Investment In Renewable Generation Research Articles

Renewable electricity capacity planning with uncertainty at multiple scales

We formulate and compare optimization models of investment in renewable generation using a suite of social planning models that compute optimal generation capacity investments for a hydro-dominated electricity system where inflow uncertainty results in a risk of energy shortage. The models optimize the expected cost of capacity expansion and operation allowing for investments in hydro, geothermal, solar, wind, and thermal plant, as well as battery storage for smoothing load profiles. A novel feature is the integration of uncertain seasonal hydroelectric energy supply and short-term variability in renewable supply in a two-stage stochastic programming framework. The models are applied to data from the New Zealand electricity system and used to estimate the costs of moving to a 100% renewable electricity system by 2035. We also explore the outcomes obtained when applying different forms of CO2 constraint that limit respectively non-renewable capacity, non-renewable generation, and CO2 emissions on average, almost surely, or in a chance-constrained setting, and show how our models can be used to investigate the merits of a proposed pumped-hydro scheme in New Zealand’s South Island.

On the use of time varying marginal and time varying average emissions factors in building energy analysis

Calculation of greenhouse gas emissions from grid consumed electricity requires use of grid emissions intensity factors. In current building emissions regulations annual fixed factors are used, though there is growing recognition of the benefits of time-varying factors that take account of the varying emissions intensity as different generators ramp up/down. There are two main types of time varying emissions factors: marginal (MEF) and average (XEF) emissions intensity. Regulators seeking to draft policy around use of time varying emissions factors will need to evaluate the most appropriate methodology. Several studies in the scientific literature state MEF should be used to calculate savings from flexible demand strategies since it is the marginal generator that is the first to respond to small changes in demand. Here we suggest that XEF should in fact be the preferred approach in most scenarios. This recommendation is based on three factors; (i) alignment with historical emissions accounting methods; (ii) a more consistent basis from which to evaluate the alternative (baseline) scenario; and (iii) greater clarity over the incentive that it provides for investment in renewable generation. These factors are explored using examples and data from the Australian National Electricity Market.

Managing the energy trilemma of reliability, affordability and renewables: Assessing consumer demands with discrete choice experiments

AbstractIn response to the looming climate crisis, many countries are adopting technologies to reduce the accumulation of greenhouse gases. However, national energy policies are often multiobjective and resolution deeply divisive. The result is a policy trilemma between the energy mix and the trade‐offs with other policy objectives, including cost and reliability. Utilising a discrete choice experiment (DCE), the objective of this study is to explore Australian household preferences for alternative electricity contracts containing features reflecting changes in future energy policy. The first set of features include investments in renewable generation and community‐based energy storage. The second set of features reflect demand‐side management policies, including installing smart meters and consumption limits being imposed on households during peak demand. Two versions of the DCE were developed to obtain both willingness to pay and willingness to accept estimates for the same features. In line with the literature, differences in the two sets of estimates were observed, with the willingness to accept estimates being statistically larger for some features. These dollar value measures can be used to support public policy decision‐making – the choice of which depending on the context of the policy problem being considered.

A distributionally ambiguous two-stage stochastic approach for investment in renewable generation

The optimal expansion of a power system with reduced carbon footprint entails dealing with uncertainty about the distribution of the random variables involved in the decision process. Optimisation under ambiguity sets provides a mechanism to suitably deal with such a setting. For two-stage stochastic linear programs, we propose a new model that is between the optimistic and pessimistic paradigms in distributionally robust stochastic optimisation. When using Wasserstein balls as ambiguity sets, the resulting optimisation problem has nonsmooth convex constraints depending on the number of scenarios and a bilinear objective function. We propose a decomposition method along scenarios that converges to a solution, provided a global optimisation solver for bilinear programs with polyhedral feasible sets is available. The solution procedure is applied to a case study on expansion of energy generation that takes into account sustainability goals for 2050 in Europe, under uncertain future market conditions.

The contribution of taxes, subsidies, and regulations to British electricity decarbonization

<h2>Summary</h2> Great Britain's carbon emissions from electricity generation fell two-thirds between 2012 and 2019, providing an important example for other nations. This rapid transition was driven by a complex interplay of policies and events: subsidized investment in renewable generation, regulation-driven closure of coal power stations, rising carbon prices, and energy efficiency measures. Previous studies ignore the interactions of these simultaneous measures with each other and with exogenous changes to fuel prices and the weather. Here, we use Shapley values—a concept from game theory—to disentangle these and precisely attribute outcomes (changes to CO₂ emissions, electricity prices, and fossil fuel consumption) to individual drivers. We find the effectiveness of each driver remained stable despite the broad transformation of the power system. The four main drivers each saved 19–29 MtCO₂ per year in 2019, reinforcing the view that there is no "silver bullet" and that a multi-faceted approach to deep decarbonization is essential.

Investing in generation and storage capacity in a liberalised electricity market: An agent based approach

The power sector is undergoing a period of significant change, in terms of the mix of generation technologies, as well as the structure of energy markets, regulation and assets ownership. The shift to a more liberalised electricity system has resulted in an increase in the number of decision makers. This paper demonstrates an agent-based approach for investigating the long-term investment in the GB power generation sector, whilst considering the operability of the system. A key focus of this study is to investigate the efficacy of a range of policies to reduce the emissions and facilitate investment in renewable generation and battery storage. In order to capture the value of battery storage, the hourly operation of the electricity system, considering short-term variation of demand, renewable generation and wholesale electricity prices (including negative prices during high renewable and low demand events), was incorporated in the long-term investment model. The modelling results show while the cost of battery storage is expected to decrease gradually in future, a substantial subsidy is still required to justify investment in battery storage. The deployment of battery storage provides a significant reduction in the overall power generation system cost, peak demand and carbon emissions.

The Contribution of Taxes, Subsidies and Regulations to British Electricity Decarbonisation

Great Britain’s carbon emissions from electricity generation fell by two-thirds between 2012 and 2019, providing an important example for other nations. This rapid transition was driven by a complex interplay of policies and events: investment in renewable generation, closure of coal power stations, raising carbon prices and energy efficiency measures. Previous studies of the impact of these simultaneous individual measures miss their interactions with each other and with exogenous changes in fuel prices and the weather. Here we use Shapley values, a concept from cooperative game theory, to disentangle these and precisely attribute outcomes (CO2 saved, changes to electricity prices and fossil fuel consumption) to individual drivers. We find the effectiveness of each driver remained stable despite the transformation seen over the 7 years we study. The four main drivers each saved 19–29 MtCO2 per year in 2019, reinforcing the view that there is no ‘silver bullet’, and a multi-faceted approach to deep decarbonisation is essential.

The Contribution of Taxes, Subsidies And Regulations to British Electricity Decarbonisation

Great Britain’s carbon emissions from electricity generation fell by two-thirds between 2012 and 2019, providing an important example for other nations. This rapid transition was driven by a complex interplay of policies and events: investment in renewable generation, closure of coal power stations, raising carbon prices and energy efficiency measures. Previous studies of the impact of these simultaneous individual measures miss their interactions with each other and with exogenous changes in fuel prices and the weather. Here we use Shapley values, a concept from cooperative game theory, to disentangle these and precisely attribute outcomes (CO2 saved, changes to electricity prices and fossil fuel consumption) to individual drivers. We find the effectiveness of each driver remained stable despite the transformation seen over the 7 years we study. The four main drivers each saved 19–29 MtCO2 per year in 2019, reinforcing the view that there is no ‘silver bullet’, and a multi-faceted approach to deep decarbonisation is essential.

When the Wind of Change Blows, Build Batteries? Optimum Renewable Generation and Energy Storage Investments.

Renewables have become the cheapest energy source in most of the world, but their generation remains variable and difficult to predict. Recent technological advances have rendered large-scale electricity storage economically viable, thus mitigating the renewable intermittency issue and enabling combinations of renewable generation plus storage, e.g., wind farms and batteries, to be potentially viable candidates to replace fossil fuel power plants. However, it is not yet well-understood how to jointly determine optimal capacity for their generation and storage. Our work aims to shed light on this question by developing a two-product newsvendor model of a utility's strategic capacity investment in renewable generation and storage to match demand with supply, while using fossil-fuel backup, if needed. We establish optimal capacity guidelines contingent on market and technology parameters. We find that renewable generation and storage are strategic complements, except in cases with very high penetration of either technology, when they surprisingly turn into strategic substitutes. We also develop sufficient and necessary conditions, as well as a simple heuristic, to determine which of the many storage technologies can turn a profit in the broadest set of market conditions, and thus is likely to be adopted first (for multi-hour storage). In particular, we show that low-efficiency, cheap technologies, such as thermal, can turn a profit easier than high-efficiency, expensive ones, such as lithium-ion batteries. Using real-life data from Europe and the US, we calibrate our model to provide insight on the role that large-scale storage plays in both the short- and long-term, while technology improves, emission taxes are levied, and renewables become cheaper. Finally, we show that, when generation and storage investment decisions are made by different players, investment levels decrease relative to the monopoly outcome.

Stochastic model to aid decision making on investments in renewable energy generation: Portfolio diffusion and investor risk aversion

Brazil has a different energy matrix from the world, from electricity generation with renewable resources. With the increase in energy demand, environmental problems of hydroelectric plants show a need to increase the installed capacity of renewable sources. Investments in renewable generation are required, decision affected by different aspects, such as variability in generation, uncertainties in the energy market and investor risk aversion, the company’s current portfolio. The paper presents a stochastic decision support model for renewable energy investments, maximizing the expected return for a given level of risk aversion. To assess uncertainties, the Conditional Value-at-Risk (CVaR) is used. Scenarios are generated based on past generation and NEWAVE output data. Simulations with the investment option are related in this energy complementation. The risk for intermittent source involves the company using CVaR assessment. The results conclude that the diversification of the firm’s assets and the complementary composition of the sources of generation reduce the investor’s portfolio’s financial risks and the risk aversion level of the decision maker influences the market position that the company must adopt. Model tending to more conservative solutions when risk aversion is higher, confirming the literature, the existence of a trade-off between risk aversion and expected return.

Analyzing the energy system impacts of price-induced demand-side-flexibility with empirical data

This paper assesses the potential effects on the energy system from a full roll out of a smart phone app designed to connect household electricity consumers with their consumption and price data. The effects of the app in allowing greater demand-side flexibility from household consumers is estimated based on data from an 18-month field trial involving 1557 Austrian households. These estimates are given as hourly price elasticities of electricity demand and hourly energy efficiency treatment effects from consumer engagement with the app. In a novel methodological coupling, the econometric estimates are input into the Balmorel energy system model, which is used to analyze future scenarios of full renewable energy deployment in the Austrian energy system. The results demonstrate that the impact of the flexible residential demand for electricity is small but significant to future system costs. The total discounted system cost increases by 20–24% in the renewable energy scenarios, compared to a business as usual scenario, due to heavy investments in renewable generation. However, system cost is reduced by 4–7% in renewable energy scenarios where the observed demand-side flexibilities are considered. The results are subject to several methodological caveats, but they give a clear signal that ICT-enabled demand side flexibility can be an important cost-saving element that should be integrated into the future energy system and considered in system-level models.

A Benders’ Decomposition Approach for Renewable Generation Investment in Distribution Systems

A model suitable to obtain where and when renewable energy sources (RES) should be allocated as part of generation planning in distribution systems is formulated. The proposed model starts from an existing two-stage stochastic mixed-integer linear programming (MILP) problem including investment and scenario-dependent operation decisions. The aim is to minimize photovoltaic and wind investment costs, operation costs, as well as total substation costs including the cost of the energy bought from substations and energy losses. A new Benders’ decomposition framework is used to decouple the problem between investment and operation decisions, where the latter can be further decomposed into a set of smaller problems per scenario and planning period. The model is applied to a 34-bus system and a comparison with a MILP model is presented to show the advantages of the model proposed.

Moving the Coal-Posts: Ottawa’s Wrong Turn on Carbon Pricing for Electricity Generation

New federal government carbon pricing regulations for electricity generators will reduce the average carbon cost for coal plants, while decreasing incentives for cleaner wind, solar and hydro projects, says a new report from the C.D. Howe Institute. As phase two of the carbon-pricing “backstop”, originally enacted in 2018 under the Greenhouse Gas Pollution Pricing Act, the federal government has proposed an emissions benchmark for electricity generators that will vary by the type of fuel used. In “Moving the Coal-Posts: Ottawa’s Wrong Turn on Carbon Pricing for Electricity Generation,” author Grant Bishop examines Ottawa’s proposed plan and finds that a fuel-specific approach creates an unlevel playing field between different sources of electricity. The federal government’s fuel-specific method contrasts with Alberta’s current Carbon Competitiveness Incentive Regulation, which uses a uniform benchmark for all fuels used to generated electricity. The report finds that relative to a uniform standard for output-based allocations, Ottawa’s fuel-specific benchmark risks distorting the order in which plants are dispatched (i.e. turned on and off as demand requires) – potentially pushing high-emission coal plants ahead of lower-emission natural gas plants. In addition, unlike Alberta’s approach, Ottawa’s system does not allocate credits to zero-emission electricity producers such as hydro, wind and solar, and will diminish incentive for new investment in renewable generation. “Ottawa’s approach undercuts the concept of a single economy-wide carbon price and the economic rationale behind carbon pricing,” says Bishop. “While the federal government is likely concerned to avoid spikes in power prices in those provinces where power generation currently relies on coal, Ottawa risks distorting provincial power markets and investment incentives by differentiating carbon costs for producers of the same product.”

Are Renewables Profitable in 2030? A Comparison between Wind and Solar across Europe

The European Commission (EC) and the European Council have set ambitious energy targets for 2030 in order to secure clean and efficient energy in the European Union. In 2014, EU countries agreed that by 2030, the share of renewables should be 27% of total energy consumption in order to achieve the overall target of 40% GHG emission reduction. In 2018, this overall renewable target has been increased to 32% of total energy consumption. This target holds at the EU level, so all countries should work together either by reducing the energy demand or increasing generation from renewable energy sources (RES), to achieve the overall goals. Electricity generation is one of the sectors affected by the EU targets together with transport, agriculture and industry, as it is one of the major sectors responsible for total emissions. Following the track started with the 2020 targets on emission reductions, renewable electricity generation (RES-E) should increase to 49% of total electricity demand by 2030 in order to be consistent with the overall target on total energy demand, as noted by the in its own impact assessment analysis. The installed capacity in renewable energy has increased strongly during the last decade, when every EU country set up different incentives to promote the investment in renewable generation. There are several works that focus on the costs and the regulatory changes needed to promote the investments in renewable energy. All these works highlight that subsidies given to renewables are positively related with the investment in this type of generation in all EU countries. Despite the positive correlation between subsidies and investment in RES-E, it is widely recognised that the use of subsidies is suboptimal with respect to the first best solution of carbon-pricing, so a rigorous analysis is needed to assess under which conditions investments in renewable energy are economically profitable without subsidies. Looking at 2030, investment costs associated with renewables should decrease over time, making the investment in renewable energy more attractive to market operators. However, European countries are quite different in terms of natural resources and the availability of wind and solar irradiation, thus a careful analysis of the profitability of investments in renewable technologies is required to determine where it is optimal to invest in the future, as capital costs of renewables are often higher than for fossil fuels. Despite the importance of the subject, there are not many studies focusing on the profitability of renewable technologies in 2030 or beyond, in particular when it comes to comparing countries across Europe.

Renewable generation expansion under different support schemes: A stochastic equilibrium approach

Following the deregulation of electricity markets, a current challenge of policy makers is to facilitate the transition to a sustainable power system at the highest welfare for society. In this paper we investigate the efficiency of different support schemes, such as a feed-in tariff, a feed-in premium and tradable green certificates, with respect to incentivizing the required investments in renewable generation. We consider a number of generation expansion problems, and formulate stochastic equilibrium models that account for uncertainty in demand and renewable supply, the risk-aversion of investors and the competitiveness of the market. The problem of the policy maker is formulated as a mathematical program with equilibrium constraints (MPEC) and as a non-linear complementarity problem (NCP) for the feed-in schemes and the certificate market, respectively. Our models are solved for a small illustrative example and a larger case study based on the Danish power system. The results confirm that the main driver for the optimal choice of renewable support scheme is the aversion of power producers towards price and volume risk, while the competitiveness of the market rarely affects such choice.

Game-theoretic modeling of curtailment rules and network investments with distributed generation

Renewable energy has achieved high penetration rates in many areas, leading to curtailment, especially if existing network infrastructure is insufficient and energy generated cannot be exported. In this context, Distribution Network Operators (DNOs) face a significant knowledge gap about how to implement curtailment rules that achieve desired operational objectives, but at the same time minimise disruption and economic losses for renewable generators. In this work, we study the properties of several curtailment rules widely used in UK renewable energy projects, and their effect on the viability of renewable generation investment. Moreover, we propose a new curtailment rule which guarantees fair allocation of curtailment amongst all generators with minimal disruption. Another key knowledge gap faced by DNOs is how to incentivise private network upgrades, especially in settings where several generators can use the same line against the payment of a transmission fee. In this work, we provide a solution to this problem by using tools from algorithmic game theory. Specifically, this setting can be modelled as a Stackelberg game between the private transmission line investor and local renewable generators, who are required to pay a transmission fee to access the line. We provide a method for computing the equilibrium of this game, using a model that captures the stochastic nature of renewable energy generation and demand. Finally, we use the practical setting of a grid reinforcement project from the UK and a large dataset of wind speed measurements and demand to validate our model. We show that charging a transmission fee as a proportion of the feed-in tariff price between 15% and 75% would allow both investors to implement their projects and achieve desirable distribution of the profit. Overall, our results show how using game-theoretic tools can help network operators to bridge the knowledge gap about setting the optimal curtailment rule and determining transmission charges for private network infrastructure.

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

Integration of renewable generation can lead to both diversification of energy sources (which can improve the overall economic performance of the power sector) and cost increase due to the need for further resources to provide flexibility and thus secure operation from unpredictable, variable and asynchronous generation. In this context, we propose a cost-risk model that can properly plan generation and determine efficient technology portfolios through balancing the benefits of energy source diversification and cost of security of supply through the provision of various generation frequency control and demand side services, including preservation of system inertia levels. We do so through a scenario-based cost minimization framework where the conditional value at risk (CVaR), associated with costs under extreme scenarios of fossil fuel prices combined with hydrological inflows, is constrained. The model can tackle problems with large data sets (e.g. 8760 hours and 1000 scenarios) since we use linear programming and propose a Benders-based method adapted to deal with CVaR constraints in the master problem. Through several analyses, including the Chilean main electricity system, we demonstrate the effects of renewables on hedging both fossil fuel and hydrological risks; effects of security of supply on costs, risks and renewable investment; and the importance of demand side services in limiting risk exposure of generation portfolios through encouraging risk mitigating renewable generation investment.

CVaR Constrained Planning of Renewable Generation with Consideration of System Inertial Response, Reserve Services and Demand Participation

Integration of renewable generation can lead to both diversification of energy sources (which can improve the overall economic performance of the power sector) and cost increase due to the need for further resources to provide flexibility and thus secure operation from unpredictable, variable and asynchronous generation. In this context, we propose a cost-risk model that can properly plan generation and determine efficient technology portfolios through balancing the benefits of energy source diversification and cost of security of supply through provision of various generation frequency control and demand side services, including preservation of system inertia levels. We do so through a scenario-based cost minimization framework where the conditional value at risk (CVaR), associated with costs under extreme scenarios of fossil fuel prices combined with hydrological inflows, is constrained. The model can tackle problems with large data sets (e.g. 8760 hours and 1000 scenarios) since we use linear programming and propose a Benders-based method adapted to deal with CVaR constraints in the master problem. Through several analyses, including the Chilean main electricity system, we demonstrate the effects of renewables on hedging both fossil fuel and hydrological risks; effects of security of supply on costs, risks and renewable investment; and the importance of demand side services in limiting risk exposure of generation portfolios through encouraging risk mitigating renewable generation investment.

Alternative low-carbon electricity pathways in Switzerland and it’s neighbouring countries under a nuclear phase-out scenario

Switzerland and Europe are currently at a crossroads with respect to its electricity policy. Several existing nuclear and fossil fuel power plants are to be retired in the coming years. Meanwhile, ambitious carbon dioxide (CO2) emission reductions are envisaged, which could penalise fossil power plants and encourage investments in renewable-based electricity generation. Switzerland and a few other countries in Europe have decided to phase-out their nuclear capacity, thereby removing a low-carbon source of electricity in the medium- to long-term future. In order to understand possible electricity transition pathways for Switzerland, electricity supply options for Switzerland and its four neighbouring countries namely Austria, France, Germany and Italy are analysed to assess non-nuclear alternatives under a stringent climate mitigation policy. The cross border Swiss TIMES electricity model (CROSSTEM), a cost optimisation framework with a long time horizon and an hourly temporal resolution, is used for this analysis. In the absence of any CO2 emission reduction targets, gas-based generation supplemented by electricity imports is the cost-effective alternative to nuclear for Switzerland. For a low carbon electricity system, natural gas based generation with Carbon Capture and Storage (CCS), complemented by an accelerated investment in renewable generation is required. Traditional electricity trade patterns have to be revised, with significant increases in cross-border interconnector capacities necessary to transfer electricity to Switzerland from countries with abundant renewable resources such as Germany and Italy. The average cost of electricity in a decarbonised electricity sector would increase in the range of 30–120% by 2050 compared to today, depending on the scenario assumptions. The availability of CCS technology and the requirement for electricity storage are particularly important to achieve a complete decarbonisation of the electricity sector with a nuclear phase-out.

Dynamic Modeling of Thermal Generation Capacity Investment: Application to Markets With High Wind Penetration

Modeling the dynamics of merchant generation investment in market environments can inform the making of policies whose goals are to promote investment in renewable generation while maintaining security of supply. Such models need to calculate expected output, costs and revenue of thermal generation subject to varying load and random generator outages in a power system with high penetrations of wind. This paper presents a dynamic investment simulation model where the short-term energy market is simulated using probabilistic production costing using the Mix of Normals distribution (MOND) technique to represent residual load (load net of wind output). Price mark-ups due to market power are accounted for. An “energy-only” market setting is used to estimate the economic profitability of investments and forecast the evolution of security of supply. Simulated results for a Great Britain (GB) market case study show a pattern of increased relative security of supply risk during the 2020s. In addition, many new investments can recover their fixed costs only during years in which more frequent supply shortages push energy prices higher.