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Abstract Variable electricity generation from wind and solar influences the design of a cost-efficient and reliable energy system. This paper presents a method that uses stochastic programming to represent variable renewable electricity generation in long-term energy system models, and demonstrates this on a Norwegian TIMES model. First, we derive hourly PV- and wind-generation data by modifying satellite-based data, based on a comparison with historical generation data. Second, the satellite-based dataset is transformed into a manageable set of scenarios that is used as an input to the stochastic energy-system model. This is done using six different scenario generation methods. Third, we solve the energy-system model with three of the scenario-generation methods and evaluate the quality of the corresponding model value by stability tests. We demonstrate that scenarios generated from the six methods have significantly different moment-based and Wasserstein distance error relative to the dataset. Further, the energy system model results show that the number of scenarios needed to achieve stability differs between the three used scenario generation methods.

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.

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.

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 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.