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Abstract Commercial potential in developing countries has always received a great attention from international investors, but this is not the case in Waste-to-Energy sector. Waste-to-Energy is bound up with various uncertainties rooted in its long-term nature therefore incorporating risks regarding political matters in developing countries makes it more complex. The present study substantiates the incompatibility of classic valuation methods in risky projects. Consequently, to deal with the riskiness of Waste-to-Energy investment in less developed countries, the combination of binomial tree analysis and Decoupled NPV is proposed. The hybrid approach is deployed to value a Waste-to-Energy project in Iran, and all evidence attest to the robustness of the method. The contribution of this paper can open up new vistas for investing in Waste-to-Energy industry, thus abating the catastrophic effects of landfill gas emissions.

Wastewater containing ammonia nitrogen compounds is considered as a harmful material to environment due to eutrophication and toxicity effects; hence, finding practical methods for treating this type of wastewater seems necessary. In this study, performance of two sequencing batch reactors have been assessed for simultaneous nitrification and denitrification in treating synthesized wastewater containing ammonium nitrogen, using leachate obtained from cow dung as the biomass. The leachate obtained from cow dung as the source of bio‐sludge added to the reactors. Experiments were designed according to central composition design and response surface methodology with four operating variables including pH (6, 7.5, 9), cycle time (CT) (4, 12, 20 h), ratio (5, 10, 15) and carbon source (Sodium acetate: , Glucose: ). According to statistical analysis, experimental responses were in acceptable agreement with model predictions. CT was the most important operating variable in chemical oxygen demand (COD) and removal. The maximum percentage of ammonium nitrogen removal was attained at pH 7.5 and CT 21.5 h. The optimum conditions were composed of pH 7.67, CT 19.15 h, 10.95 and sodium acetate as carbon source, while COD, and total nitrogen removals were 94.96%, 94.93% and 93.60%, respectively. © 2017 American Institute of Chemical Engineers Environ Prog, 37: 1638–1646, 2018

Abstract A three dimensional numerical model of the northwest (NW) Sabalan geothermal system was developed on the basis of the designed conceptual model from available field data. A numerical model of the reservoir was expressed with a grid system of a rectangular prism of 12 km × 8 km with 4.6 km height, giving a total area of 96 km2. The model has 14 horizontal layers ranging in thickness between 100 m to 1000 m extending from a maximum of 3600 to −1000 m a.s.l. Fifteen rock types were used in the model to assign different horizontal permeabilities from 5.0 × 10−18 to 4.0 × 10−13 m2 based on the conceptual model. Natural state modeling of the reservoir was performed, and the results indicated good agreements with measured temperature and pressure in wells. Numerical simulations were conducted for predicting reservoir performances by allocating production and reinjection wells at specified locations. Three different exploitation scenarios were examined for sustainability of reservoir for the next 30 years. Effects of reinjection location and required number of makeup wells to maintain the specified fluid production were evaluated. The results showed that reinjecting at Site B, immediate adjacent to production zone, is most effective for pressure maintenance of the system. On the base of existing data and assumptions the reservoir can sustain producing fluid equivalent to 50 MWe of electricity for more than 30 years. The reservoir can produce the maximum amount of fluids equivalent to 90–100 MWe for only 5 years, but the production capacity decreases to 50 MWe after 20 years of operation because of pressure and enthalpy drop. The reservoir can sustain 50 MWe over 100 years that can be defined as a sustainable production level of the field.

Abstract Motivated by a real-world case, this paper deals with uncertainty issues in the resilient and sustainable electricity supply chain network design. Uncertainty is always found in electricity demand prediction and perfect foresight is not possible. The resilience of electricity networks in face of uncertainty can prevent catastrophic impacts. Besides, due to the growing concerns about social impacts, considering social measures along with the other measures is becoming critical when analyzing the network costs. In this paper, a multi-objective optimization model is developed, which consists of the total cost in the first objective, resiliency measures in the second objective, and some aspects of corporate social responsibility in the third objective. The resiliency measures minimize de-resiliency, including successive establishment, distributed generators inadequacy, congestion through electrical lines, and energy dissatisfaction level. A novel robust approach is also proposed to deal with the uncertainty of electricity demand based on the light robust programming and possibilistic theory in the fuzzy logic. By investigating a real case in Iran, the contributions of the considered measures, and the effects of uncertainty are analyzed. The analyses reveal that by applying the proposed model the decision-makers can enhance corporate social responsibility and resiliency by 50% and 20%, respectively, although it increases the total cost by 50%. Moreover, the results of applying the proposed fuzzy-robust approach show that how the decision-makers can effectively allocate the uncertainty budget and protection level to attain more robust solutions in terms of standard deviation and mean value of the total cost.

Abstract Inter-area generation expansion planning in a multi-period timescale with minimization of expansion cost and pollution is proposed in this paper. The expansion decision is made in a deregulated environment from the viewpoint of the strategic competitor. In this regard, the GENCO can expand its generation units accessing various generation technologies as candidate expansion plans in the main area. The main area consists of several buses while the adjacent areas are modeled as a single bus. The behavior of the other competitors is considered predictable by the strategic GENCO. In this study, the uncertainties of load, electricity price and wind speed are included. To confront them, rough neural network methodology is employed to forecast the uncertain parameters. In order to achieve cleaner environment and sustainable development policies, pollution constraint and taxes are employed in addition to the main costs where different carbon emission penalties are applied to each generation technology production. Different scenarios are proposed in order to investigate the effect of both multi-market and pollution barriers considerations on generation companies’ decision-making and profitability. In the field of multi-markets, the scenarios are designed so as to evaluate the extra income opportunities triggered by selecting optimal locations which have appropriate electrical accessibility to adjacent markets. In the scope of pollution barriers, the purpose of the suggested scenarios is to observe the companies’ investment combinations and future revenues by applying regulatory constraints like carbon taxes and quotas. Indeed, the findings underline that by engaging pollution control policies, the total emissions can be dramatically reduced up to 45% while maintaining an acceptable income for the generation companies taking advantage of participating in adjacent electricity markets.

With the spotlight on smart grid development around the world, it is critical to recognize the key factors contributing to changing power system characteristics. This is more apparent in distribution systems with the integration of renewable energy sources, energy storage, and microgrid development. Utilities are also focusing on the reliability and resiliency of the grid. These activities require distribution automation (DA) strategies that take advantage of available technologies while promoting newer solutions. It is necessary to create a roadmap for holistic DA strategies in a smarter grid. Sustainable and resilient grid development is a paradigm shift requiring a new line of thinking in the engineering, operation, and maintenance of the power system. International perspectives on DA are also addressed, with the understanding that one solution will not fit all. Integrating technical, business, and policy decisions into the challenges will generate the development of technologies, standards, and implementation of the overall solution. The challenges in the development of industry standards are also discussed. This paper explores the challenges and opportunities in the changing landscape of the distribution systems. Evolution of technologies and the business case for infrastructure investment in distribution systems are covered in another paper by the same authors.

The use of agricultural wastes as livestock feed is an appropriate method to convert these materials into high value-added materials. These wastes have low nutritional value per unit volume and high transportation, storage and labor costs when they are used in original form. Compaction of wastes in the form of pellet is a proper solution to solve these problems. The pellet drying stage is one of the most important pellet production processes that affect the quality of the pellets. In the present study, the impacts of moisture content, particle size, inlet air temperature of dryer and infrared power of dryer were investigated on properties of physical (unit and bulk density and shrinkage) and thermal (effective moisture diffusivity and specific energy consumption) properties of pellets produced from food and agriculture wastes. The results indicated that all independent variables had a significant negative effect on unit and bulk densities. The effective moisture diffusivity increased with the increase in particle size, infrared power and air temperature dryer. Also specific energy consumption in infrared-convection drying of pellets increased with finer grinding of raw materials.

Abstract Iran's demographic profile is sharply youth oriented and this upcoming generation's needs for employment and housing, coupled with low-energy efficiency vectors and consumption patterns, has created a constant rise in energy demand and greenhouse gas (GHGs) emissions in the residential sector. Improved energy efficiency as a national policy lynchpin for demand reduction and GHGs mitigation, has become commonplace. OPEC countries however, Iran included, suffer an obvious lack of consumer incentive because of low fuel prices. This study evaluates the twin impacts of price reform and efficiency programs on energy carriers’ consumption and GHGs mitigation in the Iranian housing sector. For this purpose, the demand functions for energy carriers, has been developed by econometrics process models. The results reveal that price elasticity for electricity demand in the Constant Elasticity Model for the short-run while the long-run is −0.142 and −0.901, respectively. In the Variable Elasticity Model the 250% increase in electricity rates in the short-run resulted in a price elasticity change from −0.02 to −0.475, hence the 250% increase in electricity pricing for the long-run resulted in the price elasticity change from −0.15 to −2.0. Finally, aided by a Scenario-Based Approach the impact of fuel pricing and efficiency improvement in trends of energy demand and GHGs emission were assessed in a Scenarios Base, developed on two different cases of Business-as-Usual (BAU) and Management. The results indicate that in the BAU case between 2000 and 2011, the energy demand and CO2 emission increases with an annual growth rate of 7.5% and 6.8%, respectively. Comparatively, if the energy carriers’ price is increased to border price and energy efficiency programs are implemented, they will stimulate carriers’ demand and CO2 emissions growth rate decreases to 4.94% and 3.1%, respectively.

Biomass is expected to play an increasingly significant role in the ‘greening’ of energy supply. Nevertheless, concerns are rising about the sustainability of large-scale energy crop production. Impacts must be assessed carefully before deciding whether and how this industry should be developed, and what technologies, policies and investment strategies should be pursued. There is need for a comprehensive and reliable sustainability assessment tool to evaluate the environmental, social and economic performance of biomass energy production. This paper paves the way for such a tool by analysing and comparing the performance and applicability of a selection of existing tools that are potentially useful for sustainability assessment of bioenergy systems. The selected tools are: Criteria And Indicators (C&I), Life Cycle Assessment (LCA), Environmental Impact Assessment (EIA), Cost Benefit Analysis (CBA), Exergy Analysis (EA) and System Perturbation Analysis (SPA). To evaluate the tools, a framework was constructed that consists of four evaluation levels: sustainability issues, tool attributes, model structure, area of application. The tools were then evaluated using literature data and with the help of a Delphi panel of experts. Finally, a statistical analysis was performed on the resulting data matrix to detect significant differences between tools. It becomes clear that none of the selected tools is able to perform a comprehensive sustainability assessment of bioenergy systems. Every tool has its particular advantages and disadvantages, which means that trade-offs are inevitable and a balance must be found between scientific accuracy and pragmatic decision making. A good definition of the assessment objective is therefore crucial. It seems an interesting option to create a toolbox that combines procedural parts of C&I and EIA, supplemented with calculation algorithms of LCA and CBA for respectively environmental and economic sustainability indicators. Nevertheless, this would require a more comprehensive interdisciplinary approach to align the different tool characteristics and focuses.