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The smart grid is a fully automatic delivery grid for electricity power with a two-way reliable flow of electricity and information among different equipment on the grid. Smart meters and sensors monitoring the system provide a huge amount of data in various part of smart grid. To logically manage this trouble, a new lossy data compression approach for big data compression is proposed. The optimal singular value decomposition (SVD) is applied to a matrix that achieves the optimal number of singular values to the sending process, and the other ones will be neglected. This goal is done due to the quality of retrieved data and the compression ratio. In the presented scheme, to implement the optimization framework, various intelligent optimization methods are used to determine the number of optimal values in the elimination stage. The efficiency and capabilities of the proposed method are examined using a wide range of data types, from electricity market data to image processing benchmarks. The comparisons show that the compression level obtained by the proposed method can dominate the points given by the existing SVD rank reduction methods. Also, as the other finding of this article, the performance of the rank reduction methods depends on the application and data types. It means that a rank reduction method can reveal a good performance in one application and performs unacceptably for another purpose. So, the optimized rank reduction can pave the way toward a robust and reliable performance. [ABSTRACT FROM AUTHOR]

In this paper, a stable fuzzy controller in the form of linear matrix inequalities (LMIs) is designed for an electric vehicle (EV). The proposed controller evaluates the stabilization of the EV speed with robust disturbance rejection approach throughout both transient and steady states and in the presence of external disturbances and parametric uncertainties. In order to achieve an efficient control, EV's battery voltage, as control input, and EV speed, as system output, are constrained. The proposed control strategy is based on Takagi–Sugeno (T-S) fuzzy model and the parallel distributed compensation (PDC) fuzzy controller. Proposed controller can be practically implemented due to low computational load of control input that is owing to the offline process of obtaining the feedback gains, very small amplitude of control input and low power consumption. To evaluate the performance of the designed controller, simulations in five steps are conducted on an EV equipped with a brushed direct current (BDC) motor as a case study in MATLAB simulation environment. Ultimately, the real-time digital simulation results using real-time digital power system simulator (RTDS) confirm the efficiency of the proposed input-output constrained robust disturbance rejection stable fuzzy controller (IOCRDRSFC) in stabilizing the EV speed. The simulation results obtained from MATLAB and the RTDS confirm the energy-efficient and robust performance of the proposed controller in quick stabilization of the EV speed in the presence of all structured and unstructured uncertainties. [ABSTRACT FROM AUTHOR]

This paper describes a new probabilistic model for generation and transmission expansion planning (G&TEP) problem considering reliability criteria. Probabilistic reliability criteria accounts for random generator or line outages with known historical forced outage rates (FOR). The resultant model considers the installation and operation costs as well as the cost of expected energy not supplied (EENS) to optimally determine the number and location of new generating units and circuits in the network, power generation capacity for those units and the voltage phase angle at each node. Also, efficient linear formulations are introduced in this paper to deal with the nonlinear nature of the problem including objective functions and constraints. Modified 6-bus test system, IEEE 24-bus RTS and IEEE 118-bus test system are utilized to illustrate the effectiveness of the proposed framework. [ABSTRACT FROM PUBLISHER]

In this paper, a stochastic multiobjective framework is proposed for day-ahead joint market clearing. The proposed multiobjective framework can concurrently optimize competing objective functions including augmented generation offer cost and security indices (overload index, voltage drop index, and voltage stability margin). Besides, system uncertainties including generating units and branches contingencies and load uncertainty are explicitly considered in the stochastic market clearing scheme. The solution methodology consists of two stages, which firstly, employs roulette wheel mechanism and Monte Carlo simulation (MCS) for random adaptive 24-h scenario generation wherein the stochastic multiobjective market clearing procedure is converted into its respective deterministic equivalents (scenarios). In the second stage, for each deterministic scenario, a multiobjective mathematical programming (MMP) formulation based on the c-constrained approach is implemented for provision of spinning reserve (SR) and nonspinning reserve (NSR) as well as energy. The MMP formulation of the market clearing process is optimized while meeting ac power flow constraints and expected interruption cost (EIC). The IEEE 24-bus Reliability Test System (RTS 24-bus) is used to demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a new modulation strategy based on the carrier-based switching strategy for the non-square direct matrix converters (MC) is proposed to control the active power of distributed generation (DG) units. In this strategy, the active power of DGs is controlled by the central input current control of the non-square direct MC independent from the voltage and frequency. Conventionally, each DG has a converter, and for supplying a load with N number of DGs, N number of converters are needed and each converter has its own modulation switching and control strategy to control the power output of each DG. Needless to say, in a microgrid with N number of DGs, the control strategy of each converter has more complex structure than that of a microgrid with one converter, and surely the former strategy entails more volume and price. Using the proposed converter in this paper, it is possible to supply a load with N number of DGs through one converter. Also, the power outputs of all DGs are controlled by a central control strategy. The proposed central control strategy is described and simulated for a typical microgrid. Experimental and simulation results validate the effectiveness of the proposed converter and the proposed strategy. The results demonstrate the applicability and efficiency of the system and verify the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper presents a new optimization algorithm, named modified teaching-learning algorithm, to solve a more practical formulation of the reserve constrained dynamic economic dispatch of thermal units considering the network losses and operating limitations of the generating units (i.e., the valve loading effect and ramp rate limits). Unlike the previous approaches, three types of the system spinning reserve requirements are explicitly modeled in the problem and a new constraint-handling is proposed to satisfy them. The proposed teaching-learning optimization algorithm is a new population-based optimization method features between the teacher and learners (students). Therefore, this algorithm searches for the global optimal solution through two main phases: 1) the “teacher phase” and 2) the “learner phase”. Nevertheless, these two phases are not adequate for learning interaction between the teacher and the learners in the entire search space. Thus, in this paper a new phase named “modified phase” based on a self-adaptive learning mechanism is added to the algorithm to improve the process of knowledge learning among the learners and accordingly generate promising candidate solutions. The proposed framework is applied to 5-, 10-, 30-, 40-, and 140-unit test systems in order to evaluate its efficiency and feasibility. [ABSTRACT FROM PUBLISHER]

This paper proposes a new strategy for an independent system operator (ISO) to trade demand response (DR) with different DR aggregators while considering various operational constraints. The ISO determines the energy scheduling and reserve deployment in a pre-emptive market while setting DR contracts with the DR aggregators. The ISO applies a two-stage stochastic programming to cope with the uncertainty associated with wind power production. DR aggregators’ behavior is modeled through a profit maximization function. Aggregators determine their DR trading shares with ISO and customers through three DR options, including load curtailment, load shifting, and load recovery. A stochastic bilevel problem is formulated, in which in the upper level, the ISO minimizes the total operation cost, and in the lower level, the DR aggregator maximizes the profit. Afterwards, the problem is transferred to a single-level mathematical problem with equilibrium constraints by replacing the lower level program with its Karush–Kuhn–Tucker (KKT) conditions. As a result, the total operation cost is reduced using the proposed method comparatively to run the program without considering the lower level. [ABSTRACT FROM AUTHOR]

Reliability worth analysis is of great importance in the area of distribution network planning and operation. The reliability worth's precision can be affected greatly by the customer interruption cost model used. The choice of the cost models can change system and load point reliability indices. In this study, a cascade correlation neural network is adopted to further develop two cost models comprising a probabilistic distribution model and an average or aggregate model. A contingency-based analytical technique is adopted to conduct the reliability worth analysis. Furthermore, the possible effects of adding distributed generation units into the network are evaluated. The proposed approach has been tested on a radial distribution test network evaluating the reliability worth. The results show that the probabilistic distribution model provides a more realistic model for the reliability analysis. [ABSTRACT FROM PUBLISHER]