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The variability of wind power generation increases the uncertainties in modern power system, affecting its physical operation. Owning to the fast response capability, battery energy storage system (BESS) has offered an answer to this problem. In this paper, a novel sizing methodology is proposed for BESS planning, which strikes a balance between economic cost and wind smoothing performance. First, a novel variable-interval reference signal optimization approach and a fuzzy control-based charging/discharging scheme are presented to smooth wind power, maintaining the state-of-health of BESS in the meanwhile. And then, power and energy capacities are determined according to a statistical model of charge/discharge power and the economic cost model, respectively. Finally, case studies are carried out to demonstrate the performance of the proposed method. The impact from wind power forecasting error during real-time operation is also analyzed. [ABSTRACT FROM PUBLISHER]

With increasing large‐scale wind farms being integrated into the power grids, transmission expansion planning (TEP) increasingly requires more flexibility to account for the intermittency as well as other uncertainty factors involved in the process. In this study, a probabilistic TEP model is proposed for planners to tackle the variability and uncertainty factors associated with grid connected wind farms. Both load forecast and wind power output uncertainties are considered in the proposed model. Other factors considered in the model include the forced outage rates of transmission lines and generators, and the wind speed correlation between wind farms. Moreover, the incentive‐based demand response (IBDR) program is introduced as a non‐network solution instead of the conventional network expansion approaches. The utilities will pay IBDR providers for their contributions to peak demand reduction. The proposed TEP model can find the optimal trade‐off between transmission investment and demand response expenses. The hierarchical Bender's decomposition algorithm integrated with Monte Carlo simulation is employed to solve the proposed model. Case studies are given using the Garver's six‐bus system and the IEEE‐reliability test system to show the effectiveness of the method. [ABSTRACT FROM AUTHOR]

In this paper, we argue that Asia's unique geography, abundant low-emission energy resources, rapid economic growth, and rising energy demands merit consideration of a Pan-Asian Energy Infrastructure. In our study, we focus on development of wind and solar resources in Australia, China, Mongolia, and Vietnam as the potential foundation for an electricity grid stretching from China to Australia. Hourly climate data for a full year are used to estimate renewable energy generation, electricity demand, generation capacity are projected forward to the year 2025, and economic dispatch in an international market is simulated to demonstrate cost benefits. Intermittency, connectivity, future dispatch orders, storage, line losses, and engineering and financial issues are all addressed. [ABSTRACT FROM PUBLISHER]

Offshore wind energy, as one of the featured rich renewable energy sources, is getting more and more attention. The cable connection layout has a significant impact on the economic performance of offshore wind farms. To make better use of the wind resources of a given sea area, a new method for optimal construction of offshore wind farms with different types of wind turbines has emerged in recent years. In such a wind farm, the capacities of wind turbines are not identical which brings new challenges for the cable connection layout optimization. In this work, an optimization model named CCLOP is proposed for such wind farms. The model incorporates both the cable capital cost and the cost of power losses associated with the cables in its objective function. To get an optimized result, a Voronoi diagram based adaptive particle swarm optimization with local search is proposed and applied. The simulation results show that the proposed method can help find a solution that is 12.74% outperformed than a benchmark. [ABSTRACT FROM AUTHOR]

Currently, the maximization of wind energy extraction for variable speed wind turbines (VSWT) is usually transformed into the maximum power point tracking (MPPT), i.e., the rotor speed is continuously regulated to follow the optimal rotor speed as a function of wind speed. However, it has been recognized that the increasing rotor inertia and the high fluctuation of wind speed result in the turbines’ being unable to instantly track the optimal rotor speed and tracking losses. This indicates that maximizing wind energy extraction based on MPPT is not suitable for large-inertia VSWTs under turbulence. Moreover, it is revealed and demonstrated in this letter that a conservative speed reference matching up with the slow dynamic behavior of VSWTs can effectively improve wind energy production, which provides a new approach for maximizing wind energy extraction of VSWTs under turbulence. [ABSTRACT FROM AUTHOR]