No Arabic abstract
As the cost of renewable energy falls below fossil fuels, the most important challenge to enable widespread sustainable power generation has become making renewables dispatchable. Low cost energy storage can provide this dispatchability, but there is no clear technology that can meet the need. Pumped hydroelectric and compressed air storage have low costs, but they are geographically constrained. Similarly, lithium-ion batteries are becoming ubiquitous, but even their lower bounding asymptote cost is too high to enable cost-competitive dispatchable renewables. Here, we introduce a concept based on thermal energy grid storage (TEGS) using a multijunction photovoltaic heat engine (MPV) with promising initial experimental results that could meet the low cost required to enable cost competitive dispatchable renewables. The approach exploits an important tradeoff between the accession of an extremely low cost per unit energy stored, by storing heat instead of electricity directly, while paying the penalty of a lower round trip efficiency. To understand why this tradeoff is advantageous, we first introduce a framework for evaluating storage technologies that treats round trip efficiency (RTE) as a variable, in addition to cost per unit energy stored (CPE) and cost per unit power (CPP). It is from this perspective that the TEGS-MPV concept offers a compelling economic proposition.
In this paper, we propose to model the energy consumption of smart grid households with energy storage systems as an intertemporal trading economy. Intertemporal trade refers to transaction of goods across time when an agent, at any time, is faced with the option of consuming or saving with the aim of using the savings in the future or spending the savings from the past. Smart homes define optimal consumption as either balancing/leveling consumption such that the utility company is presented with a uniform demand or as minimizing consumption costs by storing energy during off-peak time periods when prices are lower and use the stored energy during peak time periods when prices are higher. Due to the varying nature of energy requirements of household and market energy prices over different time periods in a day, households face a trade-off between consuming to meet their current energy requirements and/or storing energy for future consumption and/or spending energy stored in the past. These trade-offs or consumption preferences of the household are modeled as utility functions using consumer theory. We introduce two different utility functions, one for cost minimization and another for consumption balancing/leveling, that are maximized subject to respective budget, consumption, storage and savings constraints to solve for the optimum consumption profile. The optimization problem of a household with energy storage is formulated as a geometric program for consumption balancing/leveling, while cost minimization is formulated as a linear programming problem. Simulation results show that the proposed model achieves extremely low peak to average ratio in the consumption balancing/leveling scheme with about 8% reduction in consumption costs and the least possible amount for electricity bill with about 12% reduction in consumption costs in the cost minimization scheme.
We present a multijunction detailed balance model that includes the effects of luminescent coupling, light trapping and nonradiative recombination, suitable for treatment of multijunction solar cells and photonic power converters -- photovoltaic devices designed to convert narrow-band light. The model includes both specular and Lambertian reflections using a ray-optic formalism and treats nonradiative processes using an internal radiative efficiency. Using this model, we calculate and optimize the efficiency of multijunction photonic power converters for a range of material qualities and light-trapping schemes. Multijunction devices allow increased voltage with lower current, decreasing series resistance losses. We show that efficiency increases significantly with increased number of junctions, even without series resistance, when the device has an absorbing substrate. Such an increase does not occur when the device has a back reflector. We explain this effect using a simplified model, which illustrates the origin of the decreased radiative losses in multijunction devices on substrates.
Adding thermal conductivity enhancements to increase thermal power in solid-liquid phase-change thermal energy storage modules compromises volumetric energy density and often times reduces the mass and volume of active phase change material (PCM) by well over half. In this study, a new concept of building thermal energy storage modules using high-conductivity, solid-solid, shape memory alloys is demonstrated to eliminate this trade-off and enable devices that have both high heat transfer rate and high thermal capacity. Nickel titanium, Ni50.28Ti49.36, was solution heat treated and characterized using differential scanning calorimetry and Xenon Flash to determine transformation temperature (78deg-C), latent heat (183 kJm-3), and thermal conductivity in the Austenite and Martensite phases (12.92/12.64 Wm-1K-1). Four parallel-plate thermal energy storage demonstrators were designed, fabricated, and tested in a thermofluidic test setup. These included a baseline sensible heating module (aluminum), a conventional solid-liquid PCM module (aluminum/1-octadecanol), an all-solid-solid PCM module (Ni50.28Ti49.36), and a composite solid-solid/solid-liquid PCM module (Ni50.28Ti49.36/1-octadecanol). By using high-conductivity solid-solid PCMs, and eliminating the need for encapsulants and conductivity enhancements, we are able to demonstrate a 1.73-3.38 times improvement in volumetric thermal capacity and a 2.03-3.21 times improvement in power density as compared to the conventional approaches. These experimental results are bolstered by analytical models to explain the observed heat transfer physics and reveal a 5.86 times improvement in thermal time constant. This work demonstrates the ability to build high-capacity and high-power thermal energy storage modules using multifunctional shape memory alloys and opens the door for leap ahead improvement in thermal energy storage performance.
Energy storage units (ESUs) enable several attractive features of modern smart grids such as enhanced grid resilience, effective demand response, and reduced bills. However, uncoordinated charging of ESUs stresses the power system and can lead to a blackout. On the other hand, existing charging coordination mechanisms suffer from several limitations. First, the need for a central charging coordinator (CC) presents a single point of failure that jeopardizes the effectiveness of the charging coordination. Second, a transparent charging coordination mechanism does not exist where users are not aware whether the CC is honest or not in coordination charging requests among them in a fair way. Third, existing mechanisms overlook the privacy concerns of the involved customers. To address these limitations, in this paper, we leverage the blockchain and smart contracts to build a decentralized charging coordination mechanism without the need for a centralized charging coordinator. First ESUs should use tokens for anonymously authenticate themselves to the blockchain. Then each ESU sends a charging request that contains its State-of-Charge (SoC), Time-to-complete-charge (TCC) and amount of required charging to the smart contract address on the blockchain. The smart contract will then run the charging coordination mechanism in a self-executed manner such that ESUs with the highest priorities are charged in the present time slot while charging requests of lower priority ESUs are deferred to future time slots. In this way, each ESU can make sure that charging schedules are computed correctly. Finally, we have implemented the proposed mechanism on the Ethereum test-bed blockchain, and our analysis shows that execution cost can be acceptable in terms of gas consumption while enabling decentralized charging coordination with increased transparency, reliability, and privacy preserving.
Adopting thin Si wafers for PV reduces capital expenditure (capex) and manufacturing cost, and accelerates the growth of PV manufacturing. There are two key questions about thin Si today: (a) how much can we still benefit economically from thinning wafers? (b) what are the technological challenges to transition to thin wafers? In this work, we re-evaluate the benefits and challenges of thin Si for current and future PV modules using a comprehensive techno-economic framework that couples device simulation, bottom-up cost modeling, and a cash-flow growth model. When adopting an advanced technology concept that features sufficiently good surface passivation, similarly high efficiencies are achievable for 50-um wafers as for 160-um ones. We then quantify the economic benefits for thin Si wafers in terms of poly-Si-to-module manufacturing capex, module cost, and levelized cost of electricity (LCOE) for utility PV systems. Particularly, LCOE favors thinner wafers for all investigated device architectures, and can potentially be reduced by more than 5% from the value of 160-um wafers. With further improvements in module efficiency, an advanced thin-wafer device concept with 50-um wafers could reduce manufacturing capex by 48%, module cost by 28%, and LCOE by 24%. Furthermore, we apply a sustainable growth model to investigate PV deployment scenarios in 2030. It is found that the state-of-the-art industry concept could not achieve the climate targets even with very aggressive financial scenarios, therefore the capex reduction benefit of thin wafers is needed to facilitate more rapid PV growth. Lastly, we discuss the remaining technological challenges and areas for innovation to enable high-yield manufacturing of high-efficiency PV modules with thin Si wafers.