Paschalis A. Gkaidatzis

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.

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.

Comparative analysis of heuristic techniques applied to ODGP

In this paper, a comparative analysis and evaluation of several heuristic techniques, when applied to the Optimal Distributed Generation Placement (ODGP) problem, is presented. Loss minimization is considered as the objective, while the networks technical characteristics as the constraints. Three versions of Particle Swarm Optimization (PSO), Global, Local and Unified (GPSO, LPSO and UPSO, respectively), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS) and Harmony Search (HS) are compared. The implemented analysis demonstrates that all Heuristic Techniques examined can solve the ODGP problem efficiently, although UPSO emerge as the most promising, in terms of solution and convergence performance, whereas regarding execution time, HS is the most prominent. The study is evaluated upon typical 33 and 30 bus systems.

Optimal application order of network reconfiguration and ODGP for loss reduction in distribution networks

In this paper loss reduction in Distribution Networks is performed under proper consideration regarding Distributed Generation (DG) siting and sizing and network reconfiguration. The optimal solution for each of the aforementioned techniques, when applied individually, is highly dependent on the network layout and on its load composition. Thus, the question that arises when both techniques are utilized, concerns their application order towards the most efficient solution. The analysis in this work examines how the sequence regarding the implementation of these two techniques affects the optimal solution in terms of DG capacity penetration and number of switching operations. Various scenarios with different available DG units are examined regarding their optimal siting and sizing before and after the implementation of the network reconfiguration technique. Finally, an alternative scenario with the simultaneous application of both techniques is also examined. In this latter case the complexity of the problem is highly increased and the solution efficiency is also affected. A special PSO variant is utilized as the solution algorithm, namely Unified-PSO, accordingly modified for each scenario and the IEEE 33 bus system is utilized as the reference distribution network. The analysis concludes about the optimal application order of these two techniques for practical implementation.

Optimal Distributed Generation Placement Problem for Power and Energy Loss Minimization

This chapter introduces the Optimal Distributed Generation Placement problem towards power and energy loss minimization. Several solving methods are applied in order for the most suitable to emerge. Apart from technical and DG constraints, recent raised issues due to high Distributed Generation penetration like the reverse power flow effect is considered as well. The load and generation variability and their impact in integrating Renewable Energy Sources are examined, aided by the use of Capacity Factors implementation. In addition, the impact of Optimal Distributed Generation Placement problem in conjunction with Network Reconfiguration and Optimal Energy Storage Systems Placement is introduced aiming to examine how joined management schemes could be efficiently combined in order to maximize the potential loss and energy reduction.

Techno-economic analysis for optimal energy storage systems placement considering stacked grid services

The increasing penetration of Renewable Energy Sources (RES) and generation uncertainties, brought to the fore new challenges and problems regarding efficient Distribution Networks (DNs) operation. Energy Storage Systems (ESS) can play a significant role in more reliable, secure and flexible DN operation since they can deal with difficult-to-predict changes. This study provides a detailed methodology among the corresponding mathematical formulation for the optimal sizing and allocation of ESS considering optimum operation schedule.