可再生能源发电项目投资决策的实物期权方法

由于传统DCF分析方法不能完全满足可再生能源发电项目投资决策的需要,本文提出了在可再生能源发电项目投资决策中引入实物期权的思路,初步讨论了可再生能源发电项目投资的实物期权模型,并以风力发电项目投资为例具体讨论了实物期权模型的应用方法.     

可再生能源绿证价格季节性测算方法研究

  目的  在市场化交易模式消纳可再生能源发电的发展趋势下，“市场电价+绿证收入”将成为未来可再生能源发电企业的主要经营模式。以可再生能源发电参与现货电能量市场为研究大背景，对可再生能源绿色电力证书的价格进行研究。  方法  基于现货电能量市场的优化出清模型，应用可再生能源全生命周期成本测算理论，以满足可再生能源发电企业的内部收益为目的，建立了绿证-电能量市场耦合的优化模型，并结合可再生能源季节性出力特性，提出了可再生能源绿证价格季节性曲线及其波动区间的仿真和测算方法。  结果  不同类型的可再生能源绿证价格不同，不同类型的可再生能源绿证价格的气候相关性亦不相同。  结论  绿证价格的科学合理测算不仅可以帮助可再生能源发电企业进行收益评估和制定更为准确的投资决策，还为电力市场主体与交易中心提供相关决策支持。     

考虑效率和效益提升的配网多能源系统投资决策模型

针对配网多能源系统当前能量供给难以适应负荷容量不断增加的问题,文章在研究通过投资建设提升系统可靠运行能力的同时,兼顾投资建设过程后设备的利用效率及收益,利用可再生能源发电设备、储能设备和改造线路在多时空间尺度上对配电网的支撑作用,进行投资容量和选址决策。首先,给出了配网多能源系统拓扑结构,在此基础上分别建立配网多能源系统储能、可再生能源发电和线路改造投资收益模型。然后,对于所需投资设备进行分期规划,建立考虑效率效益双目标的投资时段决策模型;再基于所提出的改进枚举求解方法,对储能设备、可再生能源发电设备和线路改造求解容量和分期投资决策模型。最后,基于某地区多能源电网实际运行数据,建立配网多能源系统投资决策仿真模型,仿真结果验证了所提投资决策模型能够提升配网多能源系统投资的效率和效益。     

分布式光伏发电项目的投资影响因素研究

为应对全球气候变化,中国提出了碳达峰、碳中和的战略目标。作为中国实现该目标的重要途径之一,分布式光伏发电不仅能够减少和替代化石能源消费,而且有利于构建安全、高效的局域微电网,近年来在政策的影响下得到快速发展。目前,分布式光伏发电项目的投资竞争已经进入白热化阶段,优质的项目资源越来越少,同时又面临产业链上游环节原材料价格的波动和补贴退坡等问题,分布式光伏发电项目的投资决策越发困难。首先对分布式光伏发电系统的优势进行了分析,然后从补贴政策、产业链价格传导、售电收入的不确定性这3个方面对新能源投资企业在分布式光伏发电项目投资决策中需要考虑的几个关键影响因素进行了分析。研究结果可为日后在更加严峻的投资环境中的项目投资决策提供借鉴和参考。     

欧盟可再生能源产业去补贴化趋势及对中国的影响

受欧债危机和新政的影响,欧盟可再生能源产业遭遇投资不足以及去补贴化的趋势,致使其发展速度放缓。探究其因,一是可再生能源发电补贴使欧盟成员国面临日益严重的财政困难;二是欧盟希望通过去补贴措施,增加可再生能源产业的市场竞争力;三是可再生能源已成为国内政治博弈的关注点。欧盟可再生能源产业政策的调整将对在欧投资决策以及制定我国的可再生能源政策产生影响,应予以高度重视。     

区域多能源电网可再生能源发电容量规划及投资收益优化模型

针对地区型多种能源形式接入的区域电网,研究了可再生能源发电容量与区域电网内其他多能源形式协调优化及可再生能源发电设备运行效益问题。以高比例风力发电、光伏发电、固体蓄热式电锅炉、电制天然气、燃气储能、热力储能系统构成的区域多能源系统模型研究为基础,提出基于不同调度时间尺度的区域可再生能源发电容量规划和投资效益优化模型。首先,研究区域电网内多能源供能、传输和负荷设备的能源供给和消耗特性,建立基于高比例可再生能源发电不确定性的多能源电网功率平衡模型;其次,研究大规模可再生能源不确定性、动态电价响应、系统运行状态的约束模型,建立区域可再生能源容量配置与收益多目标优化模型及其求解方法;最后,利用东北某地区多能源电网实际运行数据,建立可再生能源发电容量规划及投资收益优化仿真模型。仿真结果表明,文章所建立的可再生能源容量和效益优化模型,能够在保证较高投资效益的前提下,实现较小的可再生能源容量配置。     

可再生能源发电系统虚拟惯量动态控制仿真模型

针对可再生能源发电系统大规模接入电网后,因电网总惯量减小而导致小干扰稳定性能减弱的问题,提出了能够有效提高系统转动惯量的多可再生能源发电系统虚拟惯量动态鲁棒控制方法。文章研究了风电、光伏和储能系统虚拟惯量及其控制机理,建立多可再生能源发电系统的转子运动方程;研究基于多可再生能源发电系统转动惯量动态控制和电网小扰动稳定判别模型的系统稳定性鲁棒控制,建立可再生能源出力和负荷波动下的虚拟惯量动态鲁棒控制模型;基于ADPSS仿真平台,针对多可再生能源虚拟惯量动态控制模型,搭建了风电、光伏和储能与无穷大系统并联模型。对所建立虚拟惯量控制模型进行的仿真分析表明,该模型能够有效地提高多可再生能源发电系统接入条件下的电力系统稳定性。     

考虑碳配额的风电—光热—天然气能源系统储热容量规划研究

可再生能源发电的随机性和波动性已成为影响其大规模应用的主要因素.大规模、低碳的储热系统能够为可再生能源发电系统提供稳定、可靠、经济的电力调节.可再生能源发电系统中的储热容量需要被优化设计以兼顾可靠性、低碳性、经济性.考虑碳配额因素,建立了风电—光热—天然气能源系统储热容量优化模型,验证了风电—光热—天然气能源系统配置储...     

基于配额制的西北可再生能源跨区跨省电力交易经济性评价

预测了西北5省（区）非水电可再生能源的配额量和交易量,建立了配额制下可再生能源电力跨区、跨省交易经济性评价模型和方法,分析了新疆、甘肃和青海等西北可再生能源主要基地跨区、跨省交易送出的主要目标市场,提出了争取可再生能源电力输电补贴、水电比重偏大省份可再生能源发电企业的结算价格采用本省平均购电价格等政策建议。     

德国政府调整可再生能源入网补贴政策

正德国联邦政府2016年通过《可再生能源法》改革方案,对可再生能源发电设施扩建及入网补贴政策予以调整,以期降低成本,鼓励竞争,防止可再生能源发电投资过热。按照德国目前的入网补贴政策,电网运营商必须优先并以较高的指定价格收购利用可再生能源所发绿色电力,并将多出的成本转嫁到消费者     

Joint electricity and carbon market for Northeast Asia energy interconnection

Decarbonization of the electricity sector is crucial to mitigate the impacts of climate change and global warming over the coming decades. The key challenges for achieving this goal are carbon emission trading and electricity sector regulation, which are also the major components of the carbon and electricity markets, respectively. In this paper, a joint electricity and carbon market model is proposed to investigate the relationships between electricity price, carbon price, and electricity generation capacity, thereby identifying pathways toward a renewable energy transition under the transactional energy interconnection framework. The proposed model is a dynamically iterative optimization model consisting of upper- level and lower-level models. The upper-level model optimizes power generation and obtains the electricity price, which drives the lower-level model to update the carbon price and electricity generation capacity. The proposed model is verified using the Northeast Asia power grid. The results show that increasing carbon price will result in increased electricity price, along with further increases in renewable energy generation capacity in the following period. This increase in renewable energy generation will reduce reliance on carbon-emitting energy sources, and hence the carbon price will decline. Moreover, the interconnection among zones in the Northeast Asia power grid will enable reasonable allocation of zonal power generation. Carbon capture and storage (CCS) will be an effective technology to reduce the carbon emissions and further realize the emission reduction targets in 2030-2050. It eases the stress of realizing the energy transition because of the less urgency to install additional renewable energy capacity.     

A real options model for renewable energy investment with application to solar photovoltaic power generation in China

This paper proposes a real options model for evaluating renewable energy investment by considering uncertain factors such as CO₂ price, non-renewable energy cost, investment cost and market price of electricity. A phase-out mechanism is built into the model to reflect the long-term changes of subsidy policy. We apply the proposed model to empirically evaluate the investment value and optimal timing for solar photovoltaic power generation in China. Our empirical results show that the current investment environment in China may not be able to attract immediate investment, while the development of carbon market helps advance the optimal investment time. A sensitivity analysis is conducted to investigate the dynamics of investment value and optimal timing under the changes of unit generating capacity, subsidy level, market price of electricity, CO₂ price and investment cost. It is found that the high investment cost and the volatility of electricity and CO₂ prices, are not conducive to attract immediate investment. Instead, increasing the level of subsidy, promoting technological progress and maintaining the stability of market are useful to stimulate investment.     

Low-carbon generation expansion planning considering uncertainty of renewable energy at multi-time scales

With the development of carbon electricity, achieving a low-carbon economy has become a prevailing and inevitable trend. Improving low-carbon expansion generation planning is critical for carbon emission mitigation and a low- carbon economy. In this paper, a two-layer low-carbon expansion generation planning approach considering the uncertainty of renewable energy at multiple time scales is proposed. First, renewable energy sequences considering the uncertainty in multiple time scales are generated based on the Copula function and the probability distribution of renewable energy. Second, a two-layer generation planning model considering carbon trading and carbon capture technology is established. Specifically, the upper layer model optimizes the investment decision considering the uncertainty at a monthly scale, and the lower layer one optimizes the scheduling considering the peak shaving at an hourly scale and the flexibility at a 15-minute scale. Finally, the results of different influence factors on low-carbon generation expansion planning are compared in a provincial power grid, which demonstrate the effectiveness of the proposed model.     

State-scale evaluation of renewable electricity policy: The role of renewable electricity credits and carbon taxes

We have developed a state-scale version of the MARKAL energy optimization model, commonly used to model energy policy at the US national scale and internationally. We apply the model to address state-scale impacts of a renewable electricity standard (RES) and a carbon tax in one southeastern state, Georgia. Biomass is the lowest cost option for large-scale renewable generation in Georgia; we find that electricity can be generated from biomass co-firing at existing coal plants for a marginal cost above baseline of 0.2–2.2 cents/kWh and from dedicated biomass facilities for 3.0–5.5 cents/kWh above baseline. We evaluate the cost and amount of renewable electricity that would be produced in-state and the amount of out-of-state renewable electricity credits (RECs) that would be purchased as a function of the REC price. We find that in Georgia, a constant carbon tax to 2030 primarily promotes a shift from coal to natural gas and does not result in substantial renewable electricity generation. We also find that the option to offset a RES with renewable electricity credits would push renewable investment out-of-state. The tradeoff for keeping renewable investment in-state by not offering RECs is an approximately 1% additional increase in the levelized cost of electricity.     

An energy pricing scheme for the diffusion of decentralized renewable technology investment in developing countries

The purpose of this paper is to investigate price support for market penetration of renewable energy in developing nations through a decentralized supply process. We integrate the new decentralized energy support: renewable premium tariff, to analyze impacts of tariff incentives on the diffusion of renewable technology in Senegal. Based on photovoltaic and wind technologies and an assessment of renewable energy resources in Senegal, an optimization technique is combined with a cash flow analysis to investigate investment decisions in renewable energy sector. Our findings indicate that this support mechanism could strengthen the sustainable deployment of renewable energy in remote areas of Senegal. Although different payoffs emerged, profits associated with a renewable premium tariff are the highest among the set of existing payoffs. Moreover in analyzing impacts of price incentives on social welfare, we show that price tariffing schemes must be strategically scrutinized in order to minimize welfare loss associated with price incentives. Finally we argue that a sustainable promotion of incentive mechanisms supporting deployment of renewable technology in developing nations should be carried out under reliable institutional structures. The additional advantage of the proposed methodology is its ability to integrate different stakeholders (producers, investors and consumers) in the planning process.     

Offshore transmission for wind: Comparing the economic benefits of different offshore network configurations

It has been argued that increasing transmission network capacity is vital to ensuring the full utilisation of renewables in Europe. The significant wind generation capacity proposed for the North Sea combined with high penetrations of other intermittent renewables across Europe has raised interest in different approaches to connecting offshore wind that might also increase interconnectivity between regions in a cost effective way. These analyses to assess a number of putative North Sea networks confirm that greater interconnection capacity between regions increases the utilisation of offshore wind energy, reducing curtailed wind energy by up to 9 TWh in 2030 based on 61 GW of installed capacity, and facilitating a reduction in annual generation costs of more than €0.5bn. However, at 2013 fuel and carbon prices, such additional network capacity allows cheaper high carbon generation to displace more expensive lower carbon plant, increasing coal generation by as much as 24 TWh and thereby increasing CO₂ emissions. The results are sensitive to the generation “merit order” and a sufficiently high carbon price would yield up to a 28% decrease in emissions depending on the network case. It is inferred that carbon pricing may impact not only generation investment but also the benefits associated with network development.     

Valuing investment decisions of renewable energy projects considering changing volatility

Volatility is an important parameter when evaluating investments using the real options method. For renewable energy investments, the volatility of cash flow continuously changes, because as new information and knowledge are gathered, there is less foreseen variation. This paper proposes an extended recombining trinomial tree model, where the changing volatility is used to generate transition probabilities. The changing volatility is generated using a consolidation process where multiple random variables, including the market price of electricity, carbon price, and lending rate, are integrated into a low-dimension stochastic process. A two-factor learning curve is used to model the changes of investment cost. We apply the proposed model to analyze solar photovoltaic (PV) power generation investment in China. The results show volatility with changing feature. Compared with constant volatility, changing volatility may advance investment decisions and change the project value. Complete grid parity policy in the solar PV industry is infeasible because the opportunity cost brought by the option of delaying cannot be offset. The changing volatility may produce a lower and equally effective subsidy level compared with constant volatility. A carbon emission trading scheme is helpful in advancing investments in renewable energy, which is reflected in improvements in project value, advancements in investment decisions, and reductions in the required subsidy level.     

Evaluating the power investment options with uncertainty in climate policy

This paper uses a real options approach (ROA) for analysing the effects of government climate policy uncertainty on private investors’ decision-making in the power sector. It presents an analysis undertaken by the International Energy Agency (IEA) that implements ROA within a dynamic programming approach for technology investment choice. Case studies for gas, coal and nuclear power investment are undertaken with the model. Illustrative results from the model indicate four broad conclusions: i) climate change policy risks can become large if there is only a short time between a future climate policy event such as post-2012 and the time when the investment decision is being made; ii) the way in which CO₂ and fuel price variations feed through to electricity price variations is an important determinant of the overall investment risk that companies will face; iii) investment risks vary according to the technology being considered, with nuclear power appearing to be particularly exposed to fuel and CO₂ price risks under various assumptions; and iv) the government will be able to reduce investors' risks by implementing long-term (say 10 years) rather than short-term (say 5 years) climate change policy frameworks. Contributions of this study include: (1) having created a step function with stochastic volume of jump at a particular time to simulate carbon price shock under a particular climate policy event; (2) quantifying the implicit risk premium of carbon price uncertainty to investors in new capacity; (3) evaluating carbon price risk alongside energy price risk in investment decision-making; and (4) demonstrating ROA to be a useful tool to quantify the impacts of climate change policy uncertainty on power investment.     

Multi-timescale robust dispatching for coordinated automatic generation control and energy storage

The increasing penetration of renewable energy into power grids is reducing the regulation capacity of automatic generation control (AGC). Thus, there is an urgent demand to coordinate AGC units with active equipment such as energy storage. Current dispatch decision-making methods often ignore the intermittent effects of renewable energy. This paper proposes a two-stage robust optimization model in which energy storage is used to compensate for the intermittency of renewable energy for the dispatch of AGC units. This model exploits the rapid adjustment capability of energy storage to compensate for the slow response speed of AGC units, improve the adjustment potential, and respond to the problems of intermittent power generation from renewable energy. A column and constraint generation algorithm is used to solve the model. In an example analysis, the proposed model was more robust than a model that did not consider energy storage at eliminating the effects of intermittency while offering clear improvements in economy and efficiency