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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.

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.

This paper presents a new framework for optimal planning of electrical, heating, and cooling distributed energy resources and networks considering smart buildings' contribution scenarios in normal and external shock conditions. The main contribution of this paper is that the impacts of smart buildings' commitment scenarios on the planning of electrical, heating, and cooling systems are explored. The proposed iterative four-stage optimization framework is another contribution of this paper, which utilizes a self-healing performance index to assess the level of resiliency of the multi-carrier energy system. In the first stage, the optimal decision variables of planning are determined. Then, in the second stage, the smart buildings and parking lots contribution scenarios are explored. In the third stage, the optimal hourly scheduling of the energy system for the normal condition is performed considering the self-healing performance index. Finally, in the fourth stage, the optimization process determines the optimal scheduling of system resources and the switching status of electrical switches, heating, and cooling pipelines’ control valves. The proposed method was successfully assessed for the 123-bus IEEE test system. The proposed framework reduced the expected values of aggregated system costs and energy not supplied costs by about 49.92% and 93.64%, respectively, concerning the custom planning exercise. ; © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). ; fi=vertaisarvioitu|en=peerReviewed|

Refrigerators have considerable share of residential consumption. They can be, however, flexible loads because their operating time and consumption patterns can be changed to some extent. Accordingly, they can be selected as a target for the study of Demand Side Management plans. In this paper, two experimental models for a refrigerator are derived. In obtaining the first model, following assumptions are made: the ambient temperature of refrigerator is assumed to be constant and the refrigerator door is remained closed. However, in the second model the variation of ambient temperature and door-opening effects are considered according to some general patterns. Further, two strategies are proposed to reduce the annual electricity cost and electric power consumption at peak-load times. These strategies together with the aforementioned models form an optimization problem which is, then, solved by Particle Swarm Optimization algorithm. Simulation results indicate a reduction of more than 28.61% in the annual cost. Also, the annual electricity consumption has decreased more than 20.46% and load shifting from the peak periods has achieved about 40%. In addition, these approaches are implemented in laboratory and their performance is confirmed by experimental results.

Abstract In this study, an industrial combined cooling, heat and power (CCHP) generation system in a tile factory was simulated and optimized by the genetic algorithm approach taking into account electricity, heating and cooling loads. Modeling and optimization were performed based on thermodynamic, environmental and economic analyzes. A multi-criteria function (energy, economic, and environmental) called relative annual benefit (RAB) with a gas engine (with partial load operation) as the prime mover was used in the optimization process. The analysis was performed for three different scenarios of the possibility of selling (selling scenario or SS) and impossibility of selling electricity (no-selling scenario or NS) to the grid and the possibility of selling electricity with similar capacities. The designing variables including the number of prime movers, nominal capacity of movers, backup boiler capacity and the capacity of compression and absorption chillers were optimized. The CCHP system for the tile factory showed the better performance of selling scenario using a gas engine with a capacity of 5000 and 700. However, the nominal capacity of the prime movers in the selling scenario was higher than that in the no-selling strategy. The results showed that the relative annual benefit decreased by choosing a similar capacities.

Abstract A novel integrated energy system based on a geothermal heat source and a liquefied natural gas heat sink is proposed in this study for providing heating, cooling, electricity power, and drinking water simultaneously. The arrangement is a cascade incorporating a flash-binary geothermal system, a regenerative organic Rankine cycle, a simple organic Rankine cycle, a vapor compression refrigeration cycle, a regasification unit, and a reverse osmosis desalination system. Energy, exergy, and exergoeconomic methods are employed to analyze the suggested system. A parametric study based on decision variables is carried out to better assess the system performance. Four different multi-objective optimization problems are also carried out. At the most excellent trade-off solution specified by the TOPSIS method, the system attains 29.15% exergy efficiency and 1.512 $/GJ total product cost per exergy unit. The main output products are consequently calculated to be 101.07 kg/s cooling water, 570.44 kW net output power, and 81.57 kg/s potable water.

Abstract This paper presents an active distribution network expansion planning framework, which concurrently uses the renewable distributed generations and energy storage systems as capacity expansion options. In order to enhance the network reliability, the model takes into account the island mode operation of the renewable resources and energy storage systems. The proposed planning framework, which is modeled as a probabilistic bi-level optimization problem quantifies and controls the economic risk level associated with the stochastic nature of these resources. The master level is devoted to the here-and-now decisions in the planning phase, whereas the slave level, which is formulated as a two-stage model, is related to the wait-and-see decisions in the operational phase. At the first stage of the slave level, the network operational behaviour is determined by performing an optimal power flow modeled as a mixed-integer linear programming problem. At the second stage of the slave problem, the network reliability is optimized considering island mode operation and taking into account energy-limited nature of storage systems. The effectiveness of the proposed active distribution network planning model is demonstrated through several case studies. Simulation results demonstrate that the proposed approach can result in a flexible low-risk plan for the expansion of the network.

-Semi-dynamic behavior of natural gas distribution network and nodal gas consumptions are predicted. Traditional Hardy-Cross method for analysis of the gas network is replaced with a direct mathematical solution of mass conservation equations at network nodes to yield nodal static pressures and volumetric flow rates for the coming days. After the calculation of static pressure distribution in a network for near future days, the problem of pressure drop in the network which is a serious problem in cold seasons can be managed in advance. TSK (Takagi-Sugeno-Kang) fuzzy system is used for forecasting. Structure identification of the system is carried out using CVIs (Cluster Validity Indices) and PFCM (Possibilistic Fuzzy C-Means algorithm) to determine number of rules which is also chosen such that testing error of the system does not exceed a predefined value. Premise and t-norm parameters of the TSK system are tuned by GAs (Genetic Algorithms) and their consequent parameters are adjusted using LSE (Least Square Estimate). Comparison of testing error of the TSK system for modeling benchmark data with other popular methods demonstrates its suitability for forecasting nodal gas consumptions.