بررسی اثرات قیمت انرژی استوکستیک در حالات انرژی اقتصادی بلند مدت
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|6736||2007||10 صفحه PDF||سفارش دهید||6180 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Energy, Volume 32, Issue 12, December 2007, Pages 2340–2349
In view of the currently observed energy prices, recent price scenarios, which have been very moderate until 2004, also tend to favor high future energy prices. Having a large impact on energy-economic scenarios, we incorporate uncertain energy prices into an energy systems model by including a stochastic risk function. Energy systems models are frequently used to aid scenario analysis in energy-related studies. The impact of uncertain energy prices on the supply structures and the interaction with measures in the demand sectors is the focus of the present paper. For the illustration of the methodological approach, scenarios for four EU countries are presented. Including the stochastic risk function, elements of high energy price scenarios can be found in scenarios with a moderate future development of energy prices. In contrast to scenarios with stochastic investment costs for a limited number of technologies, the inclusion of stochastic energy prices directly affects all parts of the energy system. Robust elements of hedging strategies include increasing utilization of domestic energy carriers, the use of CHP and district heat and the application of additional energy-saving measures in the end-use sectors. Region-specific technology portfolios, i.e., different hedging options, can cause growing energy exchange between the regions in comparison with the deterministic case.
Prices for fossil energy carriers, in particular oil and natural gas, have shown a steep rise since 2003. However, until 2004 very moderate assumptions of future energy prices (View the MathML source20–30US$2000/bbl oil over the next two decades) had been used in most energy-related studies (e.g.  and ). Nowadays such estimates are judged to be at least questionable and even predictions of up to 100 US$/bbl can be found  and . In view of such changing expectations, a question arises: How can such different and apparently fast changing assumptions (cf. ) be even rudimentarily incorporated into energy systems models that are frequently used to aid scenario analysis in energy-related studies? In energy models uncertainties are typically treated—if at all—by analyzing multiple deterministic scenarios. In stochastic energy systems models, usually only uncertainties related to future investment costs for technologies or restrictions (demands, emissions, etc.) are considered (see e.g. ,  and ). On the other hand, energy price uncertainties are sometimes explicitly treated in supply-oriented models, e.g. to optimize the production portfolio of utilities (see e.g. ). However, the effects of energy price uncertainties on the competitiveness of energy-saving measures cannot be analyzed in supply-oriented models. The impact of uncertain energy carrier import prices on the supply structures and the interaction with measures in the demand sectors, in particular within mitigation scenarios, is therefore a focus of the present paper. Apart from energy price uncertainties, the increasing intertwining of national economies in the context of globalization and, in particular, the liberalization of energy markets in the European Union, especially for electricity and natural gas, have a significant impact on the structures of national energy systems. An increasing interaction of the energy systems of the EU member countries is the result of this process. In addition, the reduction of greenhouse gas emissions is a global problem and as such can only be solved by joint action by the majority of countries. International agreements like the Kyoto Protocol are the foundations of global efforts to reduce GHG emissions. Hence, a multi-regional structure is a desirable feature of models to analyze the impact on the interaction between national energy systems via energy carrier exchange.
نتیجه گیری انگلیسی
Linear programming models tend to favor single solutions and simplistic developments. This behavior can generally be reduced by including a stochastic risk function into the model's objective function, leading to a diversification of investment decisions. Apparently, this diversification only concerns technologies that are affected by stochastic parameters. In contrast to scenarios with stochastic investment costs for a (limited) number of technologies, the inclusion of stochastic energy import prices directly affects virtually all parts of the energy system. In view of the currently observed volatility of energy prices, it is particularly useful for the treatment of uncertain energy prices and thus for the development of risk-hedging strategies. Including the stochastic risk function, elements of high energy price scenarios can be found in reference price scenarios with a moderate future development of energy prices as for instance in  and . Robust elements of hedging strategies include increasing utilization of domestic primary energy carriers (e.g. lignite, renewables) and the application of additional energy-saving measures in the end-use sectors. Also, the use of CHP and district heat becomes more attractive, because of a reduction of TPES and thus reduced energy imports. Different hedging options in the model regions (e.g. lignite or nuclear power) can cause growing electricity exchange between the regions in comparison with the deterministic case. The technology portfolio of different countries to react to the increasing import price risks thus has a significant impact on their competitiveness. By gradually increasing the risk aversion a ranking of hedging options can be derived to identify the cheapest measures to reduce risks. The relatively simple implementation of this stochastic optimization approach, as well as the manageable computational overhead in comparison with the deterministic model version, makes the approach an attractive extension for energy systems models. A promising application of the method is emission reduction scenarios, which generally show a fuel switch from coal to natural gas in the conversion sector. This strategy is questionable under volatile gas prices and the presented approach could be very useful to identify viable alternatives to this fuel switch, taking into account the stochastic nature of energy prices. Also, the inclusion of stochastic carbon prices could be an interesting extension in this context.