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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.

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.

Abstract In the paper a novel approach to thermochemical utilization of low rank coal, flotation concentrates and municipal refuse derived fuels was presented. The economic attractiveness of low rank coals and flotation concentrates is limited and that is why they are commonly stored at excavation heaps causing additional costs and the risk of endogenous fires occurrence. One of the crucial parameters determining the attractiveness and usability of a fuel in the gasification process is its reactivity. In the study several low rank coals, flotation concentrates and municipal refuse derived fuels were tested in terms of their reactivity in the process of steam gasification. The reactivity of low rank coal and flotation concentrates at 50% of carbon conversion, R50, varied between 1.46·10−4 and 2.39·10−4 s−1, whereas the maximum reactivity, Rmax, from 3.28·10−4 to 4.62·10−4 s−1. Advanced mathematical models were developed to investigate the similarities and dissimilarities between the studied fuels as well as the relationships between the physical and chemical parameters and the reactivities of fuel chars in steam gasification. On this basis, a low rank coal was selected and blended with 20%w/w of municipal refuse derived fuel in co-gasification experiments. The aim of the research was to utilize the low rank coal characterized by the lowest reactivities (R50 and Rmax of 1.46·10−4 and 3.28·10−4 s−1, respectively) in steam co-gasification to hydrogen-rich gas with an alternative fuel in a fixed bed reactor at the temperature of 800 °C. The selected low rank coal was blended with 20%w/w of municipal refuse derived and the resulting fuel yielded the average concentration of hydrogen in the produced gas of 58.99%vol.

Renewable resources and energy storage systems integrated into microgrids are crucial in attaining sustainable energy consumption and energy cost savings. This study conducts an in-depth analysis of diverse storage systems within multi-energy microgrids, including natural gas and electricity subsystems, with a comprehensive focus on techno-economic considerations. To achieve this objective, a methodology is developed, comprising an optimization model that facilitates the determination of optimal storage system locations within microgrids. The model considers various factors, such as operating and emission costs of both gas and electricity subsystems, and incorporates a sensitivity analysis to calculate the investment and maintenance costs associated with the storage systems. Due to the incorporation of voltage and current relations in the electricity subsystem as well as gas pressure and flow considerations in the natural gas subsystem, the developed model is classified as a mixed-integer nonlinear programming model. To address the inherent complexity in solving, a decomposition approach based on Outer Approximation/Equality Relaxation/Augmented Penalty is developed. This study offers scientific insights into the costs of energy storage systems, potential operational cost savings, and technical considerations of microgrid operation. The results of the developed decomposition approach demonstrate significant advantages, including reduced solving time and a decreased number of iterations.

District heating systems operation can be improved by control strategies. One of the options is the introduction of predictive control model. Predictive models of heat load can be applied to improve district heating system performances. In this article, short-term multistep-ahead predictive models of heat load for consumers connected to district heating system were developed using SVMs (Support Vector Machines) with FFA (Firefly Algorithm). Firefly algorithm was used to optimize SVM parameters. Seven SVM-FFA predictive models for different time horizons were developed. Obtained results of the SVM-FFA models were compared with GP (genetic programming), ANNs (artificial neural networks), and SVMs models with grid search algorithm. The experimental results show that the developed SVM-FFA models can be used with certainty for further work on formulating novel model predictive strategies in district heating systems.