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The calculated levelized cost of electricity for single junction and tandem devices show that when the time for perovskite devices to degrade to 85% of the original output (T85) is only 3 years, tandem device are economically favorable.
In this project, TDA developed a new mixed metal oxide-based sorbent that converts CO 2 (captured from coal fired power plant) to CO, which can then be combined with renewable H 2 generated by water electrolysis to produce different liquid fuels. TDA’s absorbent-based CO 2 conversion process uses a redox process, which splits the reverse water gas shift reaction steps into two stages: CO 2 reduction to CO and H 2 oxidation to H 2 O and eliminates the equilibrium limitations. The CO produced in the two-stage reactor system can then be further reacted with renewable H 2 to produce methanol, naphtha, diesel, or gasoline. We worked with the Gas Technology Institute (GTI) and Advanced Power & Energy Program (APEP) of University of California, Irvine (UCI) to design and develop the liquid fuel synthesis process that is built around this new material. We demonstrated the techno-economic viability of the new sorbent based redox process to convert CO 2 into synthesis gas by: 1) demonstrating continuous carbon dioxide reduction in a prototype test system for over 500 hours while converting up to 0.4 kg CO 2 /day. With the successful completion of the R&D effort, the technology is now ready for a larger pilot-scale demonstration and the technology readiness has been raised from TRL 3 to TRL 5. In collaboration UCI, we also completed a high-fidelity process design and economic analysis. The required selling price (RSP) for methanol (Case 1 H2-MeOH) is $\$$3.06/gal, naphtha and diesel (Case 2 H2-FT) are $\$$6.21/gal and $\$$8.91/gal, and for gasoline (Case 3 H2-MTG) is $\$$6.96/gal on a 2011 dollar basis. To put these costs in perspective, the recent (June 2020) U.S. price for methanol made from natural gas is about $\$$1.19/gal according to Methanex, a major methanol producer in North America. The recent (June 2, 2020) California prices for gasoline and diesel with California’s strict specifications are $\$$3.35/gal and $\$$3.73/gal according to the U.S. Energy Information Agency. On the other hand, it should be noted that the gasoline and diesel produced by the designs of Case 2 and Case 3 would be both nitrogen and sulfur free. These RSPs are based on a cost of imported electricity of $\$$64/MWh based on the low-end current wind generated electricity cost (Genevieve and Ramsden, Wind Electrolysis: Hydrogen Cost Optimization, Technical Report, June 2011, NREL/TP-5600-50408). This cost is by far the largest component of the variable costs used in computing the RSPs. The cost of the imported electricity has to approach zero for the RSP to be competitive for methanol produced in Case 1 and naphtha and diesel produced in Case 2 while the cost of electricity has to be less than $\$$10/MWh for gasoline produced in Case 3.
Photovoltaics and concentrating solar thermal power are two ways for generating electricity from sunlight, albeit through different methods. Parabolic trough style powerplants represent 3.6 GW of electricity production, but many of these plants are aging and being replaced with photovoltaics. An alternative option that could be employed to leverage the sunk capital cost associated with the primary optics would be the design of a pure photovoltaic retrofit working within the existing plant architecture. Here, a secondary optical concentrator is designed to use the existing primary optics of a parabolic trough type solar thermal powerplant. The design is a v-shaped secondary concentrator resulting in a predicted concentration ratio on a 20 mm wide target of 94. The concentrating photovoltaic receiver for retrofit of an RP-3 based parabolic trough has been constructed using multi-junction concentrator photovoltaic cells and experimentally demonstrated here for the first time. Calculated performance of the cells based on cell specifications should result in 31% efficiency at 85 °C. On-sun efficiencies were measured at an average value of 21% with operational temperatures between 55 and 120 °C. Levelized cost of electricity calculations predict the system to have the potential to be below 7¢/kWh based on predicted efficiencies and 13¢/kWh based on the measured values at cell costs of $5/cm 2 .
Electric vehicle aggregators (EVAs) that utilize vehicle-to-grid (V2G) technologies can function as both controllable loads and virtual power plants, providing key energy management services to the distribution system operator (DSO). EVAs can also balance the grid’s reactive power as a virtual static VAR compensator (SVC) and provide voltage stability by utilizing advanced electric vehicle (EV) chargers that are capable of four-quadrant operations to provide reactive power management. Finally, managed charging can benefit EVAs themselves by minimizing power factor penalties in their electricity bills. In this paper, we propose a novel EV charging scheduling algorithm based on a hierarchical distributed optimization framework that minimizes peak load and provides reactive power compensation for the DSO by collaboration with EVAs that manage both the active and the reactive charging and discharging power of participating EVs. Utilizing the alternative direction method of multipliers (ADMM), the proposed distributed optimization approach scales well with increased EV charging infrastructure by balancing active and reactive power while decreasing computational burden. In our proposed hierarchical approach, each EVA schedules the active and reactive EV charging and discharging power for 1) reactive power compensation in order to minimize power factor penalty and electricity cost accrued by the EVA, 2) satisfaction of each EV’s energy demand at minimal charging cost, and 3) peak shaving and load management for the DSO. When compared with an uncoordinated charging model, the efficacy of this proposed model is successfully demonstrated through a 300% decreased peak EV load for the DSO, 28% lower electricity costs for EV users, and 98.55% smaller power factor penalty, along with 17.58% lower overall electricity costs, for EVAs. The performance of our approach is validated in a case study with 50 EVs at multiple EVAs in an IEEE 13-bus test case and compared the results with uncoordinated EV charging.
In the transition toward sustainable agriculture, farms have emerged as eco-friendly pioneers, harnessing clean hybrid wind and solar systems to improve farm performance. A concern in this paradigm is the effective sizing of renewable energy systems to ensure optimal energy use within budget considerations. This research focuses on optimizing renewable energy sizing in small-scale ammonia production to meet specific farm demands and enhance local resilience, emphasizing the interplay between environmental and economic factors. These findings promise increased energy efficiency and sustainability in this innovative agricultural sector. Additionally, our approach considers small-scale ammonia plant needs and the dynamic relationships between ammonia, water, and farm demands. Simulations demonstrate substantial cost savings in farm electricity consumption. Specifically, scenarios with renewable energy integration in the farm can reduce at least 13% electricity cost compared to a grid-dependent system in the 15-year simulation.
A new decentralised demand response (DR) model relying on bi‐directional communications is developed in this study. In this model, each user is considered as an agent that submits its bids according to the consumption urgency and a set of parameters defined by a reinforcement learning algorithm called Q‐learning. The bids are sent to a local DR market, which is responsible for communicating all bids to the wholesale market and the system operator (SO), reporting to the customers after determining the local DR market clearing price. From local markets’ viewpoint, the goal is to maximise social welfare. Four DR levels are considered to evaluate the effect of different DR portions in the cost of the electricity purchase. The outcomes are compared with the ones achieved from a centralised approach (aggregation‐based model) as well as an uncontrolled method. Numerical studies prove that the proposed decentralised model remarkably drops the electricity cost compare to the uncontrolled method, being nearly as optimal as a centralised approach.
This report summarizes important considerations for implementing BEBs in the three national park fleets, detailing information about current buses at each fleet, electric bus demonstration vehicles, as well as performance evaluations of BEBs in Zion, Bryce, and Yosemite. Results include in-use data collection results reporting metrics such as average bus speed, energy usage per trip, and daily distance traveled, as well as effects of high heating, ventilation, and air conditioning (HVAC) system use to both heat and cool the buses, emissions estimations before and after use of electric buses, operating costs, electric vehicle infrastructure, maintenance, and bus driver user experience survey information. Analysis results from this project will help the NPS understand how BEBs and future expansion of BEBs could assist in meeting their bottom line and operational goals and assist NPS in choosing appropriate locations for future BEB deployments.
a two-step CO 2 electroreduction process for selective production of acetate and ethylene was successfully developed at the kW-scale. The CO 2 and CO electrolyzers were first investigated individually on the Watt-scale to achieve high current densities and more durable operation by using a reinforced GDL. High CO 2 conversion was obtained in the first CO 2 electrolyzer to produce a CO-dominant gas stream with minimal CO 2 , and highly pure acetate stream was produced in the second CO electrolyzer by using NiFe anode, which promoted alcohol oxidation to carboxylates. The two-step process operated stably for 200 h with acetate and ethylene as the major C 2+ products. Degradation mechanism study revealed that the flooding and the salt formation in the GDL is likely the biggest contributor to the performance degradation for both CO 2 and CO electrolyzers. A 1,000 cm 2 CO electrolyzer stack was then designed, fabricated, and operated up to a total current of 300 A along with a 500 cm 2 CO 2 electrolyzer stack which was operated up to a total current of 100 A. The CO electrolyzer stack demonstrated good stability at 300 A for at least 125 h at a carbon selectivity >96%. The impact of CO 2 , O 2 , N 2 , SO x , and NO x gas impurities on the CO electrolyzer stack was studied and a relatively high resistance to these contaminants was demonstrated. Electroreduction of CO 2 into acetic acid was also demonstrated to be environmentally favorable when compared to the traditional production of acetic acid when powered by renewable electricity. Additionally, electricity cost was identified as the primary source of cost sensitivity indicating substantial economic improvements could be achieved by continuing to drive down the cost of renewable electricity. Overall, the presented approach demonstrates the feasibility of the two-step electrochemical CO 2 reduction process for the effective production of C 2+ products at the kW-scale which should inspire future scaling efforts accelerating commercialization.
Electric water heaters represent 14% of the electricity consumption in residential buildings. An average household in the United States (U.S.) spends about USD 400–600 (0.45 ¢/L–0.68 ¢/L) on water heating every year. In this context, water heaters are often considered as a valuable asset for Demand Response (DR) and building energy management system (BEMS) applications. To this end, this study proposes a model-free deep reinforcement learning (RL) approach that aims to minimize the electricity cost of a water heater under a time-of-use (TOU) electricity pricing policy by only using standard DR commands. In this approach, a set of RL agents, with different look ahead periods, were trained using the deep Q-networks (DQN) algorithm and their performance was tested on an unseen pair of price and hot water usage profiles. The testing results showed that the RL agents can help save electricity cost in the range of 19% to 35% compared to the baseline operation without causing any discomfort to end users. Additionally, the RL agents outperformed rule-based and model predictive control (MPC)-based controllers and achieved comparable performance to optimization-based control.
Incorporating battery electric buses into bus fleets faces three primary challenges: a BEB’s extended refuel time, the cost of charging, both by the consumer and the power provider, and large compute demands for planning methods. When BEBs charge, the additional demands on the grid may exceed hardware limitations, so power providers divide a consumer’s energy needs into separate meters even though doing so is expensive for both power providers and consumers. Prior work has developed a number of strategies for computing charge schedules for bus fleets; however, prior work has not worked to reduce costs by aggregating meters. Additionally, because many works use mixed integer linear programs, their compute needs make planning for commercial-sized bus fleets intractable. This work presents a multi-program approach to computing charge plans for electric bus fleets. The proposed method solves a series of subproblems where the solution to the charge problem becomes more refined with each problem, moving closer to the optimal schedule. The results demonstrate how runtimes are reduced by using intermediate subproblems to refine the bus charge solution so that the proposed method can be applied to large bus fleets of 100+ buses. Not only will we demonstrate that runtimes scale linearly with the number of buses but we will also show how the proposed method scales to large bus fleets of over 100 buses while managing the monthly cost of energy.
Aviation contributed approximately 10% of the U.S. transportation sector's greenhouse gas (GHG) emissions and about 3% of the nation's total GHG production before the coronavirus disease (COVID-19) pandemic (EPA 2022). Unlike the ground transportation sector, which can be decarbonized by using batteries and hydrogen fuel cell powertrain technologies, the technical and economic challenges of aviation electrification open the opportunity for CO 2 utilization (CO2U) using clean power sources. The U.S. government set a goal to produce 3 billion gallons per year of sustainable aviation fuels (SAF) by 2030 and scale up the production to 35 billion gallons per year by 2050 (Bioenergy Technologies Office 2022). In this report, we examine three potential locations, in California, Iowa, and Louisiana, for SAF production using two production pathways that are expected to be available by 2030. We analyze the electricity cost to satisfy 10% of the SAF production potential for the select locations. Specifically, we consider (1) retail electricity cost from the default local utility in each location; (2) physical power purchase agreement (PPA) for renewable power and battery storage hybrid systems with preset prices; (3) financial PPA from a dedicated renewable plant; and (4) estimated real-time pricing (RTP) from the wholesale power market with utility delivery adders. Retail rates are available for the three potential locations; however, the developer should negotiate with the local utility in Iowa for new tariff riders, because the load of the proposed SAF plant (1.4 GW) is significantly higher than the current industrial tariff structure requirement (200 kW). The developers are not able to claim federal credits (i.e., the Inflation Reduction Act of 2022) when sourcing electricity from local utilities. Developers could consider prioritizing physical and financial PPAs as purchase options. While physical PPAs can be imported out of state, this structure imposes availability issues as developers must locate in the same electricity grid regions. Financial PPAs do not have the same location restrictions and provide the same cost savings as physical PPAs, but financial PPAs impose financial risks in the event of system curtailment and grid interruption. RTPs are available for customers in California with flexible load and hourly load management capability; however, utilities in our studied regions in Louisiana and Iowa only offer time-of-use and curtailment programs to incentivize lower energy usage in peak hours. These utilities do not currently offer RTP programs, and the developer may need to have further negotiations with the local utilities to access RTP programs. The results show that purchased electricity prices may range from 2.6 cents/kWh to 7.1 cents/kWh for the three studied plants.
The increasing integration of distributed energy resources (DERs) on the electric grid brings new challenges and opportunities for utility grid operations. With the rapid deployment of DERs, there is emerging interest in integrating these controllable devices with utility operations at all levels for monitoring and management. Different types of DERs such as Photovoltaics (PV), energy storage, electric vehicles (EVs), etc. have varying effects on the grid based on how and where they are deployed. Again, the DER deployment and its impact are network-dependent, while the traditional electric grids were not designed to host DERs. To understand the challenges with increasing behind-the-meter (BTM) DERs and to identify the needs in deploying advanced controls, a comprehensive grid impact study is indispensable. This paper investigates the impacts of integrating a mix of DERs in the utility distribution network in Colorado, U.S. Firstly, BTM DERs at the residential scale including distributed PV; battery energy storage systems (BESS); heating, ventilation, and air-conditioning (HVAC) load; electric water heater (EWH) load; and EVs are modeled. The impacts of integrating these resources on the distribution network are evaluated by conducting time-series simulations for different scenarios considering different days to capture the worst-case conditions. Monte Carlo simulations are conducted to generate the realistic EV charging profile. The voltage issues, substation transformer loadings, and critical nodes in the network are identified with the incorporation of uncoordinated EV charging loads and other DER in the network. This analysis helps visualize the DER impact on the grid, identify the challenges, and provides an insight into the new distribution management and control needs to enable reliable and resilient distribution grid operations. Additionally, further analysis is performed to estimate the daily residential electricity cost with the inclusion of DERs under time-of-use tariffs. The result shows the daily residential electricity cost reduced by 20:7% on average with DERs compared to the case without DERs.
Decarbonizing the electricity system to zero-carbon emission is crucial for climate change mitigation. Previous studies have shown that such a transition in the United States (U.S.) may lead to higher system cost compared to a business-as-usual case, but it is not well-known how the cost of electricity generation varies at sub-regional level under the transition, and studies have rarely evaluated the trade-off between the cost and avoided climate damages, as well as the potential roles of negative emission technologies (NETs) in the electricity decarbonization. In this work, we present a regionally resolved national model to quantify the cost of decarbonizing the U.S. electricity system under a set of possible scenarios. The results show that, compared to the reference scenario without a decarbonization policy, reaching zero CO 2 emission by 2050 would incur, depending on the scenarios, 335-494 billion USD additional cost to the U.S. electric power sector during 2020-2050. The regional costs of electricity generation ranges from 2.4 to 4.7 cent/kWh, largely due to the generation profiles and renewable resources availability of those regions. The additional costs can be translated to an average CO 2 abatement cost of 29-59 USD/metric ton CO 2 (with 2%-7% discount rates), which are comparable to the social cost of carbon in the literature at around 4% discount rate. The results also show that the cost of mitigating the last few percent CO 2 emission from the U.S. electricity system may exceed the costs of NETs, indicating an opportunity for NETs to contribute to electricity decarbonization.
The LCOE Content Model provides data submitters with an easy and consistent means of uploading data that can be used to calculate the levelized cost of energy for MHK devices. These data are important to DOE and will be used to develop data products that provide quantitative information to guide and support programmatic decisions. Data will also be used to evaluate MHK products and determine costs and can help data users compare the combination of capital costs and operations and maintenance over time. The ultimate goal is to use these data to perform research and tailor programs to best benefit the industry.
Here this study investigates the applicability of a lumped building modeling approach to model-based predictive control (MPC) to alleviate the complex modeling process of the grey-box multi-zone building model. Based on experimental data, two building models were estimated in this study. The detailed model as a reference case and a lumped model were estimated with decentralized and conventional approaches, respectively. Then, simulations were performed with two boundary conditions, including the comfort bound and electricity cost structure. The performances of the MPC with the detailed and lumped models were analyzed compared to the feedback control. More savings was achieved with a larger comfort bound and more aggressive electricity cost structure. The savings potential of the proposed lumped model approach was not as high as that of the detailed model. However, the proposed method yields good control performance, whose savings was approximately 8.6% over that of feedback control. These results suggest that the proposed method can be used to facilitate MPC implementation in multi-zone building applications.
Through Net Zero World, the National Renewable Energy Laboratory (NREL), the National Energy Secretariat of Argentina, La Pampa's Energy and Mining Secretariat, Pampetrol, and the Administracion Provincial de Energia de La Pampa (APELP) conducted an integrated analysis of the power system of the La Pampa province in Argentina. The study assessed the impact that solar and battery energy storage systems could have on the region grid's reliability and electricity costs, providing insights into long-term decarbonization strategies across Argentina. The analysis demonstrated that these systems are expected not only to help with decarbonization efforts but also to bolster grid resiliency, reduce line losses, and improve voltage regulation while lowering electricity costs. The paper offers a replicable framework for similar studies across other Argentine provinces, which could inform a national grid reliability plan and promote regional coordination. Such coordination will play a key role in supporting a nationwide clean energy transition as Argentina increases its share of renewable energy. By coordinating subnational energy plans with national strategies, Argentina can secure a resilient clean energy future, leveraging the substantial renewable resources available across its provinces.
This paper studies the performance of a model predictive control (MPC) algorithm in a home energy management system (HEMS) as the set of controllable resources varies and under both a constant and a time-of-use (TOU) electricity price structure. The set of controllable resources includes residentially-owned photovoltaic (PV) panels, a home battery system (HBS), an electric vehicle (EV), and a home heating, ventilation, and air conditioning (HVAC) system. The HEMS optimally schedules the set of controllable resources given user preferences such as indoor thermal comfort and electricity cost sensitivity. The home energy management system is built on a chance constrained, MPC-based algorithm, where the chance constraint ensures the indoor thermal comfort is satisfied with a high probability given uncertainty in the outdoor temperature and solar irradiance forecasts. Simulation results for varying sets of controllable resources under two different electricity price structures demonstrate the variation in the HEMS control with respect to HBS operation, electricity cost, and grid power usage.
The electric sector simultaneously faces two challenges: decarbonization to mitigate, and adaptation to manage, the impacts of climate change. In many regions, these challenges are compounded by an interdependence of electricity and water systems, with water needed for hydropower generation and electricity for water provision. Here, we couple detailed water and electricity system models to evaluate how the Western Interconnection grid can both adapt to climate change and develop carbon-free generation by 2050, while accounting for interactions and climate vulnerabilities of the water sector. We find that by 2050, due to climate change, annual regional electricity use could grow by up to 2% from cooling and water-related electricity demand, while total annual hydropower generation could decrease by up to 23%. To adapt, we show that the region may need to build up to 139 GW of additional generating capacity between 2030 and 2050, equivalent to nearly thrice California's peak demand, and could incur up to $\$$150 billion (+7%) in extra costs.