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The rapid development of advanced metering infrastructure provides a new data source—building electrical load profiles with high temporal resolution. Electric load profile characterization can generate useful information to enhance building energy modeling and provide metrics to represent patterns and variability of load profiles. Such characterizations can be used to identify changes to building electricity demand due to operations or faulty equipment and controls. In this study, we proposed a two-path approach to analyze high temporal resolution building electrical load profiles: (1) time-domain analysis and (2) frequency-domain analysis. The commonly adopted time-domain analysis can extract and quantify the distribution of key parameters characterizing load shape such as peak-base load ratio and morning rise time, while a frequency-domain analysis can identify major periodic fluctuations and quantify load variability. We implemented and evaluated both paths using whole-year 15-minute interval smart meter data of 188 commercial office building in Northern California. The results from these two paths are consistent with each other and complementary to represent full dynamics of load profiles. The time- and frequency-domain analyses can be used to enhance building energy modeling by: (1) providing more realistic assumptions about building operation schedules, and (2) validating the simulated electric load profiles using the developed variability metrics against the real building load data.

Abstract This paper presents a novel multi-time-stage input–output-based modeling framework for simulating the dynamics of bioenergy supply chains. One of the key assumptions used in the model is that the production level at the next time-stage of each segment of the energy supply chain adjusts to the output surplus or deficit relative to targets at the current time period. Furthermore, unlike conventional input–output models, the technology matrix in this approach need not be square, and thus can include coefficients denoting flows of environmental goods, such as natural resources or pollutants. Introducing a feedback control term enables the system to regulate the dynamics, thus extending the model further. This is an important feature since the uncontrolled dynamic model exhibits oscillatory or unstable behavior under some conditions; in principle, the control term allows such undesirable characteristics to be suppressed. Numerical simulations of a simple, two-sector case study are given to illustrate dynamic behavior under different scenarios. Although the case study uses only a hypothetical system, preliminary comparisons are made between the simulation results and some broad trends seen in real bioenergy systems. Finally, some of the main policy implications of the model are discussed based on the general dynamic characteristics seen in the case study. In particular, insights from control theory can be used to develop policy interventions to impart desirable dynamic characteristics to nascent or emerging biofuel supply chains. These interventions can be used to guide the growth of bioenergy supplies along final demand trajectories with minimal fluctuation and no instability.

Abstract Reversible carbonation/calcination of CaCO3 is a promising technology for thermochemical energy storage in concentrated solar power plants. However, the major drawback of this technology is the rapid deactivation of CaO due to sintering. In this study, newly developed CaCO3/graphite nanosheets composites were synthesized as the heat storage medium through a one-pot route varying the graphite nanosheets load (3–12 wt%). The impregnation of the composites with H3BO3 solutions enhance the initial weightlessness temperature of graphite nanosheets from 900 °C to 1050 °C in pure CO2 atmosphere, thus upgrading the stability of graphite nanosheets during heat storage/release process. The performances of the synthesized composites were evaluated by thermogravimetric analysis, which simulates the heat storage cycle. The composites showed improved heat storage/output capacity and faster heat input/output rate compared to pure CaCO3. Only 3 wt% of graphite nanosheets was needed to effectively stabilize the heat storage capacity of the material. The heat storage capacity of composites with 3 wt% graphite nanosheets is 1313 kJ/kgcomposite after 50 cycles, corresponding to 2.9 times as much as that of pure CaCO3. This high stability is attributed to the unique synthetic strategy in which the CaCO3 nanoparticles uniformly coated on graphite nanosheets surface, and their sintering and aggregation were prevented. This work brings the development of this technology to a level closer to its industrial application.

Shale gas, due to its clean-burning and efficient nature, is becoming an increasingly promising alternative energy resource. It is commonly held that promoting shale gas development will gradually play a significant role in meeting the energy needs of economic and social development as well as reducing harm to the environment. Given the significant implications, many countries are pursuing shale gas opportunities. However, numerous concerns have been raised about the economics of shale gas development, as it is difficult to evaluate. Accurately evaluating the economic viability of shale gas development to reduce investment risks and increase investment opportunity is the key issue that needs to be urgently addressed. This paper presents a systematic review and examination of the technical and economic evaluation techniques for the development of shale gas to provide an overview of their current status. Over time, some progress has been made in existing technical–economic evaluation techniques. It is worth noting that these techniques need to be further improved to more precisely assess the economic feasibility of developing shale gas for assisting investment decisions effectively. For this reason, various potentially useful ideas and approaches are presented to propose some potential improvement in evaluation techniques for shale gas development, which may materialize in possible future trends.

Abstract In solid fuel (such as lignite) chemical looping combustion, solid fuels undergo pyrolysis and gasification. The volatiles from pyrolysis and the gasification product (CO/H 2 ) react with oxygen carriers. The gas conversion (to CO 2 /H 2 O) in the fuel reactor is a key point. However, char particles of different sizes and conversion ratios cause segregation in the fuel reactor, which influences the contact time between fuel gases and the carrier, thereby changing the gas conversion behavior. In order to gain information on obtaining a high gas conversion in the fuel reactor, this work focused on the effect of the char particle segregation on gas conversion. Different factors – the char particle size, the fluidizing gas velocity, and the oxygen carrier reactivity – were taken into account. Smaller char particles with low density would float on top of the fluidized bed, corresponding to a low gas conversion ( U mf in the Fe63Al bed) can reduce the segregation effect, resulting in a higher CO conversion. High reactivity carriers can convert CO completely although segregation exists, whereas low reactivity carriers exhibit the segregation effect and thus corresponds to a low CO conversion.

Abstract With increasing prosumers employed with flexible resources, advanced demand-side management has become of great importance. To this end, integrating demand-side flexible resources into electricity markets is a significant trend for smart energy systems. The continuous double auction (CDA) market is viewed as a promising P2P (peer to peer) market mechanism to enable interactions among demand side prosumers and consumers in distribution grids. To achieve optimal operations and maximize profits, prosumers in the electricity market must act as price makers to simultaneously optimize their operations and trading strategies. However, the CDA-based market is difficult to model explicitly because of its information-based clearing mechanism and the stochastic bidding behaviors of its participants. To facilitate prosumers actively participating in the CDA market, this paper proposes a novel prediction-integration strategy optimization (PISO) model. A surrogate market prediction model based on Extreme Learning Machine (ELM) is developed, which learns the interaction relationship between prosumer bidding actions and market responses from historical transaction data. Moreover, the prediction model can be conveniently transformed and integrated into the prosumer operation optimization model in the form of constraints. Therefore, prosumer operations and market trading strategies can be jointly optimized through the proposed approach, facilitating the integration of flexible resources into electricity markets. Numerical studies demonstrate the effectiveness of the proposed model by comparing with existing CDA trading strategies under various market conditions.

Abstract Advanced combustion engine (ACE) research is typically carried out on single-cylinder research engines. These engines are designed to tightly control fueling and conditions at intake valve closure (IVC) and to precisely measure in-cylinder conditions and emissions. However, to be able to measure and control engine operation so precisely, these research engines typically do not feature intake and exhaust tracts that resemble those in production engines, specifically in regards to turbomachinery, heat exchangers, and exhaust gas recirculation (EGR) systems. For this reason, these research engines are effective for understanding in-cylinder combustion parameters such as heat release rate, burn duration, combustion efficiency, pollutant formation, and exhaust valve opening (EVO) conditions. This paper applies high fidelity simulations to determine the feasibility of achieving a chosen single cylinder engine operating point on a production type homogeneous charge compression ignition (HCCI) engine, using a partial fuel stratification (PFS) strategy. To accomplish this, a Converge 3 dimensional (3D) – computational fluid dynamics (CFD) model of the experimental combustion chamber and intake and exhaust runners was created to simulate the experimental engine. This model was used to simulate an operating point achieved experimentally, as well as to determine the sensitivity of the operating point to variations in intake pressure, intake temperature, injection timing, injected mass, and EGR fraction. The results from these simulations were fed into a 1-dimensional engine simulation created in AVL Boost, featuring production-type intake and exhaust systems, including turbomachinery and heat exchangers necessary to create the required IVC conditions. This full engine simulation was used to assess the cycle efficiency of the engine at the experimental operating condition, and to assess whether changes to this operating point in intake temperature, intake pressure, direct injection timing, or fueling are beneficial to the cycle efficiency and engine-out emissions. In addition, the sensitivity of promising engine operating points to injection timing and injection mass are determined to evaluate the potential stability of these operating points.

Abstract In the past, most studies considered solar as a drawback to road pavement, especially the asphalt pavement. Not only its radiation causes the asphalt aging, simultaneously with high temperature, asphalt pavement contributes to the urban heat island (UHI) effect. In recent, as the world becomes more supportive towards implementing sustainable energy as the alternatives to the scarcity of non-renewable resources, utilizing the abundant solar radiation as the main source of energy has gradually attracted attention from both industries and academia with its potentials, including solar application on roads. Nevertheless, more researches have focused on the potential energy collection with less consideration to simultaneously overcome the side effects of abundant solar radiation on its lifecycle. This study found the necessity to review a comprehensive correlation between solar radiation and asphalt pavement from both positive and negative effects. The negative effects included asphalt aging and its effects on surroundings i.e. UHI effect. Meanwhile, the positive effects included exhaust purification and solar energy conversion into other forms of energy that can be used by humans using pavement solar heat collection for winter snowmelt, thermoelectric technology and photoelectric technology to convert solar energy into electrical energy. The cases in various application scenarios were analysed and summarized and the existing problems in the current research were proposed in this paper. Also, the reflection on the transformation of road design concept from “avoiding harm” to “seeking profit” was included. Finally, a new concept of “solar-road harmonious symbiosis” for future road application was proposed based on a comprehensive review of previous published works. It is also the new suggestion to scholars on the treatment of solar diseases on roads to prolong its symbiosis functions.

In power system operation, characterizing the stochastic nature of wind power is an important albeit challenging issue. It is well known that distributions of wind power forecast errors often exhibit significant variability with respect to different forecast values. Therefore, appropriate probabilistic models that can provide accurate information for conditional forecast error distributions are of great need. On the basis of Gaussian mixture model, this paper constructs analytical conditional distributions of forecast errors for multiple wind farms with respect to forecast values. The accuracy of the proposed probabilistic models is verified by using historical data. Thereafter, a fast sampling method is proposed to generate scenarios from the conditional distributions which are non-Gaussian and interdependent. The efficiency of the proposed sampling method is verified.