Elsevier

Energy Economics

Volume 75, September 2018, Pages 261-273
Energy Economics

Quantifying the effects of uncertain climate and environmental policies on investments and carbon emissions: A case study of Chile

https://doi.org/10.1016/j.eneco.2018.08.014Get rights and content

Highlights

  • We quantify the effect of uncertainty of climate change policies on investments.

  • We compute bounds using two different equilibrium models.

  • The cost of uncertainty ranges between 3.2.

  • Uncertainty also increases carbon emissions.

Abstract

In this article we quantify the effect of uncertainty of climate and environmental policies on transmission and generation investments, as well as on CO2 emissions in Chile. We use a two-stage stochastic planning model with recourse or corrective investment options to find optimal portfolios of infrastructure both under perfect information and uncertainty. Under a series of assumptions, this model is equivalent to the equilibrium of a much more complicated bi-level market model, where a transmission planner chooses investments first and generation firms invest afterwards. We find that optimal investment strategies present important differences depending on the policy scenario. By changing our assumption of how agents will react to this uncertainty we compute bounds on the cost that this uncertainty imposes on the system, which we estimate ranges between 3.2% and 5.7% of the minimum expected system cost of $57.6B depending on whether agents will consider or not uncertainty when choosing investments. We also find that, if agents choose investments using a stochastic planning model, uncertain climate policies can result in nearly 18% more CO2 emissions than the equilibrium levels observed under perfect information. Our results highlight the importance of credible and stable long-term regulations for investors in the electric power industry if the goal is to achieve climate and environmental targets in the most cost-effective manner and to minimize the risk of asset stranding.

Introduction

In 2014 the Intergovernmental Panel on Climate Change concluded that changes in climate due to global warming are a major threat to humans, a large number of economic activities, and most ecosystems on earth (Field et al., 2014). In response to this message, many countries and regions have enacted or are discussing the implementation of a series of environmental and economic policies that aim at reducing greenhouse gas emissions across all economic and industrial activities. In the electricity industry, these policies include carbon taxes and cap-and-trade programs (Chen and Tseng, 2011), Renewable Portfolio Standards (RPSs) (Lyon and Yin, 2010), feed-in tariffs (Couture and Gagnon, 2010), production tax credits (Wiser et al., 2007), and energy efficiency programs (Arimura et al., 2011), among others. To date, more than 100 countries have either binding or voluntary renewable targets (REN21, 2015) and nearly 40 national jurisdictions have put a price on carbon emissions (Kossoy and Guigon, 2012).

A salient feature of these policies is that they are highly uncertain, which complicates investment decisions for both power transmission planners and generation firms. Part of the uncertainty of climate policies is a direct consequence of the uncertainty of multi-decadal climate forecasts and the potential effects of increasing levels of greenhouse emissions on the atmosphere (Allen et al., 2000, Murphy et al., 2004, Tol, 2003). Naturally, as prediction models improve and new information becomes available, policy makers can adjust the stringency of environmental regulations in order to mitigate the effects of climate change in the most economically efficient manner. Furthermore, the use of different methodologies can also have relatively large impacts on, for instance, estimates on the social cost of carbon emissions (Tol, 2008, Ackerman and Stanton, 2012), which are ultimately used to guide environmental policies such as carbon taxes.

A changing political landscape also leads to uncertain environmental policies. For instance, generation firms and transmission planners in the US now face large regulatory uncertainties given the goal of the current administration to basically dismantle most of the environmental and climate policies enacted during the two previous administrations (Kraft, 2017). In Europe, the UK has had a history of inconsistent energy policies to reduce greenhouse gas emissions and to incentivize investments and generation from renewable energy technologies (Newbery, 2016). Spain, a country that fostered a large amount of investments in renewables through feed-in tariffs, decided to drastically reduce the subsidies to solar PV installations by nearly 30% in 2011 after the financial crisis, which likely affected investment decisions in the Spanish electricity market (Hofman and Huisman, 2012).

As it has been demonstrated in prior studies, an optimal portfolio of investments for one scenario of market, regulatory, and technological conditions can be highly suboptimal if the future turns out to be different to the planned one (van der Weijde and Hobbs, 2012, Munoz et al., 2014, Munoz et al., 2015). In particular, more uncertainty increases the risk of stranded transmission and generation assets (Wright, 2012).

In this paper, we focus on quantifying how uncertain climate policies as a consequence of changing political landscapes can affect both transmission and generation investments in Chile. We define a set of five different policy scenarios inspired by current regulations in Chile and on international policies and use a two-stage stochastic transmission and generation planning model with recourse to find optimal investment policies. Under the assumption of a perfectly competitive generation market, aligned objectives, inelastic demand, and proactive transmission investments that are 100% paid for by demand, this solution is equivalent to an equilibrium on investments and operations for a deregulated generation market. We employ this model to estimate bounds on the cost of uncertain climate and environmental policies. The lower bound results from the assumption that all generation firms and the transmission planner will develop plans considering all scenarios explicitly, which minimizes the expected system cost of meeting future demand and climate policies. The upper bound results from imposing deterministic investment plans, assuming that both generators and the transmission planner will act myopically, ignoring uncertainty.

Our results indicate that, for our set of scenarios, the cost of uncertain climate and environmental policies for the Chilean system is between 3.2% ($1.8 Billion, lower bound) and 5.7% ($3.3 Billion, upper bound) of the optimal total system cost of the stochastic model. Furthermore, uncertain policies can result in nearly 18% higher CO2 emissions than under perfect information if agents explicitly account for uncertainty in their decisions using a stochastic planning model, which could result in a high regret if more stringent emissions policies are imposed in the future. Consequently, an uncertain political landscape can impose a rather high cost in the Chilean power system, even if all agents plan investments using sophisticated planning tools such as stochastic programming.

The main message for regulators and planning authorities is that, if possible, policy uncertainties should be reduced to a minimum. Otherwise, planning with explicit consideration of uncertain policies would yield the lowest expected system cost. For planning authorities that are also responsible for shaping long-term energy and environmental policies, such as in Chile, this means that transmission infrastructure should be selected taking into consideration that these policies could change significantly in the near future under a different political administration or as new information becomes available. To our best knowledge, this is the first study that quantifies the investment effects and costs of uncertain climate and environmental policies on investments, costs, and carbon emissions in Chile, taking investments in transmission and generation as a response to these policies. Yet, models that are similar to the one we use in this study have been employed before in other contexts (e.g., to quantify the value of stochastic instead of deterministic transmission planning models in regions of Europe (van der Weijde and Hobbs, 2012) and the US (Munoz et al., 2014)).

We structure the rest of the paper as follows. In Section 2 we present a literature review focused on previous research studies that have quantified the effect climate change and climate change policy uncertainty on generation and transmission investment decisions. In Section 3 we present a two-stage stochastic transmission and generation planning model. In Section 4 we summarize our case study. This includes a simplified representation of the Chilean power system and our assumptions of policy scenarios. In Section 5 we present our numerical results and analyze their implications. Finally, in Section 6 we conclude and provide some policy recommendations.

Section snippets

Literature review

Climate change will have a large impact in the energy sector (Parkpoom et al., 2005). The rise of temperatures, changes in precipitation, humidity variation, and the number of sunny days per year will affect both the consumption and production of energy (Feenstra et al., 1998). It is expected that consumption patterns of electricity will change as average temperatures increase (Franco and Sanstad, 2008, Hamlet et al., 2010). Multiples studies have shown that climate change will lead to a

Methodology

We quantify the effect of uncertain climate and environmental policies using two different equilibrium models. One is deterministic and assumes that all market agents have access to perfect information. The other one is stochastic and assumes that all agents are risk-neutral and make decisions trying to minimize expected system cost (transmission planner) or maximize expected profits (generation firms). We model uncertainty of climate and environmental policies using a set of scenarios that

Case study

Our case study is a network reduction of the Chilean power system. We considered the two largest power systems in the country that currently operate in a single synchronized grid known as the National Electric System (SEN) operated by the national system operator: The Northern Interconnected System (SING) and the Central Interconnected System (SIC). The model consists of 28 buses, 36 transmission elements, and 351 aggregated generators with an installed capacity of 22.74 GW. We model investment

Results

The model outlined in Section 3.2 is a mixed-integer linear program that was formulated in AIMMS version 4.25 (64-bit) and solved using CPLEX 12.6.3 in a computer with an Intel Core i5-2450 M CPU @ 2.50 GHz with 6GB of RAM. The size of the stochastic optimization model is 3.2 million variables (555 integer) and 3.4 million constraints. We solved all optimization problems to a 0.5% optimality gap,9

Conclusions

Several countries around the world have already enacted or are considering implementing climate change or environmental policies to reduce greenhouse emissions, particularly in the electric power industry. Unfortunately, changing political agendas and uncertain estimates of the social costs of CO2 emissions and other pollutants make investment planning in new transmission and generation infrastructure very difficult, mainly because these are long-lived assets.

In this paper we use a two-stage

Acknowledgments

The research in this article was supported by FONDECYT #11150029, CONICYT/FONDAP/15110019 (SERC-CHILE), and CONICYT-Basal Project FB0008. We thank the editor and two anonymous reviewers for their constructive comments, which helped us to improve earlier versions of this manuscript.

References (79)

  • R.S. Go et al.

    Assessing the economic value of co-optimized grid-scale energy storage investments in supporting high renewable portfolio standards

    Appl. Energy

    (2016)
  • L. Hirth

    The benefits of flexibility: the value of wind energy with hydropower

    Appl. Energy

    (2016)
  • D.M. Hofman et al.

    Did the financial crisis lead to changes in private equity investor preferences regarding renewable energy and climate policies?

    Energy Policy

    (2012)
  • W.R. Huss

    A move toward scenario analysis

    Int. J. Forecast.

    (1988)
  • A. Inzunza et al.

    CVaR constrained planning of renewable generation with consideration of system inertial response, reserve services and demand participation

    Energy Econ.

    (2016)
  • T.K. Mideksa et al.

    The impact of climate change on the electricity market: a review

    Energy Policy

    (2010)
  • F.D. Munoz et al.

    New bounding and decomposition approaches for MILP investment problems: multi-area transmission and generation planning under policy constraints

    Eur. J. Oper. Res.

    (2016)
  • F.D. Munoz et al.

    Aiming low and achieving it: a long-term analysis of a renewable policy in Chile

    Energy Econ.

    (2017)
  • F.D. Munoz et al.

    Does risk aversion affect transmission and generation planning? A Western North America case study

    Energy Econ.

    (2017)
  • F.D. Munoz et al.

    Optimizing your options: extracting the full economic value of transmission when planning under uncertainty

    Electr. J.

    (2015)
  • D.M. Newbery

    Towards a green energy economy? The EU Energy Union's transition to a low-carbon zero subsidy electricity system-lessons from the UK's electricity market reform

    Appl. Energy

    (2016)
  • S. Pryor et al.

    Climate change impacts on wind energy: a review

    Renew. Sust. Energ. Rev.

    (2010)
  • A.H. van der Weijde et al.

    The economics of planning electricity transmission to accommodate renewables: using two-stage optimisation to evaluate flexibility and the cost of disregarding uncertainty

    Energy Econ.

    (2012)
  • R. Wiser et al.

    Using the federal production tax credit to build a durable market for wind power in the United States

    Electr. J.

    (2007)
  • G. Wright

    Facilitating efficient augmentation of transmission networks to connect renewable energy generation: the australian experience

    Energy Policy

    (2012)
  • F. Ackerman et al.

    Climate risks and carbon prices: revising the social cost of carbon

    Economics: The Open-Access, Open-Assessment E-Journal

    (2012)
  • M.R. Allen et al.

    Quantifying the uncertainty in forecasts of anthropogenic climate change

    Nature

    (2000)
  • T.H. Arimura et al.

    Cost-effectiveness of electricity energy efficiency programs

  • BCN, 2016. Ley 20936, Establece un nuevo sistema de transmision electrica y crea un organismo coordinador independiente...
  • N. Bloom et al.

    Uncertainty and investment dynamics

    Rev. Econ. Stud.

    (2007)
  • CE, 2016. Reporte Anual 2016, Coordinador Electrico Nacional....
  • Y. Chen et al.

    Inducing clean technology in the electricity sector: tradable permits or carbon tax policies?

    Energy J.

    (2011)
  • R.T. Clemen et al.

    Combining probability distributions from experts in risk analysis

    Risk Anal.

    (1999)
  • CMI, 2015. Estudio de Transmision Troncal 2015-2018, Informe 4. Consorcio de Mercados Interconectados....
  • CNE, 2015. Informe Costos de Inversión por Tecnología de Generación, Comisión Nacional de Energía....
  • R. Cooke et al.

    Experts in Uncertainty: Opinion and Subjective Probability in Science

    (1991)
  • EE, 2015. Explorador de Energía Eólica, Universidad de Chile....
  • EIA, 2013. Updated Capital Cost Estimates for Utility Scale Electricity Generating Plants, Energy Information...
  • ES, 2015. Explorador de Energía Solar, Universidad de Chile....
  • Cited by (0)

    View full text