Kallisthenis I. Sgouras

Cyber attack impact on critical Smart Grid infrastructures

Electrical Distribution Networks face new challenges by the Smart Grid deployment. The required metering infrastructures add new vulnerabilities that need to be taken into account in order to achieve Smart Grid functionalities without considerable reliability trade-off. In this paper, a qualitative assessment of the cyber attack impact on the Advanced Metering Infrastructure (AMI) is initially attempted. Attack simulations have been conducted on a realistic Grid topology. The simulated network consisted of Smart Meters, routers and utility servers. Finally, the impact of Denial-of-Service and Distributed Denial-of-Service (DoS/DDoS) attacks on distribution system reliability is discussed through a qualitative analysis of reliability indices.

Impact of penetration schemes to optimal DG placement for loss minimisation

This paper examines the impact of different penetration schemes to the optimal distributed generation placement problem for loss minimisation. The four variables of the problem are presented and a concept based on degrees of freedom (DoF), representing the number of the variables that undergo any kind of limitation during the solution process, is introduced. Four commonly utilised penetration schemes subject to various limitations are examined and compared with a fifth penetration scheme, which is unconstrained and is proposed as the optimal one. All schemes are implemented under a local-particle swarm optimisation-variant algorithm and applied on the IEEE 33 and IEEE 118 bus systems. The results indicate that the proposed penetration scheme with four DoF provides the optimal solution both in terms of loss minimisation and voltage profile improvement.

Event detection for load disaggregation in Smart Metering

The reduction of consumption is an objective of the Smart Grid paradigm. The pursuit of efficient solutions requires the knowledge that can be derived from each installations energy consumption measurements through Smart Metering. This work presents an event detection methodology, aimed to help in the disaggregation of the total measured energy consumption in an installation to a number of partial curves corresponding to individual appliances. The work has been conducted within the scope of the EU funded FP7 project “CASSANDRA - A multivariate platform for assessing the impact of strategic decisions in electrical power systems”.

Application and evaluation of UPSO to ODGP in radial Distribution Networks

In this paper, a new effort regarding the Optimal Distributed Generation Placement (ODGP) problem is presented. Loss minimization is considered as the objective while considering the networks technical characteristics as constraints, i.e. node voltage and line thermal limits. The proposed method, called Unified Particle Swarm Optimization technique (UPSO), combines the advantages while at the same time extinguishes the disadvantages of the two basic PSO variants, the Global and Local PSO. The implemented analysis demonstrates that an enhanced performance is achieved, both in terms of a better optimal solution as well as faster convergence. The method is evaluated upon IEEE-16 and IEEE-33 bus systems and compared with other techniques.

Energy loss reduction in Distribution Networks via ODGP

In this paper the Optimal Distributed Generation Problem (ODGP) towards energy minimization is solved for a large number of scenarios regarding power loss minimization. Load variations are taken into account by the formulation of different snapshots concerning the networks operational status. These snapshots refer to various load compositions and for each one the ODGP problem is applied. Load variations are formed stochastically under a uniform distribution while the initial loading conditions are considered as the mean load profile of the network. The solution algorithm relies on a Local PSO Variant and the results indicate that for not extreme load variations some specific nodes tend to participate in the majority of the different solutions. Thus, the analysis proposes a fixed solution that could yield the highest energy reduction despite the fact that it is not the optimal for each individual operating state with different load composition.

Short-term risk assessment of botnet attacks on advanced metering infrastructure

An ongoing evolution of the power grids into more intelligent and sophisticated ones has been taking place since the beginning of the 21st century. The underlying objective of the power systems is to deliver electrical energy with high-security standards, i.e. to supply power to the consumers uninterruptedly. However, the integration of information technology into the smart grid introduces new vulnerabilities related to cyber-security which the authors should address extensively. This study discusses the impact of coordinated cyber-attacks on the advanced metering infrastructure. In this work, emulations of distributed denial-of-service attacks in a closed testbed environment using a topology of smart meters that participate in an electricity market are being performed. This study proposes a method to evaluate the impact on the reliability of such attacks. The results demonstrate that the proposed method can serve as a tool for the evaluation of the short-term risk of botnet attacks during load shifting in smart distribution networks.

Siting and sizing of Photovoltaic Systems subject to several parameters

In this paper a methodology regarding the siting and sizing of Photovoltaic Systems (PVS) under the consideration of several parameters is presented. In the proposed algorithm equal distribution of the available PVS is initially considered and at the next level appropriate coefficients are formalized in order to quantify each respective parameter. The initial equal distributed capacity of PVS is being resized due to the impact of various critical parameters. Moreover, the participation of each parameter to the resizing procedure is being defined by respective weight factors. In the paper, the Interconnected Greek Transmission System (IGTS) is examined as the test case and useful conclusions are depicted regarding the algorithms results. The proposed algorithm could constitute a useful guideline for efficient allocation of PVS in regard to specific predefined requirements and in turn it could define the most appropriate solution among a number of alternatives.

Quantitative Risk Management by Demand Response in Distribution Networks

Demand response (DR) is a versatile tool capable of providing sophisticated solutions and competitive services. Currently, the utilities pursue such services, as the reliability improvement, by continuous infrastructure investment and maintenance. In many cases, DR can provide reliability benefits, as it allows distribution network operators to reshape the load profile when a contingency is imminent. The quantification of the DR benefits is necessary to understand its economic and financial impact on the power sector. Methodologies used in risk management can be adapted for this purpose. In this paper, we propose a method to build a detailed reliability model, to assess the expected reliability indices, and to manage the financial risk of the reliability performance by DR in distribution networks subject to performance-based regulation. The outcome of the proposed method is the quantification of the relation between the risk and return of DR portfolios, in terms of conditional value-at-risk and ex-pected return, respectively. The results demonstrate that the method can be used as a decision support system for optimal DR allocation to trade off efficiently between the reliability performance risk and the expected return.