Systems Engineering Procedia
Available online at www.sciencedirect.com
Systems Engineering Procedia 00 (2011) 000–000
Systems Engineering Procedia 3 (2012) 92 – 99
www.elsevier.com/locate/procedia
The 2nd International Conference on Complexity Science & Information Engineering
Modelling and Simulation of Power Grid Engineering Project based on System Dynamics on the Background of Smart Grid LI Chen *, ZHOU Lisha, LI Na, ZENG Ming North China Electric Power University, NO.2 Beinong Road, Changping District, Beijing and 102206, China
Abstract Power grid engineering project has the features of dynamics and complexity on the background of smart grid. Firstly, the modelling method based on System Dynamics is proposed. Secondly, on the basis of defined key factors and their mutual relations, a dynamic model with the fundamental structure of casual loop and stock-flow diagrams is built according to complex and dynamic nature of the project. Then this model is used to simulate the management process of the power grid engineering project. The simulation results shows that System Dynamics model can be used efficiently in modelling and optimization of power grid engineering project management for its clear causality, suitable complexity and exact describe characteristics.
© by Elsevier ElsevierLtd. Ltd.Selection Selectionand andpeer-review peer-reviewunder under responsibility of Desheng Dash © 2011 2011 Published Published by responsibility of Desheng Dash WuWu. Open access under CC BY-NC-ND license.
Keywords: smart grid; project management; system dynamics; modeling and simulation
* Corresponding author. Tel.: +86-10-51963855, +8613581822110; fax: +86-10-51963851. E-mail address:
[email protected].
2211-3819 © 2011 Published by Elsevier Ltd. Selection and peer-review under responsibility of Desheng Dash Wu. Open access under CC BY-NC-ND license. doi:10.1016/j.sepro.2011.11.013
LI Chen et al. / Systems Engineering Procedia 3 (2012) 92 – 99
1. Introduction Power grid is the critical infrastructure for energy supply, strengthen the power grid engineering project management is of great importance to improve the quality of power grid construction and guarantee reliable supply of energy and electricity. In recent years, many countries generally increase their grid investment, and propose smart grid strategy in line with their actual situation of power grid. In the background of smart grid, the expansion of power grid investment scale and the introduction of intelligent technology not only improve the operational state of power grid, but also challenge the traditional way of power grid engineering project management. The smart grid engineering projects are usually with large scale and high technique difficulty, and need to consume lots of people, financial, and material resources. Resource factors are playing an increasingly prominent role to constrain the quality, cost and schedule of power grid engineering project. This requires the project managers not only clarify the relationship between three key factors of quality, cost and schedule, but also further clarify the dynamic relationship between resource factor and other key factors. In this circumstance, the traditional pre-planned methods for project management can not effectively deal with the problem of complexity and dynamic nature, so it is important to introduce a more scientific method for power grid engineering project management. The traditional method for power grid engineering project management is built on the basis of reductive thinking [1]. Affected by reductive thinking, projects are seen as simple sum of many tasks which are cognizable, controllable and manageable in the view of traditional power grid engineering project management approaches, such as WBS, CPM, PERT and so on. The traditional power grid engineering project management approaches have two deficiencies: first, with these approaches, the project is considered to be simple sum of each task, and the dynamic nature of the project are not taken into account; second, these approaches cannot deal with the problem of great complexity, the key factors and their complex mutual relations can not analyzed with these approaches [2]. As a cross discipline based on control theory, decision theory, simulation technology and computer applied technology, System Dynamics (SD) has advantages on dealing with dynamic and complex problems in complex system [3]. It is widely used in many economic and social systems research [4-6]. In recent years, the advantages of System dynamics are gradually exhibited [7-10], and it is applied to more microscopic field, such as evaluation and decision-making of a project [11-13]. When taking a research on the dynamic nature and optimization methods of power grid engineering project management, it is necessary to consider both the role of macro-law and the behavior of microscopic individual, then the System Dynamics will demonstrate its unique advantages. Aiming to solve the uncertainties in the process of power grid engineering project management on the background of smart grid, this paper establishes a system model of power grid engineering project management with qualitative and quantitative analyzing methods of System Dynamics, and conducts a simulation and calculation using the computer. The information obtained was used to analyze and study the structure and behavior of power grid engineering project, and provide decision support for establishing a rational project management mode. 2. Modeling basics 2.1. Overview of System Dynamics System Dynamics (SD) has been widely used in industrial business management, macro-economic planning, social economic development, environment protection and other fields. It particularly emphasizes the recognition of integrity and the nonlinear nature of complex system. It is considered that the system function is determined by its structure, and the system behavior pattern depends on the dynamic structure and the internal feedback mechanisms of the system. It is also considered that the
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system is developing and evolving according to certain rules under the internal and external forces and constraints. The method of System Dynamics, which is used to deal with complex systems problems, is a combined method of qualitative and quantitative analysis, and based on system thinking and general reasoning. With computer simulation, the System Dynamics model can solve various problems in complex system. 2.2. Fundamental structure of System Dynamics Simulation through System Dynamics models is to simulate structure and function of the objective system. The system dynamics model mainly includes the causal loop diagrams (CLD) and the stock and flow diagrams (SFD). The causal loop diagram gives a qualitative description of system problems, and is the basis of modelling and simulating with System Dynamics, as shown in Figure 1 (a). According to the causal loop diagrams, the stock and flow diagrams are drawn using the specific symbol of System Dynamics, and aim to depict the mathematical or logical relationships between the factors in the objective system, as shown in Figure 1 (b).
Fig. 1. (a) example of the causal loop diagram; (b) example of the stock and flow diagrams
2.3 Steps of System Dynamics modelling System Dynamics modelling process contains 4 steps, shown as Figure 2. These steps reflect the cycle optimization process of supposing, questioning, testing and refining. Repeated cycle process is not only the process of approaching reality, but also the process of constant learning, experiencing, and getting a better understanding.
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Define the problem, determine (or adjust) the system bound
Confirm the problem Define the key factors Determine the research period
Determine (or optimize) the model structure of the system
Define the interaction rules of factors Divide the model boundaries and subsystem Draw the casual loop diagrams Draw the stock and flow diagrams
Model (or adjust) the dynamic relationship between factors
Determine the initial conditions Model quantification Model parameter estimation
Model test
Boundary, structural test Consistency test Robustness test Policies improving test
System Dynamics model
Fig. 2. modelling steps of System Dynamics
3. Modelling 3.1. Applicability analysis of System Dynamics On the background of smart grid, the performance of power grid engineering project is affected by various internal and external factors. So it can be seen as a system which is typically with dynamics and complexity. In the view of System Dynamics, the dynamic and complex relations in power grid engineering project system can be modeled. And using the simulation software Vensim, it is convenient to quantitatively analyze and dynamically evaluate the performance of the project management. Therefore, it is proper to introduce systematic analysis ideology, and apply System Dynamics techniques to solve its problems. 3.2. Factors and sets of variables The implementation process of power grid engineering project is the allocating and consuming process of resources. The implementation of project guides the resources consumption; on the contrary, resources consumption constrains the implementation of project. So resources consumption and project needs must be matched, and the matching degree has a great influence on the performance of project management. Therefore, on the background of smart grid, the performance of power grid engineering project management depends on resources consumption, project quality, project progress and project implementation cost. Additionally, these four aspects are affected by extrinsic factors, such as the planned project number of grid, the resource consumption structure adjusted schemes and so on. And this exactly exhibits the complexity and multi-stage feature of power grid engineering project. The causal loop diagram is shown as Figure 3. As shown in Figure 3, there are 24 key variables in power grid engineering project management system: resource consumption of project, gross estimated residual projects, and so on. In addition, there are some constants, such as resource consumption periodic coefficient, productivity smoothing time coefficient, planned adjusting time coefficient, and so on.
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Fig. 3. the causal loop diagram of power grid engineering project
3.3. Stock and flow structure model According to Figure 3, the stock and flow structure model of power grid engineering project based on System Dynamics is built, shown in Figure 4. This model is based on two assumptions: first, labor productivity of project personnel remains constant throughout the duration of the project; second, four stock variables of this model are resource consumption of project, awaiting rework projects undiscovered, project implementation costs and project progress. This model includes 34 variables. This paper just gives detailed description to the above four stock variables and the five change rate which is resource consumption rate, occurrence rate of awaiting rework projects, detection rate of awaiting rework projects, cost growth rate and growth rate of project period. In the SD project management model of power grid, the equations of the stock variables and the change rate are as follow: resource consumption of project. K = resource consumption of project. J+DT × resource consumption rate resource consumption rate = (planned resource used amount - resource consumption of project) / resource consumption period awaiting rework projects undiscovered. K = awaiting rework projects undiscovered. J+DT × ( occurrence rate of awaiting rework projects - detection rate of awaiting rework projects) occurrence rate of awaiting rework projects = gross completion rate × (1- standard-reaching rate of project) detection rate of awaiting rework projects = awaiting rework projects undiscovered / detection time of awaiting rework projects project implementation costs. K = project implementation costs. J+DT × cost growth rate cost growth rate = gross completion rate × standard-reaching rate of project project progress. K = project progress. J+DT× growth rate of project period growth rate of project period = (estimated completion date of the project-project progress) / planned adjusting time
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Fig. 4. the stock and flow diagram of power grid engineering project
4. Simulation and results analysis In the simulation experiment with System Dynamics model, the initial parameters (current 1) are set as follows: the gross productivity is 100%, the standard-reaching rate of project is 70%, the resource consumption periods are 3 months and the planned adjusting time are 6 months. In order to investigate the impact of the resource consumption periods on the performance of power grid engineering project, modify the parameter value of the resource consumption periods from 3 months to 6 months, while other parameter values remain constant. And this is current 2 state of the SD model. Compare the simulation results of the current 2 state and the current 1 state, the results are shown in Figure 5. Figure 5 shows that when the resource consumption periods increase from 3 months to 6 months, the resource consumption of project decreased significantly. The project implementation costs also decrease significantly and the project progress would be delayed. This shows that the resource consumption periods has a great impact on the performance of power grid engineering project management. The government or grid enterprises should improve resource utilization, in order to meet the demand of optimal resources allocation of project.
resource consumption of project current 2： resource consumption of project current 1： project implementation costs current 2： project implementation costs current 1： awaiting rework projects undiscovered current 2: awaiting rework projects undiscovered current 1: project progress current 2： project progress current 1：
1 2
1 2
3 4 5 6 7 8
3 4 5 6 7 8
unit/month unit/month Kw Kw month month Kw Kw
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Fig. 5. the stock and flow diagram of power gird engineering project
In order to investigate the impact of the standard-reaching rate of project on the performance of power grid engineering project management, modify the parameter of the resource consumption periods from 70% to 90%, while other parameters remain constant. This is the current 3 state of the SD model. Compare the simulation results of the current 3 state and the current 1 state, the comparing results are show in Figure 6. Figure 6 shows that when the standard-reaching rate of project increase from 70% to 90%, the awaiting rework projects undiscovered decrease greatly. This means that the project quality has been greatly improved. Meanwhile, the project could be completed ahead of schedule when consuming the same amount resources. Therefore, standard-reaching rate of project also has a great impact on the performance of power grid engineering project management. The government or grid enterprises should pay great attention to the progress management and improve the construction level of project. Then, the standard-reaching rate of project could be improved constantly and the project should be completed successfully.
resource consumption of project current 3： resource consumption of project current 1： project implementation costs current 3： project implementation costs current 1： awaiting rework projects undiscovered current 3: awaiting rework projects undiscovered current 1: project progress current 3： project progress current 1：
1 2
1 2
3 4 5 6 7 8
3 4 5 6 7 8
unit/month unit/month Kw Kw month month Kw Kw
Fig. 6. the comparing results of current 1 state and current 3 state
5. Conclusion To solve the management problem of power grid engineering project on the background of smart grid, this paper proposes a modelling method based on System Dynamics, and build a dynamic model with the fundamental structure of casual loop and stock-flow diagrams according to complex and dynamic nature of the project. Then this model is used to simulate the power grid engineering project, the simulation result shows that the factors of project standard-reaching rate and resource consumption periods play a decisive role in power grid engineering project management, and, relatively, the standard-reaching rate plays a more prominent role. This paper shows that System dynamics model has characteristics of clear causal relationship and appropriate sophistication degree, and is powerful to describe the reality. It can be powerful tool for modelling and simulation of the power grid engineering project management. References [1] Camci A., Kotnour T.. Technology complexity in projects: Does Classical project management work?, in proceedings of PICMET 2006, Turkey, 2006, Jul., 9-13.
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[2] Yujing Wang. A method of project management based on System Dynamics. Statistics and Decision 2010, 12: 34-36 (in Chinese). [3] John D. Sternman. Business dynamics: systems thinking and modeling for a complex world. McGraw Hill Publications, 2000. [4] Erma Suryani, Shuo-Yan Chou, Chih-Hsien Chen. Air passenger demand forecasting and passenger terminal capacity expansion: A system dynamics frame work, Export Systems with Applications, 2010, 37[3]: 2324-2339. [5] Peng H.L., Huang H.H., Hsiao C.T., Han K.C., Lin C.T.. System dynamics approach to the financial crisis in elementary education system-a case in Taiwan, in proceedings of ICMIT 2010, Singapore, 2010, Jun., 2-5. [6] Junghua Wu, Yilung Huang, Changchen Liu. Effect of floating pricing policy: An application of system dynamics on oil market after liberalization. Energy Policy, 2011, 39[7]: 4235-4252. [7] E. Alishahi, M. Parsa Moghaddam, M.K. Sheikh-El-Eslami. A system dynamics approach for investigating impacts of incentive mechanisms on wind power investment. Renewable Energy, 2011, 37[1]: 310-317. [8] Klaus Vogstad. Combining system dynamics and experimental economics to analyze the design of Tradable Green Certificates, in proceedings of the 38th Hawaii International Conference on System Sciences, Big Island, USA, 2005, Jan., 3-6. [9] Hollmann M., Voss J.. Modeling of decentralized energy supply structures with “system dynamics”, in proceedings of the International Conference on Future Power Systems, Amsterdam, 2005, Nov., 16-18. [10] Hongming Yang, Gaojie Wang, Lixing Zhou, Renjun Zhou. A study of power market dynamics based on system dynamics modeling power system technology, in proceedings of POWERCON 2006, Chongqing, 2006, Oct., 22-26. [11] Jeng J.J., Lianjun An. System Dynamics Modeling for SOA Project Management, in proceedings of the International Conference on Service-Oriented Computing and Applications, Newport Beach, California, 2007, Jun., 19-20. [12] KeungSik Choi, Doo-Hwan Bae. Dynamic project performance estimation by combining static estimation models with system dynamics. Information and Software Technology, 2009, 51[1]: 162-172. [13] A.S. White. A control system project development model derived from System Dynamics, International Journal of Project Management, 2011, 29[6]: 696-705.
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