Increasing penetration of intermittent renewable energy sources and the development of advanced information give rise to questions on how responsive loads can be managed to optimise the use of resources and assets. In this context, demand response as a way for modifying the consumption pattern of customers can be effectively applied to balance the demand and supply in electricity networks. This study presents a novel stochastic model from a microgrid (MG) operator perspective for energy and reserve scheduling considering risk management strategy. It is assumed that the MG operator can procure energy from various sources, including local generating units and demand-side resources to serve the customers. The operator sells electricity to customers under real-time pricing scheme and the customers response to electricity prices by adjusting their loads to reduce consumption costs. The objective is to determine the optimal scheduling with considering risk aversion and system frequency security to maximise the expected profit of operator. To deal with various uncertainties, a riskconstrained two-stage stochastic programming model is proposed where the risk aversion of MG operator is modelled using conditional value at risk method. Extensive numerical results are shown to demonstrate the effectiveness of the proposed framework.

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Abstract:In recent deregulated power systems, demand response (DR) has become one of the most cost-effective and efficient solutions for smoothing the load profile when the system is under stress. By participating in DR programs, customers are able to change their energy consumption habits in response to energy price changes and get incentives in return. In this paper, we study the effect of various time-based rate (TBR) programs on the stochastic day-ahead energy and reserve scheduling in residential islanded microgrids (MGs). An effective approach is presented to schedule both energy and reserve in presence of renewable energy resources (RESs) and electric vehicles (EVs). An economic model of responsive load is also proposed on the basis of elasticity factor to model the behavior of customers participating in various DR programs. A two-stage stochastic programming model is developed accordingly to minimize the expected cost of MG under different TBR programs. To verify the effectiveness and applicability of the proposed approach, a number of simulations are performed under different scenarios using real data; and the impact of TBR-DR actions on energy and reserve scheduling are studied and compared subsequently.

Abstract:Increasing the penetration levels of renewable energy sources (RESs) in microgrids (MGs) may lead to frequency instability issues due to intermittent nature of RESs and low inertia of MG generating units. On the other hand, presence of electric vehicles (EVs), as new high-electricityconsuming appliances, can be a good opportunity to contribute in mitigating the frequency deviations and help the system stability. This paper proposes an optimal charging/discharging scheduling of EVs with the goal of improving frequency stability of MG during autonomous operating condition. To this end, an efficient approach is applied to reschedule the generating units considering the EVs owners' behaviors. An EV power controller (EVPC) is also designed to determine charge and discharge process of EVs based on the forecasted day-ahead load and renewable generation profiles. The performance of the proposed strategy is tested in different operating scenarios and compared to those from non-optimized methodologies. Numerical simulations indicate that the MG performance improves considerably in terms of economy and stability using the proposed strategy.

Summary In this paper, a stochastic model for scheduling of AC security‐constrained unit commitment associated with demand response (DR) actions is developed in an islanded residential microgrid. The proposed model maximizes the expected profit of microgrid operator and minimizes the total customers' payments for electricity consumption under load and renewable energy sources uncertainties. In this regard, a stochastic dispatch model for responsive load is developed in order to investigate the effect of price‐based DR programs in a microgrid environment. In this scheme, customers are able to participate in real‐time price program with shifting and curtailing their responsive loads. A multi‐segment AC‐optimal power flow, which allows the voltage and reactive power to be considered directly, is also used to study the effects of DR participation on microgrid operation. The proposed AC security‐constrained unit commitment and DR scheduling problem is modeled as a mixed‐integer programming problem and is solved using the CPLEX solver in the general algebraic modeling system.

In the present paper, a plate and frame heat exchanger is considered. Multi-objective optimization using genetic algorithm is developed in order to obtain a set of geometric design parameters, which lead to minimum pressure drop and the maximum overall heat transfer coefficient. Vividly, considered objective functions are conflicting and no single solution can satisfy both objectives simultaneously. Multi-objective optimization procedure yields a set of optimal solutions, called Pareto front, each of which is a trade-off between objectives and can be selected by the user, regarding the application and the project’s limits. The presented work takes care of numerous geometric parameters in the presence of logical constraints. A sensitivity analysis is also carried out to study the effects of different geometric parameters on the considered objective functions. Modeling the system and implementing the multi-objective optimization via genetic algorithm has been performed by MATLA

SUMMARY Higher penetration of distributed generation in distribution systems makes the islanding operation more realistic. In this paper, an adequacy assessment is presented in the presence of wind energy sources in distribution system for an islanded area. Wind speed simulation is performed using auto‐regressive moving average time series. For adequacy assessment, well‐being indices are introduced and calculated for an islanded area through an islanding algorithm. Then, sensitivity analysis is conducted to investigate the dependency of well‐being indices on key parameters of analysis. Finally, the results are presented and discussed. Copyright © 2012 John Wiley & Sons, Ltd.

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