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Abstract This paper uses quantile regression to demonstrate how electricity price distributions are linked to fundamental supply and demand variables. It investigates the California electricity market (zone SP15) for selected trading hours using data from January 8, 2013 to September 24, 2016. The approach quantifies a non-linear relationship between the fundamentals and electricity prices, just as predicted by the merit order curve. Natural gas, greenhouse gas allowance prices and load all have a positive effect on electricity prices, with the effect increasing with the quantiles. In contrast, solar production and wind production both have a negative effect on electricity prices. The effect of solar production increases with quantiles, whereas the effect of wind production decreases with quantiles. This paper also includes a stress testing case study in which a producer faces the risk of high solar and wind production, and investigates the effect on the lower tail of the price distribution. Overall, the results demonstrate how the proposed approach can be a helpful risk management tool for participants in the electricity market.

Abstract The use of compressed air in industry is an important and yet overlooked energy carrier. Although there are different energy-saving measures discussed in the specialized literature, there is little discussion on the energy performance of the production and use of compressed air. This study developed a new approach to assess the energy performance of compressed air systems based on a six-step local energy benchmarking methodology. The methodology includes an energy management procedure to monitor and control the electricity consumption and sustain the energy performance of compressed air systems in time. The procedure monitors the production and use of compressed at plant and at manufacturing section levels based on the real-time monitoring of relevant variables to calculate energy performance indicators, energy baselines, and CUSUM charts. Monitoring the consumption of compressed air at the section level in a case study reduced the demand between 11 and 47%. While electricity consumption to produce compressed air at the plant level reduced by an estimated 23%. This approach permits the rapid detection of inefficiencies in the production and demand sides of the compressed air system, highlighting inefficiencies that are frequently hidden in the total electricity consumption of manufacturing plants.

Abstract Using the Bakken shale play as a case study, the previous part of this two-part series demonstrated how small-scale mobile plants could be used to monetize associated or stranded gas effectively. Here, we address the issue of uncertainty in future supply, demand and price conditions. To this end, we modified our multi-period optimization framework to a stochastic programming framework to account for various scenarios with different parameter realizations in the future. The maximum ENPV (expected net present value) obtained was $2.01 billion, higher than the NPV obtained in the previous part. In addition, the value of the stochastic solution was 0.11% of the optimal ENPV, indicating that the flexible nature of mobile plants affords them a great advantage when dealing with uncertainty.

Abstract A CCP (combined cooling and power) system, which integrated a flash-binary power generation system with a bottom combined cooling and power subsystem operating through the combination of an organic Rankine cycle and an ejector refrigeration cycle, was developed to utilize geothermal energy. Thermodynamic and exergoeconomic analyses were performed on the system. A performance indicator, namely the average levelized costs per unit of exergy products for the overall system, was developed to assess the exergoeconomic performance of the system. The effects of four key parameters including flash pressure, pinch point temperature difference in the vapor generator, inlet pressure and back pressure of the ORC turbine on the system performance were evaluated through a parametric analysis. Two single-objective optimizations were conducted to reach the maximum exergy efficiency and the minimum average levelized costs per unit of exergy products for the overall system, respectively. The optimization results implied that the most exergoeconomically effective system couldn't obtain the best system thermodynamic performance and vice versa. An exergy analysis based on the thermodynamic optimization result revealed that the biggest exergy destruction occurred in the vapor generator and the next two largest exergy destruction were respectively caused by the steam turbine and the flashing device.

Abstract This work presents a simultaneous mixed-integer nonlinear programming (MINLP) optimisation model and an efficient iterative solution strategy that can be successfully applied to various heat-integrated water networks (HIWNs), including large-scale problems with a large number of water streams. These problems are highly nonlinear, non-convex and combinatorial. To circumvent such difficulties, including network complexity as well as identifying the roles of water streams in the heat exchanger network (HEN) whether they are hot or cold, a modified convex hull formulation proposed by Ahmetovic and Kravanja [1] is applied. The overall model combines the water network (WN), wastewater treatment network (WTN), heat integration (HI), and heat exchanger network synthesis (HENS) models. This model is iteratively solved in three steps including targeting and design steps. The proposed model and solution strategy are tested on large-scale problems. To the best of our knowledge, the results obtained for all the problems in this paper are better than those reported in the current literature.

Abstract As electricity markets deregulate and energy tariffs increasingly expose customers to commodity price volatility, it is difficult for energy consumers to assess the economic value of investments in technologies that manage electricity demand in response to changing energy prices. The key uncertainties in evaluating the economics of demand–response technologies are the level and volatility of future wholesale energy prices. In this paper, we demonstrate that financial engineering methodologies originally developed for pricing equity and commodity derivatives (e.g., futures, swaps, options) can be used to estimate the value of demand-response technologies. We adapt models used to value energy options and assets to value three common demand–response strategies: load curtailment, load shifting or displacement, and short-term fuel substitution—specifically, distributed generation. These option models represent an improvement to traditional discounted cash flow methods for assessing the relative merits of demand-side technology investments in restructured electricity markets.

Abstract A mixed-integer linear programming model was developed to optimize the multiple biomass feedstock supply chains, including feedstock establishment, harvest, storage, transportation, and preprocessing. The model was applied for analyses of multiple biomass feedstocks at county level for 13 states in the northeastern United States. In the base case with a demand of 180,000 dry Mg/year of biomass, the delivered costs ranged from $67.90 to $86.97 per dry Mg with an average of $79.58/dry Mg. The biomass delivered costs by county were from $67.90 to 150.81 per dry Mg across the northeastern U.S. Considered the entire study area, the delivered cost averaged $85.30/dry Mg for forest residues, $84.47/dry Mg for hybrid willow, $99.68 for switchgrass and $97.87 per dry Mg for Miscanthus. Seventy seven out of 387 counties could be able to deliver biomass at $84 per dry Mg or less a target set by US DOE by 2022. A sensitivity analysis was also conducted to evaluate the effects of feedstock availability, feedstock price, moisture content, procurement radius, and facility demand on the delivered cost. Our results showed that procurement radius, facility capacity, and forest residue availability were the most sensitive factors affecting the biomass delivered costs.

Abstract Chemists, biologists, ecologists, and engineers have been developing a data-base to identify and evaluate risks to humans and the environment and strategies to minimize potential risks from large-scale coal liquefaction. Coal-liquids produced by various processes and under various stages of design and operating conditions have been screened for potential health and environmental effects. Toxicologically active materials have been fractionated and chemical constituents of biologically active fractions have been identified, and the environmental fate of problematic agents is being determined. Results indicate that coal-derived liquids are generally more toxicologically active than shale oil and petroleum crudes. Bioactive agents include primary aromatic amines (PAA), polynuclear aromatic hydrocarbons (PAH), phenolics, and others. Some components of coal-derived materials are taken up by biota and metabolized. Hydrotreating reduces PAA, PAH, and phenol content, as well as toxicological response to coal-liquids. Selective distillation restricts PAA and PAH content, and mutagenicity and carcinogenicity to high-boiling-range materials. Other process conditions and environmental factors also influence chemical characteristics and toxicological activity of coal-derived liquids. Recent findings indicate that biological responses to a given coal-derived liquid component vary, depending on whether that material is presented to the organism or environment as a pure compound or in a complex mixture. The data-base described provides input for assessment and has been used by developers when selecting process modifications and product slates that minimize risk to humans and the environment. These data have also been used in developing occupational health and industrial hygiene practices and may aid in selection of control technologies, mitigative strategies, special handling and accident prevention procedures, or spill-clean-up options to enhance the environmental acceptability of a coal liquefaction industry.