Amirsaman Arabali

Genetic-Algorithm-Based Optimization Approach for Energy Management

This paper proposes a new strategy to meet the controllable heating, ventilation, and air conditioning (HVAC) load with a hybrid-renewable generation and energy storage system. Historical hourly wind speed, solar irradiance, and load data are used to stochastically model the wind generation, photovoltaic generation, and load. Using fuzzy C-Means (FCM) clustering, these data are grouped into 10 clusters of days with similar data points to account for seasonal variations. In order to minimize cost and increase efficiency, we use a GA-based optimization approach together with a two-point estimate method. Minimizing the cost function guarantees minimum PV and wind generation installation as well as storage capacity selection to supply the HVAC load. Different scenarios are examined to evaluate the efficiency of the system with different percentages of load shifting. The maximum capacity of the storage system and excess energy are calculated as the most important indices for energy efficiency assessment. The cumulative distribution functions of these indices are plotted and compared. A smart-grid strategy is developed for matching renewable energy generation (solar and wind) with the HVAC load.

A Framework for Optimal Placement of Energy Storage Units Within a Power System With High Wind Penetration

This paper deals with optimal placement of the energy storage units within a deregulated power system to minimize its hourly social cost. Wind generation and load are modeled probabilistically using actual data and a curve fitting approach. Based on a model of the electricity market, we minimize the hourly social cost using probabilistic optimal power flow (POPF) then use a genetic algorithm to maximize wind power utilization over a scheduling period. A business model is developed to evaluate the economics of the storage system based on the energy time-shift opportunity from wind generation. The proposed method is used to carry out simulation studies for the IEEE 24-bus system. Transmission line constraints are addressed as a bottleneck for efficient wind power integration with higher penetration levels. Distributed storage is then proposed as a solution to effectively utilize the transmission capacity and integrate the wind power more efficiently. The potential impact of distributed storage on wind utilization is also evaluated through several case studies.

Stochastic Performance Assessment and Sizing for a Hybrid Power System of Solar/Wind/Energy Storage

This paper proposes a stochastic framework for optimal sizing and reliability analysis of a hybrid power system including the renewable resources and energy storage system. Uncertainties of wind power, photovoltaic (PV) power, and load are stochastically modeled using autoregressive moving average (ARMA). A pattern search-based optimization method is used in conjunction with a sequential Monte Carlo simulation (SMCS) to minimize the system cost and satisfy the reliability requirements. The SMCS simulates the chronological behavior of the system and calculates the reliability indices from a series of simulated experiments. Load shifting strategies are proposed to provide some flexibility and reduce the mismatch between the renewable generation and heating ventilation and air conditioning loads in a hybrid power system. Different percentages of load shifting and their potential impacts on the hybrid power system reliability/cost analysis are evaluated. Using a compromise-solution method, the best compromise between the reliability and cost is realized for the hybrid power system.

Energy Storage Application for Performance Enhancement of Wind Integration

This paper proposes a stochastic framework to enhance the reliability and operability of wind integration using energy storage systems. A genetic algorithm (GA)-based optimization approach is used together with a probabilistic optimal power flow (POPF) to optimally place and adequately size the energy storage. The optimization scheme minimizes the sum of operation and interrupted-load costs over a planning period. Historical wind speed, load and equipment failure data are used to stochastically model the wind generation, load and equipment availability. Using Fuzzy C-Means (FCM) clustering, wind and load samples are grouped into 40 clusters of days with similar sample points to account for seasonal variations. The IEEE 24-bus system (RTS) is used to evaluate the performance of the proposed method and realize the maximum achievable reliability level. A cost-benefit analysis compares storage technologies and conventional gas-fired alternatives to reliably and efficiently integrate different wind penetration levels and determine the most economical design. Storage distribution and its effect on performance enhancement of wind integration are examined for higher wind penetrations.

A Multi-Objective Transmission Expansion Planning Framework in Deregulated Power Systems With Wind Generation

Integration of renewable energy resources into the power system has increased the financial and technical concerns for the market-based transmission expansion planning. This paper proposes a stochastic framework for transmission grid reinforcement studies in a power system with wind generation. A multi-stage multi-objective transmission network expansion planning (TNEP) methodology is developed which considers the investment cost, absorption of private investment and reliability of the system as the objective functions. A non-dominated sorting genetic algorithm (NSGA II) optimization approach is used in combination with a probabilistic optimal power flow (POPF) to determine the Pareto optimal solutions considering the power system uncertainties. Using a compromise-solution method, the best final plan is then realized based on the decision-maker preferences. The proposed methodology is applied to the IEEE 24-bus Reliability Tests System (RTS) to evaluate the feasibility and practicality of the developed planning strategy.

Fault Location in Distribution Networks by Compressive Sensing

This paper proposes a novel method for fault location in distribution networks using compressive sensing. During fault and prefault voltages are measured by smart meters along the feeders. The voltage sag vector and impedance matrix produce a current vector that is sparse enough with one nonzero element. This element corresponds to the bus at which a fault occurs. Due to the limited number of smart meters installed at primary feeders, our system equation is underdetermined. Therefore, the l1-norm minimization method is used to calculate the current vector. Primal-dual interior point (PDIP) and the log barrier algorithm (LBA) are utilized to solve the optimization problem with and without measurement noises, respectively. Our proposed method is implemented on a real 13.8-kV, 134-bus distribution network when single-phase, three-phase, double-phase, and double-phase-to-ground short circuits occur. Simulation results show the robustness of the proposed method in noisy environments and satisfactory performance for various faults with different resistances.

Flexible-Voltage DC-Bus Operation for Reduction of Switching Losses in All-Electric Ship Power Systems

The multizonal medium voltage dc architecture of all-electric ship shipboard power systems (SPSs) includes a variety of distributed energy resources, energy storage devices, and power electronic voltage source converters. Due to the limited fuel supply and tight space, control of the converters should be aimed at increasing their efficiency and compactness. In this paper, a novel approach to voltage control in all-electric ship power systems is proposed to fulfill these goals. In contrast to the traditional SPSs that emulate the land-based electricity grid, maintaining a steady bus voltage is no longer an objective here. On the contrary, the voltage is compelled to fluctuate in order to minimize switching losses in the converters. A method for calculation of the minimum required dc voltage level is described, and results of computer simulations of an SPS with a flexible-voltage dc bus are presented. The minimum-voltage operation of the system results in a significant efficiency increase.

Electric drive vehicle to grid synergies with large scale wind resources

Vehicle to grid (V2G) provides an opportunity for the electric vehicles (EVs) to feed back their power to the electric grid as they are connected to the grid and not in use. However, during the charging period of these vehicles, the power is drawn from the electric grid to charge the battery. This paper examines the V2G capability of the EV fleet for its potential for synergies between the storage of the fleet and the intermittent nature of wind resources. Towards this end, a distribution feeder is considered with the wind power as its primary resource. Assuming an EV with V2G for each residence, different scenarios are studied to evaluate the capability of the EV fleet as the wind electric storage. An energy management method based on an evolutionary optimization approach is proposed to minimize the cost of the conventional generation required to supplement the wind power while maximizing the utilization of wind generation. Smart grid technologies such as real-time communication, smart metering and home area networks (HANs) are proposed to enhance the V2G capability for coordinated charging and discharging of the EV fleet in a distribution feeder. Flexibility of the system is assessed for the studied scenarios and the most appropriate solution is determined based on simulation results.

A stochastic framework for power system operation with wind generation and energy storage integration

This paper proposes a probabilistic optimal power flow (POPF) to evaluate the power system operation with renewable energy generation. Probability density functions (PDFs) are used to stochastically model the wind speed and load. A (2m+1)-point estimation considers the system uncertainties for power flow analysis. The proposed method incorporates energy storage into the POPF model to evaluate its application for wind power integration. The IEEE 24-bus system is used to evaluate the feasibility and practicality of the developed POPF framework. Different scenarios are studied to investigate the economic advantage of wind and storage co-location over the cases where the wind and storage units are installed at different buses.

Modeling and simulation of a DFIG-based wind-power system for stability analysis

This paper deals with the modeling and simulation of a double-fed induction generator (DFIG) based wind-power unit which is connected to the grid through a series-compensated transmission line. A detailed model of a DFIG in the rotor flux reference frame, along with its controllers, is developed in this paper. This paper proposes a comprehensive set of differential equations for grid connection of DFIGs that takes into account the dynamics of the grid side line filter and transmission line. Time domain simulations are performed in Matlab/Simulink. Based on the model and simulation results, system stability is investigated for different series compensation levels. Also, the effect of grid stiffness on the system stability is studied. Potential of sub-synchronous resonance (SSR) oscillations is investigated and the proper system and controller parameters which make the system stable and mitigate hazards due to SSR are identified.