Understanding electricity consumption and production patterns is a necessary first step toward reducing the health and climate impacts of associated emissions. In this work, the economic input–output model is adapted to track emissions flows through electric grids and quantify the pollution embodied in electricity production, exchanges, and, ultimately, consumption for the 66 continental US Balancing Authorities (BAs). The hourly and BA-level dataset we generate and release leverages multiple publicly available datasets for the year 2016. Our analysis demonstrates the importance of considering location and temporal effects as well as electricity exchanges in estimating emissions footprints. While increasing electricity exchanges makes the integration of renewable electricity easier, importing electricity may also run counter to climate-change goals, and citizens in regions exporting electricity from high-emission-generating sources bear a disproportionate air-pollution burden. For example, 40% of the carbon emissions related to electricity consumption in California’s main BA were produced in a different region. From 30 to 50% of the sulfur dioxide and nitrogen oxides released in some of the coal-heavy Rocky Mountain regions were related to electricity produced that was then exported. Whether for policymakers designing energy efficiency and renewable programs, regulators enforcing emissions standards, or large electricity consumers greening their supply, greater resolution is needed for electric-sector emissions indices to evaluate progress against current and future goals.
In highly renewable power grids, the availability of clean power from water, wind, and solar resources will vary by location, season and time of day. Environmental accounting tools can no longer rely on annual and national averages to capture the underlying physical reality. As more detailed data becomes available, such tools must now evolve to capture heterogeneity at the appropriate scales in space and time. Only then will they accurately monitor and guide future energy transitions.
Decarbonization of electricity generation together with electrification of energy-and-carbon-intensive services such as heating and cooling is needed to address ambitious climate goals. Here we show that city-scale electrification of heat with large-scale thermal storage also cost-effectively unlocks significant additional operational benefits for the power sector. We build an optimization model of fully electrified district heating and cooling networks integrated with other electric loads. We leverage real-world consumption and operational data from a first-of-a-kind facility that meets heating, cooling and electrical energy requirements equivalent to a city of 30,000 people. Using our model, we compute optimal operational strategies for the controllable loads and thermal storage in this system under different economic hypotheses. In our example, electrifying the previously gas-based heating and cooling infrastructure has led to a 65% reduction in the overall campus carbon footprint. Through least-cost scheduling, the load shape of the aggregate energy system can be flattened and annual peak power demand can be reduced by 15%. Through carbon-aware scheduling that takes advantage of variations in grid power carbon intensity, heating and cooling emissions could further decrease by over 40% in 2025 compared to the 2016 baseline, assuming a policy-compliant electricity mix for California. However, rethinking electricity rates based on peak power usage will be needed to make carbon-aware scheduling economically attractive.
District heating and cooling systems incorporating heat recovery and large-scale thermal storage dramatically reduce energy waste and greenhouse gas emissions. Electrifying district energy systems also has the effect of introducing city-scale controllable loads at the level of the electrical substation. Here we explore the opportunity for these systems to provide energy services to the grid through capacity-based demand response mechanisms. We present both a planning approach to estimate available demand-side capacity and a control framework to guide real-time scheduling when the program is active. These tools are used to assess the technical feasibility and the economic viability of participating in capacity-based demand response in the context of a real-world, megawatt-scale pilot during the summer of 2018 on the Stanford University campus.
The goal of this work is to build computational tools to aid decision making for the modelling and operations of integrated urban energy systems that actively interact with the future power grid. District heating and cooling networks incorporating heat recovery and large-scale thermal storage, such as the Stanford campus system, dramatically reduce energy waste and greenhouse gas emissions. They have historically played a small, but important role at a local level. Here we explore the potential for other co-benefits, including the provision of load following services to the electrical grid, carbon emissions reductions or demand charge management. We formulate and solve the problem of optimally scheduling daily operations for different energy assets under a demand-charge-based tariff, given available historical data. We also explore the interaction and interdependence of an electrified thermal energy network with actively managed power sources and sinks that concurrently draw from the same electrical distribution feeder. At Stanford University, large-scale electric vehicle charging, on-site photovoltaic generation and controllable building loads could each separately represent up to 5 MW, or 15% of the aggregate annual peak power consumption in the very near future. We co-optimize financial savings from peak power reductions and shifting consumption to lower price periods and assess the flexibility of both the different components and the integrated energy system as a whole. We find that thermal storage, especially complemented with electric vehicle charging, can play the role that is often proposed for electrochemical storage for demand charge management applications and quantitatively evaluate potential revenue generators for an integrated urban energy system. Although there is little value to smart charging strategies for low penetrations of electric vehicles, they are needed to avoid significant increases in costs once penetration reaches a certain threshold – in the Stanford case, 750-1,000 vehicles, or 25% of the vehicle commuter population.
In a saturated solution with dispersed clusters of a second phase, the mechanism by which the larger clusters grow at the expense of the smaller ones is called Ostwald Ripening. Although the mechanism is well understood in situations where multiple clusters of gas exist in a liquid solution, evolution is much more complicated to predict when the two phases interact with a solid matrix, since the solid plays a significant role in determining the shape of the interface. In this paper, we study capillary dominated regimes in porous media where the driving force is inter-cluster diffusion. By decomposing the Ostwald ripening mechanism into two processes that operate on different time scales – the diffusion of solute gas in the liquid and the readjustment of the shape of the gas- liquid interface to accommodate a change in mass – we develop a sequential algorithm to solve for the evolution of systems with multiple gas ganglia. In the absence of a solid matrix, thermodynamic equilibrium is reached when all of the gas phase aggregates to form one large bubble. In porous media on the other hand, we find that ripening can lead to equilibrium situations with multiple disconnected ganglia, and that evolution is highly dependent on initial conditions and the structure of the solid matrix. The fundamental difference between the two cases is in the relationship between mass and capillary pressure.
The large-scale penetration of renewable energy is a challenge for grid operators both at the transmission and distribution levels. Demand-side management has been gaining traction for offering new ways of controlling the balance between supply and demand of electricity that is so critical to reliable grid operations. Key questions remain as to the flexibility that different dispatchable loads can provide in real-time operations, especially since they act at very different time scales (ramp rates, cycle frequency and duration). The goal of this work is to model and understand how to design and control complex energy systems or ecosystems that interact with not one but several energy carriers such as electricity, heat and fuels; and to explore the potential of flexible energy system components that can increase the security and affordability of our energy system. Specifically, we study the optimal behavior of the Stanford campus energy system under three different California energy mixes, and under different pricing structures for both energy and carbon. This study highlights the synergies that can be gained from a district energy system that couples the supply of heating, cooling and electric power and provides key insights into the relative impacts of the carbon intensity of the electric grid and different pricing structures for carbon and energy on effective decarbonization pathways for a campus energy system.
A multi-scale synchrotron-based X-ray microtomographic dataset of residually trapped air after gravity-driven brine imbibition was acquired for three samples with differing pore topologies and morphologies; image volumes were reconstructed with voxel sizes from 4.44 µm down to 0.64 µm. Capillary pressure distributions among the population of trapped ganglia were investigated by calculating interfacial curvature in order to assess the potential for remobilization of residually-trapped non-wetting ganglia due to differences in capillary pressure presented by neighbor ganglia. For each sample, sintered glass beads, Boise sandstone and Fontainebleau sandstone, sub-volumes with different voxel sizes were analyzed to quantify air/brine interfaces and interfacial curvatures and investigate the effect of image resolution on both fluid phase identification and curvature estimates. Results show that the method developed for interfacial curvature estimation leads to reliable capillary pressure estimates for gas ganglia. Higher resolution images increase confidence in curvature calculations, especially for the sandstone samples that display smaller gas-brine interfaces which are then represented by a higher number of voxels when imaged with a micron or sub-micron voxels size. The analysis of sub-volumes from the Boise and Fontainebleau dataset highlights the presence of a residually-trapped gas phase consisting of ganglia located in one or few pores and presenting significantly different capillary pressures, especially in the case of Fontainebleau sandstone. As a result, Ostwald ripening could occur, leading to gas transfer from ganglia with higher capillary pressure to surrounding ganglia with lower capillary pressures. More generally, at the pore-scale, most gas ganglia do present similar capillary pressures and Ostwald ripening would then not represent a major mechanism for residually-trapped gas transfer and remobilization.
The stability of residually trapped CO2 is often taken for granted in the simulation studies used for predicting the long-term fate of CO2 in geological storage reservoirs. Ostwald ripening is one of the mechanisms that could potentially remobilize residually trapped CO2. This mechanism would cause the gradual growth of ganglia with low capillary pressures at the expense of ganglia with higher capillary pressures. The growth of ganglia becomes an issue if the ganglia start expanding in the vertical direction. In porous media, capillary pressure at the top of a column of gas is directly related to the column height. Above a critical height, the capillary pressure at the top of the column could overcome the capillary entry pressure of the pores directly above it, and induce gravitational remobilization. Capillary heterogeneities at the pore scale are known to affect large-scale migration of gas plumes however. Ostwald ripening will be driven by differences in capillary pressure between ganglia, and subsequent diffusion of dissolved CO2 through the aqueous phase. In a bulk liquid medium, a bubble of gas is observed to be spherical. The capillary pressure of a spherical bubble of gas is inversely proportional to the bubble radius. In porous media on the other hand, the gas phase (observable through microtomographic imaging) takes the form of ganglia with complicated shapes and sizes. Capillary pressures of individual gas ganglia are thought to depend not on total ganglion volume, but rather on pore geometry and topology. A stable equilibrium where disconnected ganglia of different sizes share the same capillary pressure can be imagined. Critical questions relate to understanding and measuring the distribution of capillary pressure in isolated, disconnected ganglia, as well as studying their evolution in time. The goal of this study is therefore two-fold. We develop reliable methods to estimate the capillary pressure distributions for populations of disconnected ganglia of gases that are trapped during imbibition experiments in sandstones. Multi-resolution X-ray microtomography datasets from air-water spontaneous imbibition experiments in sintered glass beads and sandstone samples were acquired at the Advanced Light Source, in the Lawrence Berkeley National Laboratory. A series of computational techniques to process microtomography images; estimate curvature at the interface between two immiscible fluids; and then link these curvature estimates to the capillary pressures of the ganglia were developed based on these data sets. The work flow we develop allows us to estimate curvature distributions for disconnected gas ganglia and assess the reliability of the estimates. Pore structure as well as resolution are found to have a significant impact on curvature estimation. The capillary pressures of disconnected ganglia are also found to be controlled by neighboring pore throat radii. The capillary pressure at different locations on interface of a single ganglion is found to be similar, such that the average capillary pressure for a ganglion is well defined and displays little variability. The distribution of the average capillary pressures for disconnected ganglia is quantified in the different samples and across samples in order to assess the potential for Ostwald Ripening. A second research effort presented in this report focuses on simulating the evolution of multi-ganglia systems governed by ripening mechanisms in porous media. Using reduced dimension representations of the pore space, we study evolution in the context of simple physical laws to find equilibrium positions and guide physical intuition. The final equilibrium situation as well the time scales for evolution are found to be highly dependent on system initialization as well as on pore structure. The simulations also highlight the different evolution regimes of a multi-ganglia system in the bulk and porous medium settings.