A detailed MILP optimization model for combined cooling, heat and power system operation planning

Bischi, Aldo; Taccari, Leonardo; Martelli, Emanuele; Amaldi, E.; Manzolini, Giampaolo; Silva, Paolo; Campanari, Stefano; Macchi, Ennio

doi:10.1016/j.energy.2014.02.042

Cited by 242 publications

(98 citation statements)

References 14 publications

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“…As shown in Fig. 2 efficiency of the MT is defined in (13)- (16) through piecewise linearization [12]. Because the nonlinear efficiency curve is changed to several line segments, the control problem can be modelled as a MILP:…”

Section: Optimization Objectivementioning

confidence: 99%

“…Chance constrained programming and particle swarm optimization were used in [11] to optimize economic dispatch of combined heat and power (CHP) system. The optimization model for planning operation of CCHP systems was presented in [12][13][14], and the mixed integer nonlinear programming model was then converted into a linear programming model by appropriate piecewise linear approximation of the nonlinear performance curve.…”

Section: Introductionmentioning

confidence: 99%

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A robust optimization method for energy management of CCHP microgrid

Luo

et al. 2017

J. Mod. Power Syst. Clean Energy

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Energy management is facing new challenges due to the increasing supply and demand uncertainties, which is caused by the integration of variable generation resources, inaccurate load forecasts and non-linear efficiency curves. To meet these challenges, a robust optimization method incorporating piecewise linear thermal and electrical efficiency curve is proposed to accommodate the uncertainties of cooling, thermal and electrical load, as well as photovoltaic (PV) output power. Case study results demonstrate that the robust optimization model performs better than the deterministic optimization model in terms of the expected operation cost. The fluctuation of net electrical load has greater effect on the dispatching results of the combined cooling, heating and power (CCHP) microgrid than the fluctuation of the cooling and thermal load. The day-ahead schedule is greatly affected by the uncertainty budget of the load demand. The economy of the optimal decision could be achieved by adjusting different uncertainty budget levels according to control the conservatism of the model.

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Section: Optimization Objectivementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A robust optimization method for energy management of CCHP microgrid

Luo

et al. 2017

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

show abstract

“…Extraction-condensing steam turbine-based CHP is very popular because the ratio between heat and electricity output could be adjusted according to various loading ratios between the two loads, providing more flexibility during peak and off-peak hours [19].…”

Section: Basic Diagram Of Combined Heat and Powermentioning

confidence: 99%

Optimal Operation of Combined Heat and Power System Based on Forecasted Energy Prices in Real-Time Markets

Xie

Sun

et al. 2015

Energies

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This paper develops a discrete operation optimization model for combined heat and powers (CHPs) in deregulated energy markets to maximize owners' profits, where energy price forecasting is included. First, a single input and multi-output (SIMO) model for typical CHPs is established, considering the varying ratio between heat and electricity outputs at different loading levels. Then, the energy prices are forecasted with a gray forecasting model and revised in real-time based on the actual prices by using the least squares method. At last, a discrete optimization model and corresponding dynamic programming algorithm are developed to design the optimal operation strategies for CHPs in real-time. Based on the forecasted prices, the potential operating strategy which may produce the maximum profits is pre-developed. Dynamic modification is then conducted to adjust the pre-developed operating strategy after the actual prices are known. The proposed method is implemented on a 1 MW CHP on a typical day. Results show the optimized profits comply well with those derived from real-time prices after considering dynamic modification process.

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“…The total operating costs are determined by the fuel cost C f , which is a function of the power of each DG at each time interval, cost associated with the start-up and shut-down of DG, and costs due to curtailment of PV power [31].…”

Section: Equations Of the Mip Modelmentioning

confidence: 99%

Optimization Models for Islanded Micro-Grids: A Comparative Analysis between Linear Programming and Mixed Integer Programming

et al. 2017

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This paper presents a comparison of optimization methods applied to islanded micro-grids including renewable energy sources, diesel generators and battery energy storage systems. In particular, a comparative analysis between an optimization model based on linear programming and a model based on mixed integer programming has been carried out. The general formulation of these models has been presented and applied to a real case study micro-grid installed in Somalia. The case study is an islanded micro-grid supplying the city of Garowe by means of a hybrid power plant, consisting of diesel generators, photovoltaic systems and batteries. In both models the optimization is based on load demand and renewable energy production forecast. The optimized control of the battery state of charge, of the spinning reserve and diesel generators allows harvesting as much renewable power as possible or to minimize the use of fossil fuels in energy production.

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A detailed MILP optimization model for combined cooling, heat and power system operation planning

Cited by 242 publications

References 14 publications

A robust optimization method for energy management of CCHP microgrid

A robust optimization method for energy management of CCHP microgrid

Optimal Operation of Combined Heat and Power System Based on Forecasted Energy Prices in Real-Time Markets

Optimization Models for Islanded Micro-Grids: A Comparative Analysis between Linear Programming and Mixed Integer Programming

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