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[en] The most important constraint toward the successful implementation of the energy efficiency investments in Greece remains the absence of appropriate and competitive financial schemes. Energy efficiency improvements seems to be one of the areas that JESSICA instrument should focus on in the case of Greece, considering that energy efficiency constitutes a major component of sustainable urban development. The scope of this paper is to present, in an updating way, an analysis of energy efficiency investments' environment in Greece as well as to examine the potential role of the JESSICA instruments, aiming to the supporting of the real implementation of energy efficiency investments in Greece. In addition, a rigid methodological note is presented for the analysis as well as evaluation of the JESSICA instruments for energy efficiency projects in Greece.
[en] Research highlights: → Multi-objective optimization model of short-term environmental/economic hydrothermal scheduling. → A hybrid multi-objective cultural algorithm (HMOCA) is presented. → New heuristic constraint handling methods are proposed. → Better quality solutions by reducing fuel cost and emission effects simultaneously are obtained. -- Abstract: The short-term environmental/economic hydrothermal scheduling (SEEHS) with the consideration of multiple objectives is a complicated non-linear constrained optimization problem with non-smooth and non-convex characteristics. In this paper, a multi-objective optimization model of SEEHS is proposed to consider the minimal of fuel cost and emission effects synthetically, and the transmission loss, the water transport delays between connected reservoirs as well as the valve-point effects of thermal plants are taken into consideration to formulate the problem precisely. Meanwhile, a hybrid multi-objective cultural algorithm (HMOCA) is presented to deal with SEEHS problem by optimizing both two objectives simultaneously. The proposed method integrated differential evolution (DE) algorithm into the framework of cultural algorithm model to implement the evolution of population space, and two knowledge structures in belief space are redefined according to the characteristics of DE and SEEHS problem to avoid premature convergence effectively. Moreover, in order to deal with the complicated constraints effectively, new heuristic constraint handling methods without any penalty factor settings are proposed in this paper. The feasibility and effectiveness of the proposed HMOCA method are demonstrated by two case studies of a hydrothermal power system. The simulation results reveal that, compared with other methods established recently, HMOCA can get better quality solutions by reducing fuel cost and emission effects simultaneously.
[en] Highlights: • Three novel schemes about integrating solar energy into the boiler are proposed. • Solar-coal hybrid system under fuel saving mode and power boosting mode are studied. • Sankey diagram is used to analyze the exergy destruction of key components. - Abstract: A Solar Tower Aided Coal-fired Power (STACP) system utilizes a solar tower coupled to a conventional coal-fired power system to reduce pollutants, greenhouse gas emissions and the investment of solar energy facilities. This paper examines three different schemes for integrating solar energy into a conventional boiler. For each scheme, an energy and exergy analysis of a 600 MWe supercritical coal-fired power system is combined with 53 MWth of solar energy in both a fuel saving mode and a power boosting mode. The results show that, for all these integration schemes, the boiler’s efficiency and system’s efficiency are reduced. However, the standard coal consumption rate is lower in comparison to conventional power plants and the standard coal consumption rate in the fuel saving mode is lower than that in the power boosting mode for all three schemes. Comprehensively considering both the standard coal consumption rate and efficiency, the scheme that uses solar energy to heat superheat steam and subcooled feed-water is the best integration option. Compared with a coal-fired only system, the saved standard coal consumption rate of the above mentioned scheme in fuel saving mode and power boosting mode can reach up to 11.15 g/kWh and 11.11 g/kWh, respectively. Exergy analysis shows, for STACP system, exergy losses of boiler and solar field contribute over 88% of whole system’s exergy loss.
[en] Highlights: • A gas-fired power system using InGaAsSb TPV cells is designed, tested and developed. • The thermal radiation was emitted by a novel super alloy emitter in the TPV system. • The electric output of InGaAsSb TPV cells is characterized under various conditions. • A power density of 0.65 W/cm2 is produced at the emitter temperature of 1197 °C. • TPV power generation is feasible in gas-fired furnaces for combined heat and power. - Abstract: In this paper, electricity generation using very low bandgap InGaAsSb thermophotovoltaic (TPV) cells whose bandgap is 0.53 eV was investigated in a gas-fired furnace system where thermal radiation was emitted from a metal alloy emitter. The electric output of the InGaAsSb TPV cells was characterized under various operating conditions. The cell short circuit density was measured to be 3.01 A/cm2 at an emitter temperature of 1197 °C. At this emitter temperature, an electric power density of 0.65 W/cm2 was produced by the TPV cells. Experimental results show that direct thermal to electrical energy conversion was achieved in a gas-fired heating furnace system. Such a system could be employed to form a micro-combined heat and power (micro-CHP) process where exhaust heat is utilized for home heating needs. The TPV integrated energy system provides an effective means for primary energy savings
[en] Highlights: • Energy quality management is applied from individual building to district. • A novel time-effective multi-objective design optimization scheme is proposed. • The scheme searches for exergy efficient and environmental solution for districts. • System reliability is considered and addressed in this paper. - Abstract: Renewable energy systems entail a significant potential to meet the energy requirements of building clusters and districts (BCDs) provided that local energy sources are exploited efficiently. Besides improving the energy efficiency by reducing energy consumption and improving the match between energy supply and demand, energy quality issues have become a key topic of interest. Energy quality management is a technique that aims at optimally utilizing the exergy content of various renewable energy sources. In addition to minimizing life-cycle CO2 emissions related to exergy losses of an energy system, issues such as system reliability should be addressed. The present work contributes to the research by proposing a novel multi-objective design optimization scheme that minimizes the global warming potential during the life-cycle and maximizes the exergy performance, while the maximum allowable level of the loss of power supply probability (LPSP) is predefined by the user as a constraint. The optimization makes use of Genetic Algorithm (GA). Finally, a case study is presented, where the above methodology has been applied to an office BCD located in Norway. The proposed optimization scheme is proven to be efficient in finding the optimal design and can be easily enlarged to encompass more relevant objective functions
[en] Highlights: • We develop an economic model different from related models. • We evaluate the initial investment cost of a plant built in northwest China. • We analyze the cost and benefit of a plant built in northwest China. • By the sensitivity analysis, we examine the sensitivity of TNPV to many parameters. - Abstract: This paper develops a model different from existing models to analyze the cost and benefit of a reinforced concrete solar chimney power plant (RCSCPP) built in northwest China. Based on the model and some assumptions for values of parameters, this work calculates total net present value (TNPV) and the minimum electricity price in each phase by dividing the whole service period into four phases. The results show that the minimum electricity price in the first phase is higher than the current market price of electricity, but the minimum prices in the other phases are far less than the current market price. The analysis indicates that huge advantages of the RCSCPP over coal-fired power plants can be embodied in phases 2–4. In addition, the sensitivity analysis performed in this paper discovers TNPV is very sensitive to changes in the solar electricity price and inflation rate, but responds only slightly to changes in carbon credits price, income tax rate and interest rate of loans. Our analysis predicts that RCSCPPs have very good application prospect. To encourage the development of RCSCPPs, the government should provide subsidy by setting higher electricity price in the first phase, then lower electricity price in the other phases
[en] Highlights: • Mixture experimental design was used which allowed evaluating various responses. • Predictive equation was presented that allows verifying the behavior of the mixtures. • The results depicted that the obtained biodiesel dispensed the use of any additives. - Abstract: The quality of biodiesel is a determining factor in its commercialisation, and parameters such as the Cold Filter Plugging Point (CFPP) and Induction Period (IP) determine its operability in engines on cold days and storage time, respectively. These factors are important in characterisation of the final product. A B100 biodiesel formulation was developed using a multiresponse optimisation, for which the CFPP and cost were minimised, and the IP and yield were maximised. The experiments were carried out according to a simplex-centroid mixture design using soybean oil, beef tallow, and poultry fat. The optimum formulation consisted of 50% soybean oil, 20% beef tallow, and 30% poultry fat and had CFPP values of 1.92 °C, raw material costs of US$ 903.87 ton−1, an IP of 8.28 h, and a yield of 95.68%. Validation was performed in triplicate and the t-test indicated that there were no difference between the estimated and experimental values for none of the dependent variables, thus indicating efficiency of the joint optimisation in the biodiesel production process that met the criteria for CFPP and IP, as well as high yield and low cost
[en] Highlights: • Analyzing of wind farm project investment. • Net present value (NPV) maximization of the wind farm project. • Adaptive neuro-fuzzy (ANFIS) optimization of the number of wind turbines to maximize NPV. • The impact of the variation in the wind farm parameters. • Adaptive neuro fuzzy application. - Abstract: A wind power plant which consists of a group of wind turbines at a specific location is also known as wind farm. To maximize the wind farm net profit, the number of turbines installed in the wind farm should be different in depend on wind farm project investment parameters. In this paper, in order to achieve the maximal net profit of a wind farm, an intelligent optimization scheme based on the adaptive neuro-fuzzy inference system (ANFIS) is applied. As the net profit measures, net present value (NPV) and interest rate of return (IRR) are used. The NPV and IRR are two of the most important criteria for project investment estimating. The general approach in determining the accept/reject/stay in different decision for a project via NPV and IRR is to treat the cash flows as known with certainty. However, even small deviations from the predetermined values may easily invalidate the decision. In the proposed model the ANFIS estimator adjusts the number of turbines installed in the wind farm, for operating at the highest net profit point. The performance of proposed optimizer is confirmed by simulation results. Some outstanding properties of this new estimator are online implementation capability, structural simplicity and its robustness against any changes in wind farm parameters. Based on the simulation results, the effectiveness of the proposed optimization strategy is verified
[en] Highlights: • Developed hourly-indexed ARX models for robust cooling-load forecasting. • Proposed a two-stage weighted least-squares regression approach. • Considered the effect of outliers as well as trend of cooling load and weather patterns. • Included higher order terms and day type patterns in the forecasting models. • Demonstrated better accuracy compared with some ARX and ANN models. - Abstract: This paper presents a robust hourly cooling-load forecasting method based on time-indexed autoregressive with exogenous inputs (ARX) models, in which the coefficients are estimated through a two-stage weighted least squares regression. The prediction method includes a combination of two separate time-indexed ARX models to improve prediction accuracy of the cooling load over different forecasting periods. The two-stage weighted least-squares regression approach in this study is robust to outliers and suitable for fast and adaptive coefficient estimation. The proposed method is tested on a large-scale central cooling system in an academic institution. The numerical case studies show the proposed prediction method performs better than some ANN and ARX forecasting models for the given test data set
[en] Highlights: • A thermodynamic analysis of two recuperated ICE plants is undertaken. • The overall plant efficiency and CO2 emissions are analysed. • Chemical recuperation without a secondary heat source is unlikely. • Using a renewable secondary heat source reduces the CO2 emission of the plant. - Abstract: This paper is the first of a two part study that analyses the technical and financial performance of particular, recuperated engine systems. This first paper presents a thermodynamic study of two systems. The first system involves the chemical recuperation of a reciprocating, spark ignited, internal combustion engine using only the waste heat of the engine to power a steam–methane reformer. The performance of this system is evaluated for different coolant loads and steam–methane ratios. The second system is a so-called ’hybrid’ in which not only the waste heat of the engine is used, but also a secondary heat source – the combustion of biomass. The effects of the reformer’s temperature and the steam–methane ratio on the system performance are analysed. These analyses show that the potential efficiency improvement obtained when using only the engine waste heat to power the recuperation is marginal. However, results for the hybrid show that although the overall efficiency of the plant, defined in terms of the energy from both the methane and biomass, is similar to that of the conventional, methane fuelled engine, the efficiency of the conversion of the biomass fuel energy to work output appears to be higher than for other biomass fuelled technologies currently in use. Further, in the ideal limit of a fully renewable biomass fuel, the burning of biomass does not contribute to the net CO2 emissions, and the CO2 emission reduction for this second plant can be considerable. Indeed, its implementation on larger internal combustion engine power plants, which have efficiencies of around 45–50%, could result in CO2 emissions that are as much as 10–20% lower than typical natural gas combined cycle (NGCC) power stations. This appears to be a significant result, since NGCCs are commonly considered to have the lowest CO2 emissions of all forms of fossil fuelled, power generation currently in use