Forward contracts are widely proposed as tools for mitigating market power and ensuring resource adequacy in electricity markets, yet their design and regulation in hydropowerrich systems lack a systematic analytical foundation. This paper shows that while forward contracts can reduce spot prices elevated by market power, they offer no guarantee of doing so: a strategic hydropower producer may deliberately sustain high spot prices to inflate contract prices, capturing revenues in both markets simultaneously. We further show that risk aversion among producers introduces a forward premium above expected spot prices, whose magnitude depends on the system's energy balance, and that mandatory contracting obligations on the retail side can effectively substitute for voluntary hedging demand but create significant vulnerability to forward market manipulation when competition is limited. These findings emerge from stochastic equilibrium models, enabling the representation of perfect competition or strategic behaviour in both the spot and forward markets. It is applied to a case study inspired by the Brazilian power system, which combines a large hydropower fleet, rapidly rising shares of variable renewable energy, and regulated long-term contract auctions. The results highlight important trade-offs between resource adequacy policies and market power mitigation that should inform the design of forward contract regulation in hydropower-rich systems.

The study introduces a two-step approach for creating profitable and operationally feasible hydropower bids in the day-ahead electricity market. The first step builds a wide range of realistic production profiles by solving a detailed short-term scheduling problem that reflects reservoir dynamics, water values, and turbine startup behavior. The second step evaluates these profiles under uncertain market prices and selects a limited set of mutually exclusive block bids that best balance expected revenue and operational cost. By separating profile generation from bid selection, the method keeps the problem computationally manageable while still capturing the physical and economic complexity of hydropower operation. Tests on a real Norwegian system indicate that the approach produces high-quality bids quickly and performs on par with a traditional hourly bidding model, but with far lower computational effort.

Electricity forward contracts are key instruments for managing price volatility in liberalized power markets, where non-storability and real-time balancing create complex price dynamics. These contracts differ from traditional derivatives as they are defined over delivery periods, leading to overlapping maturities and interdependent forward curves. This structure, combined with low liquidity and sparse data in long-term horizons, poses challenges for accurate forecasting. This work proposes a novel probabilistic forecasting framework for electricity forward curves, addressing multivariate dependencies, seasonality, and data sparsity. The approach involves three steps: (i) forward curve estimation with seasonal adjustment, arbitrage-free constraints, and a non-parametric smoothing error model; (ii) dimensionality reduction, and orthogonalization of elementary errors; (iii) probabilistic forecasts using autoregressive models, bootstrap, and Generalized Autoregressive Score (GAS) models for residuals. The framework supports bidirectional estimation and forecasting. By enhancing forecast accuracy and capturing forward curve dynamics, the method facilitates more informed decision-making for energy market participants. Results confirm the model’s effectiveness in capturing key multivariate structures for portfolio risk management.

This paper examines ex post and ex ante risk premiums on Nordic electricity futures over different time horizons, using commercial-grade forecasts from 2013-2024. It assesses the variation in risk premiums with supply scarcity and with seasons, and analyzes higher moments (skewness and kurtosis) in these contexts. The study finds that ex post premiums usually surpass ex ante premiums, with significantly negative premiums in summer across all contracts. Additionally, risk premiums tend to increase during periods of limited hydropower production opportunities. The analysis highlights the importance of skewness in risk premiums, noting positive skewness during autumn and winter.

The changing and rising uncertainty within power systems may result in more frequent and severe misalignments between hydropower storage management and society's interests. This work examines how risk aversion can influence the use of stored hydropower energy in centralized and liberalized power systems. We investigate the impact of different uncertainty sources and compare an equilibrium with risk-averse hydropower producers to risk-neutral and risk-averse central dispatch optimization approaches. Our main finding is that risk aversion can impact the scheduling strategies of competing hydropower producers in liberalized markets and those of a risk-averse central planner in opposite ways. Notably, risk-averse competing producers may have an incentive to spare less water in the cases of an expected energy deficit at the system level and higher price variability, potentially jeopardizing energy security. Meanwhile, contrary to strategic behaviour, widespread risk aversion in hydropower storage management can decrease or increase the producers' revenues at an aggregated level, depending on the energy situation.

This paper proposes a two-phase optimization framework for short-term hydropower scheduling in the day-ahead electricity market using profile block bids grouped in exclusive sets. The first phase solves a nonlinear deterministic model that generates a diverse and operationally feasible set of production blocks by accounting for startup costs, opportunity costs, and hydrological constraints. In the second phase, a two-stage stochastic program is used to select a subset of blocks for market submission under price uncertainty. The proposed approach captures a wide range of production scenarios while ensuring compliance with market design rules. By decomposing the problem and relaxing the binary variables, the proposed framework reduces computational time while still reaching the optimal solution. Profile block bids can be effective in complex systems with multiple reservoirs and interconnections, systems with production constraints over consecutive hours, limited operational flexibility, or high startup and shutdown costs. Numerical experiments show that the proposed model achieves comparable or better profits than hourly bidding strategies, while requiring significantly less computational time.

As the uncertainty and time granularity of short-term electricity markets increase and as intraday trading gains importance, deriving good trading decisions becomes increasingly complex. This paper analyses the potential benefit of coordinating bids in three sequential electricity markets using a three-stage stochastic optimisation. The modelled markets include a typical European market setting consisting of a balancing reserve, a day- ahead, and an intraday market. Due to limited intraday market liquidity, the trading strategies also take price impacts into account. The results indicate that coordinated bidding can increase profitability, with the extent of gains depending on the price impacts. In a case study with a biomass and photovoltaic portfolio operating in Germany, we find that coordinated bidding increases the average revenue by around 18% over all analysed type days. As renewable generation continues to increase, trading strategies that coordinate bids across markets are expected to become increasingly important.

Zonal markets and nodal pricing are the dominant designs for liberalized electricity markets. We propose an alternative design that changes zones in each bidding period according to the estimated most efficient dispatch. These flexible electricity market clearing zones consider the grid's physical constraints to a larger degree than zonal markets but maintain their bidding simplicity and few price areas. We propose a proof-of- concept framework for flexible electricity market clearing zones, including a method to enumerate all zonal configurations. We illustrate the performance of this framework on a case study in the Nordic countries using flow-based market clearing (FBMC), considering a model for the day-ahead market and a real-time balancing market. Our results suggest that flexible electricity market clearing zones on sequential day-ahead and real-time balancing markets achieve costs slightly above nodal stochastic clearing. But, contrary to stochastic clearing, it can guarantee short-term revenue adequacy and cost recovery. Moreover, the flexible market design increases day-ahead market price levels and price variability at the nodal level, particularly in scenarios with high renewable generation, demonstrating its capacity to align price signals with network congestion and real-time supply conditions. Flexible electricity market clearing zones can thus facilitate the integration of renewables by enhancing system adaptability and promoting more efficient resource allocation.

This paper demonstrates that sequentially cleared electricity markets may provide look-ahead benefits equivalent to what is seen in centrally dispatched systems when the market for flexibility is complete. Power systems that use sequentially cleared auctions for short-term operations might suffer from resource waste because of insufficient look-ahead to unexpected (high or low) renewable-based generation in real-time, which leads to dispatches that are costly to change. We formulate the flexibility market as bilateral contracts, which ensure sufficient flexible capacity at the first-stage market phase and adhere to sequential clearing. Such contracts are arranged so that surplus or deficit renewable generation is physically fully compensated by a flexible generator. Our findings contribute to the electricity market design debate and support the current design of European electricity markets.

We study the interplay between coal-fired electric power generator retirements, capacity markets, and environmental policies. We focus on the Reliability Pricing Model (RPM) electricity capacity market in PJM Interconnection L.L.C. (PJM). We find that RPM acts as a channel through which environmental regulations affect the supply mix. Surprisingly, increases in RPM prices lead to more coal retirements even when we control directly for increases in generator profitability induced by higher capacity payments. Increases in RPM prices signal the need for expensive capital improvements and increase the probability of coal retirement.

Aneo is one of the first Nordic power companies to apply stochastic programming for day-ahead bidding of hydropower. This paper describes our experiences in implementing, testing, and operating a stochastic programming-based bidding method aimed at setting up an automated process for day-ahead bidding. The implementation process has faced challenges such as generating price scenarios for the optimization model, post-processing optimization results to create feasible and understandable bids, and technically integrating these into operational systems. Additionally, comparing the bids from the new stochastic-based method to the existing operator-determined bids has been challenging, which is crucial for building trust in new procedures. Our solution is a rolling horizon comparison, benchmarking the performance of the bidding methods over consecutive two-week periods. Our benchmarking results show that the stochastic method can replicate the current operator-determined bidding strategy. However, additional work is needed before we can fully automate the stochastic bidding setup, particularly in addressing inflow uncertainty and managing special constraints on our watercourses.

The transition to a zero-emission passenger vehicle fleet has become imperative because of the growing concerns about climate change. Here, we investigate the trends and socioeconomic determinants influencing emitting and battery electric vehicle ownership using longitudinal data of Norwegian households with any vehicle ownership record from 2005 to 2022, accounting for over 2.4 million unique households. Intriguingly, more than half of the households owning battery electric vehicles had three or more of these vehicles in 2022, indicating an unbalanced ownership distribution concentrating on the wealthiest. Moreover, almost one in ten households once owned battery electric vehicles discontinued ownership by 2022. Our population-level panel data analysis indicates that lower income, having children, and working away from the residence municipality are positively linked to owning emitting vehicles, while demonstrating the opposite effect for battery electric vehicle ownership. Household size and educational attainment also appear to drive vehicle ownership positively. The type of vehicles owned by Norwegians depends on household size, income and work commute, and battery electric vehicle ownership is concentrated in the wealthier part of the population, according to an analysis of socio-economic data of 2.4 million households from 2005 to 2022.

Upgrading the capacity of existing hydropower plants is an important lever to achieve a carbon-neutral power grid. We investigate a hierarchical perspective towards capacity upgrade at a hydropower plant based on work with a European hydropower producer. We develop a nested Markov decision process model that captures both the timing flexibility to delay upgrades and hourly-production flexibility. The former is typically not considered in practice, while the latter is not accurately captured in dynamic upgrade models in academia. Our work bridges this gap. We combine policy and price model structure with options valuation to show that dynamic upgrade policies that capture hourly-production flexibility can be accurately and efficiently valued. We perform extensive numerical experiments on instances calibrated to real market and operational data. We find that our approach allows a hydropower producer to disentangle the value of optimizing the timing of a capacity upgrade versus the value of accurately capturing hourly-production flexibility. Accurately valuing hourly-production flexibility results in more capacity addition, regardless of whether the upgrade timing is optimized. It is thus relevant to NPV models that are popular in practice. Dynamic upgrade policies are viewed as a means of increasing plant value by optimizing upgrade timing. We discover that a significant alternative benefit of optimized upgrade time lies in recovering the upgrade investment cost substantially sooner, even when the increase in market value is marginal.

Analyses of climate policies often assume the economy is in a first-best equilibrium with well-functioning markets. This paper studies policy effects in power systems characterized by a market failure known as the missing market problem, whereby the incompleteness of long-term markets leaves investors exposed to uninsured risk. We find that renewable tax credits and CO 2 taxes may partly correct this market failure, thus providing an economic benefit additional to climate change mitigation. Consequently, illustrative experiments show the costs of these policies to be lower, and in some cases even negative, in power systems with missing risk markets.

Quantity and price risks are key uncertainties market participants face in electricity markets with increased volatility, for instance, due to high shares of renewables. From day ahead until real-time, there is a large variation in the best available information, leading to price changes that flexible assets, such as battery storage, can exploit economically. This study contributes to understanding how coordinated bidding strategies can enhance multi-market trading and large-scale energy storage integration. Our findings shed light on the complexities arising from interdependencies and the high-dimensional nature of the problem. We show how stochastic dual dynamic programming is a suitable solution technique for such an environment. We include the three markets of the frequency containment reserve, day-ahead, and intraday in stochastic modelling and develop a multi-stage stochastic program. Prices are represented in a multidimensional Markov Chain, following the scheduling of the markets and allowing for time-dependent randomness. Using the example of a battery storage in the German energy sector, we provide valuable insights into the technical aspects of our method and the economic feasibility of battery storage operation. We find that capacity reservation in the frequency containment reserve dominates over the battery's cycling in spot markets at the given resolution on prices in 2022. In an adjusted price environment, we find that coordination can yield an additional value of up to 12.5

Replacing conventional cars and trucks with battery electric vehicles requires a rapid expansion of fast-charging infrastructure. However, private sector charging infrastructure investments are delayed by unfavorable project economics and uncertainty in future demand. Prior research has addressed the former using standard net present value (NPV) methods, but neglected the latter. To address this gap, this paper introduces a real options model of charging investments, which quantifies the option value of delaying investment under uncertainty. We use this model to assess the implications of optionality in a representative case. Our analysis provides indicative estimates of how investment timing is impacted by alternative policy options: grants, long-term contracts, demand charge re-design, and Zero Emission Vehicle standards. We estimate that if grant subsidies are informed by a traditional NPV analysis, firms would delay investing by more than 5 years. Perhaps surprisingly, even low levels of risk incentivize long delays. We find that policies targeting optionality are substantially more cost-effective than the traditionally used grants. Specifically, we calculate that long-term contracts for differences can trigger immediate investments at a cost 68% lower than grants. A simpler but relatively cost-effective alternative is to introduce a phase-out schedule for grants to discourage investment delays.

Once a subsidy scheme is close to reaching its goal or loses political support, it may be terminated. An important question for policy makers is how to minimize the negative impact of the risk of subsidy termination on industrial investment. We assume the social planner aims to increase capacity and welfare and uses a subsidy, which has an uncertain lifetime, for the purpose. We examine a monopolist supplying an uncertain demand, faced with the option to expand capacity by irreversibly investing in small increments. We find that the firm installs capacity expansions sooner and, consequently, installs a larger capacity than a firm without a subsidy. A firm’s total investment during the subsidy’s lifetime increases with both the subsidy size and the likelihood of subsidy withdrawal. However, this happens at the cost of less investment directly after the subsidy has been retracted. The optimal subsidy size strongly depends on the point in time at which the social planner aims to maximize the welfare — the further into the future, the larger the welfare optimal subsidy. Furthermore, the welfare optimal subsidy size strongly depends on the social planner’s discretion over adjustments to the subsidy size.