Multi-stage Stochastic Optimal Operation of Energy-efficient Building with Combined Heat and Power System

Liu, Ping; Yong, Fu; Marvasti, Amin Kargarian

doi:10.1080/15325008.2013.862324

Cited by 31 publications

(21 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, for the multi-stage SP model, the problem becomes extremely large when the number of stages increase [32], even though a relatively small number of nodes are allowed in each stage. With an RHC strategy, as proposed in the next section, the two-stage SP model can also sequentially make the decisions with hourly updated forecast information, which compensates for the lack of additional stages for uncertainty unveiling.…”

Section: Two-stage Stochastic Programming Formulation For Microgrid Ementioning

confidence: 99%

“…The multi-stage SP model [32][33][34] allows revision of the planning decisions sequentially when the uncertainty realized hourly [33]. However, for the multi-stage SP model, the problem becomes extremely large when the number of stages increase [32], even though a relatively small number of nodes are allowed in each stage.…”

Section: Two-stage Stochastic Programming Formulation For Microgrid Ementioning

confidence: 99%

See 1 more Smart Citation

Combined Two-Stage Stochastic Programming and Receding Horizon Control Strategy for Microgrid Energy Management Considering Uncertainty

Zang

Zeng

et al. 2016

Energies

View full text Add to dashboard Cite

Microgrids (MGs) are presented as a cornerstone of smart grids. With the potential to integrate intermittent renewable energy sources (RES) in a flexible and environmental way, the MG concept has gained even more attention. Due to the randomness of RES, load, and electricity price in MG, the forecast errors of MGs will affect the performance of the power scheduling and the operating cost of an MG. In this paper, a combined stochastic programming and receding horizon control (SPRHC) strategy is proposed for microgrid energy management under uncertainty, which combines the advantages of two-stage stochastic programming (SP) and receding horizon control (RHC) strategy. With an SP strategy, a scheduling plan can be derived that minimizes the risk of uncertainty by involving the uncertainty of MG in the optimization model. With an RHC strategy, the uncertainty within the MG can be further compensated through a feedback mechanism with the lately updated forecast information. In our approach, a proper strategy is also proposed to maintain the SP model as a mixed integer linear constrained quadratic programming (MILCQP) problem, which is solvable without resorting to any heuristics algorithms. The results of numerical experiments explicitly demonstrate the superiority of the proposed strategy for both island and grid-connected operating modes of an MG.

show abstract

Section: Two-stage Stochastic Programming Formulation For Microgrid Ementioning

confidence: 99%

Section: Two-stage Stochastic Programming Formulation For Microgrid Ementioning

confidence: 99%

Combined Two-Stage Stochastic Programming and Receding Horizon Control Strategy for Microgrid Energy Management Considering Uncertainty

Zang

Zeng

et al. 2016

Energies

View full text Add to dashboard Cite

show abstract

“…The purpose of the robust rcome the drawbacks introduced by uncertainties [40]. The limits and dynamics of electrical energy stored in the BESS of m th MG are given by quation (5a) and (5b).…”

Section: Uncertainty Boundsmentioning

confidence: 99%

“…The purpose of the robust ounterpart is to overcome the drawbacks introduced by uncertainties [40]. The uncertainty bounds re formulated prior to worst-case model realization, in order to cater the influence of intermittent enewable power generation and variable customer loads.…”

Section: Robust Counterpartmentioning

confidence: 99%

Robust Optimization-Based Scheduling of Multi-Microgrids Considering Uncertainties

2016

View full text Add to dashboard Cite

Scheduling of multi-microgrids (MMGs) is one of the important tasks in MMG operation and it faces new challenges as the integration of demand response (DR) programs and renewable generation (wind and solar) sources increases. In order to address these challenges, robust optimization (RO)-based scheduling has been proposed in this paper considering uncertainties in both renewable energy sources and forecasted electric loads. Initially, a cost minimization deterministic model has been formulated for the MMG system. Then, it has been transformed to a min-max robust counterpart and finally, a traceable robust counterpart has been formulated using linear duality theory and Karush-Kuhn-Tucker (KKT) optimality conditions. The developed model provides immunity against the worst-case realization within the provided uncertainty bounds. Budget of uncertainty has been used to develop a trade-off between the conservatism of solution and probability of unfeasible solution. The effect of uncertainty gaps on internal and external trading, operation cost, unit commitment of dispatchable generators, and state of charge (SOC) of battery energy storage systems (BESSs) have also been analyzed in both grid-connected and islanded modes. Simulations results have proved the robustness of proposed strategy.

show abstract

“…[19] used mixed integer programming and Ref. [20] applied auto-regressive moving average, time series model and stochastic mixed integer programming for a solar-based CHP system. Ref.…”

Section: Literature Reviewmentioning

confidence: 99%

Scheduling of a Renewable Hybrid Tri-generation

Ercan¹,

Kayakutlu²

2017

JOCET

View full text Add to dashboard Cite

Abstract-Hybrid energy resources are generally accepted with the concern of global pollution and warming. Optimization studies on the detailed scheduling of hybrid energy usage are trying to find solutions for complex problems including uncertainty and lots of data. This paper proposes a stochastic model for detailed scheduling of a tri-generation system using wind and solar sources with thermal collectors. The proposed hourly scheduling will allow balancing the production and consumption as well as giving more realistic commitments the day ahead. The model is currently working with the simulated data and the case application has started.

show abstract

Multi-stage Stochastic Optimal Operation of Energy-efficient Building with Combined Heat and Power System

Cited by 31 publications

References 15 publications

Combined Two-Stage Stochastic Programming and Receding Horizon Control Strategy for Microgrid Energy Management Considering Uncertainty

Combined Two-Stage Stochastic Programming and Receding Horizon Control Strategy for Microgrid Energy Management Considering Uncertainty

Robust Optimization-Based Scheduling of Multi-Microgrids Considering Uncertainties

Scheduling of a Renewable Hybrid Tri-generation

Contact Info

Product

Resources

About