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Abstract: This paper investigates the technical and economic potential of energy-intensive industries to provide demand-side management (DSM) in electricity and balancing markets through 2030. Increasing shares of renewables will lead to a rising demand for ancillary services at the same time that less conventional plants will be available to provide these services. This paper makes projections on the extent to which DSM from industrial processes can provide economic benefits in electricity markets with renewables by providing tertiary reserve capacity. Different industrial processes and their specific technical and economic properties are investigated and compared with other storage devices and electricity generation technologies. Based on an extension of an existing European electricity market model, simulations are used here to make long-term forecasts for market prices, dispatch and investments in the electricity markets through linear optimization. [ABSTRACT FROM AUTHOR]

Abstract: Increasing levels of intermittent renewables, especially wind power, in energy systems require accurate temporal characterisation of the resources’ availability at seasonal, daily and hourly scales. This is crucial for isolated energy systems, where increasing wind power penetration is limited due to costly backup power generation requirements. In the case studies presented in this paper, the energy systems of two islands are simulated using a new methodology for synthetic wind speed scenarios including daily wind patterns. A trade-off analysis was conducted in terms of surplus wind power and renewables penetration rate, with the objective of supplying decision support on wind turbine placement. Results show that there may be a significant advantage in locating future wind parks on sites where wind speed patterns better match electricity demand patters, rather than just choosing a site with the highest mean wind speed, but only if the annual mean wind speed is still sufficiently high to make the investment economically feasible. [Copyright &y& Elsevier]

Abstract: Two of the most significant challenges in the 21st century will be to improve energy security and reduce the greenhouse gas emissions associated with energy consumption. A co-beneficial solution to these challenges is seen as increasing the use of renewable energy for the production of electricity. Some renewable sources, such as wind are often presented as a way to reduce greenhouse gas emissions; however, since wind’s variability increases uncertainty and risk in expected generation, it can be detrimental to energy security. One of the ways in which wind’s contribution to a jurisdiction’s energy security and greenhouse gas reduction strategies can be improved is to employ a forecasting method that can help reduce risks. This paper proposes a method that applies risk and reliability analysis techniques to obtain the most-likely RL (Resistance–Load) scenario using a set of historical data for wind-supply or generation and load. RL estimates the reliability of a wind-energy system by simulating an anticipated resistance (the electrical generation) attempting to meet a load (the electricity demand) for a future year. The method is demonstrated through a case study and its results are compared with real-time data from a 12MW wind farm to prove its efficacy. [Copyright &y& Elsevier]