1,721,005 research outputs found
Integrated energy and ancillary services optimized management and risk analysis within a pay-as-bid market
No full textIn liberalized electricity markets, trading energy between generators and consumers occurs primarily on the Day-Ahead Market (DAM) one day in advance. However, the scheduled programs may not comply with grid requirements or real-time conditions. To ensure grid stability and sufficient reserves, system operators procure resources on the Ancillary Services Market (ASM). With the increasing share of renewable energy sources, many programmable generators are shifting their business model, from generating energy at base load to providing grid services. In this context, a DAM-based traditional approach to dispatch scheduling, widely adopted by existing techno-economics analysis, may result significantly suboptimal. This paper presents a novel model for dispatch optimization maximizing profits simultaneously on both the DAM and ASM, utilizing a mixed integer linear programming (MILP) formulation and a machine learning algorithm considering a pay-as-bid pricing system and predicting the probability of offer acceptance based on historical data. The proposed framework is modular and flexible, allowing for separate use of the MILP dispatch optimizer and the machine learning offer acceptance prediction model. A risk propensity factor is defined and the impact on the optimal bidding strategy, the expected profits, and their variability, is studied. A Montecarlo approach is used to evaluate the profits probability density function. The performance obtained (i.e. 20 min to optimize one week of operation of a Combined Cycle Gas Turbine) allows in applying the proposed methodologies for both long term energy system planning and daily production offer schedulin
Gas turbine combined cycle start-up and stress evaluation: A simplified dynamic approach
No full textThe main topic of this work is the development and validation of a simplified approach for the dynamic analysis of a Gas Turbine Combined Cycle (GTCC), with a particular focus on start-up procedure and associated mechanical stresses on the steam turbine (ST). The currently deregulated energy market led GTCC to undergo frequent startups, a condition often not considered during plant design. Moreover, the time required for the start-up is crucial under an economical viewpoint, though it is constrained by mechanical stresses imposed to thick components by thermal gradients. The framework proposed in this work aims to improve the accessibility to simulation software by applying commonly used office suite â Microsoft Excel/Visual Basic â with acceptable reduction in accuracy. Simplicity of model allow fast computation and its exploitation can be pursued by non-qualified plant operators. The obtained tool can be than adopted to support decision process during plant operations. The developed tool has been validated for a hot start-up against field measurements supplied by Tirreno Power S.p.A. Italy. Data are recorded through control and monitoring sensors of a 390 MW multi-shaft combined cycle based on the GT AEN94.3 A4 frame, but the results can be easily generalized to other layouts. Simulation result and stress evaluations around the steam turbine (ST) rotor show good agreement with experimental data
METODI PER IL RICIRCOLO DEI GAS DI SCARICO IN SISTEMI BASATI SU TURBINA A GAS PER DIMINUIRE IL MINIMO CARICO TECNICO AMBIENTALE
No full textShort-Term Optimization of a Combined Cycle Power Plant Integrated With an Inlet Conditioning Unit
No full textUnder new scenarios with high shares of renewable electricity, combined cycle gas turbines (CCGTs) are required to improve their flexibility to help balance the power system. Simultaneously, liberalization of electricity markets and the complexity of its hourly price dynamics are affecting the CCGT profitability, leading the need for optimizing its operation. An inlet conditioning unit (ICU) offers the benefit of power augmentation and “minimum environmental load” (MEL) reduction by controlling the gas turbine (GT) inlet temperature using cold thermal energy storage (TES) and a heat pump (HP). Consequently, an evaluation of a CCGT integrated with this unit including a day-ahead optimized operation strategy was developed in this study. To establish the hourly dispatch of the power plant and the operation mode of the ICU, a mixed-integer linear programing (MILP) optimization was formulated, aiming to maximize the operational profit of the plant within a 24 h horizon. To assess the impact of the unit operating under this dispatch strategy, historical data have been used to perform annual simulations of a reference power plant located in Turin, Italy. Results indicate that the power plant's operational profit increases by achieving a wider operational range during peak and off-peak periods. For the specific case study, it is estimated that the net present value (NPV) of the CCGT integrated with the ICU is 0.5% higher than the CCGT without it. Results also show that the unit reduces the MEL by approximately 1.34% and can increase the net power output by 0.17% annually.</p
Advanced Control System for Grid-Connected SOFC Hybrid Plants: Experimental Verification in Cyber-Physical Mode
No full textThis paper presents a Model Predictive Controller (MPC) operating an SOFC Gas Turbine hybrid plant at end-of-life performance condition. Its performance was assessed with experimental tests showing a comparison with a Proportional Integral Derivative (PID) control system. The hybrid system operates in grid-connected mode, i.e. at variable speed condition of the turbine. The control system faces a multivariable constrained problem, as it must operate the plant into safety conditions while pursuing its objectives. The goal is to test whether a linearized controller design for normal operating condition is able to govern a system which is affected by strong performance degradation. The control performance was demonstrated in a cyber-physical emulator test rig designed for experimental analyses on such hybrid systems. This laboratory facility is based on the coupling of a 100 kW recuperated microturbine with a fuel cell emulation system based on vessels for both anodic and cathodic sides. The components not physically present in the rig were studied with a real-time model running in parallel with the plant. Model output values were used as set-point data for obtaining in the rig (in real-time mode) the effect of the fuel cell system. The result comparison of the MPC tool against a PID control system was carried out considering several plant properties and the related constraints. Both systems succeeded in managing the plant, still the MPC performed better in terms of smoothing temperature gradient and peaks
Gross error detection based on serial elimination: applications to an industrial gas turbine
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