Hybrid genetic multi objective/fuzzy algorithm for optimal sizing and allocation of renewable DG systems (original) (raw)
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A combination of MADM and genetic algorithm for optimal DG allocation in power systems
2007
Distributed Generation (DG) can help in reducing the cost of electricity to the customer, relieve network congestion, provide environmentally friendly energy close to load centers as well as promote system technical characteristics such as loss reduction, voltage profile enhancement, reserve mitigation and many other factors. Furthermore, its capacity is also scalable and it can provide voltage support at distribution level. The planning studies include penetration level and placement evaluation which are influenced directly by DG type. Most of the previous publications in this field chose one or two preferred parameter as basic objective and implement the optimizations in systems. But due to small size of DGs output, placement according to one or two of just technical parameters usually leads to more theoretical results and with incorporation of less DG resources. Furthermore, optimization of one parameter might degrade another system attribute. In this paper a multi-objective placement and penetration level of Distributed Generators (DGs) is examined, concerning both technical and economical parameters of power system using Genetic Algorithm (GA) combined with Multi-Attribute Decision Making (MADM) method. In fact, by using GA best plans for system with incorporation of DG are determined. For approaching such aim, 4 technical parameters of system, including total losses, buses voltage profile, lines capacity limits and total reactive power flow, are consider with appropriate priorities applied to each objective. In the next step, Analytic Hierarchy Process (AHP) along with Data Envelopment Analysis (DEA) is used as a multi attribute decision making technique to form a decision making framework for selecting the best capacity and place of DG units. The attributes are defined as technical and economical parameters. The technical parameters are the voltages on the buses, the reactive power and losses in the transmission lines and the economical parameters are the emi- - ssions, congestion and capital cost. The proposed approach is illustrated by case studies on IEEE 30 bus distribution system which demonstrate significant improvement in optimization through this procedure.
Renewable resource based DG-unit allocation in distribution systems
RENEWBUILD International Conference on Application of Efficient & Renewable Energy Technologies in Low Cost Buildings and Infrastructure, 2013
Increasing power demand and limited sources give rise to researches for sustainable energy supplies. Renewable energy sources like solar cell, wind turbine, fuel cell, and hydro turbine are the common technologies for sustainable, exhaustless and nonpolluting energy. In recent years, great interests have been directed to integration of embedded resources to the grid. Embedded resources have place in electric power networks as distributed generation units. DG-units have many advantages as providing loss reduction, improving voltage profile, and increasing reliability of system. Using renewable sources as a DG-unit has many parameters to assess. In this paper DG-units which have been considered as renewable resources are integrated at optimal place with optimal size into distribution system. This optimization process is performed with Artificial Bee Colony (ABC) algorithm. ABC algorithm is a meta-heuristic approach inspired by the intelligent foraging behavior of honey bee swarms. ABC algorithm has two parameters to estimate. Therefore updating them for effective values to get higher success is easier than other meta-heuristic methods. ABC algorithm is used in this study to determine DG-unit’s optimal size and place in order to minimize total system power loss. 33 and 69 bus radial test systems and 229 bus real distribution system are used in order to show the performance of ABC algorithm in solving nonlinear optimization problems. Results are compared with grid search method and success of ABC algorithm is approved.
Clean Technologies, 2021
Distributed generation (DG) is becoming a prominent key spot for research in recent years because it can be utilized in emergency/reserve plans for power systems and power quality improvement issues, besides its drastic impact on the environment as a greenhouse gas (GHG) reducer. For maximizing the benefits from such technology, it is crucial to identify the best size and location for DG that achieves the required goal of installing it. This paper presents an investigation of the optimized allocation of DG in different modes using a proposed hybrid technique, the tunicate swarm algorithm/sine-cosine algorithm (TSA/SCA). This investigation is performed on an IEEE-69 Radial Distribution System (RDS), where the impact of such allocation on the system is evaluated by NEPLAN software.
A Review on Optimal Allocation and Sizing Techniques for DG in Distribution Systems
International Journal of Renewable Energy Research, 2018
Distributed Generation (DG) offers the reliable and economic source of electricity to consumers. These are connected directly to the distribution system at consumer load points. Integration of DG units into an existing system has significantly high importance due to its innumerable advantages. However, Optimal DG (ODG) allocation and sizing is always a challenging task for utilities as well as consumers. The major objective of ODG allocation and sizing is to improve system overall efficiency with minimum power loss, maximum system security, voltage stability, and reliability. Analytical techniques are performing well for small and simple systems, not suitable for a system with large and complex networks. However, various meta-heuristic techniques are performing better in terms of accuracy and convergence for extensively large and complex networks. A hybrid optimization is a combination of two or more optimization techniques. This technique offers efficient and reliable global optimum solutions for complex multi-objective problems. In this context, a comprehensive literature review of DG fundamentals and the different technical approaches for DG integration into the distribution system are analyzed here. Furthermore, an attempt has been made for comparison of analytical, classical (non-heuristic), meta-heuristic and hybrid optimization techniques with respect to objective function, test system, advantages, and disadvantages. This present study will give in-depth knowledge and acts as a forthright reference for imminent investigators and investors for ODG allocation and sizing in a distribution system.