies have been performed to demonstrate the efficacy of the GBM and coordination algorithms, and evaluate the benefits of using their flexibility to provide grid and end user services. We show that optimal coordination yields significant cost saving and revenue. Moreover, the best option for grid services is to provide energy arbitrage and frequency regulation, because spinning reserve cannot compete with regulation service. Furthermore, when coordinating flexible loads with energy storage to provide end user services, it is recommended to consider both the time-of-use (TOU) price and demand charge rather than the TOU price only in order to flat the aggregate power profile.

Hao, He↗
DOE OSTI2020

A Penalty Scheme for Mitigating Uninstructed Deviation of Generation Outputs From Variable Renewables in a Distribution Market

With rapid growth of distributed renewable generation, the establishment of electricity distribution markets has attracted widespread concerns. Different from existing transmission grid-scale electricity markets, an electricity distribution market is featured by numerous small-scale prosumers, and zero marginal cost and intermittency of renewable generation units. Against this background, this paper first extends an average pricing market (APM) mechanism for pricing renewable generation outputs with zero marginal cost in the distribution network concerned. Then, to mitigate the uninstructed volatility of renewable generation outputs and power demand, a penalty scheme is proposed for deviations between the real-time demand/output and market cleared bid/offer, with frequency regulation service (FRS) from energy storage systems (ESSs) considered. It is proved that the market volatility can be well controlled within an expected limit through properly setting the penalty prices for load demand and generation output fluctuations. Also, with this mechanism a non-negative market surplus could always be attained. Case studies are carried out to demonstrate the feasibility and efficiency of the proposed distribution market mechanism and penalty scheme.

electricity distribution market↗
DOE OSTI2014

Agriculture and Climate Change in Global Scenarios: Why Don't the Models Agree

Agriculture is unique among economic sectors in the nature of impacts from climate change. The production activity that transforms inputs into agricultural outputs makes direct use of weather inputs. Previous studies of the impacts of climate change on agriculture have reported substantial differences in outcomes of key variables such as prices, production, and trade. These divergent outcomes arise from differences in model inputs and model specification. The goal of this paper is to review climate change results and underlying determinants from a model comparison exercise with 10 of the leading global economic models that include significant representation of agriculture. By providing common productivity drivers that include climate change effects, differences in model outcomes are reduced. All models show higher prices in 2050 because of negative productivity shocks from climate change. The magnitude of the price increases, and the adaptation responses, differ significantly across the various models. Substantial differences exist in the structural parameters affecting demand, area, and yield, and should be a topic for future research.

Nelson, Gerald↗
DOE OSTI2017

Switchgrass nitrogen response and estimated production costs on diverse sites

Switchgrass ( Panicum virgatum L.) has been the principal perennial herbaceous crop investigated for bioenergy production in North America given its high production potential, relatively low input requirements, and potential suitability for use on marginal lands. Few large trials have determined switchgrass yields at field scale on marginal lands, including analysis of production costs. Thus, a field–scale study was conducted to develop realistic yield and cost estimates for diverse regions of the USA. Objectives included measuring switchgrass response to fertility treatments (0, 56, and 112 kg N ha –1 ) and generating corresponding estimates of production costs for sites with diverse soil and climatic conditions. Trials occurred in Iowa, New York, Oklahoma, South Dakota, and Virginia, USA. Cultivars and management practices were site specific, and field–scale equipment was used for all management practices. Input costs were estimated using final harvest–year (2015) prices, and equipment operation costs were estimated with the MachData model (2015). Switchgrass yields generally were below those reported elsewhere, averaging 6.3 Mg ha –1 across sites and treatments. Establishment stand percent ranged from 28% to 76% and was linked to initial year production. No response to N was observed at any site in the first production year. In subsequent seasons, N generally increased yields on well–drained soils; however, responses to N were nil or negative on less well–drained soils. Greatest percent increases in response to 112 kg N ha –1 were 57% and 76% on well–drained South Dakota and Virginia sites, where breakeven prices to justify N applications were over 70 and 63 Mg –1 , respectively. For some sites, typically promoted N application rates may be economically unjustified; it remains unknown whether a bioenergy industry can support the breakeven prices estimated for sites where N inputs had positive effects on switchgrass yield.

09 BIOMASS FUELS↗
DOE OSTI2022

Electricity Bill Calculator (elecprice) v0.1.0

The Electricity Bill Calculator Library is a generic tool for manipulating the tariffs of electricity, with an emphasis on commercial Time-Of-Use (TOU) rates in the U.S. and residential Net Energy Metering tariffs in CA. Typical uses of the package are price signal generation and bill computation. To generate price signals from a specific tariff and given a time window, the tool creates a pandas dataframes containing the various components of the electricity charges, at each time step. This is particularly useful for Demand Response (DR) applications, such as price-based optimization of energy in buildings. The tool also computes bills given a building's power consumption and a specific tariff and returning the corresponding cost of electricity, as well as a breakdown per type of rate.

Prakash, Anand Krishnan↗
DOE OSTI2025

Hybrid Energy Resources Bidding Model (HERB) v1.0

The HERB Model provides a stochastic optimization framework for hybrid power plants (Storage plus Renewables) participating in day-ahead electricity markets. It considers uncertainty in market prices and renewable energy generation and generated risk-constrained price-quantity bid curves with limited, non-decreasing price steps.

Heleno, Miguel [Lawrence Berkeley National Laborat↗
DOE OSTI2024

HP-FLEX MPC v0.1.0

HP-FLEX MPC is control software developed by Lawrence Berkeley National Laboratory with support from the California Energy Commission (CEC) through EPIC-19-301. HP-FLEX aims to provide load flexibility for heat pumps (HPs) in response to dynamic grid signals (including Time-of-Use, Dynamic Pricing, and Critical Peak Pricing) while maintaining thermostat temperatures within user-specified bounds. The software includes a system-identification module, which models the dynamics of the building envelope with thermostat data, and a control module based on a model predictive controller (MPC) to make optimal decisions. HP-FLEX receives forecasts of outdoor air temperature, solar irradiation, and internal gain (if available), as well as trajectories of energy price, temperature lower and upper bounds over a prediction horizon. It then optimizes heating and cooling capacities to minimize energy cost and peak power (with a user-defined weight on peak power) over the prediction horizon, while maintaining room air temperature within the temperature constraints, and outputs the optimal thermostat setpoints.

Kim, Donghun↗
DOE OSTI2024

yuyao-jia/Bio-based_HMF_coproduction

5-Hydroxymethyl furfurals (HMF) is one of the versatile platform chemicals. However, green routes to produce it directly from lignocellulosic biomass are lacking. A significant amount of HMF produced during the hydrothermal valorization of lignocellulosic biomass is considered undesired and ends up in a waste stream. The study transformed the undesired byproduct into a valuable coproduct by advancing the existing biofuel production process. A detailed economic and environmental sustainability analysis of the integrated biorefinery design was performed. The evaluation showed that the biorefinery could afford a maximum feedstock purchasing price of $115.17 per MT and produce HMF with a minimum selling price of $4.54 per kg which is ∼75% lower than the commercial price of HMF. The median global warming potential of HMF was estimated to be 3.92 kg CO2-eq. per kg HMF which was ∼32% less than its counterpart bio-based p-xylene. Diverse coproducts produced in the biorefinery using transgenic feedstock positively impacted sustainability.

Jia, Yuyao↗
DOE OSTI2025

BioSTEAMDevelopmentGroup/BLocS

BioSTEAM Location-Specific Evaluation. This module allows BioSTEAM users to consider the impacts of economic and environmental parameters that vary by location. Current location-specific data includes income, property, fuel producer, and sales tax rates; feedstock prices (corn, corn stover, and sugarcane); electricity prices; natural gas prices; location capital cost factors (LCCFs), and tax incentives (available as of February 2020) for all 50 states in the US. Stewart et al. is the first paper to utilize BLocS to explore the influence of policy incentives and location-specific economic parameters on the financial viability of three different biorefineries.

Stewart, Dalton↗
DOE OSTI2026

EASY-SHIFT v Alpha

The software is a generic, price- and load-responsive control algorithm integrating heat pumps with thermal energy storage. The algorithm leverages simple models of the system and easily accessible data to schedule operation of heat pumps and thermal energy storage in ways that minimize the cost of operating the heating/cooling system. This tool is specifically designed to be easy to interact with, and something that industry partners are able to adopt. There are two current state of the art approaches. Industry tends to develop very simple algorithms, with predetermined schedules that are not capable of changing operation in response to changes in operating environment. For example, a control designed to avoid high-price electricity from 5-8 PM will not be able to adapt if the high-price period changes to 4-9 PM. Academia commonly develops algorithms called Model predictive control (MPC). MPC requires extensive data and highly trained staff to develop a specific type of simulation model of the building, connect the building to optimization algorithms, and leverage powerful computers. Industry, with limited time/finance budgets for any project, is resistant to adopting MPC due to the associated high complexity and cost.

Grant, Peter [Lawrence Berkeley National Laborator↗