实物期权理论在风力发电项目投资决策中的应用

由于风力发电项目投资具有很高的不可逆性、不确定性、竞争性和可持续发展特性,传统DCF分析方法不能很好地处理这些特性．DCF分析方法不能完全满足风力发电项目投资决策的需要。分析了风力发电项目投资的特点,提出了在风力发电项目投资决策中引入实物期权的基本思路和模型,并讨论了实物期权模型的应用方法。     

发电量估算不确定性对风电项目投资决策的影响

正风电场开发属于典型的资本密集型产业,风险控制是开发商做出投资决策时必须考虑的重要因素。控制风险需要尽量减少项目评价时的不确定因素,从电价、投资和发电量这三个影响风电项目收益最重要的因素来看,我国已确立的风电标杆价格政策帮助开发商锁定了电价风险;风电装备、施工、监理水平随着近年来风电的大规模开发取得了长足进步,经验的积累使得相关风险可以得到较好的     

风力发电的成本效益分析——以张家口为例

中国风能资源十分丰富,推行风力发电在我国具有普遍适用性和可行性。成本收益是影响可再生能源推广应用的重要因素,因此采用全生命周期法将风力发电项目划分为融资建设期、运营维护期、回收报废期三个阶段,再通过动态平直供电成本法对风电项目的成本效益进行分析。如果不考虑政府干预因素,风力发电效益根本上取决于风电上网电量的多少和上网电价的高低。风力发电成本由非系统成本和系统成本构成,同时也会产生增值税、企业所得税和教育费附加等税费,因此,项目的经济效益=售电收益+补贴-系统成本-非系统成本-应纳税总额。风电项目不能单纯考虑经济收益,要更加注重环境收益,由于风电机组在运行期内基本不排放污染物,为环境治理节约了成本,所以环境效益也要考虑在内。以张家口为例,Ⅱ类风资源区风电的度电成本在0.4264～0.4294元/(kW·h)之间,风电上网电价为0.45元/(kW·h),风电大规模上网具有经济性。整个寿命周期内,度电环境收益约为1.571元/(kW·h)。通过敏感性分析,得出对成本效益影响最大的是上网电价,设备成本和风能资源量的影响次之。投资者应综合考虑经济收益和环境收益,努力降低投资成本,延长设备使用寿命;政府应保持上网电价的稳定性。     

计及CVaR的含光热电站的鲁棒机组组合模型

针对大规模可再生能源并网发电产生的不确定性问题,提出计及条件风险价值（CVaR）的含光热电站（CSP）的鲁棒机组组合模型。首先,建立了CSP机组持续且稳定供电的数学表达;然后,引入CVaR概念用于度量不确定风电给调度运行带来的风险损失,建立计及CVaR的含CSP的鲁棒机组组合模型,构造包含区间、时间和空间的多维不确定性集合,实现对风力发电的不确定性调节;最后,以IEEE 6节点和IEEE 118节点系统为例,验证了模型的有效性。     

三种方法分析光伏发电上网电价

与常规能源发电相比,目前光伏发电电价还不具备竞争优势,制定一个合理的上网电价是推动我国光伏产业健康发展的关键。上网电价取决于光伏电站的建设和运营成本,而影响光伏电站投资成本的因素很多,如太阳能辐射量、器件和设备价格、电站规模、项目融资方式、贷款利率、政府补贴额定等,很难制定出统一的上网电价。本文从具体实例出发,在云南某地拟建一个10MW的单晶硅并网光伏电站,将辐射量、设备价格、电站规模及财务参数等因素都具体化,从电站的建设到运行,考虑所有影响投资成本的因素来计算建设成本。然后利用财务分析中的净现值法、内涵报酬率法、项目回收期法三种方法分析计算相应的较为合理的光伏发电上网电价,比较三种方法得到的结果,讨论不同因素对电价的影响效果。     

光伏发电成本的数学模型分析

光伏发电从2005年进入产业化以来,成本不断降低.目前,我国发改委制定了1元/kWh的光伏发电上网标杆电价.但许多投资者对于光伏发电的成本却感到难以分析,不敢贸然投资.本文给出了光伏发电成本的数学分析模型,讨论了影响光伏成本电价的因素,如装机成本、日照时间、贷款状况、预期的投资回收期以及运营费用等.并根据该模型对现阶段光伏发电的投资效益进行了投资分析.计算结果表明,在我国西北地区,按照1元/kWh的上网电价,目前投资光伏电站的投资回收期为10年.     

基于贝叶斯网络节能住宅的投资决策

为了使投资者对节能住宅投资风险的高低有一定的了解,做出投资与否的正确判断,文中将贝叶斯网络引入节能住宅投资风险研究中,同时采用GeNie2．0贝叶斯分析软件进行建模,构建了节能住宅投资风险决策的贝叶斯网络模型,通过模型的分析可以表明,此时投资者适不适合投资,如果当风险因素发生变化时,可以通过模型的自动更新功能,及时地更新整个网络,从而使投资者更加准确地把握各种信息,并运用一个案例验证了该模型的有效性。     

PJM电力市场交易价格分布特征及波动风险度量

根据PJM电力市场披露的逐时电力交易价格信息,采用假设检验方法,对日前市场、实时市场及辅助服务市场部分交易品种的电价分布特征进行了统计建模,并针对电价分布的尖峰厚尾特性,结合VaR、CVaR尾部风险度量指标,对各市场中的电价波动风险进行精确度量。研究结果表明：PJM电力市场负荷与日前市场电价、实时市场电价具有强相关性,日前市场及实时市场电价服从对数正态分布,辅助服务价格差异较大,分布拖尾特征显著;在给定置信水平下,负荷、日前市场电价和实时市场电价波动风险逐渐增大,但显著小于辅助服务价格波动风险。     

基于财务评价体系的光伏项目含税LCOE分析

该文结合财务评价系统的成本分类,配合国内增值税、所得税等税金的计算方式,推导通用的项目含税平准化度电成本(LCOE)公式,并提出项目含税LCOE值盈亏平衡点的概念和计算方法,来表征项目含税成本与上网电价间的盈亏平衡点.具体实例分析结果表明,含税LCOE公式可以适应项目全生命周期税率和上网电价变化的情况.项目扣减进项税或...     

赋权多级平滑法在水电工程项目群协调风险预测中的应用

为有效预测协调风险可能对水电工程项目群带来的损失程度,提出了赋权多级平滑法,在分析水电工程项目群协调风险及其预测可行性的基础上,将水电工程项目群协调风险造成的损失表述为风险值,并构造协调风险的一组时间序列预测样本,借助一次、二次、三次、四次指数平滑法对样本数据进行分析与计算,在此基础上对计算出的结果按其准确性赋予不同的权重,得出最终预测出的风险值,按照大小确定其对应的风险可控程度,得出该风险可能对水电工程项目群带来的损失程度。案例应用结果表明,该水电工程项目群协调风险属于较高风险,与实际风险值相符。可见所提方法可行、有效。     

Levelized cost of electricity (LCOE) of renewable energies and required subsidies in China

The development and utilization of renewable energy (RE), a strategic choice for energy structural adjustment, is an important measure of carbon emissions reduction in China. High cost is a main restriction element for large-scale development of RE, and accurate cost estimation of renewable power generation is urgently necessary. This is the first systemic study on the levelized cost of electricity (LCOE) of RE in China. Results indicate that feed-in-tariff (FIT) of RE should be improved and dynamically adjusted based on the LCOE to provide a better support of the development of RE. The current FIT in China can only cover the LCOE of wind (onshore) and solar photovoltaic energy (PV) at a discount rate of 5%. Subsidies to renewables-based electricity generation, except biomass energy, still need to be increased at higher discount rates. Main conclusions are drawn as follows: (1) Government policy should focus on solving the financing problem of RE projects because fixed capital investment exerts considerable influence over the LCOE; and (2) the problem of high cost could be solved by providing subsidies in the short term and more importantly, by reforming electricity price in the mid-and long-term to make the RE competitive.     

Dynamic model for market-based capacity investment decision considering stochastic characteristic of wind power

This paper proposes a decentralized market-based model for long-term capacity investment decisions in a liberalized electricity market with significant wind power generation. In such an environment, investment and construction decisions are based on price signal feedbacks and imperfect foresight of future conditions in electricity market. System dynamics concepts are used to model structural characteristics of power market such as, long-term firms’ behavior and relationships between variables, feedbacks and time delays. For conventional generation units, short-term price feedback for generation dispatching of forward market is implemented as well as long-term price expectation for profitability assessment in capacity investment. For wind power generation, a special framework is proposed in which generation firms are committed depending on the statistical nature of wind power. The method is based on the time series stochastic simulation process for prediction of wind speed using historical and probabilistic data. The auto-correlation nature of wind speed and the correlation with demand fluctuations are modeled appropriately. The Monte Carlo simulation technique is employed to assess the effect of demand growth rate and wind power uncertainties. Such a decision model enables the companies to find out the possible consequences of their different investment decisions. Different regulatory policies and market conditions can also be assessed by ISOs and regulators to check the performance of market rules. A case study is presented exhibiting the effectiveness of the proposed model for capacity expansion of electricity markets in which the market prices and the generation capacities are fluctuating due to uncertainty of wind power generation.     

The impact of Chinese carbon emission trading scheme (ETS) on low carbon energy (LCE) investment

China is planning to introduce emission trading scheme (ETS) to decrease CO₂ emission. As low carbon energy (LCE) will play a pivotal role in reducing CO₂ emissions, our paper is to assess the extent and the conditions under which a carbon ETS can deliver LCE investment in China. We chose wind technology as a case study and a real-option based model was built to explore the impact of a number of variables and design features on investment decisions, e.g. carbon and electricity price, carbon market risk, carbon price floor and ceiling and on-grid ratio. We compute critical values of these variables and features and explore trade-offs among them. According to our work, a carbon ETS has a significant effect on wind power plant investment although it cannot support investment in wind power on its own. Carbon price stabilization mechanisms such as carbon price floor can significantly improve the effect of carbon ETS but the critical floor to support investment is still much higher than the carbon price in China pilot ETSs. Our results show that other policy measures will be needed to promote low-carbon energy development in China.     

Pricing offshore wind power

Offshore wind offers a very large clean power resource, but electricity from the first US offshore wind contracts is costlier than current regional wholesale electricity prices. To better understand the factors that drive these costs, we develop a pro-forma cash flow model to calculate two results: the levelized cost of energy, and the breakeven price required for financial viability. We then determine input values based on our analysis of capital markets and of 35 operating and planned projects in Europe, China, and the United States. The model is run for a range of inputs appropriate to US policies, electricity markets, and capital markets to assess how changes in policy incentives, project inputs, and financial structure affect the breakeven price of offshore wind power. The model and documentation are made publicly available.     

偏差电量考核下风光储混合发电商的市场均衡博弈研究

为了更直观地分析风光储混合发电商对市场均衡博弈结果和市场力的影响,在偏差电量考核衡量风光储混合发电出力随机波动性的基础上,综合考虑各发电商的出力限制和储能系统的运行约束,建立以风光储混合发电商和传统发电商收益最大化为目标的市场均衡博弈模型,调用CPLEX软件求解利用非线性互补函数处理得到的混合整数规划模型,并对比分析了风光储混合发电商作为价格接受者和影响者参与电力市场竞争对均衡博弈电价和投标出力的影响。结果表明,风光储混合发电商参与电力市场竞争可抑制传统发电商的市场力,对电价有显著的"削峰填谷"作用;当风光储混合发电商作为价格影响者时,对电价的影响会减弱,但其收益有所增加。     

The role of financial incentives in promoting renewable energy in Jordan

In this research paper the effect of introducing financial incentives to promote green electricity generation, in Jordan, was studied. The incentives investigated include tax reduction, introduction of a grace period, provision of capital or reduced discount rate, reduced depreciation life of assets, and the usage of accelerated depreciation methods. The obtained results show that implementing such tools leads to positive financial improvements that serve in encouraging private sector to invest in renewable energy technologies. It is revealed that variations of both grace period and taxation rate lead to minor impacts on internal rate of return and net present value for such projects. On the other hand, the increase in depreciation period makes electricity generated from renewable sources more attractive, in terms of unit price of generated electricity, using the straight line depreciation method. In the contrary, the choice of accelerated depreciation method leads to better attractiveness as the depreciation period is reduced. The effect of discount rate variations is noticeable, and affects economics of such systems significantly. Finally, the results confirmed that wind energy is ranked first, followed by PV and concentrated solar power schemes are the last under the studied conditions in Jordan.     

A dynamic risk aversion model for virtual energy plant considering uncertainties and demand response

To full use clean energy to meet load demand of electrical and thermal, the paper proposed a novel concept of virtual energy plant (VEP) including wind power plant (WPP), photovoltaic power generation (PV), combined heat and power generation (CHP), solar collectors (SC), electric boiler (EB), heat storage tank (HSK), and incentive‐based demand response (IBDR). Firstly, the basic structure of VEP is designed, including three subsystems, namely, electricity, heating, and energy storage. Then, a basic scheduling model is constructed under the objective of maximizing operating revenue without considering uncertainty. Thirdly, the conditional value at risk (CVaR) method and the robust optimization theory are used to handle the uncertainty factors in objective functions and condition constraints, and the risk aversion scheduling model is proposed. Finally, industrial park group in northern China are chosen, for example, analysis results show (1) VEP could convert the abandoned clean energy, use HSK to store heating energy during the valley load period, and supply heating energy in the peak period to obtain the excess economic benefits. (2) Lower‐prediction accuracy will amplify the uncertainty risk, when the robust β∈[0.8,0.825]&(0.925,1], the increase of confidence level β will lead to larger increase in CVaR. Especially when β∈(0.925,1), decision makers are extremely disgusted with the risks brought by the uncertainty factors, and correspondingly, the output of clean energy becomes minimum. (3) When the capacity ratio of HSK, EB and the electricity price of peak, valley are lower than 3, the values of revenue, VaR and CVaR change faster, but the ratios are larger than 3, the values change slower, which indicates that the scale of HSK capacity needs to be properly controlled to optimize the use of clean energy, and price‐based demand response could improve the operation profit while controlling risk properly. In general, the proposed scheduling model can maximize the use of clean energy to obtain economic benefits while rationally controlling risks.     

Risk-averse based optimal operational strategy of grid-connected photovoltaic/wind/battery/diesel hybrid energy system in the electricity/hydrogen markets

The techno-economic advantages of grid-connected hybrid energy system (HES) exploit synergies to improve reliability and economic efficiency while maintaining grid stability. Therefore, this paper proposes a risk-averse optimal operational strategy of grid-connected photovoltaic/wind/battery/diesel HES to participate into two energy markets including electricity and hydrogen markets. The grid company can flexibly trade power into two markets to maximally achieve profits based on price arbitrage. The risk influences of the uncertainties, i.e., photovoltaic/wind generation, and electricity prices on the expected revenue are evaluated with CVaR model. For a better exhibition of seasonal variability effects on HES optimal operation strategy, two typical Spring/Summer days are chosen. The proposed risk-averse optimal operational strategy is formulated as a two-stage mixed-integer linear programming (MILP) model. The results in a Spring day simulation under non-risk situation indicate that the overall expected revenue can be improved 2.74 times larger if considering hydrogen market. Moreover, the optimal operational strategy of hydrogen production is considerably affected by unpredictable wind farm. Sensitivity analysis also validates that the changes of PV/WT curtailment penalty have a profound influence than battery degradation coefficient on the HES expected revenue.     

云南风电项目枯平期发电量占比计算方法探讨

2020年9月30日,云南“8 + 3”风光项目落地,并出台配套的结构电价政策,即风电项目枯平期2 000 h以内采用燃煤发电上网基准电价,丰水期500 h以内采用集中交易撮合下限价格,超额部分执行竞争性电价,因此对云南结构电价下风电项目发电量评估方法提出了新的要求。基于该电价规则,以云南某个拟建设的风电项目为例,分析实测数据、中尺度数据的每年枯平期风速以及风功率密度、发电量占全年比值,对比采用不同机型计算枯平期发电量占比的变化;并采用长时间序列数据计算风电场枯平期发电量占比,与周边已建成风电场进行对比分析;提出云南结构电价下风电项目的枯平期发电量占比计算方法及项目评估建议。研究结果可为类似云南结构电价下的风电项目发电量评估提供依据。     

A combined modeling approach for wind power feed-in and electricity spot prices

Wind power generation and its impacts on electricity prices has strongly increased in the EU. Therefore, appropriate mark-to-market evaluation of new investments in wind power and energy storage plants should consider the fluctuant generation of wind power and uncertain electricity prices, which are affected by wind power feed-in (WPF). To gain the input data for WPF and electricity prices, simulation models, such as econometric models, can serve as a data basis.This paper describes a combined modeling approach for the simulation of WPF series and electricity prices considering the impacts of WPF on prices based on an autoregressive approach. Thereby WPF series are firstly simulated for each hour of the year and integrated in the electricity price model to generate an hourly resolved price series for a year. The model results demonstrate that the WPF model delivers satisfying WPF series and that the extended electricity price model considering WPF leads to a significant improvement of the electricity price simulation compared to a model version without WPF effects. As the simulated series of WPF and electricity prices also contain the correlation between both series, market evaluation of wind power technologies can be accurately done based on these series.