سرمایه گذاری در انرژی تجدید پذیر: اثرات بازار و سیاست
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|9985||2012||6 صفحه PDF||16 صفحه WORD|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Applied Energy , Volume 97, September 2012, Pages 249–254
فهرست علائم و اختصارات
2. یک مدل گزینههای واقعی با قیمتهای درونزا
جدول 1: دادههای هزینه. منبع: محاسبهشده از IEA .
جدول 2: دادههای فرایند قیمت.
4. تحلیلهای سیاسی
4.1 فشار باد ثابت در مقابل متغیر
جدول 3: مقایسه ارزش شرکت (با تنها یک فرصت سرمایهگذاری: باد).
4.2 سیاست انرژی و عدم اطمینان نظارتی
The liberalization of electricity markets in recent years has enhanced competition among power-generating firms facing uncertain decisions of competitors and thus uncertain prices. At the same time, promoting renewable energy has been a key ingredient in energy policy seeking to de-carbonize the energy mix. Public incentives for companies to invest in renewable technologies range from feed-in tariffs, to investment subsidies, tax credits, portfolio requirements and certificate systems. We use a real options model in discrete time with lumpy multiple investments to analyze the decisions of an electricity producer to invest into new power generating capacity, to select the type of technology and to optimize its operation under price uncertainty and with market effects. We account for both the specific characteristics of renewables and the market effects of investment decisions. The prices are determined endogenously by the supply of electricity in the market and by exogenous electricity price uncertainty. The framework is used to analyze energy policy, as well as the reaction of producers to uncertainty in the political and regulatory framework. In this way, we are able to compare different policies to foster investment into renewables and analyze their impacts on the market.
The liberalization of electricity markets in recent years has enhanced competition among power-generating firms facing uncertain decisions of competitors and thus uncertain prices. At the same time, promoting renewable energy has been a key ingredient in energy policy aiming at the de-carbonization of the energy mix. Public incentives for companies to invest in renewable technologies include inter alia feed-in tariffs, investment subsidies, tax credits, portfolio requirements and certificate systems. A problem connected to these new technologies in contrast to the traditional ones is that they often yield uncertain amounts of electricity depending on the current environment (variable amount of sunny days, high vs. low wind speeds, etc.). Moreover, the political frameworks and regulations differ between the individual countries of the European Union (EU), change over time and are thus often subject to major uncertainties themselves. Although costs for renewable technologies are falling (e.g. ), installed and established capacity such as coal-fired plants still benefit from relatively low investment and operations and maintenance (O&M) costs. Renewable technologies (such as wind power), however, have positive external effects, e.g. by emitting less to no CO2, and creating jobs or energy security, which could trigger support from public administrations. This support has played an important role in encouraging wind power respectively renewable power development and could e.g. take the form of tax and financial incentives, CO2 costs or feed-in tariffs. With respect to the latter, Blancoa and Rodrigues  quote the current German feed-in tariff for wind power to be 90 €/MW h. We introduce a modeling framework, which captures (a) the specific properties of electricity markets (e.g. high up-front sunk costs and flexibility to time installations differently), where (b) large companies can have an impact on prices in the market. Furthermore, we model (c) the dynamic nature of investment decisions, (d) the associated uncertainties emanating from both markets and environment and analyze, and (e) the impact of policy and the uncertainty surrounding it. For the latter part we pick Germany as a case study, which has a feed-in tariff system since 1991, which has often been cited as a success case and example for other countries. Renewable energy producers receive a fixed tariff from the grid operator, who has the obligation to accept the electricity. The tariff depends on the type of technology – and in the case of wind also location – and is fixed for up to 20 years.1 Moreover, Germany’s renewables share has more than doubled between 2000 and 2009, where wind is the most important of the supported renewable energy carriers. For this reason we pick wind power as the subject of our study. In the discrete time model developed in this paper, we are analyzing the investment decisions of a firm, producing a homogenous and non-storable good, over a fixed planning horizon. The firm decides whether to irreversibly invest in new capacities or not at the end of each time period. When deciding about investing in new capacities, which are lumpy, the firm can choose between different technologies implying different cost structures and production uncertainties. The yields of some technologies depend on the state of the environment, e.g. wind power plants with high or low wind speeds over a specific period. Electricity prices are stochastic. Furthermore, market prices are influenced by changes in the total supply, e.g. supply fluctuations, new capacity investments. Firms in this environment have to consider the effects of their own and competitors’ investment decisions (which are modeled indirectly in this study) and their impact on their firm value instead of an isolated investment. Standard real options models, which are described e.g. by Trigeorgis  or Dixit and Pindyck , have traditionally been applied to similar timing and investment decision problems. The requirements to apply real options analysis of the flexibility of the producer (to decide whether to invest in new capacities or not), the uncertainties pervading the future paths of price, production and policy, as well as the irreversibility of the investments are given in the markets observed by our model. Examples of lumpy investment real options models are e.g.  and . These models, however, do not capture the effect of an investment on the value of past and future investments. In our model, we do not assume that firms can continuously (infinitely small increments) add capacity without any adjustment costs. Real option models have been applied to questions in the electricity industry most recently e.g. by Siddiqui and Fleten  or Murto and Liski . Also, non-real options models have been used to determine the optimal incentive scheme for renewable energy uptake (e.g. ). In contrast to the existing literature we extend the model introduced by Reuter et al.  addressing points (a)–(e), as explained above. Strictly speaking, with respect to point (e), regulatory uncertainty has been addressed in the case of the energy market by previous work in real options modeling (e.g. ,  and ), where the uncertainty emanates from variability in different scenarios of future CO2 price paths. These studies generally find a negative response of investment to regulatory uncertainty. In this paper we are also interested to examine whether uncertainty about the durability (or re-introduction) of feed-in tariffs has a similar impact when there are feedbacks of decisions to the market. The paper is organized as follows. Section 2 presents the basic framework and notations, whereas Section 3 offers an overview and explanation of the data and the sources they come from. Section 4 describes the policy experiments and their results, which are then further analyzed and put into the policy context in the conclusion in Section 5.
نتیجه گیری انگلیسی
In this study we have introduced a real options model to investigate some policy-relevant issues concerning investment in the energy sector under proclaimed government ambitions of decarbonisation. The model contributes to existing models by taking into account that large companies can have an impact on prices in the market and explicitly modeling and capturing the associated uncertainties emanating from both markets and environment, which are thought to provide obstacles to renewable investment. The framework has then been used to examine the impact of policy and the uncertainty surrounding it, where we chose Germany as a case study, since it has often been cited as a success case and example for other countries. Our findings corroborate the intuition that environmental uncertainties such as the variability of renewable loads need to be modeled explicitly due to the impact on profit distributions, the firm value and thus investment decision-making. This has not yet been explicitly stated and explored in the relevant literature. In addition, feed-in tariffs have found to be an effective means of promoting renewable investment with the current feed-in tariffs in Germany close to the optimal level. An important caveat is, however, that if there is uncertainty about the future development of feed-in tariffs, much higher levels will be needed to make renewable investment attractive for energy companies. This uncertainty of feed-in tariff and its consequences is new to the relevant literature and should be subject to further research. Further research should explore alternative ideas to stabilize profits from renewable energy carriers. One solution which has entered the debate in recent years, for example, is the combination of wind farms with hydro and pumped storage technology to this effect . However, such equipment is extremely costly and it is questionable whether the premium from profit stabilization would make up for this deficiency and whether therefore public funding should rather be directed at R&D targeted at cost reductions in the first place. In a different study, we have built a real options model to look into this specific possibility , but further work is obviously needed.