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With the integration of a high penetration of wind power into distribution system, the ability to cope with the voltage fluctuation issue arising from the intensified uncertainty should be improved. Energy storage system (ESS) with effective control strategies keeps a key role in smoothing the voltage fluctuation caused by the uncertainty of wind turbine generators (WTGs). Considering the constraints of state of charge (SOC), charge-discharge efficiency and continuous operation of ESS, this paper proposes a novel approach to determine the minimum capacity of ESS to satisfy the voltage fluctuation limit based on unbalanced three-phase interval power flow. An unbalanced forward-backward sweep interval power flow considering the constraints of ESS and the voltage fluctuation limit is proposed. The interval model of uncertain WTG output is established on basis of wind speed fluctuation curve. The interval model of the practical ESS output is obtained on basis of the present SOC, nominal capacity and comprehensive efficiency. The output voltages of power flow can directly demonstrate the voltage fluctuation rate due to their interval forms. Therefore, a feedback mechanism is established between the output voltage fluctuation rate and the input ESS practical power, which is used to determine the minimum capacity of ESS to satisfy the fluctuation limit. A modified IEEE 33-bus system with high penetration of WTGs is implemented to test the proposed method. Results demonstrate the validity and effectiveness of the proposed approach.

The Sulphurdale Geothermal Field is located in Beaver County Utah, within the boundaries of the Cove Fort--Sulphurdale Known Geothermal Resource Area (KGRA). During the past year, three wells drilled in Section 7, T-26-S, R-6-W, have produced dry steam from a fractured volcanic formation located at a depth of about 1100 feet. Two of these three wells are currently prepared to supply steam to a power plant, and one well has been plugged and abandoned. ThermaSource, Inc. was retained by Mother Earth Industries, the operator of the field, to conduct well tests and render an opinion as to the nature of the geothermal reserves and assess the commercial potential of these reserves. Because of the limited area that has been explored to date, there can be no assurance that the reserves estimate will prove accurate. Project economics are based on parameters believed to be accurate, but there is no assurance that such cash flow projections will be realized.

In order to improve the accuracy of the battery state of charge (SOC) estimation, in this paper we take a lithium-ion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate. Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.

Studying the influence of the demand response and dynamic characteristics of the battery energy storage on the configuration and optimal operation of battery energy storage system (BESS) in the Wind-Photovoltaic (PV)-Energy Storage (ES) hybrid microgrid. A demand response model that is based on electricity price elasticity is established based on the time-of-use price. Take the capital-operating cost and direct economic benefit of the BESS and the loss of abandoned photovoltaic and wind power as the optimization objective, an optimal configuration method that considers the dynamic characteristics of the BESS and the maximum absorption of photovoltaic and wind power is proposed while using particle swarm optimization to solve. The results show that the configuration results considering the demand side response of the microgrid BESS can obtain better economy and reduce the storage capacity requirement, and the result shows that the efficiency of BESS relates to the load of the system, the distributed generation (DG) characteristics, and the dynamic characteristics of BESS. Meanwhile, the capacity and power of the energy storage configuration increase as the DG permeability increases due to the reverse load characteristic of the wind power.

Dynamic loading of transformers is the term used when the loading capacity is calculated using an appropriate thermal model while taking into account the load magnitude, load shape, thermal limits and external cooling conditions. For transformer dynamic loading, the aim is to estimate the transformer's maximum dynamic loading capacity without violating the hottest-spot temperature (HST) and top-oil temperature (TOT) thermal limits. These two temperatures are proxy measures of insulation temperature, which can be used to estimate the loss of service life. The accuracy of the TOT and HST predictions is dependent on many things, central to which is the accuracy of the thermal model. This paper introduces metrics that can be used to differentiate between reliable and unreliable transformer thermal models. The two models discussed in this paper for HST and TOT are the non-linear IEEE model and the model developed by the authors using linear regression methods.

Abstract Intermittent renewable energy generation, which is determined by weather conditions, is increasing in power markets. The efficient integration of these energy sources calls for flexible participants in smart power grids. It has been acknowledged that a large, underutilized, flexible resource lies on the consumer side of electricity generation. Despite the recently increasing interest in demand flexibility, there is a gap in the literature concerning the incentives for consumers to offer their flexible energy to power markets. In this paper, we examine a virtual power plant concept, which simultaneously optimizes the response of controllable electric hot water heaters to solar power forecast error imbalances. Uncertainty is included in the optimization in terms of solar power day-ahead forecast errors and balancing power market conditions. We show that including solar power imbalance minimization in the target function changes the optimal hot water heating profile such that more electricity is used during the daytime. The virtual power plant operation decreases solar power imbalances by 5–10% and benefits the participating households by 4.0–7.5 € in extra savings annually. The results of this study indicate that with the number of participating households, while total profits increase, marginal revenues decrease.

Abstract Numerical simulations with detailed homogeneous (gas phase) and heterogeneous (catalytic) chemistries of premixed hydrogen–oxygen mixture were performed inside a rectangular micro-combustor. The effects of catalytic walls on the homogeneous combustion were investigated via varying catalyst segment layouts and sizes. The interactions between heterogeneous and homogeneous reactions were discussed. It was shown that the heterogeneous reactions become weak when the catalyst disposition is shifted toward the outlet along the streamwise. The homogeneous combustion also becomes obviously weakened downstream behind the catalyst segmentation, except the exit. Moreover, with increasing catalyst segment size, the hydrogen conversion ratio increases and the mean outlet temperature reduces. The competition of the fresh fuel between the heterogeneous and homogeneous reactions result in the inhibition of heterogeneous reactions on homogeneous combustion. In summary, the heterogeneous reactions can obviously improve the combustion efficiency in a micro catalytic combustor. As expected, the effects of catalytic walls were more pronounced for the micro combustor systems.

Abstract For 118 million residential housing units in the U.S., there is currently a gap between the potential energy savings that can be achieved through the use of existing energy efficiency technologies, and the actual level of energy savings realized, particularly for the 37% of housing units that are considered residential rental properties. Additional quantifiable benefits are needed beyond energy savings to help further motivate residential property owners to invest in energy efficiency upgrades. This research focuses on assessing the adoption of energy efficient upgrades in U.S. residential housing and the impact on rental prices. Ten U.S. cities are chosen for analysis; these cities vary in size across multiple climate zones, and represent a diverse set of housing market conditions. Data was collected for over 159,000 rental property listings, their characteristics, and their energy efficiency measures listed in rental housing postings across each city. Following an extensive data quality control process, over thirty different types energy efficient features were identified. The level of adoption was determined for each city, ranging from 5.3% to 21.6%. Efficient lighting and appliances were among the most common, with many features doubling as energy efficient and other desirable aesthetic or comfort improvements. Then using propensity score matching and conditional mean comparison methods, the relative impact on rent charged in each city was calculated, which ranged from a 6% to 14.1% increase in rent for properties with energy efficient features, demonstrating a positive economic impact of these features, particularly for property owners. This was further subdivided into five types of energy efficiency upgrade and three housing types. Single family homes generally demanded higher premiums with energy efficient features, however there was not a consistent pattern across the types of efficient upgrades. The results of this work demonstrate that investment in energy efficient technologies has quantifiable benefits for rental property owners in the U.S. beyond just energy savings. This methodology and results can also be used in other cities and by property owners, utility companies, or others, ultimately encouraging further investment and positive economic impact in residential energy efficiency and in turn improving energy and resource conservation in the building sector.

Abstract The Seed-and-Blanket (SB it is few percent higher than that of the 2-stage PWR-ABR system. Approximately 7% of the thorium fed to the blanket is converted into energy, which makes the thorium fuel utilization approximately 12 times the utilization of natural uranium in PWRs. A comprehensive fuel cycle evaluation performed with the methodology developed by the recent U.S Department of Energy’s Nuclear Fuel Cycle Evaluation and Screening campaign concludes that the PWR-S&B system has similar fuel cycle performance characteristics as the PWR-ABR system. The S&B concept may potentially feature improved economics and resource utilization relative to the ABR.