Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem

Crisscross optimization algorithm and its application

With the increasing emphasis on energy conservation and environmental protection, fuel cost and emission should be both considered into economic dispatch. The multi-area economic/emission dispatch (MOMAED) is a complex non-convex multi-objective problem, which is not yet addressed well. Aiming at this problem, this paper proposes an efficient and powerful crisscross optimization algorithm (CSO) combining Pareto multi-objective processing strategy(MOCSO). The proposed MOCSO approach addresses the different types of complex MOMAED problem by applying two interacting search operators, namely horizontal crossover and vertical crossover. The former enhances MOCSO's global search ability in complex solution space by introducing a distinctive cross-border crossover mechanism while the latter prevents the premature convergence by a unique dimensional crossover approach. Both operators generate moderation solutions and always maintain a population of currently personal best solutions through a greedy update strategy so as to speed up the process of finding the optimal solution. The proposed approach is validated on two test systems. The effectiveness and efficiency of the MOCSO algorithm is first tested on the MOMAED problems. The results show that the proposed MOCSO method yields higher quality solutions than other methods in the literature.

Hybrid chameleon swarm algorithm with multi-strategy: A case study of degree reduction for disk Wang–Ball curves

Wind velocity distribution differences in wind wheel rotation plane caused by wind shear and tower shadow, not only causes load fluctuations of wind turbine blades, but also leads to pulsations of wind turbine aerodynamic torque and output power. In order to reduce the influence of wind shear and tower shadow on the fluctuations of three-bladed wind turbine, an individual pitch control strategy based on the pitch angle signal adjusted jointly by wind turbine output power and rotor azimuth angle is proposed. A band-pass filter is designed to filter out the three times pulsating component of the wind turbine output power, and a PID controller is used to obtain regulatory signal of pitch angle, which is then converted to tiny variable of each blade combining with the azimuth angle signal and superimposed to the reference pitch angle of the collective pitch control. Simulation results on GH Bladed platform show that the proposed individual pitch control strategy is effective to smooth blade load fluctuation, diminish aerodynamic torque ripple, reduce rotor fatigue damage, and prolong life time of wind turbine.

The instantaneous wind speed variations are ignored in the wind turbine power curves. Hence, the output power of the wind turbines becomes a function of only the average wind speed. In the variable-speed wind turbines, the output power is a function of wind speed as well as the wind dynamics because in order to extract the maximum amount of electrical energy, the control system in these turbines will adjust the rotor speed and blades' pitch angle according to the instantaneous wind speed. Since the wind dynamics is not taken into account in the power curve, the power generated by the controllers is overlooked and therefore the turbine output power is not represented accurately by the power curve. In this paper, a new method is proposed using a new index called wind turbine controllability factor in order to make it possible to calculate precisely the output power of wind turbines based on the wind dynamics. In this method, the energy pattern factor is used as an indicator of the amount of instantaneous wind speed variations and power curve is modified corresponding to the values of the energy pattern factor.

Now a days the usage of electrical power is increasing enormously and it is also becoming highly impossible in imagining the world without the electricity. So here is one of the techniques for maintaining the continuous supply i.e., by generating the electrical power in the distribution premises. Distribution Generation has been growing rapidly in power systems due to their capable solution for the issues like power system deregulation, to meet the demand of electric power and shortages in the electric power transmission capacities. Concept of Distributed generation (DG) plays an effective key role in the distribution networks. Among many of their merits, real power loss reduction, THD reduction voltage sag and voltage swell reduction and voltage profile improvement can be the salient merits of DG. Improper location of DG sources in to the electrical power system would lead to the increase energy or power loss, and also jeopardize the system operation. In this paper, determination of DG location is done by using Sensitivity Analysis and corresponding DG size is found by using Particle Swarm Optimization (PSO) to mitigate the real power losses in the distribution networks. Standard IEEE 15 bus test system is chosen to test the proposed PSO — Sensitivity Analysis approach. The results so obtained show the enhancement of voltage profile, reduction of real power loss using MATLAB.

The use of Distributed Generation (DG) throughout the world increasing. DG siting and sizing is an important engineering consideration, which is inherently influenced by the system load and DG power output uncertainties. This paper presents research results of the uncertainty influence on DG allocation problem. This influence is investigated using a constrained optimization problem for power loss reduction. The optimization is performed using Genetic Algorithm. The power system load and DG power output uncertainties are addressed using a possibilistic (α – cut method). The algorithm is applied to realistic distribution system to demonstrate its practical relevance. Results indicate that DG can reduce losses. and that uncertainties play a major role in final optimisation results. This paper contributes to the existing knowledge by applying, to a realistic test power system, a DG allocation method, which considers the influence of load and generation uncertainties on optimization results.

Most of the countries around the world are facing huge environmental impact, and the most promising solution to mitigate these is the use of renewable energy, especially wind power. Though, the use of offshore wind energy is rapidly increasing to meet the elevating electricity demand. The researchers and policymakers have become aware of the importance of providing near accurate prediction of output power. Wind energy is tied to variabilities of weather patterns, especially wind speed, which are irregular in climates with erratic weather conditions. In this paper, we predicted the output power of the wind turbines using the random forest regressor algorithm. The SCADA data is collected for two years from a wind farm located in France. The model is trained using the data from 2017. The wind direction, wind speed and outdoor temperature are used as input parameters to predict output power. We test our model for two different capacity factors. The estimated mean absolute errors for the proposed model in this study were 3.6% and 7.3% for and 0.2 capacity factors, respectively. The proposed model in this study offers an efficient method to predict the output power of wind turbine with preferably low error.

This paper contributes the optimal allocation of DSTATCOM (distribution static compensator) and DG (distributed generation) in radial distribution systems using exhaustive search method (ESM) to reduce the power loss and improvement of the voltage profile. The certain range of active and reactive powers have been injected simultaneously at each node by incorporating the corresponding size of DSTATCOMs and DGs respectively into the radial distribution system (RDS). On the basis of best reduction in power loss, the Size of DSTATCOM and DG are determined. Forward-Backward sweep load flow algorithm was used for the load flow solutions. The results obtained by proposed approach shows the optimal allocation and sizing of DSTATCOM and DG in RDS efficaciously reduces the power loss and improves the voltage profile. The IEEE-30bus RDS was used as a test system.

Distributed generation (DG) planning studies are generally conducted while assuming constant generation and load models. However, in this paper, optimal allocation of photovoltaic (PV) based DG is carried out while considering probabilistic generation and time varying voltage dependent load models. First, a novel probabilistic generation model is proposed for solar irradiance uncertainty modeling to compute the hourly output power produced via PV units. Subsequently, the obtained power values are used to determine the optimum PV allocation in the distribution system through particle swarm optimization to minimize the multi-objective function. The proposed algorithm has been validated on 33-bus and 69-bus distribution test systems. The results demonstrate that the proposed model is suitable for solar irradiance modeling and can be employed in power system planning studies.

Recent deployment of distributed generation has led to a revolution in distribution systems and the emergence of “smart grid” concepts. The smart grid mainly intends to facilitate integration of renewable energy sources and to achieve higher system reliability and efficiency. Different distributed generation types found in modern distribution systems can be classified into two main categories: intermittent- and dispatchable-based distributed generation; the latter has a unique capability of enabling successful islanding operation, thus improving system reliability. This article proposes a methodology for allocating dispatchable distributed generation units in distribution systems to economically improve system reliability. Distributed generation installation and operation costs are to be optimized against the reliability value, expressed as customers’ “willingness to pay” (WTP) to avoid power interruptions. Therefore, the main goal of this research work is to determine the optimal combination between distributed generation units to be installed and loads to be shed during all possible contingencies. A probabilistic load model is adopted to determine the optimal size of allocated distributed generation units.

Integration of various types of distributed generation (DG) into the existing power grid requires prediction of allowable DG penetration levels for a stable and reliable operation of the grid. The two major classification of the DGs namely synchronous based DGs (SDG) and inverter based DGs (IDG) exhibit different impacts on the grid which may limit their penetration level. Hence, this paper proposes a two-phase approach for maximizing the penetration of DGs complying with the collective grid constraints such as voltage harmonics and relay coordination limits exhibited by IDGs and SDGs respectively. The proposed method is tested on a standard IEEE 14-bus system with different DG penetration levels. Various case studies are conducted on the test system to demonstrate the efficacy of the proposed approach. The results obtained show the choice of the suitable optimization algorithm and the impacts of the constraints considered for optimal sizing and placement of the DGs.

<p>The rapid growth of distributed generation (DG) units has necessitated their optimization to address the increasing complexity of power grids and reduce power losses. The need for optimization of distributed generation (DG) units has been growing rapidly over the past few years. To minimize such losses, the optimal allocation of DG units needs to be correctly identified and applied. On the other hand, Garra Rufa optimization (GRO) is a mathematical optimization technique that is used to determine the high effective and efficient way to solve very complex problems to achieve optimal results. In this work, Garra Rufa optimization is used to identify the optimal placement and size of DG units in order to meet specific power loss requirements. A comparison between genetic algorithm (GA), particle swarm optimization (PSO), and GRO is done using MATLAB to validate the proposed method. The comparison shows that GRO is better than the other methods in DG allocation, especially in more than two DGs. The optimization techniques are evaluated using the IEEE standard power system case, specifically the 30-bus configuration.</p>

Directional wind energy assessment of China based on nonparametric copula models

Proliferation of electric demand and congestion in the existing network intensify challenges and problems for researchers and power system planners. Currently, these problems are mostly solved by Distributed Generation (DG). DG is placed near the customer end to meet the load demand economically, hence reducing the existing network burden. This paper attempts to present an overview of technological development carried out in the DG field. Numerous technical constraints must be considered while integrating DG in the system. The improvement in voltage stability, voltage profile, and reduction in power losses are the main benefits posed by DG integration. This paper also highlights the key issues associated with the DG allocation problem, types of DG, and methodologies for optimal DG units allocation.