Technical and Tariff Scenarios Effect on Microturbine Trigenerative Applications

Campanari, Stefano; Macchi, Ennio

doi:10.1115/1.1762904

Cited by 51 publications

(10 citation statements)

References 6 publications

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“…In our model, the microturbine is a back-pressure CHP which generates electric power by consuming natural gas. Its converting ratio of the electric and heat power output is a constant value [34]. erefore, Q MT , P MT , and G MT can be presented in the following equation:…”

Section: Two-stage Day-ahead Ed For the Integratedmentioning

confidence: 99%

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Robust Day-Ahead Dispatch for Integrated Power-Heat-Gas Microgrid considering Wind Power Uncertainty

Liu

Chen

et al. 2020

Mathematical Problems in Engineering

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Wind power generation has been widely deployed in the modern power system due to the issues of energy crisis and environment pollution. Meanwhile, the microgrid is gradually regarded as a feasible way to connect and accommodate the distributed wind power generations. Recently, more research studies also focus on incorporating various energy systems, for example, heat and gas into the microgrid in terms of satisfying different types of load demands. However, the uncertainty of wind power significantly impacts the economy of the integrated power-heat-gas microgrid. To deal with this issue, this paper presents a two-stage robust model to achieve the optimal day-ahead economic dispatch strategy considering the worst-case wind power scenarios. The first stage makes the initial day-ahead dispatch decision before the observation of uncertain wind power. The additional adjustment action is made in the second stage once the wind power uncertainty is observed. Based on the duality theory and Big-M approach, the original second-stage problem can be dualized and linearized. Therefore, the column-and-constraint generation algorithm can be further implemented to achieve the optimal day-ahead economic dispatch strategy for the integrated power-heat-gas microgrid. The experimental results indicate the effectiveness of the presented approach for achieving operation cost reduction and promoting wind power utilization. The robustness and the economy of the two-stage robust model can be balanced, of which the performances significantly outperform those of the single-stage robust model and the deterministic model.

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Section: Two-stage Day-ahead Ed For the Integratedmentioning

confidence: 99%

“…where η PtG and η EB represent the conversion efficiency of PtG and EB. In this paper, η MT , η gas , η PtG , and η EB are equal to 0.9, 0.85, 0.85, and 0.8, respectively [12,[34][35][36][37].…”

Section: Two-stage Day-ahead Ed For the Integratedmentioning

confidence: 99%

Robust Day-Ahead Dispatch for Integrated Power-Heat-Gas Microgrid considering Wind Power Uncertainty

Liu

Chen

et al. 2020

Mathematical Problems in Engineering

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“…The efficiency of the micro turbines is boosted in respect to generation output power and its characteristics can be predestined as a quadratic function of its generated power [27]. On the other hand, the efficiency of the fuel cell can be evaluated through a nonlinear function of power level [28].…”

Section: Micro-turbine and Fuel Cellmentioning

confidence: 99%

Smart Energy Management System of Environmentally Friendly Microgrid Based on Grasshopper Optimization Technique

et al. 2020

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A microgrid is a group of distributed energy resources and interconnected loads that may be operated either in isolated mode or connected mode with the main utility within electrical boundaries. Microgrids may consist of different types of renewable energy resources such as photovoltaic panels, wind turbines, fuel cells, micro turbines, and storage units. It is highly recommended to manage the dependency on these resources by implementing an energy management unit to optimize the energy exchange so that the minimum cost is achieved. In this paper, an energy management system based on the grasshopper optimization algorithm (GOA) is proposed to determine the optimal power generated by the distributed generators in the microgrid which is required to minimize the total generation cost. The proposed unit is applied to a microgrid that consists of five generating units feeding residential, commercial, and industrial loads, and results are compared to other available research in literature to validate the proposed algorithm.

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“…The fuel consumption for the controllable DG (fuel cell and microturbine) is expressed in (21), more details can be found in [30][31][32]. (21) where is the fuel price and is the efficiency rate related to the DG unit i.…”

Section: A Multiobjective Optimizationmentioning

confidence: 99%

Multiobjective Intelligent Energy Management for a Microgrid

Chaouachi

Kamel

Andoulsi

et al. 2013

IEEE Trans. Ind. Electron.

585

237

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In this paper, a generalized formulation for intelligent energy management of a microgrid is proposed using Artificial Intelligence (AI) techniques jointly with linear programming based multiobjective optimization. The proposed Multiobjective Intelligent Energy Management (MIEM) aims to minimize the operation cost and the environmental impact of a microgrid taking into account its pre-operational variables as future availability of renewable energies and load demand. An artificial Neural Network Ensemble (NNE) is developed to predict 24 hour ahead photovoltaic generation, one hour ahead wind power generation and load demand. The proposed machine learning is characterized by enhanced learning model and generalization capability. The efficiency of the microgrid operation strongly depends on the battery scheduling process, which cannot be achieved through conventional optimization formulation. In this study, a fuzzy logic expert system is used for battery scheduling. The proposed approach can handle uncertainties regarding to the fuzzy environment of the overall microgrid operation and the uncertainty related to the forecasted parameters. The results shows considerable minimization on operation cost and emission level comparing to literature microgrid energy management approaches based on opportunity charging and heuristic flowchart battery management. Index Terms-MultiobjectiveIntelligent Energy Management (MIEM), Neural Network Ensemble (NNE), Fuzzy Logic, Shortterm Forecasting, Microgrid.

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Technical and Tariff Scenarios Effect on Microturbine Trigenerative Applications

Cited by 51 publications

References 6 publications

Robust Day-Ahead Dispatch for Integrated Power-Heat-Gas Microgrid considering Wind Power Uncertainty

Robust Day-Ahead Dispatch for Integrated Power-Heat-Gas Microgrid considering Wind Power Uncertainty

Smart Energy Management System of Environmentally Friendly Microgrid Based on Grasshopper Optimization Technique

Multiobjective Intelligent Energy Management for a Microgrid

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