• Energy Research

As more uncontrollable renewable energy sources are present in the power generation portfolio, the need of more detailed and reliable tools for the optimal operation of energy systems has increased in the last years. This work presents a multi-stage stochastic Mixed Integer Linear Program with binary recourse for optimizing the day-ahead unit commitment of power plants and virtual power plants operating in the day-ahead and ancillary services markets. Scenarios reproduce the uncertainty of the ancillary services market requests, and production of photovoltaic panels. A novel decomposition algorithm is proposed to tackle the challenging multistage stochastic program. The methodology is tested on three types of large power plants: a natural gas-fired combined cycle, a combined heat and power combined cycle with thermal storage, and a virtual power plant integrating a combined cycle with battery and photovoltaic fields. Compared to the typical deterministic unit commitment approach, the proposed stochastic optimization approach allows to increase the revenues of the conventional power plant up to 13.58% and, for the combined heat and power and virtual power plant case, it allows finding a feasible and efficient operational scheduling.

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Abstract When optimizing the design of multi-energy systems, the operation strategy and the part-load behavior of the units must be considered in the optimization model, which therefore must be formulated as a two-stage problem. In order to guarantee computational tractability, the operation problem is solved for a limited set of typical and extreme periods. The selection of these periods is an important aspect of the design methodology, as the selection and sizing of the units is carried out on the basis of their optimal operation in the selected periods. This work proposes a novel Mixed Integer Linear Program clustering model, named k-MILP, devised to find at the same time the most representative days of the year and the extreme days. k-MILP allows controlling the features of the selected typical and extreme days and setting a maximum deviation tolerance on the integral of the load duration curves. The novel approach is tested on the design of two different multi-energy systems (a multiple-site university Campus and a single building) and compared with the two well-known clustering techniques k-means and k-medoids. Results show that k-MILP leads to a better representation of both typical and extreme operating conditions guiding towards more efficient and reliable designs.

This paper focuses on the multi-objective optimization of a Selexol® process for the selective removal of CO2 and H2S from coal gasification-derived syngas. A systematic analysis based on a thorough literature review of scheme options, detailed process simulation (including also a properly calibrated PC-SAFT Equation Of State) and design optimization is carried out. More in detail, the design optimization procedure enables the simultaneous optimization of process, utility design, and heat integration, and takes account all of the interactions with the rest of the plant. The multi-objective optimization is carried out with a two-stage approach combining the NSGA-II genetic algorithm with the efficient direct-search method PGS-COM. Results show that in the Selexol® process is crucial to optimize the pressures of the flash cascade releasing the CO2-rich stream to store, because the largest energy penalties are the compression power and the steam necessary for reboiling.

Abstract The use of optimized Multi-Energy Systems, including renewables, combined heat and power units and energy storages, is proven to be effective in the reduction of fossil CO2 emissions. These systems can be efficiently operated to provide electricity, heating and cooling to energy districts and buildings. To increase the share of renewable sources and further decrease CO2 emissions, incentives and/or carbon taxes are set by governments. This work proposes a novel bi-level optimization approach which mimics the actual bilevel decision process to determine the optimal renewable subsidy and carbon tax for small-medium multi-energy systems. At the upper level the government decides the incentives/tax to meet the desired emission reduction target while minimizing its costs and, at the lower level, the owner/operator of the Multi-Energy System decides the optimal design and operation to minimize its Total Annual Cost (sum of investment and operating costs). We devise an efficient heuristic approach to solve the bilevel program and apply the approach to four different real-world applications, namely a university campus, a hospital, an urban district, and an office building.

Abstract Among the technologies for carbon capture and storage (CCS) from natural gas, oxy-turbine plants are a very promising solution thanks to the high efficiency, absence of stack, and nearly 100% capture rate. This paper investigates the efficiency which can be achieved by the semi-closed oxy-combustion combined cycle (SCOC-CC) with state-of-the-art and future blade materials. In particular, the analysis considers class-H turbine superalloys with a maximum blade wall temperature of 900 °C and ceramic matrix composites with blade wall temperatures of 1300 °C. Sensitivity analyses are performed to determine the optimal pressure ratio and turbine inlet temperature. The results indicate that state-of-the-art superalloys allow the SCOC-CC to achieve 54% net electric efficiency with a 96% carbon capture rate, while ceramic matrix composite (CMC) blades boost the efficiency up to 60%. For both cases, critical factors are the high temperature gradients across the blade coatings (thermal barrier coating (TBC) for superalloy, environmental barrier coating (EBC) for CMC) and the blade thickness caused by the large heat flux exchanged between hot gases and cooling flows.

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.energy.2017.09.072 © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/