A dynamic programming model for environmental investment decision-making in coal mining

Yu, Shiwei; Gao, Siwei; Sun, Han

doi:10.1016/j.apenergy.2015.09.099

Cited by 30 publications

(15 citation statements)

References 24 publications

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“…Dynamic programming (DP) [43] is an optimization strategy that provides the global minimum of a given objective function. According to Yu et al [44], DP is more effective than linear and nonlinear programming and proves its effectiveness by solving multiple objective function problems by dividing the complex problem into interrelated subproblems [44,45]. In addition, Chen et al [46] stated that the lack of empirical coefficients and predetermined parameters constitute one of the main advantages in using DP.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Management of the Energy Flows of Interconnected Residential Users

Manservigi¹,

Cattozzo²,

Spina³

et al. 2020

Energies

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In recent years, residential users have begun to be equipped with micro-CHP (combined heat and power) generation technologies with the aim of decreasing primary energy consumption and reducing environmental impact. In these systems, the prime mover supplies both thermal and electrical energy, and an auxiliary boiler and the national electrical grid are employed as supplementary systems. In this paper, a simulation model, which accounts for component efficiency and energy balance, was developed to replicate the interaction between the users and the energy systems in order to minimize primary energy consumption. The simulation model identified the optimal operation strategy of two residential users by investigating different energy system configurations by means of a dynamic programming algorithm. The reference scenario was compared to three different scenarios by considering independent energy systems, shared thermal and electrical energy storage and also the shared prime mover. Such a comparison allowed the identification of the most suitable energy system configuration and optimized operation strategy. The results demonstrate that the optimized operation strategy smoothes the influence of the size of thermal and electrical energy storage. Moreover, the saving of primary energy consumption can be as high as 5.1%. The analysis of the economic feasibility reveals that the investment cost of the prime mover can be as high as 4000 €/kW.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal Management of the Energy Flows of Interconnected Residential Users

Manservigi¹,

Cattozzo²,

Spina³

et al. 2020

Energies

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“…Recursive function: (13) I: picked plantation blocks Cni(Xni) given by (9) B(n-a)i(y(n-a)i) given by (10) Gn-1 given by (3) or (12) The boundary conditions at N = 1 (14)…”

Section: Model Formulationmentioning

confidence: 99%

“…Moreover [11] developed dynamic programming as a method of controlling demand through the pricing of delivery time at an e-grocer receiving orders via online booking system. Meanwhile [12] developed dynamic programming model for environmental decision-making process in coal mining investment. Related to layout optimization [13] proposed a new model based on dynamic programming to make a trade-off between reliability and cost for the layout design.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Programming to Solve Picking Schedule at the Tea Plantation

Budijati¹,

Iskandar²

2018

IJET

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The tea picking schedule at PT Perkebunan Ciater is set to be the same for all plantation blocks. In fact, the altitude from sea level and the pruning age of each plantation block is different, this results in the difference of buds’ growth. The implementation of the same picking schedule causes the quality and quantity of tea buds often could not be fulfilled. This research is to determine the precise picking schedule by considering the buds’ growth of each plantation block. Two steps are implemented to solve the problem. The first step is to look for picking period and the pattern of buds’ quality for each plantation block, which corresponds to the altitude of the location and the pruning age. The regression method is applied in this first step. The buds’ quality pattern is then used to determine the cost of decreasing buds’ quality and the costs of the buds that left in the plantation. The second step is to develop the picking schedule using dynamic programming, which minimizes the total cost of picking. In addition to this, we also develop a rolling schedule, which schedule time interval is three days. The model results show that the proposed schedule gives a better total cost than the current schedule and the buds’ quality target is easier to achieve.

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“…Pertaining publications include those on fugitive emission from the mining industry (Cheng et al 2011;Shao et al 2016;Su et al 2005;Warmuzinski 2008); oil and natural gas (Elgowainy et al 2014;Elkin 2015;Motazedi et al 2017;Park et al 2010;Szklo and Schaeffer 2007;Schneising et al 2014); fossil fuel GHG emissions from the manufacturing industry (Akbostanci et al 2011;Griffina et al 2018;Laurent et al 2010;Lina and Xubc 2018;Pengab et al 2018;Ren et al 2014;Tian et al 2013;Yuab et al 2018); emissions from the mineral products, especially cement (Andrew 2018;Cai et al 2016;Liu et al 2015;Liu 2016;Rehan and Nehdi 2005;Shan et al 2016); and metal Liu et al 2016;Shi and Zhao 2016;Yang et al 2018) and food (Garnett 2011) productions. Important information was brought during assessments of mitigation potential of the industrial processes on the reduction of GHG emissions (Garnett 2011;Hao et al 2016;Laurent et al 2010;Liu et al 2014;Long et al 2016;Park et al 2010;Rehan and Nehdi 2005;Wu et al 2016;Yu et al 2016;Zhang et al 2016). Furthermore, a recent special issue of Applied Energy was published on the recent trends of industrial emissions in developing countries .…”

Section: Introductionmentioning

confidence: 99%

A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: reducing the errors and uncertainties in global emission modelling

Charkovska

Halushchak

Bun

et al. 2019

Mitig Adapt Strateg Glob Change

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Industrial processes cause significant emissions of greenhouse gases (GHGs) to the atmosphere and, therefore, have high mitigation and adaptation potential for global change. Spatially explicit (gridded) emission inventories (EIs) should allow us to analyse sectoral emission patterns to estimate the potential impacts of emission policies and support decisions on reducing emissions. However, such EIs are often based on simple downscaling of national level emission estimates and the changes in subnational emission distributions do not necessarily reflect the actual changes driven by the local emission drivers. This article presents a high-definition, 100-m resolution bottom-up inventory of GHG emissions from industrial processes (fuel combustion activities in energy and manufacturing industries, fugitive emissions, mineral products, chemical industries, metal production and food and drink industries), which is exemplified for data for Poland. The study objectives include elaboration of the universal approach for mapping emission sources, algorithms for emission disaggregation, Mitigation and Adaptation Strategies for Global Change (2019) 24:907-939 https://doi.estimation of emissions at the source level and uncertainty analysis. We start with IPCCcompliant national sectoral GHG estimates made using Polish official statistics and, then, propose an improved emission disaggregation algorithm that fully utilises a collection of activity data available at the national/provincial level to the level of individual point and diffused (area) emission sources. To ensure the accuracy of the resulting 100-m resolution emission fields, the geospatial data used for mapping emission sources (point source geolocation and land cover classification) were subject to thorough human visual inspection. The resulting 100-m emission field even holds cadastres of emissions separately for each industrial emission category. We also compiled cadastres in regular grids and, then, compared them with the Emission Database for Global Atmospheric Research (EDGAR). A quantitative analysis of discrepancies between both results reveals quite frequent misallocations of point sources used in the EDGAR compilation that considerably deteriorate high-resolution inventories. We also use a Monte-Carlo method-based uncertainty assessment that yields a detailed estimation of the GHG emission uncertainty in the main categories of the analysed processes. We found that the above-mentioned geographical coordinates and patterns used for emission disaggregation have the greatest impact on the overall uncertainty of GHG inventories from the industrial processes. We evaluate the mitigation potential of industrial emissions and the impact of separate emission categories. This study proposes a method to accurately quantify industrial emissions at a policy relevant spatial scale in order to contribute to the local climate mitigation via emission quantification (local to national) and scientific assessment of the mitigation effort (national to global). Apart from the abov...

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A dynamic programming model for environmental investment decision-making in coal mining

Cited by 30 publications

References 24 publications

Optimal Management of the Energy Flows of Interconnected Residential Users

Optimal Management of the Energy Flows of Interconnected Residential Users

Dynamic Programming to Solve Picking Schedule at the Tea Plantation

A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: reducing the errors and uncertainties in global emission modelling

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