• Energy Research
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Abstract Large amounts of heat is wasted through air coolers and water coolers for cooling low temperature ( H ) recovered in the evaporator were 8.0–8.6 MW for ORC using i-pentane as working fluid and 8.2–8.3 MW for Kalina cycle, respectively. Efficiency (η) of selected systems obtained at the highest power generated (W T ) was 10.0% (W T = 862 kW) for ORC and 10.57% (W T = 996 kW) for Kalina cycle within the design boundaries. Calculated carbon dioxide (CO 2 ) emission reduction potential was 2260 t/y for ORC and 2600 t/y for Kalina system, respectively, at advantageous process conditions. Results showed that Kalina cycle provided higher efficiency and power generation ability on expense of higher system pressure (29 bar–7 bar). Economic calculations showed that the payback time is about 5.0 year for both systems.

With increasing interest in alternative energy resources and technologies, mass penetration of PHEVs (plug-in hybrid vehicles) into the electricity grid and widespread utilization of DERs (distributed energy resources) are anticipated in the near future. As an aggregation unit, the VPP (virtual power plant) is introduced for load management and resource scheduling. In this article, we develop an energy management model for VPPs and analyze the cost and emission impacts of VPP formation and PHEV penetration. We conduct a case study for the state of California using real-world data from official resources. An average of 29.5% cost reduction and 79% CO2 and 83% NOx emission reductions are attained as shared benefits of consumers in the case study. Results are illustrative of opportunities that VPP formation can provide for the community. Sensitivity of the results to the DER costs and capacities, battery and gasoline prices are also analyzed. In addition, we prove that charging and discharging do not simultaneously occur in the solutions, which leads to a simplification in traditional energy management models.

Abstract To achieve optimal generation from a number of mixed power plants by minimizing the operational cost while meeting the electricity demand is a challenging optimization problem. When the system involves uncertain renewable energy, the problem has become harder with its operated generators may suffer a technical problem of ramp-rate violations during the periodic implementation in subsequent days. In this paper, a scenario-based dynamic economic dispatch model is proposed for periodically implementing its resources on successive days with uncertain wind speed and load demand. A set of scenarios is generated based on realistic data to characterize the random nature of load demand and wind forecast errors. In order to solve the uncertain dispatch problems, a self-adaptive differential evolution and real-coded genetic algorithm with a new heuristic are proposed. The heuristic is used to enhance the convergence rate by ensuring feasible load allocations for a given hour under the uncertain behavior of wind speed and load demand. The proposed frameworks are successfully applied to two deterministic and uncertain DED benchmarks, and their simulation results are compared with each other and state-of-the-art algorithms which reveal that the proposed method has merit in terms of solution quality and reliability.

Abstract This paper gives an experimental investigation of the effect of climatic conditions on the performance and degradation of crystalline silicon photovoltaic modules under Saharan environment in Adrar region in the south of Algeria. The first part of this study is focused on the analysis and assessment of UDTS 50 PV modules degradation after a long term outdoor exposure to these conditions (more than 12 years). The visual inspection of 62 PV modules has allowed to observe and determine the degradation modes such as, EVA discoloration, delamination, busbar corrosion, cracking of solar cell, glass breakage, AR coating and solder bond. The degradation evaluation of three modules with different defects was also performed, using (I-V/P-V) characteristics and the degradation rates of the parameters (Pmax, Imp, Vmp, Isc, Voc, FF) at Standard test conditions (STC) in order to compare with the nominal data delivered by the manufacturer of photovoltaic panels. Finally, the combination of the partial shading effect and the presence of EVA browning defect was examined to assess the changes in I-V and P-V curves caused by the drop in electrical parameters.

AbstractThe split cycle engine is a new reciprocating internal combustion engine with a potential of a radical efficiency improvement. In this engine, the compression and combustion–expansion processes occur in different cylinders. In the compression cylinder, the charge air is compressed through a quasi-isothermal process by direct cooling of the air. The high pressure air is then heated in a recuperator using the waste heat of exhaust gas before induction to the combustion cylinder. The combustion process occurs during the expansion stroke, in a quasi-isobaric process. In this paper, a fundamental theoretical cycle analysis and one-dimensional engine simulation of the split cycle engine was undertaken. The results show that the thermal efficiency (η) is mainly decided by the CR (compression ratio) and ER (expansion ratio), the regeneration effectiveness (σ), and the temperature rising ratio (N). Based on the above analysis, a system optimization of the engine was conducted. The results showed that by increasing CR from 23 to 25, the combustion and recuperation processes could be improved. By increasing the expansion ratio to 26, the heat losses during the gas exchange stroke were further reduced. Furthermore, the coolant temperatures of the compression and expansion chambers can be controlled separately to reduce the wall heat transfer losses. Compared to a conventional engine, a 21% total efficiency improvement was achieved when the split cycle was applied. It was concluded that through the system optimization, a total thermal efficiency of 53% can be achieved on split cycle engine.

Abstract Reaction kinetic parameters estimation of biomass pyrolysis is a relatively difficult optimization problem due to the complexity of pyrolysis model. Two common heuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are applied to estimate the kinetic parameters of three-component parallel reaction mechanism based on the thermogravimetric experiment in wide heating rates. The accuracy and efficiency of GA and PSO algorithms are compared with each other under the identical optimization conditions. The results indicate the better optimization abilities of PSO with the closer convergence solution to the global optimum and quicker convergence to the solution than GA based on the three-component parallel reaction mechanism of biomass pyrolysis. Especially, the improvement of best fitting value of PSO reaches up to 30% compared with that of GA. Furthermore, 14 estimated kinetic parameters of best fitting value are obtained and the mass loss rate predicted results including three separate components (hemicellulose, cellulose and lignin) are compared with experimental data.

Abstract This paper proposes a multi-year expansion planning method for enabling distribution systems to support growing penetrations of plug-in electric vehicles. As distinct from the existing studies, the temporal characteristics of charging loads and their reliability impacts are especially focused in our work. To achieve this, a novel dual-stage optimization framework is developed. The proposed method considers the capacity reinforcement of distribution systems in conjunction with their operation decisions and coordinates them under the same frame so as to minimize the total system costs for accommodating electric vehicles. The uncertainties associated with renewable energy generation, charging behaviors, and conventional load demand are represented by multiple probabilistic scenarios. To fully reveal the impacts of electric vehicle integration, both uncontrolled and coordinated charging schemes are considered in our analysis. Furthermore, as charging loads bring about extra demand to the grid, the reliability criteria is also taken into account in the proposed model. Using a heuristic algorithm combined with reliability analysis, the optimal solution for the concerned problem can be determined, which involves the best timing, locations, and capacities for installation of distributed generation units and network components. The effectiveness of the proposed framework is examined based on a 38-bus test system and the obtained results verify the performance of the approach.

Abstract Building-integrated photovoltaics (BIPV) is a growing reality worldwide and its development involves implementing techniques to log and estimate the solar resources available. In this paper an easy methodology for the pre-classification of facades in BIPV projects has been described. This step is previous to the calculation of the complete solar potential in a building, and don't include the shape and shading factors. The proposed methodology covers the development of a new model that allows the irradiation factor (IF) to be estimated on facades with only 2 input parameters: the latitude of the place and the azimuth angle of the photovoltaic generator. The necessary tools to assess the “Energetic Efficiency Rating” for BIPV facades are provided, as an initial stage to be applied by architects and engineers.