The forecasts of electricity and heating demands are key inputs for the efficient design and operation of energy systems serving urban districts, buildings, and households. Their accuracy may have a ...considerable effect on the selection of the optimization approach and on the solution quality. In this work, we describe a supervised learning approach based on shallow Artificial Neural Networks to develop an accurate model for predicting the daily hourly energy consumption of an energy district 24 h ahead. Predictive models are generated for each one of the two considered energy types, namely electricity and heating. Single-layer feedforward neural networks are trained with the efficient and robust decomposition algorithm DEC proposed by Grippo et al. on a data set of historical data, including, among others, carefully selected information related to the hourly energy consumption of the energy district and the hourly weather data of the region where the district is located. Three different case studies are analyzed: a medium-size hospital located in the Emilia-Romagna Italian region, the whole Politecnico di Milano University campus, and a single building of a department belonging to the latter. The computational results indicate that the proposed method with enriched data inputs compares favorably with the benchmark forecasting and Machine Learning techniques, namely, ARIMA, Support Vector Regression and long short-term memory networks.
A solid rocket motor (SRM) is a rocket engine that uses a fuel/oxidizer mixture in a solid state; the most commonly employed propellants are Hydroxyl-Terminated Polybutadiene (HTPB) as the fuel and ...ammonium/potassium perchlorate as the oxidizer. To increase the flight range of this kind of vehicle, the weight has to be reduced as much as possible. A possible element that can be worked on is the coating of the combustion chamber: the skirt. The aim of this paper is to investigate the behavior of a cylindrical skirt subjected to internal pressure load and axial thrust and to compare the performance of a skirt made of a standard steel for aeronautics purposes with a carbon-fiber-reinforced composite skirt. The motor test case is taken from the ONERA C1xb and the flowfield is simulated with an axisymmetric
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turbulence model. The carbon-fiber-reinforced composite skirt is a cylindrical shell with a symmetric and balanced layup 90/0/45/-45s. To check composite layer integrity, Hashin's failure criteria were adopted while linearized buckling methods were used to assess the buckling behavior of the skirt. The composite layup was modeled by adopting the classical laminate theory.
A variety of engineering applications are tackled as black-box optimization problems where a computationally expensive and possibly noisy function is optimized over a continuous domain. In this paper ...we present a derivative-free local method which is well-suited for such problems, and we describe its application to the optimization of the start-up phase of an innovative Concentrated Solar Power (CSP) plant. The method, referred to as rqlif, exploits a regularized quadratic model and a linear implicit filtering strategy so as to be parsimonious in terms of function evaluations. After assessing the performance of rqlif on a set of analytical test problems in comparison with three well-known local algorithms, we apply it in conjunction with a global algorithm based on RBFs interpolation to the start-up optimization of the CSP plant developed in the PreFlexMS H2020 project. For the test problems, rqlif provides good quality solutions in a limited number of function evaluations. For the application, the global–local strategy yields a substantial improvement with respect to the reference solution and significantly reduces the thermo-mechanical stress suffered by the plant components.
After more than one century from its first use for electric power production, steam cycles are still the object of continuous research and development efforts worldwide. Indeed, owing to its ...favorable thermodynamic properties, steam cycles are not only used in coal-fired power plants but in a large variety of applications such as combined cycles, concentrated solar power plants and polygeneration plants. On the other hand, to cope with the efficiency and flexibility requirements set by today’s energy markets, the design and the operation of steam cycles must be carefully optimized. A key rule is played by the simulation and optimization codes developed in the last 30 years. This paper provides an introduction to the main types of simulation and optimization problems (design, off-design operation and dynamic), an overview of the mathematical background (possible solution approaches, numerical methods and available software), and a review of the main scientific contributions.
This paper presents the optimization of organic Rankine cycles (ORCs) for recovering waste heat from a hypothetical aluminum production plant to be installed in Norway. The case study is particularly ...interesting because it features two hot streams at different temperatures (the pot exhaust gases and the cell wall cooling air), which make available about 16 MWth below 250°C. First, a recently proposed cycle optimization approach is adopted to identify the most promising working fluid and optimize the cycle variables (pressures, temperatures, mass flow rates) for the maximum energy performance. The analysis includes both pure fluids, including recently synthesized refrigerants, and binary zeotropic mixtures assessing in total 102 working fluids. The best pure fluid in terms of exergy efficiency turns out to be HFE-347mcc (which can achieve a target exergy efficiency of 85.28%), followed by neopentane, butane, and R114. HFO-1336mzz appears to be one of the most promising non-flammable alternatives with low Global Warming Potential (GWP). The mixture leading to the highest exergy efficiency is isobutane–isopentane, which can increase the net electrical power output by up to 3.3% compared to pure fluids. The systematic techno-economic optimization, repeated for two different electricity prices, shows that RE347mcc is the best option in both low and high electricity prices. The cost of the cycle using HFO-1336mzz is penalized by the larger evaporation heat (negatively influencing the heat integration) and the smaller regenerator.
Biogas upgrading to biomethane has received particular attention in recent years as a key strategy to produce a natural gas-substitute from biomass. Among the available upgrading technologies, ...amine-based absorption is an interesting option because of its low electric power consumption and very high methane recovery rates. This paper assesses the energy and economic performance of a biogas upgrading process involving chemical absorption with two different aqueous solvent formulations: 50%w MDEA and a 20%w/20%w MDEA/MEA blend. The process performance is evaluated with Aspen Plus and the optimal process conditions are determined with a multi-objective optimization approach targeting the maximum efficiency and minimum specific equipment cost. In the proposed scheme, to make the system energy-self-sufficient, an internal combustion engine burns a fraction of the raw biogas stream to co-generate both the electric power for the upgrading process and the thermal power required for solvent regeneration.
Aqueous MDEA turns out to be more efficient (Pareto dominant curve) and less expensive than the MDEA/MEA blend, as a result of the lower regeneration energy (0.94 kJ/Nm3BM/h vs 1.43 kJ/Nm3BM/h) which yields to lower energy consumptions and smaller engine sizes. Even though the cost estimates are subject to a higher degree of uncertainty, the MDEA option features an expected specific total equipment cost as low as 1,550 €/(Nm3BM/h)) vs. 1,850 €/(Nm3BM/h)) for the MDEA/MEA blend.
The objective of this study is to assess the technical and economic potential of four alternative processes suitable for post-combustion CO2 capture from natural gas-fired power plants. These ...include: CO2 permeable membranes; molten carbonate fuel cells (MCFCs); pressurized CO2 absorption integrated with a multi-shaft gas turbine and heat recovery steam cycle; and supersonic flow-driven CO2 anti-sublimation and inertial separation. A common technical and economic framework is defined, and the performance and costs of the systems are evaluated based on process simulations and preliminary sizing. A state-of-the-art natural gas combined cycle (NGCC) without CO2 capture is taken as the reference case, whereas the same NGCC designed with CO2 capture (using chemical absorption with aqueous monoethanolamine solvent) is used as a base case. In an additional benchmarking case, the same NGCC is equipped with aqueous piperazine (PZ) CO2 absorption, to assess the techno-economic perspective of an advanced amine solvent. The comparison highlights that a combined cycle integrated with MCFCs looks the most attractive technology, both in terms of energy penalty and economics, i.e., CO2 avoided cost of 49 $/tCO2 avoided, and the specific primary energy consumption per unit of CO2 avoided (SPECCA) equal to 0.31 MJLHV/kgCO2 avoided. The second-best capture technology is PZ scrubbing (SPECCA = 2.73 MJLHV/kgCO2 avoided and cost of CO2 avoided = 68 $/tCO2 avoided), followed by the monoethanolamine (MEA) base case (SPECCA = 3.34 MJLHV/kgCO2 avoided and cost of CO2 avoided = 75 $/tCO2 avoided), and the supersonic flow driven CO2 anti-sublimation and inertial separation system and CO2 permeable membranes. The analysis shows that the integrated MCFC–NGCC systems allow the capture of CO2 with considerable reductions in energy penalty and costs.
•Predictive scheduling optimization of Multi-Energy Systems can lower operating cost.•Forecast uncertainty can affect solution optimality and cause service interruptions.•We present an affinely ...adjustable robust formulation for day-ahead scheduling.•Uncertainty of all energy demands and non-dispatchable generators is accounted.•The robust approach is compared to a deterministic formulation with reserve margins.
Multi-energy systems and microgrids can play an important role in increasing the efficiency of distributed energy systems and favoring an increasing penetration from renewable sources, by serving as control hubs for the optimal management of Distributed Energy Resources. Predictive operation planning via Mixed Integer Linear Programming is an effective way of tackling the optimal management of these systems. However, the uncertainty of demand and renewable production forecasts can hinder the optimality of the scheduling solution and even lead to outages. This paper proposes a new Affinely Adjustable Robust Formulation of the day-ahead scheduling problem for a generic multi-energy system/microgrid subject to multiple uncertainty factors. Piece-wise linear decision rules are considered in the robust formulation, and their potential use for real-time control is assessed. Novel features include an ad hoc characterization of the polyhedral uncertainty space aimed at reducing solution conservativeness, aggregation of uncertain factors and partial-past recourse which allows speeding up the computational time. The advantages and limitations of the Affinely Adjustable Robust Formulation are thoroughly discussed and quantified through artificial and real-world test cases. The comparison with a conventional deterministic approach shows that, despite the limitations of the affine decision rules, the adjustable robust formulation can ensure full system reliability while attaining at the same time better performances.
•Novel MILP approaches to enable design of MES including seasonal energy storage.•Good accuracy and much lower computational complexity compared to current approaches.•Realistic Swiss case-study ...evaluated in terms of total annual cost and emissions.•Extensive sensitivity analysis defining design guidelines for seasonal energy storage.
Optimal design and operation of multi-energy systems involving seasonal energy storage are often hindered by the complexity of the optimization problem. Indeed, the description of seasonal cycles requires a year-long time horizon, while the system operation calls for hourly resolution; this turns into a large number of decision variables, including binary variables, when large systems are analyzed. This work presents novel mixed integer linear program methodologies that allow considering a year time horizon with hour resolution while significantly reducing the complexity of the optimization problem. First, the validity of the proposed techniques is tested by considering a simple system that can be solved in a reasonable computational time without resorting to design days. Findings show that the results of the proposed approaches are in good agreement with the full-scale optimization, thus allowing to correctly size the energy storage and to operate the system with a long-term policy, while significantly simplifying the optimization problem. Furthermore, the developed methodology is adopted to design a multi-energy system based on a neighborhood in Zurich, Switzerland, which is optimized in terms of total annual costs and carbon dioxide emissions. Finally the system behavior is revealed by performing a sensitivity analysis on different features of the energy system and by looking at the topology of the energy hub along the Pareto sets.
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