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.

Buildings currently account for over 30% of final energy use and 19% of European energy-related greenhouse gas (GHG) emissions, making reductions in this sector highly significant. Zero Emission Buildings (ZEBs) and Zero Emission Neighborhoods (ZENs) have emerged as a potential solution. They aim to achieve net-zero emissions by offsetting embodied emissions through power export from local renewable energy sources (RES), typically solar photovoltaic (PV). While accounting for material-related emissions follows well-established standards, emissions caused by electricity demand remain challenging to quantify. This paper investigates various methods for quantifying emissions linked to energy consumption and local production in a ZEN. We also examine how different time resolutions and geographical scopes impact the final outcomes. We test these calculation approaches on a ZEN case study, exploring how they influence the required investments in local RES. Our results indicate very large variations across emission factor methods and the potential for biases towards specific technologies depending on the methodological choices. In order to ensure that ZENs actually contribute to limiting GHG emissions, we recommend that the approach for calculating emission factors be region-specific and adjustable over time.

The increasing adoption of electric vehicles (EVs) in urban areas poses overloading challenges to the power distribution grid. Therefore, previous research has focused on developing different smart charging architectures, strategies, and models to mitigate potential threats in the electrification of the transportation sector. However, many existing studies focused on either optimization-based or machine-learning (ML)-based approaches separately, often within centralized architectures. This article presents a comparative assessment of smart charging control strategies in a distributed architecture, considering both optimization and ML-based models, as well as their combination. The proposed system operates within a receding horizon framework, ensuring adaptability for continuous real-life applications. A comprehensive set of scenarios is analyzed, varying upper level control strategies, forecasting time horizons, energy allocation strategies, and feedback mechanism between upper and lower level layers of the distributed control architecture. The results show that for those systems where feedback from EVs cannot be implemented, the most suitable operation control strategy in terms of satisfying different involved parties is an ML prediction-driven approach. For the scenarios where feedback can be implemented, the optimal strategy is an optimization model with fixed energy allocation per day. All scenarios with the feedback mechanism show more than 95% delivery of energy requests, while for the scenarios without feedback, the best delivery is only 79%. The obtained results can help charging point operators (CPOs), EV users, and distribution system operators (DSOs) to analyze EV charging strategies and choose the best-suited operational approach based on charging limitations and possibilities.

Artificial intelligence (AI) offers transformative opportunities for addressing critical barriers to large-scale green hydrogen deployment. Despite its promise as a clean energy carrier, green hydrogen adoption is constrained by challenges related to renewable integration, production efficiency, and participation in electricity and hydrogen markets. This short communication presents a structured literature-based analysis and conceptual framework examining how AI can support green hydrogen integration from production to market operations. We synthesize recent advances in AI-enabled renewable forecasting, electrolyzer optimization, hydrogen demand prediction, and electricity–hydrogen market coordination and organize them into a system-level perspective relevant for energy system planning. In addition, we discuss emerging directions such as AI-driven autonomous energy systems, smart hydrogen trading platforms, and personalized energy management, while highlighting key barriers related to data availability, model interpretability, cybersecurity, and regulatory adaptation. By clarifying the role of AI across the electricity–hydrogen interface, this work contributes a forward-looking framework to support researchers, system planners, and policymakers working on digitalized energy transitions.

Hydropower representations are often simplified in power system investment models, and overestimate the flexibility potential of storage technologies. More accurate hydropower representations are required as the share of variable renewable generation capacity increases, and the need for more flexible resources rises. This paper proposes a framework for modelling cascaded hydropower systems using a state-of-the-art capacity expansion model. This framework is then used in a case study of balancing variable renewable generation in Europe using pumped hydropower storage in Southern Norway. The case study results show that a cascaded hydropower system has reduced flexibility potential, leading to a change in optimal generation mix, slightly increased total costs and more generation from thermal resources.

Energy system transformations are unfolding amid profound uncertainties in technology, economics, and geopolitics. The urgency and speed at which these transformations must proceed add to the complexity, placing greater demands on forward-looking planning and decision making. At the same time, the rising frequency of extreme events, driven in part by climate change, underscores the need to enhance the resilience of energy systems. These events include natural hazards, such as severe storms, floods, and earthquakes, as well as human-induced threats, ranging from accidents to sabotage and armed conflicts. Much of the recent resilience discussion has focused on short-term operational events, like grid faults due to storms or blackouts due to frequency oscillations, often causing local and regional disruptions. Nevertheless, these events can be managed with available measures, such as increasing reserves, adding synchronous condensers, or deploying more capable inverters. Since ancillary services represent a small share of total system cost, the most significant resilience-cost tradeoffs occur at the infrastructure level, where resilient long-term planning matters most. This article takes a broader view, focusing on resilience challenges that arise in long-term planning of highly integrated energy systems. These include large-scale infrastructure decisions, such as securing more resilient gas supplies or building capital-intensive assets that reduce vulnerability during the ongoing transformation of connected commodity networks. Unlike short-term operational issues, these choices involve major investments with long-lasting consequences. The costs of addressing extreme events at the infrastructure level are substantial and raise complex tradeoffs between system resilience and cost efficiency. Offering a practitioner's guide for resilient long-term planning of integrated energy systems, this article presents a structured approach to incorporate uncertainty and resilience into the development of complex energy infrastructures. It aims to bridge today's largely deterministic planning approaches with the growing need for risk-aware and resilient strategies, particularly considering climate- and weather-related threats and system-wide shock events.

As extreme events become more frequent and severe, they require greater consideration in energy system analysis. This study addresses this need by examining the impact of a sudden halt in photovoltaic capacity expansion within a stochastic energy system model. The EMPRISE framework is used to analyse a development pathway towards 2045 for 21 European countries, based on meteorological data from 2012. By introducing an extreme event with varying occurrence probabilities, the resulting system configurations are compared regarding system costs, investment decisions, and the activation of recourse options. The results indicate that, assuming a 50% occurrence probability of the extreme event that results in 400 GWel less photovoltaic capacity, a total of 81 GWel more wind power and 27 GWel more gas-fired power plants will be installed in Europe compared to the reference scenario, with the latter leading to an increase in hydrogen consumption of up to 700 TWh/yr.

Norway’s long-term climate strategy emphasises reducing the environmental impact of waste management by mitigating fossil CO2 emissions, particularly through the integration of carbon capture and storage (CCS) in municipal Waste-to-Energy (WtE) systems. WtE facilities are important because they play a year-round role in district heating. This study applies an Analytical Hierarchy Process (AHP)-based Multi-Criteria Decision Analysis (MCDA) framework to evaluate technologies treating Municipal Solid Waste (MSW) in Trondheim, incorporating CCS retrofits at 85% and 95% capture rates. The model comprises four main criteria, twelve sub-criteria, thirteen key indicators, and eight deciding factors, which are weighted based on local performance data. These weights were processed through a scenario modelling interface to evaluate different WtE system configurations reflecting Trondheim’s existing infrastructure and potential future upgrades. The results show that retrofitting the city’s incineration plant with a 95% capture unit yields the highest composite score, improving environmental performance over the current arrangements; however, the integrated assessment also quantifies the trade-off that higher capture increases auxiliary energy demand and annualised costs, implying diminishing marginal benefits when moving from moderate to very high capture rates. The presented approach provides a transferable, data-driven tool for municipal decision-makers not only in Trondheim but worldwide to evaluate CCS unit retrofits of WtE plants under changing waste streams. By adapting to regional waste composition and policy constraints, the framework can support strategic planning for low-carbon, integrated waste management systems globally.

Municipal waste-to-energy (WtE) plants are central to district-heating (DH) supply in Nordic cities, yet they face two systemlevel challenges, including CO 2 emissions from the fossil fraction of municipal solid waste and a seasonal mismatch between heat production and varying DH demand in summer and winter. In Trondheim (Norway), this mismatch leads to summer heat rejection and winter dependence on auxiliary peak units, particularly electric boilers. This study investigates whether combining post-combustion carbon capture and storage (CCS) with large-scale seasonal thermal energy storage (STES) can reduce Trondheim's net CO 2 emissions and more effectively use excess summer heat in the DH system. Hourly simulations were performed in EnergyPLAN, where a calibrated reference energy system for Trondheim within the NO3 bidding-zone boundary was compared with a future transition configuration. The future transition energy system considers a case in which excess heat from waste incineration in summer is stored and later used for winter heat supply. It includes a CCS retrofit of the waste incineration plant in Trondheim with a 90% capture rate applied to the fossil CO 2 fraction, a pit-based STES with 90 GWh t_h storage capacity, and expanded variable renewable energy generation. The assessment includes hourly energy balances, DH technology dispatch, electricity exchange behaviour, renewable energy shares, and annual CO 2 emission; while detailed engineering design, siting, and techno-economic optimisation are outside the study boundary. The results show that the combined CCS-STES configuration enables clear inter-seasonal heat shifting from summer to winter and reduces dependence on auxiliary DH supply. Approximately 0.16 TWh of heat becomes available for storage charging, while the annual auxiliary-source contribution to DH decreases from 0.20 TWh/yr in the reference scenario to 0.18 TWh/yr in the future scenario. Electrification increases electricity demand and intensifies import-export activity, while the system remains net-exporting on an annual basis. Most importantly, the CCS-enabled 2035 case shifts the annual CO 2 balance from +0.070 Mt/yr to-0.122 Mt/yr. The results indicate that joint CCS-STES deployment can transform municipal WtE from a baseload heat supplier into a combined flexibility and deep-mitigation asset for DH-dominated cities.

The rapid growth of Distributed Energy Resources (DERs) in distribution networks is exposing a critical gap in electricity market design: existing models aggregate distribution grids into single nodal injections at transmission buses, systematically discarding the voltage constraints, reactive power coupling, and internal congestion signals that determine true locational value. Without accurate price propagation across the TSO–DSO interface, flexibility resources remain undervalued, investment signals are distorted, and welfare losses grow with DER penetration. This paper addresses the problem of price-preserving aggregation through a fundamental approach focusing on its development in the theory of spot pricing of electricity and Optimal Power Flow (OPF) problems. We organize existing approaches around the distinction between feasibility-preserving and price-preserving aggregation, and define price-propagation conditions via interface dual variables. The review highlights that no existing aggregation method simultaneously guarantees feasibility and nodal price equivalence: feasibility envelopes constrain the dispatch space but carry no dual information, while simplified OPF models neglect pricing of ancillary services, which are needed for a price-preserving aggregation. We formalize this gap through a nodal price deviation metric and argue that aggregate welfare metrics are insufficient proxies for locational signal accuracy. Finally, we present a conceptual framework for price-preserving aggregation of distribution grids in relaxed ACOPF-based market models.

This study investigates the implications of relying on robust hydropower scheduling for ensuring energy security in low-carbon power systems. A hydropower scheduling, costminimizing central dispatch model with risk aversion is used to solve two-stage problems on a rolling horizon using Benders’ decomposition. The model is applied to a simplified representation of a low-carbon, hydropower-rich power system. Moderately risk-averse water management proves valuable in a low-carbon power system with expensive flexibility sources, as it better prepares for costly, unforeseen events, thereby reducing average costs. Meanwhile, hedging too much against worst-case scenarios can lead to inefficiencies and high consumer bills. The paper concludes by discussing policy insights and reflecting on social expectations about energy security in low-carbon, hydrodominated power systems.

The integration of green hydrogen in local energy markets is often analyzed from a technical flexibility perspective, while the effect of market design rules remains less explored. This paper proposes a coordinated local electricity-hydrogen market framework in which hydrogen participation is regulated by explicit renewable access mechanisms. A mixed-integer linear programming model is developed to co-optimize electricity trading, battery operation, wind allocation and hydrogen production under centralized coordination. Six regulatory cases are examined including hydrogen supply options and access of local wind. Results are obtained for representative seasonal weeks for Norwegian energy community. Electrolyzer, when connected as rigid load, increases grid dependence, but also improves system cost when price-based participation is activated. Direct renewable access reduces grid imports, enhances wind allocation and introduces competition with households for energy distribution and system cost optimization. Furthermore, findings show that (i) hydrogen integration in local energy systems is essentially a market design problem and (ii) renewable access rules critically determine system behaviour, flexibility interactions and seasonal performance.

Hydropower is a low-carbon energy technology with a unique capability to provide flexibility and long-term energy storage, thus being a key contributor to cost-effective and reliable decarbonisation of power systems. To ensure sustainable operation, environmental regulations are normally imposed on the plants. Some of these regulations can be difficult to model in existing scheduling tools based on optimisation because of their non-convex and logical characteristics. This study contributes to the existing literature by assessing the operational impacts of two types of complex environmental reservoir constraints, with the aim to identify the operational implications as well as the economic impacts of including these constraints in medium-term hydropower scheduling. The results show that optimal reservoir management may change considerably due to these types of constraints. An important finding is that improved planning can reduce the economic loss associated with the environmental regulation, nevertheless, it is shown that the improvements depend on the power price and the characteristics of the hydropower system.

This work analyzes a net-neutral European power system in 2050 using the market simulator FanSi. The study evaluates to what extent expansions of wind, solar, and nuclear capacity can replace the energy and services supplied by hydrogen in a hydrogen-optimistic base case. Entirely wind and combined wind and solar expansions achieved similar economic performances but faced curtailment and maintained reliance on hydrogen imports. Nuclear expansion reduced reliance on hydrogen-based flexibility compared to the renewables-only alternatives and performed best economically, though depending on highly flexible reactor operation. Expanding Norwegian hydropower, pumping capacity, and interconnectors noticeably reduced curtailment across all scenarios, despite the relatively modest scale of these upgrades. Several expanded pumps proved highly profitable, with average capture prices ranging from 8.0 EUR/MWh for consumption to 123.7 EUR/MWh for generation. This analysis showed that the profitability of pumped-storage expansions improved when assumptions about future hydrogen availability were relaxed.

To achieve our climate goals, the energy sector is increasingly shifting towards zero-emission sources, mainly renewable energy sources (RES), with solar power playing a pivotal role on the global stage. Nevertheless, many forecasts for future energy systems concentrate mainly on techno-economic aspects, which may not sufficiently capture the complexities associated with the deployment of renewable energy technologies. In particular, the expansion of RES has in recent years sparked tensions related to land use for new energy infrastructure, as well as growing concerns for biodiversity and environmental degradation. This study investigates the land use implications of different renewable energy system configurations in Norway, highlighting key trade-offs between land use requirements, costs, and emissions. The research specifically investigates potential pathways for solar photovoltaic (PV) in the Norwegian energy system, aiming to assess its potential future role within the broader Nordic context. Our findings shows that achieving national climate targets could require land use ranging from 18.43 to 5149 km2, depending on the selected scenario and land use metric. While onshore wind is initially favored due to high availability and a low physical footprint, including considerations such as spacing requirements and access roads significantly increase total land impact. Conversely, solar PV systems demand greater installed capacity, raising the physical footprint. Offshore wind and rooftop PV systems offer minimal direct land use but involve higher costs and technical constraints. The analysis underscores the importance of consistent land use metrics, integrated spatial planning, and the inclusion of socio-economic factors in energy system modeling. As the expansion of RES increases land demand, careful planning is essential to balance ecological preservation, public acceptance, and climate goals.

This paper develops an optimization model that describes the interaction between local electricity trading, shared wind power allocation and hydrogen production within an energy community. Wind generation is presented as separate market agent that can be allocated among prosumers, battery storage and an electrolyzer according to prevailing electricity prices. A mixed-integer linear programming model is used to describe operational decisions under a mid-market pricing mechanism. The dataset is a Norwegian energy community with realistic load, wind power generation and hydrogen demand data for fuel cell buses. Two operational cases are analysed. A reference case is without local energy trading and another is LEM-enabled case with coordinated wind sharing. The results show that introducing a LEM leads to a reduction in total system costs of 16%, while maintaining the same level of hydrogen production. Additionally, lower grid electricity imports and increased use of locally generated wind power by the electrolyzer are also reported. The findings highlight the role of electrolyzers as valuable flexibility providers in local energy systems. This demonstrates how appropriate market design can enhance renewable energy utilisation and sector coupling at the community level.

High gas prices and increased dependency on variable renewable energy (VRE) have challenged the reliability of the European power system. This paper analyses the impacts of large-scale VRE balancing in Europe using Norwegian reservoir hydropower with new pumping capacity under elevated natural gas and carbon prices. The two-stage stochastic rolling-horizon market simulator FanSi is used to capture uncertainty in hydro inflow and VRE availability. A Northern European case study evaluates scenarios with increased hydropower flexibility (+18.2 GW), expanded HVDC interconnector transmission capacity (+11 GW) and varying natural gas prices (113-235 EUR/MWh), to assess impacts on decarbonization potential and area prices. Results show that higher gas prices reduce emission reductions from $\mathbf{- 5. 5} \mathrm{Mt} \mathrm{CO}_{2}$ to $\mathbf{- 1. 1} \mathrm{Mt} \mathrm{CO}_{2}$. Under low gas prices, adding 11 GW interconnector capacity increases the average area price in Southern Norway by $23.0 \%$, while under high gas prices the increase reaches $\mathbf{5 1. 7 \%}$.

Capacity expansion and levelized cost of energy (LCOE) models commonly annualize capital costs using a static discount rate. In practice, however, required returns evolve across project phases as construction risk is resolved, operational performance is demonstrated, and financing structures adjust. Ignoring these shifts can distort the present value of capital costs, particularly for long-lived, capital-intensive technologies. This paper introduces a Time and Risk Adjusted Capital Cost Estimation (TRACE) method, which reformulates the capital recovery factor to incorporate discrete, phase-dependent discount rates while preserving compatibility with lifetime average modeling frameworks. Stylized screening curves, capacity expansion, and simplified LCOE examples demonstrate that static discounting assumptions can materially alter optimal generation mix and standalone technology rankings, with distortions dependent on static rate choices, capital intensity, project lifetime, and operational capacity factor. TRACE provides a representation of capital costs that is better grounded in project finance behavior.

The green transition requires electrifying industries with traditionally stable energy demands. Combined with the rise of artificial intelligence (AI) and hyperscale data centers, a significant increase in grid-connected baseload is expected. These loads, with high capital and operational costs, often lack financial incentives for flexibility. This paper explores how the modeling of additional load affects the optimal energy mix under varying nuclear energy overnight construction cost (OCC) levels, highlighting nuclear energy's potential role in providing the necessary baseload for AI data centers and heavy industry electrification. By utilizing an analytical approach, the study assesses how additional load profiles match variable renewable energies (VRE) outputs to determine the mix of technologies to be responsible for accommodating additional power demands. A stylized case study using the baseload addition (BA) method showed a significant increase in the share of baseplant units, handling 95.1% of the additional load. In contrast, linear load profile scaling (LLPS) of historical loads left the energy mix unchanged. A more detailed case study with the European Model for Power system Investment with Renewable Energy (EMPIRE) confirmed the same trend as found in theory, indicating a 24% increase in nuclear generation using the BA method over historical load scaling. Moreover, a low-cost nuclear scenario (4200/kW) installed 59% more capacity than a high-cost scenario (6900/kW). Finally, higher nuclear shares are shown to significantly reduce the need for transmission, storage, VRE curtailment, and land use, emphasizing nuclear power's potential role in low-carbon power systems.