Results for “solar generation, demand charges” — Page 2

DOE OSTI2025

Spokane Eco-District Campus Performance Under Alternative Electricity Rates: Benefits for virtual power plant participants and suppliers

Here, the respective benefits for virtual power plant participants and suppliers are revealed and compared under alternative electricity rate structures, including conventional large commercial electricity rates, large commercial electricity rates with special rates for demand-side generation, and dynamic hourly transactive prices. These three scenarios were explored using the capabilities of the Eco-District campus, a virtual power plant in Spokane, Washington, that is supplied electricity by Avista Utilities. The Eco-District Campus was modeled to host solar power generation, battery energy storage, and thermal energy resources that must be coordinated with building heating and cooling needs. First, the electricity supplier’s costs for energy, infrastructure, and energy losses were modeled. Then, the virtual power plant’s performance was modeled while presuming that its manager would minimize its costs under its electricity rate structure. The demand charges of conventional commercial electricity rates managed monthly peak, as would be expected, but hourly dynamic transactive pricing resulted in a striking alignment between the costs incurred by the supplier and the virtual power plant’s energy costs.

Electricity rates↗

DOE OSTI2022

Balancing Panchromatic Absorption and Multistep Charge Separation in a Compact Molecular Architecture

A panchromatic triad and a charge-separation triad are joined in a crossbar architecture to capture solar energy. The panchromatic triad (T) is comprised of a central free-base porphyrin that is strongly coupled via direct ethyne linkages to two perylene-monoimides. Here, the charge-separation unit incorporates a free-base or zinc chlorin (C or ZnC) as a hole acceptor (or electron donor) and a perylene-diimide (PDI) as an electron acceptor, both attached to the porphyrin via diphenylethyne linkers. The free-base porphyrin is common to both light-harvesting and charge-separation motifs. The chlorin and PDI also function as ancillary light absorbers, complementing direct excitation of the panchromatic triad to produce the discrete lowest excited state of the array (T*). Attainment of full charge separation across the pentad entails two steps: (1) an initial excited-state hole/electron transfer process to oxidize the chlorin (and reduce the triad) or reduce the PDI (and oxidize the triad); and (2) subsequent ground-state electron/hole migration to produce oxidized chlorin and reduced PDI. Full charge separation for pentad ZnC-T-PDI to generate ZnC + -T-PDI¯ occurs with a quantum yield of ~30% and mean lifetime ~1 μs in dimethyl sulfoxide. For C-T-PDI, initial charge separation is followed by rapid charge recombination. The molecular designs and studies reported here reveal the challenges of balancing the demands for charge separation (linker length and composition, excited-state energies, redox potentials, and medium polarity) with the constraints for panchromatic absorption (strong electronic coupling of the porphyrin and two perylene-monoimide units) for integrated function in solar-energy conversion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Task 12 PV Sustainability - Environmental Life Cycle Assessment of Residential PV and Battery Storage Systems

Using a life cycle assessment (LCA), the environmental impacts from generating 1 kWh of electricity for self-consumption via a photovoltaic-battery system are determined. The system includes a 10 kWp multicrystalline-silicon photovoltaic (PV) system (solar irradiation about 1350 kWh/m2/year and annual yield 1000 kWh/kWp), an iron phosphate lithium-ion (LiFePO4) battery, and other components such as the control system, battery housing, and two inverters (one for the PV system and one for the battery system). Three options for the AC-coupled system with changing battery capacities (5, 10, or 20 kWh nominal capacity) are investigated. The environmental impacts are assessed using the indicators greenhouse gas emissions and cumulative energy demand (separated into total and non-renewable cumulative energy demand). In addition, the four most important impact categories for PV electricity - respiratory inorganics (particulate matter), acidification, energy carrier resource use, and minerals and metals resource use - are assessed according to the environmental footprint (EF) method. Data are drawn from the DETEC data DQRv2:2018, recent literature, and product details provided by manufacturers. The results show larger environmental impacts of PV-battery systems with increasing battery capacity; for capacities of 5, 10, and 20 kWh, the cumulative greenhouse gas emissions from 1 kWh of electricity generation for self-consumption via a PV-battery system are 80, 84, and 88 g CO 2- eq/kWh, respectively. The cumulative greenhouse gas emissions of PV electricity consumed directly or fed into the grid are 54 g CO 2 -eq/kWh. The corresponding total cumulative energy demands are 5.27, 5.40, and 5.50 MJ oil-eq/kWh, with non-renewable energy carriers contributing 1.16, 1.22, and 1.29 MJ oil-eq/kWh. In the investigated EF impact categories, we similarly observe a larger environmental burden with increasing battery capacity, except in the use of minerals and metals. Our sensitivity analyses show that using a nickel cobalt manganese oxide (NCM) lithium-ion battery, instead of an LiFePO 4 battery, leads to a comparable environmental impact in terms of greenhouse gas emissions and cumulative energy demand. However, the NCM battery increases the impact in the EF categories of acidification and respiratory inorganics by 7 and 10%, respectively, whereas energy carrier resource use decreases by 4% and minerals and metals resource use decrease by 1%. Using a copper indium selenium (CIS) PV panel instead of a multicrystalline-silicon decreases greenhouse gas emissions by 24%, non-renewable cumulative energy demand by 13%, and particulate matter emissions by 60% (the largest decrease). Furthermore, the calculated environmental impacts are sensitive to the assumed battery lifetime. A decrease from 5000 to 3000 charge cycles increases non-renewable cumulative energy demand by 24% and greenhouse gas emissions by 16%. Increasing from 5000 to 7000 charge cycles decreases the environmental impacts by 6% and 7% in terms of non-renewable cumulative energy demand and greenhouse gas emissions, respectively. A utility-scale battery system case study shows that using batteries to store PV electricity overproduction reduces greenhouse gas emissions compared to using natural gas backup electricity generation.

14 SOLAR ENERGY↗

DOE OSTI2026

OCHRE

OCHRE™ uses a variety of input data sources to run time-series simulations. Building models can be taken from the ResStock™ database or generated using the Building Energy Optimization Tool (BEopt™) or other OpenStudio-HPXML workflows. EV charging profiles can be taken from datasets used in NLR's 2030 National Charging Network project. Weather data can be taken from the National Solar Radiation Database or EnergyPlus® weather files. There are no public datasets with OCHRE outputs at this time. However, a recent project dataset on water heater and EV demand flexibility can be requested. OCHRE is a Python-based energy modeling tool designed to model flexible loads in residential buildings. OCHRE includes detailed models and controls for flexible devices including HVAC equipment, water heaters, EVs, solar PV, and batteries. It is designed to run in co-simulation with custom controllers, aggregators, and grid models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Ice storage model-predictive control in an office building with PV: scenario, error and sensitivity analysis

Thermal energy storage (TES) can enable more building-sited renewable electricity generation and lower utility bill costs for buildings owners and occupants, especially when there are high demand and variable time-of-use (TOU) charges. A model predictive control (MPC) strategy can offer additional savings over a schedule-based control with added complexity and reliance on forecasts. Here, this study examines savings for medium office buildings with chiller plants in three locations with building-installed solar photovoltaics (PV) to understand the impact of MPC. Control setpoints are fixed by a schedule-based control or optimized by nonlinear MPC. These control setpoints are actuated within EnergyPlus building models to simulate the utility cost of the chiller plant. NLP solutions can be unstable or unrealistic, but our results show that by regularizing the NLP, the solutions can be reasonably followed by the building model. MPC models make simplifications that lead to errors once the controller is participating in and changing the operation of the building. These errors average 9 % across the cases, showing that the most important parts of the system are represented. The no-thermal load costs are computed to show that the optimization can in some cases achieve both the minimum TOU and minimum monthly demand costs by demand management while reducing TOU energy costs by energy arbitrage. The MPC saves 35–66 % in the annual chiller plant operating costs, which is an additional savings above the schedule by 1–33 %. PV and TES are complementary and mostly independent, but a load with PV often results in better performance for the schedule. Our case study and sensitivity analysis show the importance of modeling and optimization for complex rates, but also the circumstances wherein a simpler strategy achieves the same performance with less potential for error.

14 SOLAR ENERGY↗

DOE OSTI2020

RODeO (Revenue Operation and Device Optimization Model) [SWR 20-67]

The Revenue, Operation, and Device Optimization (RODeO) model explores optimal system design and operation considering different levels of grid integration, equipment cost, operating limitations, financing, and credits and incentives. RODeO is a price-taker model formulated as a mixed-integer linear programming (MILP) model in the GAMS modeling platform. The objective is to maximizes the net revenue for a collection of equipment at a given site. The equipment includes generators (e.g., gas turbine, steam turbine, solar, wind, hydro, fuel cells, etc.), storage systems (batteries, pumped hydro, gas-fired compressed air energy storage, long-duration systems, hydrogen), and flexible loads (e.g., electric vehicles, electrolyzers, flexible building loads). The input data required by RODeO can be classified into three bins: 1) utility service data, which refers to retail utility rate information (meter cost, energy and demand charges), 2) electricity market data, which include energy and reserve prices, 3) other inputs, which refer to additional electrical demand, product output demand, technological assumptions, financial properties, and operational parameters.

Guerra Fernandez, Omar Jose↗

DOE OSTI2024

Optimal hybrid power plants for electric vehicle charging demand

Transmission constraints, increasing motivations to decarbonize, and concerns over peak electric vehicle (EV) load impacts on local grids have driven electric customers to consider behind-the-meter, hybrid power plant generation and storage at the distributed-grid level for EV charging. In this study, we develop capabilities to optimize hybrid power plant component capacities for EV charging. We then demonstrate these capabilities in a case study for Boulder, Colorado, using public EV charging data as well as wind and solar resource data. Our results show system designs that balance the cost of energy with load-meeting and peak shaving performance. Within the case study, systems designed for wind, solar photovoltaic (PV), and storage resulted in lower cost of energy than those optimized for PV and storage only. This indicates that in areas where wind resource exists, hybrid power plants that include wind, PV, and battery assets can better meet EV charging loads (including peak loads that are prone to overloading local grids) than PV and battery assets alone. Future work to address limitations in this paper include extending cost modeling to include performance losses (e.g., based on operations or weather) and charging station costs to estimate levelized cost of charging, and quantifying uncertainty and error in our aggregation methods for estimating EV charging loads at the hourly timescale.

14 SOLAR ENERGY↗

DOE OSTI2022

Directional detection of dark matter using solid-state quantum sensing

Next-generation dark matter (DM) detectors searching for weakly interacting massive particles (WIMPs) will be sensitive to coherent scattering from solar neutrinos, demanding an efficient background-signal discrimination tool. Directional detectors improve sensitivity to WIMP DM despite the irreducible neutrino background. Wide-bandgap semiconductors offer a path to directional detection in a high-density target material. A detector of this type operates in a hybrid mode. Here, the WIMP or neutrino-induced nuclear recoil is detected using real-time charge, phonon, or photon collection. The directional signal, however, is imprinted as a durable sub-micron damage track in the lattice structure. This directional signal can be read out by a variety of atomic physics techniques, from point defect quantum sensing to x-ray microscopy. In this Review, we present the detector principle as well as the status of the experimental techniques required for directional readout of nuclear recoil tracks. Specifically, we focus on diamond as a target material; it is both a leading platform for emerging quantum technologies and a promising component of next-generation semiconductor electronics. Based on the development and demonstration of directional readout in diamond over the next decade, a future WIMP detector will leverage or motivate advances in multiple disciplines toward precision dark matter and neutrino physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Valuation and cost reduction of behind-the-meter hydrogen production in Hawaii

A 250kW hydrogen electrolysis facility was recently installed at the Natural Energy Laboratory of Hawaii Authority's (NELHA's) campus. This facility that will begin operation in 2020 to produce hydrogen for fuel cell buses on the island to demonstrate of the application of hydrogen to decarbonize transportation. Given the size of the electrolysis station, it has the potential to significantly increase electricity costs for the campus, which is subject to energy and peak demand charges from the local utility. In this paper, we analyze the cost of hydrogen production at NELHA given the rate structure options available from the utility. Production costs are estimated using optimal versus constant scheduling of the facility to meet the buses’ demand. A model of the electrolysis station is used to capture changes in production efficiency over the power range in the optimization routine. The effects of combining the station and campus load versus standalone operation and increasing solar generation are also explored. The analyses surrounding this scenario show the importance of multiple factors on the potential profitability of hydrogen production in behind-the-meter applications and show trends that could have implications for other similar installations.

08 HYDROGEN↗

DOE OSTI2026

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Baseline vs. DER Scenario

Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗