Efficient Decentralized Economic Dispatch for Microgrids with Wind Power Integration

Decentralized energy management is of paramount importance in smart microgrids with renewables for various reasons including environmental friendliness, reduced communication overhead, and resilience to failures. In this context, the present work deals with distributed economic dispatch and demand response initiatives for grid-connected microgrids with high-penetration of wind power. To cope with the challenge of the wind's intrinsically stochastic availability, a novel energy planning approach involving the actual wind energy as well as the energy traded with the main grid, is introduced. A stochastic optimization problem is formulated to minimize the microgrid net cost, which includes conventional generation cost as well as the expected transaction cost incurred by wind uncertainty. To bypass the prohibitively high-dimensional integration involved, an efficient sample average approximation method is utilized to obtain a solver with guaranteed convergence. Leveraging the special infrastructure of the microgrid, a decentralized algorithm is further developed via the alternating direction method of multipliers. Case studies are tested to corroborate the merits of the novel approaches.Comment: To appear in IEEE GreenTech 2014. Submitted Sept. 2013; accepted Dec. 201

Power optimization for a hydrocarbon industrial plant using a genetic algorithm

In this paper, a genetic algorithm (GA) is considered for optimizing electrical power loss for a real hydrocarbon industrial plant as a single objective problem. The subject plant electrical system consists of 275 buses, two gas turbine generators, two steam turbine generators, large synchronous motors, and other rotational and static loads. The minimization of power losses (J1) objective is used to guide the optimization process, and, consequently, the injected power into the grid (PRInject) is increased. The results obtained demonstrate the potential and effectiveness of the proposed approach to optimize the power consumption. Also, in this paper a cost appraisal for the potential daily, monthly and annual cost saving will be addressed

Hybrid method for achieving Pareto front on economic emission dispatch

In this paper hybrid method, Modified Nondominated Sorted Genetic Algorithm (MNSGA-II) and Modified Population Variant Differential Evolution(MPVDE) have been placed in effect in achieving the best optimal solution of Multiobjective economic emission load dispatch optimization problem. In this technique latter, one is used to enforce the assigned percent of the population and the remaining with the former one. To overcome the premature convergence in an optimization problem diversity preserving operator is employed, from the tradeoff curve the best optimal solution is predicted using fuzzy set theory. This methodology validated on IEEE 30 bus test system with six generators, IEEE 118 bus test system with fourteen generators and with a forty generators test system. The solutions are dissimilitude with the existing metaheuristic methods like Strength Pareto Evolutionary Algorithm-II, Multiobjective differential evolution, Multi-objective Particle Swarm optimization, Fuzzy clustering particle swarm optimization, Nondominated sorting genetic algorithm-II

Demand and Storage Management in a Prosumer Nanogrid Based on Energy Forecasting

Energy efficiency and consumers' role in the energy system are among the strategic research topics in power systems these days. Smart grids (SG) and, specifically, microgrids, are key tools for these purposes. This paper presents a three-stage strategy for energy management in a prosumer nanogrid. Firstly, energy monitoring is performed and time-space compression is applied as a tool for forecasting energy resources and power quality (PQ) indices; secondly, demand is managed, taking advantage of smart appliances (SA) to reduce the electricity bill; finally, energy storage systems (ESS) are also managed to better match the forecasted generation of each prosumer. Results show how these strategies can be coordinated to contribute to energy management in the prosumer nanogrid. A simulation test is included, which proves how effectively the prosumers' power converters track the power setpoints obtained from the proposed strategy.Spanish Agencia Estatal de Investigacion ; Fondo Europeo de Desarrollo Regional