Energy loss reduction by optimal distributed generation allocation in distribution systems

Higher load growth in the distribution system and its consequences on higher voltage deviation, feeders overloading, and distribution system loss increase have a bad effect on system quality. Global warming and the dwindling supply of fossil fuels also make it imperative to search for more effective alternatives that guarantee appropriate operating level of power losses and voltage profile at different points of the distribution networks. Capacitor banks (CBs), renewable distributed generators (DGs), and automatic voltage regulators (AVRs) can be considered the most suitable enhancement devices for distribution systems that reduce both pollution and power losses, improving the voltage profile, ..., etc, if they are optimally sized and sited. The objective of this paper is to investigate the optimal placement of each of the 3 mentioned solutions (CBs, DGs, and AVRs) on a part of real Egyptian distribution system. This system is an old, long, weak, and heavily loaded distribution system with high power losses (about 29% of system loading) and poor voltage regulation (about 30%). Each introduced solution has been evaluated according to power loss minimization and investment cost minimization. A new modification to the backward-forward power flow method for radial distribution system embedded AVRs has been proposed, which facilitates the placement procedure of AVRs and reduces computation time as compared to other methods. In this paper, an optimal placement of the combined CBs, DGs, and AVRs has also been introduced based on integrated particle swarm optimization technique and rough set theory. The proposed approach aims to minimize the problem complexity and reduce the storage capacity required for the optimization problem.

Section: Introductionmentioning

confidence: 99%

Distribution system performance enhancement (Egyptian distribution system real case study)

Elsayed

Mishref²,

Farrag

2018

“…New local government policies towards reduction in greenhouse gas emission and mitigation of global warming is the main reason for such penetration. Loss reduction, on‐peak operating cost reduction, improvement in network reinforcement horizon, service quality, reliability, power quality, and voltage support are the benefits seen by the penetration of DG units into the distribution system.…”

Section: Introductionmentioning

confidence: 99%

Proportional nucleolus game theory-based locational marginal price computation for loss and emission reduction in a radial distribution system

Veeramsetty

Venkaiah

Kumar

2018

Summary In this paper, a proportional nucleolus game theory (PNGT)–based iterative method has been presented to compute locational marginal price (LMP) at buses where distributed generation (DG) units were installed in a distribution network. Proportional nucleolus theory is one of the solution concepts for a cooperative game theory problem. The PNGT‐based iterative method provides financial incentives in terms of LMP to DG owners as per their contribution in loss reduction and emission reduction at a particular loading on the distribution system. In this method, LMP values depend on the distribution company's decision maker's preference among loss reduction, emission reduction, and distribution company's additional benefit. This proposed PNGT‐based iterative method has been implemented on a Taiwan power company's distribution network consisting of 84 buses with 15 DG units using MATLAB. The computed LMP values have operated the network based on decision‐maker priority so as to enable fair competition among DG owners.

“…For this reason and considering the fact that GA is a search technique, GA search should be guided towards a specific region of variable space, in order improve the algorithm efficiency. Over the past few years, a large number of algorithms has been reported on the related research topic such as a mixed‐integer nonlinear optimization, analytical method, and metaheuristic and probabilistic method for optimal DG allocation . In particular, there has been a significant increase in artificial intelligence‐based optimization methods applied for DG allocation problem such as GA, particle swarm optimization, and whale optimisation algorithm .…”

Section: Introductionmentioning

confidence: 99%

Distributed generation allocation considering uncertainties

Šarić

Hivziefendić

Konjić

et al. 2018

Summary This paper presents a method for distributed generation (DG) allocation planning and investigates the extent to which system load and generation output uncertainties influence the final optimisation results. The problem is presented as a multiobjective constrained optimization problem in which the objective functions are power loss reduction and voltage profile improvements, while the constraints are the voltage, current, and short‐circuit power limits. The optimization is performed by using a multiobjective genetic algorithm. The best trade‐off among candidate solutions from Pareto front is achieved by using Bellman‐Zadeh method. The system load and DG power output uncertainties are addressed by using the possibilistic, α‐cut method. The proposed method is applied to a real 35 kV distribution system. It was demonstrated that appropriately planned DG allocation has potential to positively influence network losses and voltage profile. Further, it was determined that load and generation uncertainty, inherently present in the DG allocation problem, have significant impact on optimization results. This paper makes a contribution to the existing knowledge by applying, to a realistic test power system, a DG allocation method and by determining the extent to which load and DG output uncertainties influence the final optimization results.