Planning of Distributed Generation Dispatch in Distribution Networks (original) (raw)
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Distributed Generation Planning in Distribution Systems using Genetic Algorithm
Journal of emerging technologies and innovative research, 2017
A combination of public policy, incentives and economics is driving a rapid growth of distributed generation in the electric power system. The majority of states/provinces now have renewable portfolio standards, with many requiring that over 20 percent of electricity sales by generated by renewable energy sources within the next five to fifteen years. The majority of these requirements will be addressed by adding significant amounts of wind energy and growing amounts of solar energy to the bulk power system. Wind and solar power plants exhibit greater variability and uncertainty because of the nature of their "fuel" sources. Optimization is one of the tools that can be used to address concerns and costs around this variability and uncertainty. This paper discusses operational and optimal system impacts, provides background on what can be realistically expected from distributed generation power-output. Distribution generation also includes more than wind resources: both established types, like run-of-river hydro and emerging varieties, such as wave energy. While the majority of attention in this report is on wind and solar generation, most varieties of distribution generation share similar characteristics (though to a different extent) since the variability is largely driven by weather or other nonanthropogenic phenomena. Similar optimization and integration approaches are also likely to apply to these distribution generation resources as well. In fact, because load is also influenced by the weather, demand and generation optimization may eventually come.
OPTIMAL ALLOCATION OF DISPATCHABLE AND NON-DISPATCHABLE DG UNITS IN DISTRIBUTION NETWORKS
This paper presents a methodology for optimal allocation of dispatchable and non-dispatchable distributed generation (DG) units into the distribution networks with the aim of minimizing annual capital cost, operation and maintenance costs, energy loss cost and emission cost. Wind and gas turbine based DG units have been considered as the candidate DG types to be allocated. The uncertainties associated with load and wind speed are handled by employing suitable probabilistic techniques. The developed formulation is a MINLP formulation and has been solved by a GA based approach. The developed approach has been applied to 69-bus test distribution system. The results obtained shows significant improvement in the system performance in terms of reduction in active energy loss, imported active energy from the grid and emission of pollutants along with improvement in the system voltage profile.
Economic Dispatch of Distributed Generation Using Backtracking Search Optimization Algorithm
2016
Nowadays, distributed generators (DG) are most widely used in distribution system to satisfy the increasing demand. According to the demand, the dispatch of generator should be modified for economic operation. The economic dispatch (ED) of DGs are normally solved by anyone of the following methods: conventional methods such as Lambda iteration method, Dynamic Programming etc., or optimization technique such as Genetic algorithm (GA), Evolutionary Programming (EP), Differential Evolution (DE) Algorithm etc., These methods of solving ED problem require comparatively large computation time. Therefore, it is important to estimate real power dispatch values within a short period. This paper presents the ED of various DGs for different demands using Backtracking Search Optimization Algorithm (BSA). In this work two diesel engines, single units of wind turbine generator and fuel cell are used as DG. The ED problem is solved for IEEE 33 bus distribution system by BSA and DE. The test result shows that the BSA method is better for ED of DG than DE.
Genetic Algorithm based Cost Optimization Model for Power Economic Dispatch Problem
The Economic Dispatch Problem (EDP) is the optimal allocation of the load demand among the running generators while satisfying the power balance equations and the unit's operating limits. In an electrical power system, a continuous balance must be maintained between electrical generation and varying load demand, while the system frequency, voltage levels, and security also must be kept constant. Furthermore, it is desirable that the cost of such generation be minimal. Numerous classical techniques such as LaGrange based methods, linear programming, non-linear programming and quadratic programming methods has been reported in the literature. The solution of the economic dispatch problem using the classical approach presents some limitations in its implementation. One of such limitations is that there exists the possibility for this approach to be caught at the local minima when the cost functions are non-convex or piecewise discontinuous in the functional space. Furthermore, treatments of operational constraints are very difficult using the classical approach. This thesis explored the application of genetic algorithm to solve the problem of Economic power dispatch in order to circumvent the above stated limitations. Genetic Algorithms (GAs) are numerical optimization algorithms based on the principle inspired from the genetic and evolution mechanisms observed in natural systems and population of living being. Both the GA and the classical approach using the LaGrange based methods are implemented using C++ in order to be able to verify the performance of the GA approach in practical applications. Case studies of a three generators and Nigerian Grid systems are considered for two different power demands. Economic Dispatch without transmission losses were considered in both case studies. Results showed a significant improvement with the method of genetic algorithm over the classical method. This is expected to help power service companies to maximize profit while maintaining reliability and security of supply.
Interaction between Technical and Economic Benefits in Distributed Generation
Electrical and Automation Engineering, 2022
The definition of a restricted power supply area for a distribution network disqualifies this scheme as a distributed product even though it is a very common dg scheme. Power system quality is a key issue for low and medium voltage power companies and consumers, to minimize power network losses; this paper provides guidelines for guaranteed allocation and distribution of distributed generation (DG) in distribution systems for an acceptable reliability level and voltage profile. The optimization process involves the use of genetic algorithms (GA) techniques and is solved by combining systems to estimate system reliability, losses and dg impacts on the voltage profile. The fitness evaluation process leads to the determination of the ga's relationship between investment and operating costs as a benefit of setting numerical units. Estimation based on current flow method for radial networks reconciliation of scattered generator losses with profile of voltage profile.
IEEE PES Innovative Smart Grid Technologies, Europe, 2014
This paper presents a study of the impact of Distributed Generation (DG) in the operational planning and design of Medium Voltage (MV) distribution networks. The proposed model considers the planning of MV networks for reducing power losses, voltage drop and investments in reinforcements using Genetic Algorithm (GA). Planning MV distribution networks involves cable replacement, sizing and positioning of capacitor and DG and phase load balancing .The objective function to be minimized includes operational costs of proposed changes, power losses and voltage constraints. A power flow for radial distribution networks based on a backward/forward sweep using current summation was developed to validate the solutions.
A Dynamic Approach for Distribution System Planning Considering Distributed Generation
IEEE Transactions on Power Delivery, 2012
Deregulation in the power system industry and invention of new technologies for producing electrical energy have led to innovations in distribution system planning (DSP). Distributed generation (DG) is one of the most attractive technologies that brings different kinds of advantages to a wide range of entities, engaged in power systems. In this paper, a new model for considering DGs in the DSP problem is presented. In this model, an optimal power flow (OPF) is proposed to minimize capital costs for network upgrading, operation and maintenance costs, and the cost of losses for handling the load growth for the planning horizon. The term “dynamic” is used to refer to the planning over a specific period so that dynamic distribution system planning is, in fact, proposed. Besides, a modified genetic algorithm is used to find the optimal topology solution. The effectiveness of this method is demonstrated through examination on a radial distribution network.
Maximal optimal benefits of distributed generation using genetic algorithms
Electric Power Systems Research, 2010
As a result of the renewed interest for the distributed power generation (DG); meanly because of the constraints on the traditional power generation besides the great development in the DG technologies, increasing amounts of DG are being used. To accommodate this new type of generation, the existing network should be utilized and developed in an optimal manner. This paper presents an optimal proposed approach to determine the optimal sitting and sizing of DG with multi-system constraints to achieve a single or multi-objectives using genetic algorism (GA). The Linear Programming (LP) is used not only to confirm the optimization results obtained by GA but also to investigate the influences of varying ratings and locations of DG on the objective functions. The methodology is implemented and tested on a real section of the West Delta subtransmission network, as a part of Egypt network. Results are presented, demonstrating that the proper sitting and sizing of DG are important to improve the voltage profile, increase the spinning reserve, reduce the power flow in critical lines and reduce the system power losses.
Distributed Generation Capacity Evaluation Using Combined Genetic Algorithm and OPF
International Journal of Emerging Electric Power Systems, 2007
A range of techniques has been proposed to define the optimal locations and capacities of distributed generation (DG) as a means of ensuring that the maximum amount of DG can be connected to existing and future networks. However, there are limitations inherent in these methods, not least in finding the best combination of sites for connecting a predefined number of DGs. Here, a method combining optimal power flow and genetic algorithms aims to meet this requirement. Its use would be in enabling Distribution Network Operators to search a network for the best sites and capacities available to strategically connect a defined number of DGs among a large number of potential combinations. Some applications of the proposed methodology confirmed its effectiveness in sitting and sizing an assigned number of DG units.