Optimal sizing and siting techniques for distributed generation in distribution systems: A review

Outline

• Abstract
• Keywords
• 1. Introduction
• 2. Distributed Generation
• 2.1. Importance of Optimum Sizing and Siting of Dg
• 2.2. Impacts of Dg
• 2.2.1. Environmental Impacts of Dg
• 2.2.2. Economical Impacts of Dg
• 2.2.3. Technical Impacts of Dg
• 2.3. Problem Objectives
• 2.3.1. Objective Function
• 2.3.2. Constraints
• 2.3.3. Required Indices
• 2.4. Generalized Algorithm for Dg Sizing and Siting
• 3. Comprehensive Reviews for Dg Sizing and Siting Based on Various Aspects
• 3.1. Dgs Optimal Sizing and Siting Technique and Their Merits and Demerits
• 4. Methods for Optimal Sizing and Siting of Dg in Distribution System
• 4.1. Analytical Techniques
• 4.1.1. Eigen-Value Based Analysis (eva)
• 4.1.2. Index Method (ima)
• 4.1.3. Sensitivity Based Method (sbm)
• 4.1.4. Point Estimation Method (pem)
• 4.2. Classical Optimization Technique
• 4.2.1. Linear Programming (lp)
• 4.2.2. Mixed Non-Linear Programming (minlp)
• 4.2.3. Dynamic Programming (dp)
• 4.2.4. Sequential Quadratic Programming (sqp)
• 4.2.5. Ordinal Optimization (oo)
• 4.2.6. Optimal Power Flow (opf)
• 4.2.7. Continuous Power Flow (cpf)
• 4.3. Artificial Intelligent (meta-Heuristic) Techniques
• 4.3.1. Fuzzy Logic (fl)
• 4.3.2. Genetic Algorithm (ga)
• 4.3.3. Particle Swarm Optimization (pso)
• 4.3.4. Non-Dominated Sorting Ga-Ii (nsga-Ii)
• 4.3.5. Plant Growth Simulation Algorithm (pgsa)
• 4.3.6. Ant Colony Search Algorithm (acs)
• 4.3.7. Artificial Bee Colony Algorithm (abc)
• 4.4. Miscellaneous Techniques
• 4.4.1. Bellman-Zadeh Algorithm (bza)
• 4.4.2. Encoded Markov Cut Set Algorithm (emcs)
• 4.4.3. Monte Carlo Simulation (mcs)
• 4.4.4. Clustering-Based Approach
• 4.4.5. Tabu-Search Algorithm (ts)
• 4.4.6. Bat Algorithm (ba)
• 4.4.7. Big Bang Big Crunch Optimization Algorithm (bb-Bc)
• 4.4.8. Brute Force Algorithm (bf)
• 4.4.9. Backtracking Search Optimization Algorithm (bsoa)
• 4.4.10. Modified Teaching Learning Based Optimization Algorithm (mtlbo)
• 4.5. Other Promising Techniques for Future Use
• 4.5.1. Shuffled Frog Leaping Algorithm (sfla)
• 4.5.2. Imperialist Competitive Algorithm (ica)
• 4.5.3. Simulated Annealing (sa) Algorithm
• 4.5.4. Bacterial Foraging Optimization Algorithm (bfoa)
• 4.5.5. Intelligent Water Drop Algorithm (iwda)
• 4.5.6. Cuckoo Search (cs) Method
• 4.5.7. Invasive Weed Optimization Algorithm (iwo)
• 4.6. Comparison and Drawbacks of Analytical and Intelligence Optimization Techniques
• 4.6.1. Integration of Meta-Heuristic Optimization Techniques
• 5. Conclusions
• 6. Recommendations
• References

Conclusions

The present study focuses on optimum sizing and siting techniques of DGs in distribution networks. Simultaneously the study also presents the impacts of insertion of DG on distribution system operation and performance, voltage profile, system losses, loadability, stability, reliability, power quality and voltage stability margin etc., consequently various parameters need to have an extra care for optimum placement and sizing of DG. This study also focuses on the advantages and disadvantages (economical, environmental and technical) of DG installation in distribution system.

Many researchers have already disclosed that the optimum sizing and siting of DG is beneficial in many folds such as technically, environmentally as well as economically. In addition to these advantages the installation of DG defers the expansion of existing power distribution systems, since DG serve as a standby option for onsite power supply for load growth. The extended application of DG may be as protection device (since DG units are automatically disconnect when voltage at the system connection point becomes very high) for existing distribution systems in future. The reverse power flow is observed due to installation of oversize DG. Additionally, the stand alone and islanding application of DG also may be the part of current / future research for that reasons the DGs technologies are universally accepted.

As a whole the study reveals that the techniques implicated for optimum sizing and siting of DG by the researchers, carryout the researches for finding the global optimum solution of a complex problem for their single objective or multi-objective problem specially those have many local optima. Once the uncertainties associated to the DG output, load, emissions and price of the electricity is incorporated the system become more complex. The uncertainties are successfully and competently handled by newly introduced techniques. Various researchers have acknowledged numerous sizing and siting techniques for DGs in distribution network. The analytical methods are not computationally efficient for large and complex systems. In this respect, intelligent (meta-heuristic) techniques are well suited for large and complex systems. They are speedy and possess excellent convergence characteristics. It has been reported that to find a global optimal solution of a complex, multi-objective problem a hybrid of two or more meta- heuristic optimization techniques confer more effective and reliable optimum solution.

For optimum sizing and siting of DGs, newer heuristic optimization techniques such as BFOA, SA, IWD, SFLA and IWO etc. may appear promising in future.