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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 The absorption refrigeration system driven by low grade heat sources, especially the waste heat sources, becomes more and more attractive in recent decades. However, most traditional absorption systems cannot achieve a high utilization rate of the waste heat with limited heat capacity. These systems are usually designed to obtain heat in the generator, which means that the waste heat sources cannot be utilized to the temperature lower than the generator temperature. This paper proposed a new structure heated by heat conduction oil in the generator and electric heating rings around the stripping section. This structure can simulate the temperature-distributed heat sources when the electric heating rings work. It can also simulate a traditional generator when the electric heating rings do not work. Influences of different heat distributions are analyzed in detail in this paper. The results show that the heat sources utilization rate will increase with the increase of the heat in the stripping section, while the coefficient of performance will be negatively affected by the increasing heat in the stripping section. By optimizing the heating structure, the coefficient of performance can be similar to that of a traditional system when the heat is just added in the middle and lower part of stripping section. The optimum utilization rate of heat sources in this test model can reach 1.8 times to that of a traditional system. Under this heating model, the lowest temperature required in the heating section is 82 °C when the heat conduction oil inlet temperature is 169 °C. It is much lower than the temperature inside the generator, which is 137.3 °C.

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 Demand response aims to change the energy consumption patterns of normal customers in response to changes in price rate or incentive offers. This process reduces peak loads and in turn potentially lowers the energy cost for customers. In this study, we propose a new demand response scheme on the basis of an adaptive consumption level pricing scheme. On the one hand, this strategy encourages customers to manage their energy consumption and consequently lower their energy bill. On the other hand, it allows utilities to manage the aggregate consumption and predict load requirement. Unlike other pricing schemes, such as block tariff and time-of-use, the proposed pricing scheme can lower the energy bill of about 73% of customers, assuming that the total utility revenue is the same for all pricing schemes. On the basis of the currently available schemes in the literature, we find that the proposed method has significant advantages over other schemes in terms of fairness in charging customers.

Abstract Building energy consumption is vulnerable to climate change due to the direct relationship between outside temperature and space cooling/heating. This work quantifies how the relationship between climate change and building energy consumption varies across a range of building types at different spatiotemporal scales based on estimates in 925 U.S. locations. Large increases in building energy consumption are found in the summer (e.g., 39% increase in August for the secondary school building), especially during the daytime (e.g., >100% increase for the warehouse building, 5–6 p.m.), while decreases are found in the winter. At the spatial scale of climate-zones, annual energy consumption changes range from −17% to +21%, while at the local scale, changes range from −20% to +24%. Buildings in the warm-humid (Southeast) climate zones show larger changes than those in other regions. The variation of impact within climate zones can be larger than the variation between climate zones, suggesting a potential bias when estimating climate-zone scale changes with a small number of representative locations. The large variations found in the relationship between climate change and building energy consumption highlight the importance of assessing climate change impacts at local scales, and the need for adaptation/mitigation strategies tailored to different building types.

Load forecasting has long been recognized as an important building block for all utility operational planning efforts. Over the recent years, it has become ever more challenging to make accurate forecasts due to the proliferation of distributed energy resources, despite the abundance of existing load forecasting methods. In this paper, we identify one drawback suffered by most load forecasting methods: neglect to thoroughly address the impact of input errors on load forecasts. As a potential solution, we propose to incorporate input modeling and uncertainty quantification to improve load forecasting performance via a two-stage approach. The proposed two-stage approach has the following merits. (1) It provides input modeling and quantifies the impact of input errors, rather than neglecting or mitigating the impact, a prevalent practice of existing methods. (2) It propagates the impact of input errors into the ultimate point and interval predictions for the target customer's load to improve predictive performance. (3) A variance-based global sensitivity analysis method is further proposed for input-space dimensionality reduction in both stages to enhance the computational efficiency. Numerical experiments show that the proposed two-stage approach outperforms competing load forecasting methods in terms of both point predictive accuracy and coverage ability of the predictive intervals. 9 pages, 4 figures, journal

Abstract To guarantee the space heating in the heating season, conventional combined heat and power (CHP) plants operate in a heat-controlled operation mode, resulting in restricted peak-shaving ability (PSA). To improve the CHP plant’s PSA, a novel solar aided CHP (SA-CHP) system is proposed and simulated in this paper. In the new system, solar heat could be flexibly used to generate power or to supply heat according to the heating and power demands, thereby realizing the heat-power decoupling. A set of models for the SA-CHP system is developed and validated. The PSA, the standard coal consumption (SCC) and the techno-economic performances of a 330 MWe SA-CHP system are comprehensively analyzed in this paper. The results show that the SA-CHP system can significantly improve (up to double) the PSA compared with the CHP plant under the same rated heating power. The feasible operation region area of the SA-CHP system is 74.7% larger than that of the CHP plant. The annual SCC of the SA-CHP system are 17378.23 t less than that of the CHP plant. The net annual revenue of the SA-CHP system is $2.24 M. Besides, techno-economic performances of SA-CHP systems with two different heat storage systems are compared.