Faruk Ugranli

Long-term Performance Comparison of Multiple Distributed Generation Allocations Using a Clustering-based Method

Abstract Distributed generation is becoming a part of the strategic plans of electricity providers for effective system management. The proper planning of multiple distributed generation units plays an important role in modern power systems to offer a highly reliable system. Computation of power flows is one of the major tasks in system planning studies. Conventional load flow analysis methods are impractical to evaluate every possible or probable combination of loads and different allocations of distributed generation units because of the extremely large computational effort required. This article proposes an expansion method to perform load flow analysis with consideration of multiple distributed generation integration and load uncertainties. The proposed approach offers a method to handle the impacts of all possible allocations of distributed generation units without increasing computational efforts. The impacts of placement and penetration level of multiple distributed generation on power losses, voltage deviation, and line capacity are investigated under load uncertainty over a long-term period on the IEEE 57-, IEEE 30-, IEEE 14-, and 9-bus networks for future planning study purposes. The study results indicate that the proposed method has significantly reduced the computational efforts while maintaining a high degree of accuracy in evaluating various possible scenarios in which multiple distributed generation units have to be integrated into the grid.

Transmission Expansion Planning for Wind Turbine Integrated Power Systems Considering Contingency

Integration of wind turbines introduces new challenges in terms of planning criteria. In this study, a new transmission expansion planning methodology considering N-1 contingency conditions is proposed to minimize investment cost and curtailed wind energy over planning period. To deal with the uncertainty of load and output power of wind turbines, probabilistic method based on clustering is used for determination of load and wind model. To incorporate the wind power curtailment into the proposed methodology, optimal power flow which uses DC-power flow equations is utilized by including cost functions of generators and overall optimization is carried out by using an integer genetic algorithm. Finally, the proposed methodology is applied to the modified IEEE RTS 24-bus test system by considering different case studies in order to show the effects of including cost functions.

MILP Approach for Bilevel Transmission and Reactive Power Planning Considering Wind Curtailment

In this study, two important planning problems in power systems that are transmission expansion and reactive power are formulated as a mixed-integer linear programming taking into account the bilevel structure due to the consideration of market clearing under several load-wind scenarios. The objective of the proposed method is to minimize the installation cost of transmission lines, reactive power sources, and the annual operation costs of conventional generators corresponding to the curtailed wind energy while maintaining the reliable system operation. Lower level problems of the bilevel structure are designated for the market clearing which is formulated by using the linearized optimal power flow equations. In order to obtain mixed-integer linear programming formulation, the so-called lower level problems are represented by using primal-dual formulation. By using the proposed method, power system planners will be able to find economical investment plans by considering the balance between wind power curtailment and the installation of transmission lines and reactive power sources.

Neural network based distributed generation allocation for minimizing voltage fluctuation due to uncertainty of the output power

The problem of distributed generation (DG) allocation and sizing is of great importance, since improper integration of DG units cause to take a bad turn in terms of power quality and system efficiency at high penetration levels. In this reason, allocation of DG is not a trivial optimization problem. The reactive and active power fluctuation of DG will lead to voltage fluctuation, especially for wind or photovoltaic power generators. Their output powers are more unpredictable due to the intermittent wind speed and irradiation. In this paper, the effects of reactive and active powers of DG on voltage profile are analyzed by including their output power fluctuation and an artificial neural network (ANN) based decision support system are developed to be used in management and planning of DG integration. The proposed system can be used to determine suitable bus to reduce the voltage fluctuation of critical buses. The simulation results presented shows the effectiveness of the method.

Transmission expansion planning for wind turbine integrated power systems considering contingencies

Increasing penetration level of wind turbines in power systems reveal new challenges for the power system planners. Transmission expansion planning is one of the most important planning problems to maintain secure and reliable operation of power systems. In this study, a new transmission expansion planning methodology considering N-1 contingency conditions is proposed to find the location of new transmission lines while minimizing investment cost and curtailed wind energy. To deal with the uncertainty of load and output power of wind turbines, fuzzy clustering based probabilistic method is used for determination of load and wind scenarios. Proposed methodology uses the DC-power flow equations based optimal power flow and integer genetic algorithm to determine the locations of new assets and it is applied to the modified IEEE 24-bus test system.

Transmission expansion planning considering maximizing penetration level of renewable sources

Recently, integration of intermittent sources into the power systems has gained most interest in distributed generation environment. Among the others, wind based generation is the most promising renewable based technology. The volatility of these sources requires the careful planning of power systems. This paper proposed a novel genetic algorithm based method to determine the optimal trade-off between wind energy spilled and transmission line investment by deciding the location of new transmission lines. By this way, power system planners can avoid over investment of transmission lines while providing maximum usage of wind turbines. The proposed method is illustrated using the IEEE 24 bus reliability test system.

Power system planning for maximizing intermittent energy sources using AC model

As installed capacity of wind power has been increasing in the last decade, power system planners are exposed to the several challenges, with regard to the drawbacks for wind power usage because of the existing infrastructure. The transmission expansion planning (TEP) and reactive power planning (RPP) should be specified by considering the maximization of wind power usage as well as meeting load growth. In this study, a new methodology for TEP and RPP is proposed to minimize wind energy spilled due to the power system constraints such as voltage and transmission line limitations. For this reason, the power flow equations of AC model should be used. Because of the intractable nature of large scale optimization problems, the backward search approach is utilized. AC optimal power flow based economic dispatch is solved to determine optimal dispatches. The proposed methodology is illustrated on the IEEE-24 bus reliability test system to demonstrate the results.

Genetic algorithm for weight assignment in optimum planning of multiple distributed generations to minimize energy losses

Renewable distributed generation (DG) is attracting special attention in order to meet the growing demand. Wind energy will drive rapid growth of distributed renewable energy systems in rural and remote areas worldwide. The one of the major potential benefits offered by DG concept is the reduction of total system losses. Because of the time-varying characteristics of both generation and load, energy loss minimization should be considered instead of power loss minimization. In this paper, a new and simple methodology is proposed to find optimal sizes and locations of DGs in order to minimize energy losses. This method is based on genetic algorithm and weighting factor. The effectiveness of proposed method is tested on the IEEE-30 bus mesh network. The results show that the proposed method is capable of finding the optimal sizing of DG to minimize energy losses for candidate DG buses.