• Energy Research

Due to the recent price spike of the international oil and the concern of global warming, the development and deployment of renewable energy become one of the most important energy policies around the globe. Currently, there are different capacities and hub heights for commercial wind turbine generators (WTGs). To fully capture wind energy, different wind farms (WFs) should select adequate capacity of WTGs to effectively harvest wind energy and maximize their economic benefit. To establish selection criterion, this paper first derives the equations for capacity factor (CF) and pairing performance (PP) of WTGs using Weibull and Rayleigh distribution functions. Five commercial WFs in Taiwan are used as sample systems for comparative analyses. This paper combines Weibull function, cubic mean wind speed, and four existing economic-analysis methods to study different values of capacity and hub height for WTGs that have been installed in Taiwan. Important outcomes affecting wind cost of energy in comparison with economic results using the proposed economic-analysis methods for different WFs are also presented.

Abstract With the application of advanced information technology for the integration of electricity and natural gas systems, formulating an excellent energy conversion and management strategy has become an effective method to achieve established goals. Differing from previous works, this paper proposes a peak load shifting model to smooth the net load curve of an integrated electricity and natural gas system by coordinating the operations of the power-to-gas unit and generators. Moreover, the study aims to achieve multi-objective optimization while considering the economy of the system. A dynamic energy conversion and management strategy is proposed, which coordinates both the economic cost target and the peak load shifting target by adjusting an economic coefficient. To illustrate the complex energy conversion process, deep reinforcement learning is used to formulate the dynamic energy conversion and management problem as a discrete Markov decision process, and a deep deterministic policy gradient is adopted to solve the decision-making problem. By using the deep reinforcement learning method, the system operator can adaptively determine the conversion ratio of wind power, power-to-gas and gas turbine operations, and generator output through an online process, where the flexibility of wind power generation, wholesale gas price, and the uncertainties of energy demand are considered. Simulation results show that the proposed algorithm can increase the profit of the system operator, reduce wind power curtailment, and smooth the net load curves effectively in real time.

Due to uncertainties in renewable energy generation and load demands, traditional energy dispatch schemes for an integrated electricity–gas system (IEGS) considerably depend on explicit forecast mathematical models. In this study, a novel data-driven deep reinforcement learning method is applied to solve the IEGS dynamic dispatch problem with the targets of minimizing carbon emission and operating cost. Moreover, a flexible operation of carbon capture system and power-to-gas facility is proposed to attain low operating costs. The IEGS dynamic dispatch problem is formulated as a Markov game, and a soft actor–critic (SAC) algorithm is applied to learn the optimal dispatch solution. To improve training efficiency and convergence, prioritized experience replay (PER) is employed. In the simulation, the proposed PER–SAC algorithm compared with deep Q-network and SAC has fast and stable learning performance. In contrast to a modified sequential quadratic programming based on uncertainty prediction, the proposed method can reduce the target cost by 11.62% when the prediction error exceeds 10%. The computational time of scenario analysis solution on the same hardware platform is 4.58 times than that of training the PER–SAC method. Finally, the simulation results under different scenarios demonstrate that the PER–SAC-based dispatch strategy has satisfactory generalization and adaptability.

High intermittency in the nature of wind power emphasize conceptual revising in the mechanisms of electricity markets with high wind power penetration levels. This paper introduces overmuch price reduction and high price volatility as two adverse consequences in future wind dominant electricity markets. While high price volatility imposes elevated risk levels for both electricity suppliers and consumers, excessive price reduction of electricity is a disincentive for investment in new generation capacity and might jeopardizes system adequacy in long term. A comparative study between marginal to discriminatory pricing mechanisms are conducted in this paper. This particular direction has been chosen because the discriminatory approach is traditionally known for lowering price volatility while gently increasing average price. To this end an adaptive agent based simulator under two pricing mechanisms is developed. The paper indicates discriminatory pricing approach can be beneficial in high penetration of wind power because it alleviates high price variations and spikiness in one hand and prevents overmuch price reduction in wind dominant electricity markets on the other hand.

AbstractThe paper presents a model of a reference wind farm. The model considers the wind and wave climatologies for a specific site from which two different wind farm layouts are derived. These layouts are examined through the effective wake‐enhanced turbulence intensity at the hub height for a given climatology, and a simple model for the influence on capital expenditures is proposed. An electrical design is presented, the cable losses are calculated and the energy yield is determined. An operation and maintenance model is established, and the associated operating expenditure is obtained. All of the models are then summarized in terms of a levelized cost of energy using a numerical simulation tool, which allows the layouts to be compared. The data and models are freely available online for others to use and may serve as a baseline for benchmarking and allow researchers to compare and discuss their results. Copyright © 2017 John Wiley & Sons, Ltd.