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In the present study, an experimental setup for a PVT system is designed and fabricated to investigate the economic and environmental aspects of using different nano-oxides/water fluids. Selected coolant fluids are: pure water, ZnO/water, Al 2 O 3 /water and TiO 2 /water nanofluids. To make nanofluids, nanoparticles are dispersed in distilled water by 0.2% weight fractions (wt%), separately. Based on the experimental results the amount of annual emission reduction and cost saving are investigated for different coolants. Furthermore, the payback period of the PVTs is compared with that of a conventional PV unit. The results are presented in five different scenarios due to increasing of the electricity price, up to the final price and the government subsidies for the renewable energy sources (1st: 10% annual price increasing, with no subsidy, 2nd: linear increasing price, with no subsidy, 3rd: 10% the annual price increasing, 50% subsidy, 4th: linear price increasing, 50% subsidy and 5th: linear price increasing, 75% subsidy). From the energy viewpoint, by using pure water, PVT/TiO2, PVT/ZnO and PVT/Al 2 O 3 , the size reduction of the PVT system in comparison with that of the PV is 21, 32, 33 and 24%, respectively, and from the exergy viewpoint these values are about 5, 6, 7, 6%, respectively. [ABSTRACT FROM AUTHOR]

By advancement and vogue of smart grid technologies, there is a strong attitude toward utilizing different strategies for participating in demand response (DR) programs in electricity markets. DR programs can be classified into two main categories namely incentive-based programs (IBPs) and time-based rate programs (TBRPs). In this paper, an improved incentive-based DR (IBDR) model is proposed. In our proposed IBP, the concept of elasticity is improved where it depends not only on the electricity price, but also is a function of consumption hour and customer type. In this program, the incentive value which is paid to the participating consumers is not a fix value and relates to the peak intensity of each hour. The proposed IBP can participate in both of day-ahead and intra-day electricity markets. The property of considering intra-day market enables consumers to provide maximum DR if possible. The proposed model is implemented on peak load curve of Spanish electricity market and a 200-unit residential complex. Different scenarios are considered to show effectiveness of the proposed DR model from various aspects including peak shaving as well as economic indices. [ABSTRACT FROM AUTHOR]

Many irreversible long-run capital investments entail opportunities for managers to respond flexibly to changes in the economic environment. However, common levelized cost measures used to guide decision-making, such as the levelized cost of electricity, implicitly assume that the values of random economic variables are known with certainty when investment decisions are made. This assumption implies, often incorrectly, that managerial flexibility carries zero value. This paper improves levelized cost measures by deriving an expansion that accounts for both uncertainties in relevant variables and the value of managerial flexibility in responding to them. This method is applied to quantify the value of flexibility in two example decision problems. In one, an operator of a natural gas electricity generation facility evaluates whether to invest in carbon capture capabilities. Another considers retirement decisions for U.S. nuclear plants. These examples illustrate that simplified cost metrics can inaccurately guide decision-making by inflating cost estimates relative to the proposed levelized cost measure that accounts for uncertainty and flexibility. [ABSTRACT FROM AUTHOR]

Electricity storage (ES) has the potential of offering several energy system benefits but different technologies also offer different services which can be traded on different markets. In this study, a combined assessment methodology is proposed, enabling a benchmark comparison of stationary electricity storage technologies for different time and system scales, considering their technical, economic and environmental performance. The results show that for short time scale (0.01 h), battery stands out with an advantage in terms of levelised costs, while Advanced Adiabatic (AA-) and Isothermal (I-) Compressed Air Energy Storage (CAES) have relatively low life cycle Greenhouse Gas (GHG) emissions. For the medium time scale (4.5 h), I-CAES shows the best performance for small scale systems, while for large scale systems, Pumped Hydro Storage (PHS) and AA-CAES show best performance. In our long time scale (seasonal) scenario, Power-to-gas-to-power (P2G2P) has lower levelised costs due to low or avoided investment for storage of gas, but higher GHG emissions than other technologies. If existing reservoirs can be utilized for PHS, it can be economically competitive to P2G2P for seasonal storage. However, storage capacity required for seasonal storage should also be taken into account, for which P2G2P has more flexibility. [ABSTRACT FROM AUTHOR]

A cradle-to-gate life cycle assessment of rice straw-based synthetic natural gas (R-SNG) production was carried out in this study, and the inventories of rice straw cultivating and harvesting, feedstock transportation, R-SNG facility construction and dismantling, and power consumption (i.e. purchased electricity (PE) scheme and self-supply power (SP) scheme) stages were analyzed. Then, the inventory data was characterized and normalized. The results showed that the SP scheme can significantly reduce the environmental impacts of R-SNG product. A comparison of R-SNG, natural gas (NG) and coal-based synthetic natural gas (C-SNG) was conducted from the perspective of environmental impacts. The results showed that the amount of CO 2 emission from C-SNG was the largest, followed by that from NG, while the amount of CO 2 emission from R-SNG was negative. A sensitivity analysis about allocation principles of the rice straw and rough rice was carried out to evaluate the allocation principles on the environmental impacts. In conclusion, R-SNG showed great environmental benefits in the abiotic resources saving and greenhouse gases emission reduction. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective problem, whose resolution is carried out using a task management methodology and an evolutionary algorithm. The multi-objective problem includes demand response to real-time prices (RTP). Two objectives have been considered: the daily cost of electricity and consumer dissatisfaction, minimizing both. Hourly prices corresponding to a tariff currently existing in Spain have been used to evaluate the daily cost of the consumed electricity. The degree of compliance with the daily task programming required by the user has been used to evaluate the dissatisfaction of the consumer. Using a multi-objective evolutionary algorithm (NSGA-II), it has been possible to use the concept of Pareto optimality in order to determine the best solutions for the problem. The results show that the applied methodology can lead to cost savings between 6% and 12% without substantially changing the consumption habits of the consumer. If the consumer is willing to change his/her consumption habits, then the cost savings can reach 50%. In addition to cost savings, this methodology can reduce energy losses in the electric grid because the shift of consumption from peak to off-peak hours. [ABSTRACT FROM AUTHOR]

Trading of wind energy in day-ahead electricity markets entails significant risks. Wind traders are, therefore, interested in mitigating their trading risks in a number of ways. This work suggests addressing this issue in a comprehensive manner by coordinating the trading of wind resources with thermal resources and a pumped-hydro storage system (PHS) and co-optimizing the trading in energy and regulation markets. The trading strategy is modeled as a mixed integer linear stochastic program that takes into account several uncertain parameters, including wind power outputs, prices for energy, balancing, and regulation, and regulation deployments. In this optimization, the trader’s attitude toward risks is controlled by using the metric of conditional value at risk (CVaR). Simulation results show that coordinated and co-optimized trading of the generation resources and PHS significantly improve the total expected profits and mitigates trader’s risks. [ABSTRACT FROM AUTHOR]

Nuclear power plants generate over 60% of the carbon-free electricity in the U.S. Due to a decrease in electricity prices as a result of the availability of cheaper natural gas and increased low-cost renewables, many of these plants are at risk of premature retirement. If nuclear power plants retire, CO 2 emissions in many U.S. states could increase, even while the states comply with EPA legislation aimed at mitigating emissions of greenhouse gases that contribute to climate change. In this paper, we perform a Monte Carlo-based analysis to determine the breakeven price of electricity these plants must secure in order to avoid financial loses between 2015 and 2040, and find median breakeven electricity prices to range between $35 and $73 per MWh. Based on our estimates of future electricity prices under a low natural gas price scenario from the Energy Information Administration, our analysis suggests that U.S. nuclear plants would require between $8 and $44 per MWh (median results) on top of electric sales revenue in order to breakeven. Assuming natural gas plants would replace retired nuclear power plants, we estimate an equivalent cost of avoided CO 2 emissions to be $18-$30 per metric ton of avoided CO 2 (median results) for multi-reactor nuclear plants, and $47-$97 per metric ton of avoided CO 2 (median results) for single-reactor plants. Preserving the existing nuclear power plant fleet, especially multi-reactor plants, is thus a cost effective carbon-avoidance strategy compared to the social cost of carbon. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of an independently-operated large-scale energy storage system on the electricity prices of a fully competitive pool-based electricity market. From a consumer's perspective, the impact of storage operation on energy cost is investigated. The changes in the revenue of generation units and entities caused due to the arbitrage operation of storage facility are also explored. In the current study, we have considered an optimization-based scheme to schedule the operation of a large-scale storage system. Also, a number of ad-hoc strategies to participate in the energy market are examined for the sake of comparison. Modeling the impacts of a large-scale energy storage system can inform planners and operators of the potential effects of storage on the rest of the system and help them to use storage most effectively. These analyses can also inform regulators and policy-makers of the potential societal costs and benefits to energy storage deployments that are not necessarily monetizable by the energy storage owner. [ABSTRACT FROM AUTHOR]

In a typical operation of palm oil mill, oil palm residues and palm oil mill effluent (POME) would be produced. While the oil palm residues have been well harnessed as biomass fuel, regional planning of efficient POME utilisation via biogas recovery and upgrading, coupled with possible biogas compressed natural gas (bioCNG) transshipment among the palm oil mill cluster, deserves more research and development. In this study, the objective is to design a modelling tool for optimising the oil palm biomass and POME utilisation pathway as well as bioCNG distribution network for a palm oil mill cluster with maximum simultaneous economic satisfaction level. Fuzzy optimisation approach was applied for this proposed multiple-objective modelling problem. Two case studies were simulated by the developed mathematical tool to examine the economic benefit of POME utilisation pathway in multiple-site palm oil processing complex (POPC) design. Moreover, the impact of feed-in-tariff (FIT) quota variation on POPC and bioCNG transshipment pathway designs was examined. From the simulation, it was found that POME utilisation pathway incorporation had increased the overall annual worth by MYR 15,049,096 per year due to additional revenues from bioCNG and biogas-derived electricity sales as well as utility saving. [ABSTRACT FROM AUTHOR]

Abstract: In this work, the grid design and optimal allocation of wind and biomass resources for renewable electricity supply chains under uncertainties is studied. Due to wind intermittency, generation of wind electricity is not uniform and cannot be counted on to be readily available to meet the demand. Biomass represents a type of stored energy and is the only renewable resource that can be used for producing biofuels and generating electricity whenever required. However, amount of biomass resources are finite and might not be sufficient to meet the demand for electricity and biofuels. Potential of wind and biomass resources is therefore jointly analyzed for electricity generation. Policies are proposed and evaluated for optimal allocation of finite biomass resources for electricity generation. A stochastic programming model is proposed that optimally balances the electricity demand across the available supply from wind and biomass resources under uncertainties in wind speed and electricity sale price. A case study set in the American Midwest is presented to demonstrate the effectiveness of the proposed model by determining the optimal decisions for generation and transmission of renewable electricity. Sensitivity analysis shows that level of subsidy for renewable electricity production has a major impact on the decisions. [Copyright &y& Elsevier]