Abstract-The main goal of this study is to design a market operator (MO) and a distribution network operator (DNO) for a network of microgrids in consideration of multiple objectives. This is a high-level design and only those microgrids with nondispatchable renewable energy sources are considered. For a power grid in the network, the net value derived from providing power to the network must be maximized. For a microgrid, it is desirable to maximize the net gain derived from consuming the received power. Finally, for an independent system operator, stored energy levels at microgrids must be maintained as close as possible to storage capacity to secure network emergency operation. To achieve these objectives, a multiobjective approach is proposed: the price signal generated by the MO and power distributed by the DNO are assigned based on a Pareto optimal solution of a multiobjective optimization problem. By using the proposed approach, a fair scheme that does not advantage one particular objective can be attained. Simulations are provided to validate the proposed methodology.

Demand side management (DSM) plays an important role in smart grid. In this paper, a hierarchical day-ahead DSM model is proposed, where renewable energy sources (RESs) are integrated. The proposed model consists of three layers: the utility in the upper layer, the demand response (DR) aggregator in the middle layer, and customers in the lower layer. The utility seeks to minimize the operation cost and give part of the revenue to the DR aggregator as a bonus. The DR aggregator acts as an intermediary, receiving bonus from the utility and giving compensation to customers for modifying their energy usage pattern. The aim of the DR aggregator is maximizing its net benefit. Customers desire to maximize their social welfare, i.e., the received compensation minus the dissatisfactory level. To achieve these objectives, a multiobjective problem is formulated. An artificial immune algorithm is used to solve this problem, leading to a Pareto optimal set. Using a selection criterion, a Pareto optimal solution can be selected, which does not favor any particular participant to ensure the overall fairness. Simulation results confirm the feasibility of the proposed method: the utility can reduce the operation cost and the power peak to average ratio; the DR aggregator can make a profit for providing DSM services; and customers can reduce their bill. Index Terms-Artificial immune algorithm, demand response aggregator, demand side management, multiobjective problem, Pareto optimality, renewable energy sources, smart grid. NOMENCLATURE := Assignment operator. α, β Compensation coefficient. µ Bonus coefficient. θ Mutate coefficient. ε Dissatisfactory coefficient. A(n c) Current antibodies. c 0 () Conventional generation cost without DSM. c 1 () Conventional generation cost with DSM. c res () RESs generation cost. f () Multiobjective problem. f a () Objective function for the aggregator. f c () Objective function for customers. f u () Objective function for the utility. f bon Bonus function. f com Compensation function. f dis Dissatisfactory function.

This paper focuses on the problem of energy imbalance management in amicrogrid. The problem is investigated from the power market perspective. Unlike the traditional power grid, a microgrid can obtain extra energy froma renewable energy source (RES) such as a solar panel or a wind turbine. However, the stochastic input from the RES brings difficulty in balancing the energy supply and demand. In this study, a novel pricing scheme is proposed that provides robustness against such intermittent power input. The proposed scheme considers possible uncertainty in the marginal benefit and the marginal cost of the power market. It uses all available information on the power supply, power demand, and imbalanced energy. The parameters of the scheme are evaluated using an performance index. It is shown that the parameters can be obtained by solving a linear matrix inequality problem, which is efficiently solvable due to its convexity. Simulation examples are given to show the favorable performance of the proposed scheme in comparison with existing area control error pricing schemes.Comment: 9 pages, 3 figure

Abstract-A minimum Manhattan distance (MMD) approach to multiple criteria decision making in multiobjective optimization problems (MOPs) is proposed. The approach selects the finial solution corresponding with a vector that has the MMD from a normalized ideal vector. This procedure is equivalent to the knee selection described by a divide and conquer approach that involves iterations of pairwise comparisons. Being able to systematically assign weighting coefficients to multiple criteria, the MMD approach is equivalent to a weighted-sum approach. Because of the equivalence, the MMD approach possesses rich geometric interpretations that are considered essential in the field of evolutionary computation. The MMD approach is elegant because all evaluations can be performed by efficient matrix calculations without iterations of comparisons. While the weightedsum approach may encounter an indeterminate situation in which a few solutions yield almost the same weighted sum, the MMD approach is able to determine the final solution discriminately. Since existing multiobjective evolutionary algorithms aim for a posteriori decision making, i.e., determining the final solution after a set of Pareto optimal solutions is available, the proposed MMD approach can be combined with them to form a powerful solution method of solving MOPs. Furthermore, the approach enables scalable definitions of the knee and knee solutions.Index Terms-Divide and conquer (D&C) approach, knee solutions, minimum Manhattan distance approach, multicriteria decision making (MCDM), multiobjective evolutionary algorithms (MOEAs), multiobjective optimization problems (MOPs), multiple attribute decision making (MADM), multiple criteria decision making (MCDM).

This letter presents an adaptive spectrum sensing algorithm that detects wideband spectrum using sub-Nyquist sampling rates. By taking advantage of compressed sensing (CS), the proposed algorithm reconstructs the wideband spectrum from compressed samples. Furthermore, an l2 norm validation approach is proposed that enables cognitive radios (CRs) to automatically terminate the signal acquisition once the current spectral recovery is satisfactory, leading to enhanced CR throughput. Numerical results show that the proposed algorithm can not only shorten the spectrum sensing interval, but also improve the throughput of wideband CRs.Comment: 11 pages, 4 figures. This paper has been accepted to be published in IEEE Communications Letters. The associate editor coordinating the review of this letter and approving it for publication was O. Dobr

The design, fabrication and measurement of a silicon-on-insulator (SOI) two-dimensional photonic crystal ring resonator are demonstrated in this study. The structure of the photonic crystal is comprised of silicon nano-rods arranged in a hexagonal lattice on an SOI wafer. The photonic crystal ring resonator allows for the simultaneous separation of light at wavelengths of 1.31 and 1.55mum. The device is fabricated by e-beam lithography. The measurement results confirm that a 1.31mum/1.55mum wavelength ring resonator filter with a nano-rod photonic crystal structure can be realized.

Publisher's copyright statement: c 2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-An economical way to manage demand-side energy storage systems in the smart grid is proposed by using an Hoo design. The proposed design can adjust the stored energy state economically according to the price signal, while tolerating a certain degree of system uncertainty and having physical constraints on the stored energy level satisfied. Roughly speaking, batteries in the proposed design are charged during a low-price period while being discharged during a high-price period for cost control. Simulations show that the proposed energy storage system can meet the real-time power demand and save money in the long term in contrast to energy storage systems using constant-state schemes.

Abstract-Bilinear matrix inequality (BMI) problems in system and control designs are investigated in this paper. A solution method of reduction of variables (MRV) is proposed. This method consists of a principle of variable classification, a procedure for problem transformation, and a hybrid algorithm that combines deterministic and stochastic search engines. The classification principle is used to classify the decision variables of a BMI problem into two categories: external and internal variables. Theoretical analysis is performed to show that when the classification principle is applicable, a BMI problem can be transformed into an unconstrained optimization problem that has fewer decision variables. Stochastic search and deterministic search are then applied to determine the decision variables of the unconstrained problem externally and explore the internal problem structure, respectively. The proposed method can address feasibility, singleobjective, and multiobjective problems constrained by BMIs in a unified manner. A number of numerical examples in system and control designs are provided to validate the proposed methodology. Simulations show that the MRV can outperform existing BMI solution methods in most benchmark problems and achieve similar levels of performance in the remaining problems.Index Terms-Bilinear matrix inequality (BMI), BMI solution methods, method of reduction of variables (MRV), multiobjective BMI problems, spectral abscissa optimization, static output feedback.