A key feature of smart grids is the use of demand side resources to provide flexibility to the energy system and thus increase its efficiency. Multienergy systems where different energy vectors such as gas, electricity, and heat are optimized simultaneously prove to be a valuable source of demand side flexibility. However, planning of such systems may be extremely challenging, particularly in the presence of long-term price uncertainty in the underlying energy vectors. In this light, this paper proposes a unified operation and planning optimization methodology for distributed multienergy generation (DMG) systems with the aim of assessing flexibility embedded in both operation and investment stages subject to long-term uncertainties. The proposed approach reflects real options thinking borrowed from finance, and is cast as a stochastic mixed integer linear program. The methodology is illustrated through a realistic U.K.-based DMG case study for district energy systems, with combined heat and power plant, electric heat pumps, and thermal energy storage. The results show that the proposed approach allows reduction in both expected cost and risk relative to other less flexible planning methods, thus potentially enhancing the business case of flexible DMG systems.

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Flexible Distributed Multienergy Generation System Expansion Planning Under Uncertainty

Feed-in-tariffs (FITs) are widely used as policy instruments to promote investments in renewable energy sources (RES). While FITs are often regarded the most effective RES support scheme, regulators around the world continuously review their FIT schemes in the light of budget constraints and evolving policy goals. We assess the impact of adjustments to FIT schemes by quantifying the relationship between FIT levels, i.e. the guaranteed amount paid per quantity of electricity produced, and the propensity to invest in RES. Through a regime switching model, we quantify the impact of regulatory uncertainty induced by regulators considering moves from a FIT scheme to a more market-oriented regulatory regime. Our focus is on market-independent, fixed FITs, the dominant scheme in Europe receiving increasing attention globally. We find that RES investment projects under market-independent, fixed FIT schemes become now-or-never decisions and derive FIT thresholds required to induce investment. We show that uncertainty regarding future regulatory regimes only alters investment behavior for FIT levels near electricity spot market prices and high probabilities of an imminent regime switch.

Optimal Design of Feed-in-Tariffs to Stimulate Renewable Energy Investments Under Regulatory Uncertainty - A Real Options Analysis

There is a growing focus on the economics of adaptation as policy moves from theory to practice. However, the techniques commonly used in economic appraisal have limitations in coping with climate change uncertainty. While decision making under uncertainty has gained prominence, economic appraisal of adaptation still uses approaches such as deterministic cost-benefit analysis. Against this background, this paper provides a critical review and assessment of existing economic decision support tools (cost-benefit analysis and cost-effectiveness analysis) an uncertainty framework (iterative risk management) and alternative tools that more fully incorporate uncertainty (real options analysis, robust decision making and portfolio analysis). The paper summarises each method, provides examples, and assesses their strengths and weaknesses for adaptation. The tools are then compared to identify key differences, and to identify when these approaches might be appropriate for specific applications in adaptation decision making.

The use of new economic decision support tools for adaptation assessment: A review of methods and applications, towards guidance on applicability

Optimal design of feed-in-tariffs to stimulate renewable energy investments under regulatory uncertainty — A real options analysis

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

Real options (RO) theory is well known for enhancing the value of projects under uncertainty. This is achieved by modelling the flexibility that managers possess to adjust the projects in response to changes in their environments. Based on this, RO theory could be used to tackle current energy and environmental issues by enhancing the value of electricity generation projects (EGP), especially renewable energy projects (REP). The potential of RO theory to increase the value of EGP and REP has been a driver for new research in the topic. However, existing literature is still scarce, diverse, and tends to neglect the state of the art of RO theory (e.g. RO in the design of projects). RO studies tend to ignore the use of RO in the design of projects as they are difficult to formulate without the help of experts on the projects' designs. This paper aims to encourage novel research in the application of RO theory to EGP and REP. For this purpose, a critical review of RO theory, its state of the art, and its applications to EGP and REP is presented. This review identifies current areas of interest and gaps in knowledge in this research area. It is concluded that new and novel RO research should address the state of the art of RO theory, and uncertainties that are exclusive for specific types of projects. This future research will require the involvement of electrical engineers specialised in the design of EGP and REP.

F. Rivas-Dávalos

Papers

Real options theory applied to electricity generation projects: A review