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Micro grid is an epitome of a macro grid but works in low voltage comprising of various small-distributed energy resources (DERs), energy storage devices, and controllable loads being interfaced through fast acting power electronic devices. Combined heat and power (CHP) produced by DERs are utilized in the local market where Micro Grid operates either in island mode or in grid-connected mode. The CHP mode of operation makes the Micro Grid most efficient and economic. Like deregulation regime in Micro Grid market, multi agent generator providers may be considered to make the Micro Grid market competitive. The reason for competitive electricity market is to serve the consumers at a reduced price. The main purpose of this paper is to analyze and propose the pricing mechanism for Micro Grid energy in the competitive electricity market. Central controller of the Micro Grid (µcc) is the main brain behind all energy management system (EMS) activities, which includes participation in the bidding to settle marketclearing price (MCP). Two important market settlement techniques -Day-ahead and Real-time -have been discussed briefly in this paper. Uniform and Pay-as-bid pricing rules have been discussed separately for electricity pricing fixation in the context of Micro Grid. In this paper marketing strategies of some of the renewable DERs -mainly Photovoltaic (PV) and wind generator -have been considered. Wind power is now a potential candidate in non-conventional power generation family. Power available from wind and PV system cost high and is of intermittent in nature. Participation of these two renewable DERs along with Micro turbine, Diesel generator, fuel cells etc. in the bidding for market clearing price (MCP) make the market complex. This paper gives a brief guideline for marketing of PV and wind power. Consumers in the Micro Grid system are categorized as shed-able and non-shed-able according to their priority. How these loads affect demand curve have also been discussed. This paper presents a case study on price determination based on demand and supply side bidding strategies. The impacts of congestion management, market power, carbon taxation, price volatility, etc. on pricing have also been discussed in the context of Micro Grid.

This article presents a hybrid PSO with Levy flight algorithm (LFPSO) for optimization of the PID controllers and employed in automatic generation control (AGC) of nonlinear power system. The superiority of the proposed LFPSO approach has been demonstrated with comparing to recently published Lozi map-based chaotic optimization algorithm (LCOA) and Particle swarm optimization to solve load-frequency control (LFC) problem. It is found that the proposed LFPSO method has robust dynamic behavior in terms of settling times, overshoots and undershoots by varying the system parameters and loading conditions from their nominal values as well as size and locations of disturbance. Secondly, a three-area thermal power system is considered with nonlinear as Generation Rate Constraints (GRC) and outperforms to the results of Bacteria Foraging algorithm based integral controller as well as hybrid Differential Evolution and Particle Swarm Optimization based fuzzy PID controller for the similar power system. Finally, the proficiency of the proposed controller is also verified by random load patterns.

The data collected by sensor nodes over a geographical region is contaminated with Gaussian and impulsive noise. The conventional gradient-based distributed adaptive estimation algorithms exhibit good performance in the presence of Gaussian noise but perform poorly in impulsive noise environments. Therefore, the objective of this article is to propose a robust distributed adaptive algorithm that alleviates the effect of impulsive noise. An error saturation nonlinearity-based robust distributed strategy is proposed in an incremental cooperative network to estimate the desired parameters in impulsive noise. The steady-state analysis of the proposed error saturation nonlinearity incremental least mean squares (SNILMS) algorithm is carried out by employing the spatial-temporal energy conservation principle. Both theoretical and simulation results show that the presence of the error nonlinearity has made the proposed SNILMS algorithm robust to impulsive noise. ACM Reference Format:Trilochan Panigrahi, Ganapati Panda, and Bernard Mulgrew. 2014. Error saturation nonlinearities for robust incremental LMS over wireless sensor networks.

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