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DOE OSTI2021

Chapter 2: Global Value Chain and Manufacturing Analysis on Geothermal Power Plant Turbines

The global geothermal power market has shown significant growth since the last decade and is expected to reach a total installed capacity of 18.4 gigawatts electric (GWe) by the end of 2021 (GEA, 2016). The global geothermal power plant turbine market is dominated by a small number of manufacturers. Between 2005 and 2015, 82% of the geothermal steam turbines were manufactured in Japan, and 74% of the geothermal binary cycle turboexpanders were manufactured in Israel. During this period, the United States played an important role in the global trade flow of fully assembled turbine units and turbine parts, with a high volume of imports and exports. Another significant growth area was in Italian turbine/turboexpander manufacturers, who have increased their market share in the last couple of years. One other important change in the manufacturing market was in Turkey, where the bonus on feed-in-tariff (FIT) for domestic hardware components boosted the national manufacturing sector between 2010 and 2020. When planning geothermal power projects, developers customize their power plant size to fit the available geothermal resource capacity. The turbine is designed and sized to optimize the efficiency and utilization of resource and revenue production. The rest of the power plant components such as heat exchangers (HX), water-cooled cooling towers (WCCT), or air-cooled condensers (ACC) are then chosen to complement the turbine size and design. These one-off manufacturing custom design turbines have relatively higher manufacturing set-up costs, longer lead times, and higher capital costs than the standard design turbines manufactured in larger volumes. However, turbines produced in standard increments and in larger manufacturing volumes could result in lower costs per turbine, but potentially lower efficiency. Based on pipeline projects and resource assessments, there is significant potential value in creating standard turbine sizes that could offer an economic advantage, as is done for modular microturbines.

40 EE - Geothermal Technologies Office (EE-4G)↗

DOE OSTI2020

Precipitation-site competition in duplex stainless steels: Cu clusters vs spinodal decomposition interfaces as nucleation sites during thermal aging

Competing microstructural evolution mechanisms can exist simultaneously when duplex stainless steels are operating for several decades in a high temperature service environment. Such competition between different microstructural evolution pathways can be difficult to ascertain using simple model alloy systems necessitating detailed structural and compositional analysis of phase transformation mechanisms in complex alloys Thus, in this study duplex stainless steels with complex but well understood chemistries were used to investigate the relative importance of different heterogeneous nucleation sites – specifically spinodal decomposition and Cu clustering – on precipitation of Ni-Si-Mn particles during long term thermal aging. Precipitation of Ni-Si-Mn particles in duplex stainless steels and ferritic steels during thermal aging and irradiation is known to have profound effect on mechanical properties. Using duplex stainless steels with custom modified compositions along with atom probe tomography characterization and first-passage kinetic Monte Carlo (FPKMC) simulations it is revealed that while the interface between Cr and Fe formed during spinodal decomposition can be a pathway for diffusion of Ni, Si, Mn, and Cu, it is not a site that encourages precipitation of Ni-Si-Mn rich particles. Instead, the presence of a higher concentration of Cu in these steels leads to formation of small Cu clusters with high energy interfaces that act as nucleation sites for Ni-Si-Mn particles because of its strong interaction particularly with Ni increasing the chemical driving force for precipitation. These results will help inform predictive models for the use of duplex stainless steels and other precipitation-hardened alloys for extended operation at high temperatures.

Duplex stainless steel, Thermal aging, Precipitati↗

DOE OSTI2022

Impact of model predictive control-enabled home energy management on large-scale distribution systems with photovoltaics

Residential customers use more than one-quarter of the electricity in the world. Optimally managing home energy consumption is an effective way of easing the operational challenges facing the electric grid with increasing solar photovoltaics (PV). This paper studies the impact of the future proliferation of home energy management systems (HEMS) in the presence of PV on large-scale distribution systems. First, we present a stochastic HEMS model that minimizes residential customers' thermal discomfort and energy costs under uncertainty. The HEMS model schedules the optimal operations of residential appliances in the presence of PV within a mixed-integer linear programming-based model predictive control framework that links the proposed HEMS to a quasi-steady-state time-series simulation tool. Additionally, extensive simulations are conducted for a stand-alone residential home using two tariff structures and for 1977 homes on an 8,500-node distribution feeder. Simulation results quantify the impact of the future proliferation of HEMS on the large-scale distribution system with PV.

14 SOLAR ENERGY↗

DOE OSTI2018

Microgrid to enable optimal distributed energy retail and end-user demand response

In the face of unprecedented challenges in environmental sustainability and grid resilience, there is an increasingly held consensus regarding the adoption of distributed and renewable energy resources such as microgrids (MGs), and the utilization of flexible electric loads by demand response (DR) to potentially drive a necessary paradigm shift in energy production and consumption patterns. However, the potential value of distributed generation and demand flexibility has not yet been fully realized in the operation of MGs. This study investigates the pricing and operation strategy with DR for a MG retailer in an integrated energy system (IES). Based on co-optimizing retail rates and MG dispatch formulated as a mixed integer quadratic programming (MIQP) problem, our model devises a dynamic pricing scheme that reflects the cost of generation and promotes DR, in tandem with an optimal dispatch plan that exploits spark spread and facilitates the integration of renewables, resulting in improved retailer profits and system stability. Main issues like integrated energy coupling and customer bill reduction are addressed during pricing to ensure rates competitiveness and customer protection. By evaluating on real datasets, the system is demonstrated to optimally coordinate storage, renewables, and combined heat and power (CHP), reduce carbon dioxide emission while maintaining profits, and effectively alleviate the PV curtailment problem. Finally, the model can be used by retailers and MG operators to optimize their operations, as well as regulators to design new utility rates in support of the ongoing transformation of energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2018

Optimal design of microtube recuperators for an indirect supercritical carbon dioxide recompression closed Brayton cycle

This paper presents a baseline design and optimization approach developed in Aspen Custom Modeler (ACM) for microtube shell-and-tube exchangers (MSTEs) used for high- and low-temperature recuperation in a 10 MWe indirect supercritical carbon dioxide (sCO 2 ) recompression closed Brayton cycle (RCBC). The MSTE-type recuperators are designed using one-dimensional models with thermal-hydraulic correlations appropriate for sCO 2 and properties models that capture considerable nonlinear changes in CO 2 properties near the critical and pseudo-critical points. Using the successive quadratic programming (SQP) algorithm in ACM, optimal recuperator designs are obtained for either custom or industry-standard microtubes considering constraints based on current advanced manufacturing techniques. The three decision variables are the number of tubes, tube pitch-to-diameter ratio, and tube diameter. Five different objective functions based on different key design measures are considered: minimization of total heat transfer area, heat exchanger volume, metal weight, thermal residence time, and maximization of compactness. Sensitivities studies indicate the constraint on the maximum number of tubes per shell does affect the number of parallel heat exchanger trains but not the tube selection, total number of tubes, tube length and other key design measures in the final optimal design when considering industry-standard tubes. In this study, the optimally designed high- and low-temperature recuperators have 47,000 3/32 inch tubes and 63,000 1/16 inch tubes, respectively. In addition, sensitivities to the design temperature approach and maximum allowable pressure drop are studied, since these specifications significantly impact the optimal design of the recuperators as well as the thermal efficiency and the economic performance of the entire sCO 2 Brayton cycle.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

A generic microgrid controller: Concept, testing, and insights

Microgrids have garnered attention in recent years as a way to increase the reliability of the grid, increase the reliability of electricity service to customers, adapt to an increasing percentage of intermittent renewable generation, and serve both customer critical loads and the needs of adjacent communities in the case of emergencies such as natural disasters. One barrier to microgrids is the historic cost and lack of standardization associated with microgrid controllers. To reduce this cost and address standardization, specifications for a Generic Microgrid Controller (GMC) were developed with the goal to facilitate the design and ease of adaptation of microgrid controllers to various microgrids of different sizes and with different resources. A GMC must address two core functions, Transition and Dispatch, as well as several optional higher level functions such as economic dispatch, and renewable and load forecasting. The GMC specifications were demonstrated and evaluated using a commercial simulation platform for two different microgrids, a 20MW-Class community microgrid and a 10MW-Class medical center microgrid. For each microgrid, the GMC was tested in hardware-in-the-loop (HIL) using an OPAL-RT real-time digital simulator, and the two core functions were assessed. The results established that (1) the GMC is successful in seamlessly transitioning the microgrids to and from an islanded mode, (2) a load/generation mismatch at the time of separation depends on the microgrid configuration and must be lower than a specific value determined by simulation testing, and (3) the GMC Dispatch Function response is acceptable in maintaining, in the islanded mode, 60Hz for a range of load changes.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Analysis of degradation in residential battery energy storage systems for rate-based use-cases

This article examines the impact of residential battery energy storage (BES) systems' operational modes on the life (i.e. usable energy capacity) of the battery under several climatic conditions and battery chemistries. The sharp increase in residential BES installations has been a result of decreasing costs of batteries, increase in rate structures motivated applications such as solar self-consumption and time-of-use energy management, and customers purchasing these systems for backup power. While these different modes of operations provide a combination of increased bill savings, reliability of supply, and energy sustainability to the customer, their operational characteristics vary significantly between use-cases. Functional life of BES systems is known to be strongly dependent on their operating conditions. In this article, we analyze the operation of residential BES systems under different rate-based use-cases, for different battery chemistries and cell designs, and under different environmental conditions. This is conducted by simulating the control of BES operations using rate-based cycling algorithms and analyzing the prognosis of multiple battery lifetime models that consider complex nonlinear dependencies of operational stress factors such as state-of-charge, depth-of-discharge, and temperature on degradation. Significant variations in battery life are observed owing to the differences in characteristics of the uses-cases coupled with environmental conditions and battery chemistries. Such differences lead to the conclusion that choice of residential BES technologies and chemistries should account for their intended use-cases.

25 ENERGY STORAGE↗

DOE OSTI2020

A robust offering strategy for wind producers considering uncertainties of demand response and wind power

This paper proposes a risk-constrained decision-making approach for a wind power producer participating in the day-ahead market. In the developed model, a flexible demand response trading scheme between the wind power producer and different customers is employed. Through the proposed demand response mechanism, the wind power producer is able to trade demand response resource internally with different customers, and then trade energy externally with the market to increase the expected profit and the wind energy utilization. The uncertainties in the wind power and demand response are modeled by using the information gap decision theory approach from risk averse (robust) and risk-seeking (opportunistic) perspectives. The objective of the robust model is to maximize the robust level while satisfying the desired profit, whereas the opportunistic model aims to evaluate the possibility of achieving windfall profits with favorable uncertainties. The overall offering strategy problem is modeled as a bi-objective mixed integer nonlinear programming, which is linearized by proper techniques and solved efficiently by using the normal boundary intersection technique. In this work, simulation results show that utilizing demand response resource to mitigate wind power deviations can increase a wind power producer's profit and reduce potential risks. In addition, the results demonstrate that the proposed bi-objective optimization approach enables the wind power producer to select appropriate offering decisions with respect to uncertainties.

17 WIND ENERGY↗

DOE OSTI2018

Biophysical study of human induced Pluripotent Stem Cell-Derived cardiomyocyte structural maturation during long-term culture

Highlights: • hiPSC-CM structural maturation was quantified at multiple stages during 120 days. • User friendly custom-written software platform to quantify sarcomere length. • hiPSC-CMs with significant growth in cardiac fiber density and sarcomere length. • Peak of 40% multinucleated hiPSC-CMs observed at day 90. • CM content in microtissues matured to 22%, similar to adult left ventricle tissues. Human induced Pluripotent Stem Cell-derived cardiomyocytes (hiPSC-CMs) have an enormous potential for the development of drug screening and modeling cardiac disease platforms. However, early hiPSC-CMs usually exhibit low structural development, precluding the applicability of these cells. Here, we follow during 120 days the progressive structural maturation of hiPSC-CM microtissues obtained using the Wnt signaling modulation protocol. For this purpose, we designed a user friendly custom-written program to quantify cardiac fiber alignment and sarcomere length. Cardiomyocyte shape, cardiac fiber density and multinucleation were also analyzed. Derived cardiomyocytes showed significant progression in cardiomyocyte fiber density and sarcomere length during the long-term culture, with a peak at day 90 of 40% multinucleated cells. In addition, cardiomyocyte microtissues remained functional with progressive maturation leading to a decrease in the percentage of cTnT positive cells from 59% to 22% at day 120, a value similar to the content present in tissues of the adult left ventricle. These data and the framework that we provide to quantify cardiomyocyte structural features can be important to set new metrics to develop applications for drug screening and disease modeling.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Spray coating for washout tooling by binder jet additive manufacturing

We report washout tooling is a process in which a removable mold or fixture is disintegrated using a solvent. Binder Jet (BJ) additive manufacturing holds promise for creating customized washout molds from common materials. BJ-based washout tooling provides an advanced strategy in manufacturing fiber-reinforced composites (FRCs), especially for customized hollow FRCs. However, washout tools formed by BJ require an impermeable barrier between the porous tooling surface and the parts laid up on the surface to prevent the resin from infiltrating the tooling surface and bonding the tool to the FRC. The conventional method for preventing resin migration into the washout mold from the FRC is winding Teflon® tape over the entire surface, which is labor-intensive and very difficult to deploy for specific geometries. Here, we report the development of a polymer spray coating method to seal the tooling surface and prevent resin infiltration without affecting the washout properties needed for sacrificial tooling. Two water-soluble polymers, poly(sodium 4-styrenesulfonate) (PSS–Na) and poly(vinylsulfonic acid, sodium salt) (PVS–Na), were investigated for spray coating. PVS-Na maintained tighter geometric tolerances and provided excellent thermal stability with the onset of decomposition at 320 °C and glass transition temperature of 280 °C. Spray coating with PVS-Na streamlines the process of forming a protective layer for the washout molds by BJ, reduces migration of the resins from the FRC, and significantly improves the production efficiency of complex washout tools.

42 ENGINEERING↗

DOE OSTI2020

A new field solver for modeling of relativistic particle-laser interactions using the particle-in-cell algorithm

A customized finite-difference field solver for the particle-in-cell (PIC) algorithm that provides higher fidelity for wave-particle interactions in intense electromagnetic waves is presented. In many problems of interest, particles with relativistic energies interact with intense electromagnetic fields that have phase velocities near the speed of light. Numerical errors can arise due to (1) dispersion errors in the phase velocity of the wave, (2) the staggering in time between the electric and magnetic fields and between particle velocity and position and (3) errors in the time derivative in the momentum advance. Errors of the first two kinds are analyzed in detail. It is shown that by using field solvers with different -space operators in Faraday’s and Ampere’s law, the dispersion errors and magnetic field time-staggering errors in the particle pusher can be simultaneously removed for electromagnetic waves moving primarily in a specific direction. Here, the new algorithm was implemented into Osiris by using customized higher-order finite-difference operators. Schemes using the proposed solver in combination with different particle pushers are compared through PIC simulation. It is shown that the use of the new algorithm, together with an analytic particle pusher (assuming constant fields over a time step), can lead to accurate modeling of the motion of a single electron in an intense laser field with normalized vector potentials, eA / mc 2 , exceeding for typical cell sizes and time steps.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Distributionally robust facility location problem under decision-dependent stochastic demand

While the traditional facility location problem considers exogenous demand, in some applications, locations of facilities could affect the willingness of customers to use certain types of services, e.g., carsharing, and therefore they also affect realizations of random demand. Moreover, a decision maker may not know the exact distribution of such endogenous demand and how it is affected by location choices. In this paper, we consider a distributionally robust facility location problem, in which we interpret the moments of stochastic demand as functions of facility-location decisions. We reformulate a two-stage decision-dependent distributionally robust optimization model as a monolithic formulation, and then derive exact mixed-integer linear programming reformulation as well as valid inequalities when the means and variances of demand are piecewise linear functions of location solutions. We conduct extensive computational studies, in which we compare our model with a decision-dependent deterministic model, as well as stochastic programming and distributionally robust models without the decision-dependent assumption. Here, the results show superior performance of our approach with remarkable improvement in profit and quality of service under various settings, in addition to computational speed-ups given by formulation enhancements. These results draw attention to the need of considering the impact of location decisions on customer demand within this strategic-level planning problem.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Building energy simulation coupled with CFD for indoor environment: A critical review and recent applications

This paper presents a comprehensive review of the open literature on motivations, methods and applications of linking stratified airflow simulation to building energy simulation (BES). First, we reviewed the motivations for coupling prediction models for building energy and indoor environment. This review classified various exchanged data in different applications as interface data and state data, and found that choosing different data sets may lead to varying performance of stability, convergence, and speed for the co-simulation. Second, our review shows that an external coupling scheme is substantially more popular in implementations of co-simulation than an internal coupling scheme. The external coupling is shown to be generally faster in computational speed, as well as easier to implement, maintain and expand than the internal coupling. Third, the external coupling can be carried out in different data synchronization schemes, including static coupling and dynamic coupling. In comparison, the static coupling that performs data exchange only once is computationally faster and more stable than the dynamic coupling. However, concerning accuracy, the dynamic coupling that requires multiple times of data exchange is more accurate than the static coupling. Furthermore, the review identified that the implementation of the external coupling can be achieved through customized interfaces, middleware, and standard interfaces. The customized interface is straightforward but may be limited to a specific coupling application. The middleware is versatile and user-friendly but usually limited in data synchronization schemes. The standard interface is versatile and promising, but may be difficult to implement. Current applications of the co-simulation are mainly energy performance evaluation and control studies. Finally, we discussed the limitations of the current research and provided an overview for future research.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Power allocation by load aggregator with heterogeneous loads using weighted projection

With the recently increasing adoption of transactive energy markets, the role of a load aggregator is becoming increasingly important. Among the many tasks of an aggregator, the task of managing the electricity consumption among a group of grid-interactive efficient buildings according to the demand on the grid is taken up. Specifically, here, the task of allocating a certain amount of power among the participating customers while respecting their preferences and comfort is considered. Modeling this as an aggregate flexibility, the virtual battery conditions that are employed to verify the feasibility of a candidate power profile are derived. A feasible power profile, when allocated, satisfies specified comfort bands. To determine the ideal power requirement of the loads, the model-free control method is employed. The feasible power profile is allocated by embedding the customers’ preferences through weights, posing the power constraint as a hyperplane, and employing a weighted projection from the ideal power requirements onto the constraint plane to solve the considered task. The proposed method is computationally efficient and scalable to any number of heterogeneous thermostatically controlled loads in buildings.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Residential Demand Flexibility: Modeling Occupant Behavior using Sociodemographic Predictors

Demand flexibility (DF) has the potential to increase the saturation of renewables in the grid and reduce operating costs for both utilities and customers. However, less than 8% of U.S. residential electric customers are enrolled in DF programs. A major research gap on this topic is an uneven understanding of behavioral drivers of electricity use and DF program participation at the household level. In this study, we employ machine learning models to predict residential occupant behavior in activities relevant to DF. We model occupants' extensive decisions (i.e., choice of action) and intensive behaviors (i.e., amount of time spent) during peak and off-peak time periods using the publicly available American Time Use Survey, which includes activities data for approximately 200,000 respondents. In our machine learning models, predictions for both extensive and intensive behavior fell within a +/-20% error margin at the aggregate level. We identify 13 key sociodemographic predictors of DF-related intensive behavior using LASSO inference and beta coefficient ranking. However, these top predictors differ by activity, suggesting potential scope for differential user targeting for DF events and technologies during program design. This work also contributes to understanding when and who might adopt these DF technologies based on their daily routine activities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Commercial building HVAC demand flexibility with model predictive control: Field demonstration and literature insights

Model Predictive Control (MPC) for building Heating Ventilation and Air Conditioning (HVAC) systems is beginning to gain traction in the market, with a few controls companies incorporating it into their product offerings. However, it remains difficult to assess whether the energy cost savings are enough to justify the cost of MPC implementation for a particular building, given the limited number of reported demonstrations. For small commercial and residential buildings with relatively uniform systems, standardized approaches can help lower implementation costs. In contrast, for large buildings or district systems, the potential magnitude of cost savings could justify more customized solutions. Estimating the cost-effectiveness of MPC becomes more challenging for medium and large commercial buildings, where a one-size-fits-all solution may not be suitable, and the potential energy cost savings may be insufficient to justify a customized solution. To make MPC technology more appealing, incorporating additional value streams beyond energy efficiency alone can significantly increase its attractiveness. One such revenue stream is demand flexibility, in response to dynamic electricity prices, where MPC can leverage the thermal mass of the building to shift the load and support the grid. Building on an extensive literature review of MPC field studies focused on cost savings and demand flexibility, this paper presents the results of implementing MPC control in a large office building HVAC system in Berkeley, CA. Four different dynamic electricity price profiles were integrated into the MPC objective function to shift building demand while maintaining comfort, and field testing was performed with each price profile across four seasons. The results show potential for 40–65 % demand decrease percentage and up to 61 % annual cost savings compared to the existing rule-based control strategy, under the tested dynamic price scenarios. This paper also presents a sensitivity analysis on the cost savings with respect to the price profile variability, discusses the implementation effort for the price-responsive MPC, and compares the cost savings found in this study to those found in literature on the basis of dynamic price variability, or so-called Electricity Price Relative Standard Deviation.

Zanetti, Ettore↗

DOE OSTI2018

ARC algorithm: A novel approach to forecast and manage daily electrical maximum demand

This paper proposes an innovative algorithm for predicting short-term electrical maximum demand by using historical demand data. The ability to recognize in peak demand pattern for commercial or industrial customers would propose numerous direct and indirect benefits to the customers and utility providers in terms of demand reduction, cost control, and system stability. Prior works in electrical maximum demand forecasting have been mainly focused on seasonal effects, which is not a feasible approach for industrial manufacturing facilities in short-term load forecasting. The proposed algorithm, denoted as the Adaptive Rate of Change (ARC), determines the logarithmic rate-of-change in load profile prior to a peak by postulating the demand curve as a stochastic, mean-reverting process. The rationale behind this analysis, is that the energy efficient program requires not only demand estimation but also to warn the user of imminent maximum peak occurrence. This paper analyzes demand trend data and incorporates stochastic model and mean reverting half-life to develop an electrical maximum demand forecasting algorithm, which is statistically evaluated by cross-table and F-score for three different manufacturing facilities. Lastly, the aggregate results show an overall accuracy of 0.91 and a F-score of 0.43, which indicates that the algorithm is effective predicting peak demand in predicting peak demand.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Energy efficiency as a means to expand energy access: A Uganda roadmap

While energy efficiency can contribute significantly towards improving access to modern energy services, energy sector investments in many developing countries have largely focused on increasing energy access by increasing supply. This is because the links between energy efficiency and energy access, is often overlooked. This oversight of energy efficiency is frequently a missed opportunity, as efficiency is often a very cost-effective energy resource. In combination with grid expansion and new clean energy generation, efficiency efforts can help to ensure that reliable power is provided to the maximum number of customers at a lower cost than would be required to increase generation alone. Here in this paper we describe an analysis method for determining a country's energy efficiency priorities and devising an action plan to integrate energy efficiency as a resource for meeting a nation's energy access goals. We illustrate this method with a detailed case study of Uganda. If the most efficient technologies on the market were adopted in Uganda, 442 MW of generation-level demand could be offset and energy access for an additional 6 M rural customers could be achieved by 2030. Of this technical potential for efficiency, 91% is cost-effective, and 47% is economically achievable under conservative assumptions.

Sub-Saharan Africa↗