The Criticality Measure of Energy Systems - Science Direct

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ScienceDirect Energy Procedia 61 (2014) 1025 – 1028

The 6th International Conference on Applied Energy – ICAE2014

The criticality measure of energy systems Juozas Augutisa, Benas Jokšasb*, Riþardas Krikštolaitisa, Rolandas Urbonasb b

a Vytautas Magnus University, Vileikos str. 8, LT-44404 Kaunas, Lithuania. Lithuanian Energy Institute, Breslaujos str. 3, LT-44403 Kaunas, Lithuania.

Abstract National security, economic growth and population welfare depends on the safe and reliable functioning of various infrastructures very much. The disruptions in one of the infrastructures often are transferred to the other dependent infrastructures. Thus, it could have considerably negative effects for the functionality of the country’s basic administration. The critical infrastructure is defined by an asset, system, part thereof or technical networks composed of different sectors. The paper presents the assessment method of critical infrastructure modelling and elements’ criticality of the energy system. The criticality of infrastructure element is described by other systems elements functionality reduction when considered element is affected by disruption or out of order. Assessment of system ability to perform its functions is implemented by authors developed model, when energy supply is interrupted or system element is out of order. In the analysed model energy sector is composed of heat and power systems. The optimization methods were used to simulate real system (to implement allocation of demands of heat and electricity for generation technologies). © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2014 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and/or peer-review under responsibility of ICAE Peer-review under responsibility of the Organizing Committee of ICAE2014

Keywords: Network system, critical infrastructure, heat system, electricity system, criticality, modelling, optimization.

1. Introduction The country’s energy infrastructures are highly interconnected and mutually interdependent via transit gas pipelines or electricity transmission networks. Many systems are also interconnected inside the country and are dependent on each other. Critical infrastructure (CI) is defined as an asset, system or part thereof located in European Union Member States, which is essential for the maintenance of vital societal functions, health, safety, security, economic etc., and the disruption, or destruction of which would have a significant impact in a Member State as a result of the failure to maintain those functions [1].

* Corresponding author. Tel.: +370-37-401941; fax: +370-37-351271. E-mail address: [email protected]

1876-6102 © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of the Organizing Committee of ICAE2014 doi:10.1016/j.egypro.2014.11.1016

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Juozas Augutis et al. / Energy Procedia 61 (2014) 1025 – 1028

In the paper energy CI of a single country are analysed. The failure of CI may adversely affect the functioning of the country. Disruptions in one type of infrastructure often transverse to other dependent infrastructures and possibly even back to the infrastructure where the failure appeared. Many efforts are currently put in order to develop models capable to analyse interdependent infrastructure systems. Some authors analysed interdependences of infrastructures using a hybrid model. Huang et al. [2] used a combination of both the Decision-Making and the Analytic Network Process. The presented hybrid model was employed for infrastructures in Taiwan. These research results allowed to identify the infrastructure that contributes most to cascading failure. Holden et al. [3] used network model designed for interdependencies between infrastructure systems at different scales. Some authors investigate separate systems of infrastructure such as gas and electricity systems or gas and oil systems [4, 5], or heat supply system [6]. Chaudry et al. analysed infrastructure expansion planning for ecological aspect. The network planning approach allows to make assessment of the interactions between gas and electricity networks. In the paper the continuation of the work on assessment method for critical infrastructure published in [7] is presented. The model has been supplemented by optimization method for modelling as well as updates in generation technologies of analysed energy infrastructure. Optimization method was used for the strategy implementation of energy system. 2. Criticality assessment model of energy infrastructure The developed model analyses energy infrastructure composed of gas supply network (main fuel for generation technologies), district heat generation technologies (combined heat and power plants with back-pressure units, boiler houses, biofuel boiler houses), power generation technologies (CHP with extraction units, hydro power plant, and wind power plants) and final consumers for heat and electricity. The scheme of model presented in the Fig.1.

Fig. 1. (a) Energy infrastructure model; (b) Lithuania energy model scheme

The simulation of energy generation technologies were implemented by functional dependency, in the model. The generation technology is depended on availability rate, provided fuel type, installed capacity, efficiencies (which convert the primary energy), etc. All generation technologies are simulated by input-output method. The demand of heat and electricity are allocated for generation technologies by the Simplex optimization method. The optimization is performed to maximize energy generation in the each analysed city. This mathematical model allows to distribute local heat demand for local generation technologies by economic aspect. Preference is given to the technologies using renewable energy sources (hydro, wind, etc.). Gas supply network is represented as graph and is composed of set of the final pipeline nodes of graph, set of the pipeline connection nodes of graph and set of the edges represent physical pipelines, which connect nodes.

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One of the model aims is to maximize the satisfaction of consumer demands. In order to achieve this goal the maximum flow optimization method – Simplex method of linear programming was used. This mathematical model allows to evaluate the quantities of supplied gas to the final nodes (consumers). Gas supply system is composed as directed graph (balance should be in connections). A more detailed descriptions of the system functionality and criticality assessment model are presented in the paper [7]. The main aim of this presented assessment model is to identify the critical elements of energy system. It could be done using evaluated the reliability indicator for final consumer cf (when the kth element was assumed to be out of order) that shows how much the demand of energy is satisfied (0 cf 1):

¦ N

cf (k ) (t ) = 1 − i

j =1, j ≠ k N

¦

NSE ji (t )

j =1, j ≠ k

Dij (t )

, i = 1, 2, …, M,

(1)

where M – number of the final consumers in the energy system; NSEji(t) – not supplied energy from jth element for demands of ith consumer at time moment t; Dij(t) – the quantity of demand of resource from ith element to jth element at time moment t. The criticality of the kth element may be estimated using the reliability indicators of final consumers obtained in case when kth element is out of order C ( k ) (t ) = 1 − ¦ cf i(k ) (t ) ⋅ β i , k = 1, 2, …, N, M

i =1

(2)

(k ) where N – number of the elements in the energy system; cfi (t ) – the reliability indicator that shows th how much the energy demand is satisfied for the i element; ȕi – the weighted coefficient of the ith final consumer within system (for instance, weighted coefficients are estimated with regards to the energy demand of consumer, and they satisfy equality ȕ1 + … + ȕM = 1. For instance, C(k)(t) = 1 means that disruption of the ith element work stops the operation of all energy infrastructure at time moment t. 2.1. Critical elements of infrastructure evaluation (numerical experiment – Lithuania case) The developed model for the assessment of infrastructure criticality was applied for the energy infrastructure of Lithuania. The Lithuanian energy infrastructure scheme is presented in Fig.1 (b). The six cities of Lithuanian were analysed by above presented model. The energy system is composed of 7 CHP, 30 boiler houses and 19 biofuel boiler houses (located in different cities), 1 power plant (Lithuanian power plant), 2 hydro power plants and wind park. Gas supply system is composed of 129 pipeline segments. The initial data of model are demands (2008-2011) of heat and electricity. The simulation was performed in a way that in each scenario one element (different) of the system is out of order. The criticality assessment results (the criticality value for final consumers of each system elements) of the energy system is presented in the Fig. 2. In the paper only the marginal case, when the gas supply and other technologies’ availability is 100 %, is presented. This case was selected in order to investigate the most critical elements of the system. Results of nearly two-thirds of the scenarios (115 from 129) showed that the criticality of the elements is zero. It means that when element of energy system is out of order, it practically does not affect system operation. This situation was most often, because the generation technologies were diversified for consumer in Lithuanian energy system. The import gas pipeline is most critical element for Lithuanian energy system. Its criticality value for electricity system is 0.69, while for the district heat system – 0.808.

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This is therefore that all main Lithuanian energy generation technologies used natural gas as main fuel. The other most critical element is Lithuanian (thermal) power plant. Its criticality value for electricity system is 0.63. This is therefore that Lithuania power plant capacity is very large, and other generation technologies, for capacity diversification, are not installed in the system. The criticality values of pipelines (located near main Lithuanian cities) are from 0.192 to 0.57.

Fig. 2. Simulations results

3. Conclusion Lithuanian energy sector is analysed as a system of 129 elements. It was assessed that 115 elements did not directly affect the satisfaction of consumer demand. The most criticality element of Lithuanian energy infrastructure is the main pipeline for gas import to Lithuania. The criticality value of this element for the district heat systems is 0.808 (the criticality value is from interval [0; 1]) and for the electricity system is 0.69. With regard to the obtained results it could concluded that gas supply system is important (critical) for district heat and electricity system of Lithuania. The parts of gas pipeline, which are near the main Lithuanian cities, have high level of criticality (criticality values are from 0.192 to 0.57). Since the analysed matter is of great importance, the uncertainty analysis is foreseen to be performed in the near future in order to have clear understanding of the undergoing processes. References 1. 2. 3. 4. 5. 6. 7.

The Council of the European Union, Council Directive 2008/114/EC of 8 December 2008. Official Journal of the European Union, 2008. Huang, C.-N., J.J.H. Liou, and Y.-C. Chuang, A method for exploring the interdependencies and importance of critical infrastructures. Knowledge-Based Systems, 2014; 55:66-74. Holden R, Val D. V., Burkhard R, Nodwell S. A network flow model for interdependent infrastructures at the local scale. Safety Science, 2013; 53:51-60. Chaudry M, Jenkins N, Qadrdan M, Wu J. Combined gas and electricity network expansion planning. Applied Energy, 2014; 113:1171-1187. Fowler A. M., Macreadie P. I., Jones D. A multi-criteria decision approach to decommissioning of offshore oil and gas infrastructure. Ocean & Coastal Management, 2014; 87:20-29. Augutis J., Matuzienơ V. Probabilistic assessment of energy security. Analysis of Kaunas heat supply market. Energetika. ISSN 0235-7208. 2012; T. 58, Nr. 2:66-76. Augutis J., Jokšas B., Krikštolaitis R., Žutautaitơ I., Criticality assessment of energy infrastructure. Technological and economic development of economy, 2014. (In press)