Development of an interval multi-stage stochastic programming model for regional energy systems planning and GHG emission control under uncertainty

Li, Gongchen; Huang, Guohe; Lin, Qianguo; Cai, Yanpeng; Chen, Yumin; Zhang, Xiaodong

doi:10.1002/er.1867

Cited by 11 publications

(5 citation statements)

References 31 publications

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“…According to Huang [29], Li et al [30], it can be solved by decomposing it into two sets of deterministic sub-models. Using interval computation, in the minimum operating condition (OC -), the PV power is P …”

Section: Imilp Solution Methodsmentioning

confidence: 99%

Optimal Operation of a CCHP Microgrid Using Interval Mixed-Integer Linear Programming

Luo¹,

Gu²,

Gao³

et al. 2017

JOCET

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Abstract-The development of combined cooling, heating, and power (CCHP) microgrids using distributed cogeneration equipment and renewable energy sources has drawn considerable research attention. In this study, we propose an interval mixed-integer linear programming (IMILP) dispatching model for supporting the economic dispatch of a CCHP microgrid under uncertainties. Our model is based on the interval programming method, where uncertainties can be incorporated and communicated into the economic dispatch problem using interval values. The proposed IMILP dispatching model is decomposed into two sets of deterministic sub-models, f -and f + , which are solved sequentially. We use a case study of a hotel in China to demonstrate how this model can be applied to support economic dispatch decisions for a CCHP microgrid. Simulation results indicated that load and PV power uncertainties mainly affect the dispatching results of the power exchange, gas boiler and electricity chiller, while the power generation unit is consistent under different uncertainty levels. The obtained dispatching results can provide more flexibility for decision makers by generating alternative decisions for CCHP microgrid dispatching under uncertainties.Index Terms-CCHP, economic dispatch, interval programming, microgrid, uncertainty. I. INTRODUCTIONThe energy crisis and rising air pollution have led to greater worldwide focus on energy efficiency methods. Combined cooling, heating, and power (CCHP) systems are also referred to tri-generation systems. They have been widely used in small-medium scale power systems to solve energy-related problems, including increasing energy demands, higher energy costs, energy supply security, and environmental concerns [1]- [6]. A CCHP microgrid integrates power generation units (PGUs, e.g. Fuel cell or Micro turbine), auxiliary boilers, heat recovery systems, refrigeration equipment (electric chiller or absorption chiller), energy storage devices (heat storage tanks or storage batteries), and renewable energy sources (e.g. photovoltaic, wind, biomass) to implement the cascade utilization of energy. The main difference between CCHP microgrid and traditional CCHP systems is that the former can not only satisfy the cooling, Manuscript received August 28, 2015; revised February 25, 2016. This work was supported by the National High Technology Research and Development Program of China (863 Program, grant no. 2011AA05A107), the National Science Foundation of China (grant no. 51277027), the Natural Science Foundation of Jiangsu Province of China (grant no. SBK201122387), the State Grid Corporation of China.Zhao Luo, Wei Gu, Zhihe Wang, and Yiyuan Tang are with the School of Electrical Engineering, Southeast University, Nanjing, Jiangsu 210096, China (e-mail: waiting.1986@live.com).Song Gao is with Jiangsu Electric Power Company, Nanjing, Jiangsu 210024, China.heating, and power demands of certain types of customers (such as office buildings, hotels, schools, commercial malls, and industrial loads), but also interacts ...

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Section: Imilp Solution Methodsmentioning

confidence: 99%

Optimal Operation of a CCHP Microgrid Using Interval Mixed-Integer Linear Programming

Luo¹,

Gu²,

Gao³

et al. 2017

JOCET

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“…Stoyan and Dessouky [35] developed a multistage stochastic mixed-integer program model for addressing the energy technologies that may continue to be used and new clean energy technologies that should be introduced in energy generation. Li et al [36] proposed an interval multistage stochastic programming model for regional energy systems planning and GHG emission control under uncertainty, where uncertainties presented as intervals and probability density functions. Among these techniques, inexact multistage stochastic programming (IMSP) with recourse, integrated the interval-parameter programming (IPP) and multistage stochastic programming (MSP), could deal with uncertainties expressed as probability distributions and discrete intervals, and received extensive attentions over the past years [36][37][38].…”

Section: Introductionmentioning

confidence: 99%

“…Li et al [36] proposed an interval multistage stochastic programming model for regional energy systems planning and GHG emission control under uncertainty, where uncertainties presented as intervals and probability density functions. Among these techniques, inexact multistage stochastic programming (IMSP) with recourse, integrated the interval-parameter programming (IPP) and multistage stochastic programming (MSP), could deal with uncertainties expressed as probability distributions and discrete intervals, and received extensive attentions over the past years [36][37][38]. IMSP is not only useful for the decision problems, which involves in a sequence of decisions that react to outcomes that evolve over time, but also applicable to large-scale practical problems over a medium-and long-term planning context in which an analysis of policy scenarios is desired [13].…”

Section: Introductionmentioning

confidence: 99%

Carbon and air pollutants constrained energy planning for clean power generation with a robust optimization model—A case study of Jining City, China

Xie

Huang

et al. 2014

Applied Energy

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“…Jiang et al used the general distribution model to fit the real wind power distribution under different wind power prediction levels and established a stochastic scheduling model. Li et al developed an interval multistage stochastic programming regional ESP model to support regional energy systems management and greenhouse gas (GHG) control under uncertainty. Li et al developed a fuzzy‐stochastic programming model with soft constraints with imprecise and probabilistic information.…”

Section: Introductionmentioning

confidence: 99%

A dynamic risk aversion model for virtual energy plant considering uncertainties and demand response

Tan²,

Wang

2019

Int J Energy Res

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Summary To full use clean energy to meet load demand of electrical and thermal, the paper proposed a novel concept of virtual energy plant (VEP) including wind power plant (WPP), photovoltaic power generation (PV), combined heat and power generation (CHP), solar collectors (SC), electric boiler (EB), heat storage tank (HSK), and incentive‐based demand response (IBDR). Firstly, the basic structure of VEP is designed, including three subsystems, namely, electricity, heating, and energy storage. Then, a basic scheduling model is constructed under the objective of maximizing operating revenue without considering uncertainty. Thirdly, the conditional value at risk (CVaR) method and the robust optimization theory are used to handle the uncertainty factors in objective functions and condition constraints, and the risk aversion scheduling model is proposed. Finally, industrial park group in northern China are chosen, for example, analysis results show (1) VEP could convert the abandoned clean energy, use HSK to store heating energy during the valley load period, and supply heating energy in the peak period to obtain the excess economic benefits. (2) Lower‐prediction accuracy will amplify the uncertainty risk, when the robust β∈[0.8,0.825]&(0.925,1], the increase of confidence level β will lead to larger increase in CVaR. Especially when β∈(0.925,1), decision makers are extremely disgusted with the risks brought by the uncertainty factors, and correspondingly, the output of clean energy becomes minimum. (3) When the capacity ratio of HSK, EB and the electricity price of peak, valley are lower than 3, the values of revenue, VaR and CVaR change faster, but the ratios are larger than 3, the values change slower, which indicates that the scale of HSK capacity needs to be properly controlled to optimize the use of clean energy, and price‐based demand response could improve the operation profit while controlling risk properly. In general, the proposed scheduling model can maximize the use of clean energy to obtain economic benefits while rationally controlling risks.

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Development of an interval multi-stage stochastic programming model for regional energy systems planning and GHG emission control under uncertainty

Cited by 11 publications

References 31 publications

Optimal Operation of a CCHP Microgrid Using Interval Mixed-Integer Linear Programming

Optimal Operation of a CCHP Microgrid Using Interval Mixed-Integer Linear Programming

Carbon and air pollutants constrained energy planning for clean power generation with a robust optimization model—A case study of Jining City, China

A dynamic risk aversion model for virtual energy plant considering uncertainties and demand response

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