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This paper presents a hybrid algorithm that combines Particle Swarm Optimization (PSO) and Nonlinear Optimal Power Flow (OPF) in the optimal sitting and sizing of Distributed Generation (DG). The objective function considered is to minimize the power losses in distribution systems. The proposed approach makes use of a sensitivity index based on derivatives to identify the best candidate buses for sitting the DG. This index is considered in the PSO initial population aiming at reducing the search space, though, improving the convergence of the method. The OPF is then used to determine the optimal size of each DG. Results are compared with previous papers presenting different methodologies for the 69-bus radial distribution system to validate the proposed approach. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators' use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users' profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper. [ABSTRACT FROM PUBLISHER]

The concept of electrical-mobility, in opposition to the present oil-mobility, is attracting the attention of politicians and of civil society worldwide. Electrical mobility means the usage of battery powered Electric Vehicle (EV) and Plug-in Hybrid Electric Vehicle (PHEV) as the main future technology to combat greenhouse gas emissions. The burden of electric mobility will be mainly on the distribution system that, particularly during the peak hours, will be exposed to critical operation conditions by a high number of high density simultaneous loads. Vehicle-to-Grid technology by adding control capabilities to charge and discharge of cars' batteries can exalt the benefits from their whole energy storage capacity. Distributors can then be helped in the active management of the network by the services offered (e.g., VAR/volt regulation, frequency regulation, spinning reserve, integration of renewable generation). Vehicle-to-Grid is perfectly part of the emerging Smart Grid technology and is based on intelligent stations fully integrated within the distribution management system. For a full exploitation of Vehicle-to-Grid potentialities, the role of the aggregator is essential to create value to customers by offering services to the distribution system operator. In the paper, a Particle Swarm Optimization is used to define the aggregator's optimal control strategy to optimize the recharge/discharge patterns of a fleet of EVs taking into account financial contracts, driver's behavior, energy prices, etc. [ABSTRACT FROM PUBLISHER]

Demand response (DR) is becoming a key component of future smart grid that can reduce peak load and adapt elastic demand to fluctuating generations. While reducing energy bills for the participant, DR usually decreases its utility, which is different for distributed occupants inside a participating entity. A two-level distributed intelligent load management and control system is proposed in this paper to minimize the cost of the participant, where the cost is a measurement of disutility, considering differences across plug loads, with the load reduction constraint of a DR event. The control system contains a smart DR controller and distributed intelligent gateways. In the system, the cost function of a load is modeled to reflect the dissatisfaction of the occupant for switching off or dimming the load, and a two-level optimization method is deployed to minimize the participant's aggregated cost. Each intelligent gateway collects the cost functions of loads in the neighborhood of an occupant, generates its optimal cost function and sends to the smart DR controller. The smart DR controller utilizes those cost functions to allocate the load reduction among the gateways, which can then optimize the load reduction among loads for the distributed optimum. While the cost function of the loads can be modeled as either continuous or discrete functions based on the type of the load, Lagrange multipliers and particle swarm optimization (PSO) are utilized for optimization, respectively. This innovative method is implemented in a DR system of a building, and tests results show that the proposed distributed DR method is practical and promising. [ABSTRACT FROM PUBLISHER]

In power systems, power quality issues raised by various non-linear devices (NLD) might violate the allowable harmonic limits defined by IEEE-519 standard. In order to maintain the total harmonic distortion well below the allowable limits, passive harmonic filters are commonly used. This paper presents an algorithm to optimally plan passive harmonic filters in the distribution networks to minimize the total voltage harmonic distortion. The optimization considers the filter location, size, type, quality factor and tuned harmonic order with constraints of bus voltage magnitudes, total and individual harmonic limits and filter components design and operating limits. The proposed approach is developed by using Particle Swarm Optimization (PSO) algorithm and tested on IEEE 18-bus and IEEE 33-bus radial distribution systems. The evaluation demonstrates the effectiveness of the optimization solution in identifying the best filter location, capacity and type. [ABSTRACT FROM PUBLISHER]

This paper presents an application of heuristic optimization techniques such as Harmony Search Algorithm (HSA), Particle Swarm Optimization (PSO), and Genetic Algorithms (GA) for efficient operation in micro grid. For operational efficiency, the objective function in a diesel generator consists of the fuel cost function similar to the cost functions used for the conventional fossil-fuel generating plants. The wind turbine generator is modeled by the characteristics of variable output. The operating cost in fuel-cell system includes the fuel costs and the efficiency for fuel to generate electric power. The application of the heuristic optimization technique to optimal operation can save the operating costs of the distributed generation and interruption costs in micro grid. The method proposed is applied to the IEEE 13-buses system to compare its solution. [ABSTRACT FROM PUBLISHER]

In this paper, population-based artificial intelligence techniques are explored to solve distribution network reconfiguration problem. The genetic algorithm, particle swarm optimization and ant colony optimization based methods already established by the authors are further modified to improve their performance and reduce computation time. All these methods are tested on six standard distribution systems available in the literature. Finally, a comparative analysis of the proposed methods is presented and conclusions are drawn on the basis of the comparison. [ABSTRACT FROM PUBLISHER]

The objectives of the movement toward the smart grid include making the power systems more reliable and economically efficient. The rapid development of the large scale energy storage technology, such as sodium sulfur batteries, makes it an excellent candidate in achieving the goals of the smart grid. This paper proposed a modified multi-objective particle swarm optimization approach to solve the energy storage design problem in distribution systems. A Pareto front is provided by the proposed approach for decision makers to determine the desired tradeoff between multiple objectives. Within the energy storage design variables, not only the conventionally considered energy storage capacity and power rate are included, but also the energy storage operation strategy. Three energy storage operation strategies are presented and their impacts on reliability and economy are illustrated. A case study is performed to demonstrate the effectiveness of the proposed approach. Insights based on the case study results are discussed. [ABSTRACT FROM PUBLISHER]

The proliferation of distributed generators (DGs) and the concept of microgrids have altered a distribution network from a passive network to an active one. Hence, active distribution power flow methods need to account for a DG unit, which can operate either as a PV or PQ bus. However, some of the existing active distribution power flow methods have difficulty to converge if the resistance-to-reactance ratio (or R/X) is high. This paper presents a new robust three-phase distribution power flow, which incorporates the particle swarm optimization method into an existing distribution power flow to overcome these problems. An IEEE benchmark system is used to test the validity and robustness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

There are a lot of uncertainties in planning and operation of electric power system, which is a complex, nonlinear, and non-stationary system. Advanced computational methods are required for planning and optimization, fast control, processing of field data, and coordination across the power system for it to achieve the goal to operate as an intelligent smart power grid and maintain its operation under steady state condition without significant deviations. One of the important aspects to operate power system in such manner is accurate and consistent short term load forecasting (STLF). This paper presents a methodology for the STLF using the similar day concept combined with fuzzy logic approach and evolutionary particle swarm optimization (EPSO) technique. A Euclidean distance norm with weight factors considering the weather variables and day type is used for finding the similar days. Fuzzy logic is used to modify the load curves of the selected similar days of the forecast by generating the correction factors for them. The input parameters for the fuzzy system are the average load, average temperature and average humidity differences of the forecasted previous days and their similar days. These correction factors are applied to the similar days of the forecast day. The tuning of the fuzzy input parameters is done using the EPSO technique on the training data set of the considered data and tested. The results of load forecasting shows that the proposed EPSO tunes fuzzy system provides better results than the fuzzy stand alone system (without EPSO). [ABSTRACT FROM PUBLISHER]