Industrial demand side response modelling in smart grid using stochastic optimisation considering refinery process

Reka, S. Sofana; Ramesh, V.

doi:10.1016/j.enbuild.2016.05.070

Cited by 25 publications

(6 citation statements)

References 29 publications

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“…1, pp. [13][14][15][16][17][18][19][20][21] This is an open access article published by the IET under the Creative Commons Attribution -NonCommercial License (http://creativecommons.org/licenses/by-nc/3.0/) the energy supply cost, and the maintenance cost of production equipment, as described in (1) is selected as the optimisation objective function…”

Section: Objective Functionmentioning

confidence: 99%

“…Equation (15) represents that the difference between the end SOC and the initial SOC must be within the allowable error range for the air storage tank. Equation (16) shows the upper and lower limits of the air storage tank. Equations (17) and (18) indicate that the start-stop status of air compressors is determined by the pressure in the gas tank.…”

Section: Aging Machine Operation Constraintsmentioning

confidence: 99%

“…Some research efforts have been devoted to the new pricing framework and market mechanism enables industrial consumers to transfer their power consumption [14,15]. In respect of scheduling algorithms, the authors of [16,17] proposed a DR modelling scheme for an industrial sector using a resource task network scheduling process and stochastic dynamic programming. A DR energy management scheme for industrial facilities based on the state task network was given in [18].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Industrial multi‐energy and production management scheme in cyber‐physical environments: a case study in a battery manufacturing plant

Pei

Deng

et al. 2018

IET cyber-phys. syst.

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Section: Objective Functionmentioning

confidence: 99%

Section: Aging Machine Operation Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Industrial multi‐energy and production management scheme in cyber‐physical environments: a case study in a battery manufacturing plant

Pei

Deng

et al. 2018

IET cyber-phys. syst.

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“…Reference [22] proposes a management scheme for industrial parks based on the state task network (STN), and a method to model IPP as a mathematical model including discrete and continuous variables. References [23], [24] further propose optimal energy management considering on-site generation. However, current research mainly optimizes production by responding to electricity pricing and ignores the coupling of DIMS and IPP.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Distributed Integrated Multi-energy System Considering Industrial Process Based on Energy Hub

Zhang

Wang

et al. 2020

Journal of Modern Power Systems and Clean Energy

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As a typical scenario of distributed integrated multi-energy system (DIMS), industrial park contains complex production constraints and strong associations between industrial productions and energy demands. The industrial production process (IPP) consists of controllable subtasks and strict timing constraints. Taking IPP as a control variable of optimal scheduling, it is an available approach that models the IPP as material flow into an extension energy hub (EH) to achieve the optimization of industrial park. In this paper, considering the coupling between the production process and energy demands, a model of IPP is proposed by dividing the process into different adjustable steps, including continuous subtask, discrete subtask, and storage subtask. Then, a transport model of material flow is used to describe the IPP in an industrial park DIMS. Based on the concept of EH, a universal extension EH model is proposed considering the coupling among electricity, heat, cooling, and material. Furthermore, an optimal scheduling method for industrial park DIMS is proposed to improve the energy efficiency and operation economy. Finally, a case study of a typical battery factory is shown to illustrate the proposed method. The simulation results demonstrate that such a method reduces the operation cost and accurately reflects the operation state of the industrial factory. Index Terms-Distributed integrated multi-energy system (DIMS), industrial production process (IPP), energy hub (EH), optimal operation, battery production.

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“…Since data are not utilized due to industrial information security policies, experiments to create a virtual industrial process model based on statistical theory has conducted for replacing empirical data. For example, few studies have investigated the improvement in the DR operation by simply designing the process characteristics of cement, refinery, paper, and steel industries [10][11][12][13][14]. Therefore, a method must be developed to estimate and evaluate DR potential flexibility based on the data-analysis results of industrial-load resources.…”

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confidence: 99%

Evaluation of Demand Response Potential Flexibility in the Industry Based on a Data-Driven Approach

2020

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The rapid increase in renewable energy resources has resulted in the increasing need for a demand flexibility program (DFP) from industrial load resources as a solution to oversupply and peak load spikes. However, to reasonably estimate the DR potential flexibility, the load characteristics must be analyzed and potential assessment formulas must be validated. Thus, in this study, a novel method is proposed to evaluate the DR potential flexibility of industrial loads according to a process of related load-characteristic data analysis. The proposed potential-estimation model considers frequency, consistency, and DR event operation scores during designated ramp-up and ramp-down time intervals separately. A case study was conducted by considering typical cement industry process with actual power-consumption data analysis for demonstrating the test system. The results confirm that load reduction of more than half of the usual power consumption is possible if a potential score is about 0.27 in cement industry cases. Thus, the proposed method can be used as an indicator to determine how an industrial load is adequate for obtaining a DFP while suggesting meaningful implications through industrial load-resource data analysis.

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Industrial demand side response modelling in smart grid using stochastic optimisation considering refinery process

Cited by 25 publications

References 29 publications

Industrial multi‐energy and production management scheme in cyber‐physical environments: a case study in a battery manufacturing plant

Industrial multi‐energy and production management scheme in cyber‐physical environments: a case study in a battery manufacturing plant

Optimization of Distributed Integrated Multi-energy System Considering Industrial Process Based on Energy Hub

Evaluation of Demand Response Potential Flexibility in the Industry Based on a Data-Driven Approach

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