The growing adoption of electric vehicles (EVs) creates opportunities to support power system reliability by using EV batteries as decentralized storage resources. This paper proposes an optimization framework for EV aggregators participating in ancillary service markets through unidirectional smart charging (V1G) and vehicle-to-grid (V2G) operation, explicitly accounting for battery lifetime degradation. Eleven scenarios are evaluated in the context of the Swedish electricity market to examine different market participation strategies, degradation modeling approaches, and degradation compensation schemes. Two degradation models are considered: a simplified empirical model and a detailed lithium-ion degradation model based on solid electrolyte interphase (SEI) formation - a passivation layer that forms on the negative electrode during battery operation and contributes to capacity fade and internal resistance increase. Unlike most prior studies that focus only on capacity loss, the proposed framework captures both capacity fade and power capability fade, enabling more realistic scheduling and cost estimation. Results demonstrate that controlled EV charging and participation in ancillary services significantly improve economic outcomes compared to uncontrolled charging. In a case study with 55 EVs, the EV aggregator achieves a daily net revenue of up to e100, even for a relatively small-scale system. At the same time, EV owners receive charging cost reductions of up to 40% compared to the baseline uncontrolled charging scenario. Scenarios with explicit degradation compensation achieve fairer cost allocation at minimal profit reduction for the EV aggregator. A sensitivity analysis further demonstrates that the main economic conclusions remain robust across a wide range of degradation cost assumptions. Overall, the study confirms that battery degradation-aware V1G/V2G strategies can deliver significant economic benefits while respecting operational constraints.

The widespread adoption of electric vehicles (EVs) and hydrogen fuel cell electric vehicles (HVs) is tightening the interdependence between power and transportation systems (TCs), calling for better coordination between them. To address this challenge, this article proposed a distributed coordination method for the hydrogen-integrated microgrids (MGs) and transportation system. First, we introduce energy sharing among MGs, which reduces the overall system cost by 16.2%, and analyze how it improves the traffic flow. Additionally, we develop bidding models for MGs participating in joint energy and ancillary service markets, maximizing flexible resources utilization and increasing revenue by 147%. A mixed vehicle flow TS model is then established, including EVs, HVs, and gasoline vehicles (GVs). To coordinate the two individual systems efficiently, a distributed algorithm is proposed, incorporating a filtering mechanism that reduces the communication burden by 63% during the iterative process. Uncertainties and nonlinearities are handled using distributionally robust method and linearization techniques. Finally, case studies validate the effectiveness of the proposed method and highlight the mutual impact between the two systems.

The increasing electrification and integration of renewable energy in the power system present challenges in maintaining the grid balance. At the same time, the growing adoption of electric vehicles (EVs), including heavy-duty electric vehicles (HDEVs), clearly indicates the global shift toward electromobility. This study investigates the optimal planning of an energy hub consisting of a battery energy storage system and an HDEV charging station. A mathematical optimization model is developed to maximize the profit of the energy hub through simultaneous participation in ancillary services while providing EV charging services under the grid limitations. A case study based on the Swedish market context shows that the energy hub operations are profitable under different scenarios, which were investigated through sensitivity analysis.

This article investigates the potential benefits of dynamic Frequency Restoration Reserve (FRR) allocation and dimensioning in a multi-area context, focusing on the Nordic Load-Frequency Control block. Emphasis is placed on how the available information impacts the FRR dimensioning. To assess the potential benefits, a model for multi-area FRR dimensioning is proposed, applicable to both static and dynamic dimensioning approaches. The proposed FRR dimensioning model includes a new application of a methodology to simulate imbalance scenarios and sequentially dimensions FRR capacity for reference incidents and normal imbalances. The main benefit of dynamic FRR dimensioning is that the need for FRR capacity is continuously updated according to the expected short-term operating conditions, such that the daily reliability level is always close to the desired reliability level. Case study results show that dynamic FRR dimensioning can lead to a reduction in total reserve needs compared to a static approach. This reduction is significantly larger if FRR is dimensioned after the clearing of the day-ahead market.

The growing adoption of Electric Vehicles (EVs) presents challenges for the power grid, especially in meeting the peak demand without overloading the power system. Conventional grid reinforcement strategies are often costly and time-consuming, making them insufficient to address increased energy demand from simultaneous EV charging. However, when effectively managed, EV charging can be a flexible resource supporting grid stability and balance. To efficiently use this flexibility, business models play a crucial role in organising and incentivising market participation, yet the interaction between market players and grid integration remains underexplored. This article presents an extended and systematic review of over 100 state-of-the-art studies on business models for EV charging under grid limitations, presenting the most comprehensive analysis to date. Unlike the previous studies that primarily focus on technical EV-grid integration, this study combines technical and market-based solutions, focusing on the European electricity market and stakeholder perspectives. Moreover, the study identifies research gaps and proposes recommendations to improve or develop new business models for more efficient use of EV flexibility. The findings offer valuable insights for researchers, industry players, policymakers, and other actors aiming to improve the efficient usage of EV charging flexibility.

The growing adoption of electric vehicles (EVs) presents challenges for power systems, particularly due to uncontrolled charging. Such charging can lead to grid overload that requires immediate grid reinforcement. This paper proposes a planning model for an EV aggregator participating in ancillary service markets while considering the distribution grid limitations. Monte Carlo simulations capture uncertainties in mobility patterns and activations of the ancillary services. We compare uncontrolled charging with a bidirectional smart charging algorithm, which is formulated as a mixed-integer linear program. A case study focusing on the Swedish market, specifically regarding participation in the frequency containment reserve, demonstrates that smart charging benefits the EV aggregator, EV owners, and the power system. The results highlight that the flexibility of the EV can optimize the existing utilization of the grid and delay the reinforcement of the grid. The proposed planning model supports decision-making in uncertain markets, ensuring the feasibility of the EV aggregator business model.

Power systems are evolving in response to the growing integration of variable renewable resources. Considering this, balancing reserves are becoming more important for maintaining the continuous balance between total production and demand in the power system. However, the allocation of these reserves comes at a cost, given their inherent link to the energy traded in the day-ahead electricity market. This study examines the implications of different reserve allocation strategies using a modeling framework that incorporates dispatchable hydropower and intermittent wind power alongside demand to evaluate the costs of securing sufficient reserves in a highly renewable energy system. The analysis indicates that aligning reserve allocation more closely with the conditions expected at the time of operation results in improved outcomes, leading to better utilization of stored water in hydropower reservoirs and lower operational costs. Therefore, considering the increasing integration of variable renewable energy sources, the findings highlight the need to investigate more effective approaches for reserve allocation that are better aligned with anticipated system conditions.

To be able to efficiently maintain a continuous balance between supply and demand in power systems with high shares of variable renewable energy (VRE) sources, a variety of studies related to the topic are needed. A fundamental input parameter for such studies is an assessment of the power system’s physical needs for balancing power, in form of power imbalances. This article presents a new model for simulating physical power imbalances with a 1-minute time resolution based on multi-area economic dispatch simulations. Compared to existing models with the same purpose, the new model includes the combination of simulating power imbalances with 1-minute time resolution, simulating forecast uncertainty, simulating the continuous behaviour of all power system components and simulating the transmission for netting of power imbalances between balancing areas. By applying the model to a case study of the Nordic synchronous power system in year 2045, the impact of including these features in the model is highlighted. Case study results also show that the size and pattern of power imbalances much depends on the characteristics of a balancing area, in terms of electricity demand, available generation technologies and interconnections to other balancing areas.

Hydrogen electrolyzers are promising tools for frequency regulation of future power systems with high penetration of renewable energies and low inertia. This is due to both the increasing demand for hydrogen and their flexibility as controllable load. The two main electrolyzer technologies are Alkaline Electrolyzers (AELs) and Proton Exchange Membrane Electrolyzers (PEMELs). However, they have trade-offs: dynamic response speed for AELs, and cost for PEMELs. This paper proposes the combination of both technologies into a Hybrid Hydrogen Electrolyzer System (HHES) to obtain a fast response for frequency regulation with reduced costs. A decentralized dynamic power sharing control strategy is proposed where PEMELs respond to the fast component of the frequency deviation, and AELs respond to the slow component, without the requirement of communication. The proposed decentralized approach facilitates a high reliability and scalability of the system, what is essential for expansion of hydrogen production. The effectiveness of the proposed strategy is validated in simulations and experimental results.

For inclusion in large-scale power system models, various aggregations and simplifications in the modeling of relevant actors are needed. This paper focuses on reduced models of hydropower, so called area Equivalent models. They use a simplified topology but are not a direct aggregation of the real hydropower system. Instead, the area Equivalent is constructed to mimic the simulated power production of a more detailed hydropower reference model. Here, this goal is fulfilled by formulating a bilevel problem minimizing the difference in simulated power production between the area Equivalent and its reference. Solving this can be computationally heavy. Thus, for a fast solution of this bilevel problem, a single-level reduction is done, which is then solved using two methods. The first method includes McCormick envelopes to form a linear single-level problem. Second is a modified Benders with a relaxed sub-problem to handle the non-convex single-level. These are then also compared to Particle Swarm Optimization. Moreover, six new upper-level objective functions are investigated for a case study of hydropower in northern Sweden. The method using McCormick envelopes is fast (2–5 min), but the area Equivalent shows lower average performance. The modified Benders finds a solution in 5–31 min with good performance.

The growing adoption of electric vehicles (EVs) has introduced substantial challenges to the grid. Uncontrolled EV charging may lead to grid overloading, voltage instability, increased power losses, accelerated aging of distribution transformers, and risk of outages. Therefore, a strategic approach is required to tackle the adverse impacts of uncontrolled EV charging to the grid. A promising approach is using EV batteries collectively as a flexible load. Residential areas have the most pronounced EV flexibility potential due to the significant length of uninterrupted parking. In this paper, models of EV charging in residential areas are formulated, followed by Monte Carlo simulations. Three charging models are developed: uncontrolled charging, controlled charging without considering grid limitation and controlled charging considering grid limitation. An optimization problem based on quadratic programming is used in the controlled charging. A residential area based on the IEEE European LV test feeder adopting the deregulated Swedish electricity market is taken as a case study for the simulation. The case study findings indicate that incorporating grid limitation into controlled charging strategies can prevent grid overload and significantly reduce charging and battery degradation costs. In this case study, controlled charging can reduce the charging costs to approximately 42% compared to uncontrolled charging. Considering the battery degradation costs, controlled charging costs are 24% lower than uncontrolled charging. It is possible to postpone the costly grid reinforcement by applying strategic EV charging scheduling. The methods and outcomes pave the way for developing, testing, and implementing business models to manage the grid impacts of growing EV charging.

With the increasing integration of power plants into the frequency-regulation markets, the importance of optimal trading has grown substantially. This paper conducts an in-depth analysis of their optimal trading behavior in sequential day-ahead, intraday, and frequency-regulation markets. We introduce a probabilistic multi-product optimization model, derived through a series of transformation techniques. Additionally, we present two reformulations that re-frame the problem as a mixed-integer linear programming problem with uncertain parameters. Various aspects of the model are thoroughly examined to observe the optimal multi-product trading behavior of hydro power plant assets, along with numerous case studies. Leveraging historical data from Nordic electricity markets, we construct realistic scenarios for the uncertain parameters. Furthermore, we then proposed an algorithm based on the No-U-Turn sampler to provide probability distribution functions of cleared prices in frequency-regulation and day-ahead markets. These distribution functions offer valuable statistical insights into temporal price risks for informed multi-product optimal-trading decisions.

Increasing penetration of wind power poses challenges to power system balancing due to the uncertainty and variability of wind power generation. In wind integration studies, it is hence important to consider the need of balancing power caused by wind power. In this paper, we propose a new model to simulate minute resolution wind power time series in multi-area power systems, that allows for estimation of the additional need of reserves caused by wind power in future scenarios. The model is based on first simulating forecast uncertainty using a multidimensional autoregressive moving average model, and then simulating the variability by adding an autoregressive time series to a cubic spline interpolation. The model is found to generate scenarios of minute resolution wind power generation that are sufficiently realistic for long-term scenario studies. The model output can be used for analysing future reserve requirements or for scenario generation for stochastic optimisation of future power systems.

This paper presents a sequential dimensioning methodology for frequency restoration reserves in a multi-area power system based on chance-constrained optimization. In the first stage, the reserves to handle the reference incident in each area are dimensioned. Then, the transmission network usage for providing these reserves is calculated and the remaining transfer capacity is used in the next stage where the reserves to handle normal imbalances are dimensioned. The optimization problem in each stage seeks to allocate reserves such that the total volumes of reserves and the line flows are co-optimized. Reserves are dimensioned for four seasons instead of the current static approach with yearly dimensioning. By adjusting the reserve requirements based on the system’s needs, the total reserves are reduced. Also, the results demonstrate high potential in sharing reserves among bidding zones in the Nordic synchronous area.

The use of wind power has grown strongly in recent years and is expected to continue to increase in the coming decades. Solar power is also expected to increase significantly. In a power system, a continuous balance is maintained between total production and demand. This balancing is currently mainly managed with conventional power plants, but with larger amounts of wind and solar power, other sources will also be needed. Interesting possibilities include continuous control of wind and solar power, battery storage, electric vehicles, hydrogen production, and other demand resources with flexibility potential. The aim of this article is to describe and compare the different challenges and future possibilities in six systems concerning how to keep a continuous balance in the future with significantly larger amounts of variable renewable power production. A realistic understanding of how these systems plan to handle continuous balancing is central to effectively develop a carbon-dioxide-free electricity system of the future. The systems included in the overview are the Nordic synchronous area, the island of Ireland, the Iberian Peninsula, Texas (ERCOT), the central European system, and Great Britain.

Modeling large energy systems requires different forms of simplifications and aggregations. This is especially true for large hydropower systems. One way to simplify the modeling of hydropower as a part of large scale energy systems is to utilize so-called Equivalent models. The hydropower Equivalent model is a simplified hydropower area model with only one (or a few stations) which aims to mimic the behavior of an Original more detailed model containing all stations in a specific area. However, one drawback has been that the Equivalent model fails to match the highest production peaks of the real Original system. Methods to increase the maximum peaks in the Equivalent model have so far resulted in overall lower performance, where the production during lower peaks instead would be overestimated. Thus, in this paper, a method for computing hydrosystem area Equivalent models that not only have good average performance but also can capture the production peaks of the real hydropower system is developed. The new method allows for optimal partition and efficiency of different segments in the hydropower marginal production function.

To simulate a hydropower system, one can use what is known as a Detailed model. However, due to the complexity of river systems, this is often a computationally heavy task. Equivalent models, which aim to reproduce the result of a Detailed model, are used to significantly reduce the computation time for large-scale hydropower simulations. This paper computes Equivalent models for hydropower systems in Sweden by categorizing the water inflow data using a spectral clustering method. Computing the Equivalent models is done using a variant of the particle swarm optimization algorithm. Then, the Equivalent models are evaluated based on their similarity to the Detailed model in terms of power production and objective value. The Equivalent models range from 8% - 12% error in terms of the relative power production difference and the computation time is reduced by at least 99.9%.

This article presents a new approach for finding the optimal multimarket trading strategy of cascaded hydropower plants (HPPs) in the sequential electricity markets. These markets are day-ahead energy market, the market for frequency containment reserve in normal mode (FCR-N), and manual frequency restoration reserve markets for both energy production and capacity reserve. The active-time duration (ATD) of an mFRR energy offer is an important required parameter and it is uncertain at the time of day-ahead offer-function submission. Hence, we suggest a distributional regression approach for ATD modeling in an optimal multimarket setup. Also, a modified machine learning approach is proposed to generate price scenarios for the mFRR energy market taking into account uncertain ATD parameters. To illustrate our proposed approach, various numerical experiments are performed. Our numerical results show how proper modeling of ATD parameters can lead to a more realistic multimarket offer-function for cascaded HPPs. Furthermore, the results show how the inclusion of FCR-N and mFRR capacity markets change the optimal day-ahead offer-function.

Power systems are experiencing a decrease of synchronous generation along with increased penetration of inverter based renewable generation leading to reduced system inertia and a need for flexible resources.Non-generating resources such as thermostatically controlled loads (TCLs) are flexible due to their thermal energy storage capacity.When aggregated, TCLs can arbitrage energy prices and provide reserves to the power system.We approach the operational flexibility of the TCLs by modeling a risk-averse aggregator that controls decentralized TCLs and aims to maximize its own profit.The high number and low power rating of residential TCLs makes it difficult to model and assess their flexibility potential on national level.Thus, we make use of a high-level thermal energy storage model for aggregations of TCLs to quantify their flexibility potential.We present a method to aggregate temperature, TCL parameters, and building stock data into a thermal battery equivalent.We propose a multi-period multi-market multi-zonal two-stage chance constrained rolling horizon optimization problem formulation for the risk-averse day-ahead self-scheduling problem of a price-taker TCL aggregator bidding in energy and reserve markets under uncertainty and recast the problem as a linear program.We perform several case studies in the Swedish power system based on a survey of singleand two-family dwellings with electric heating and assess the flexibility potential.Additionally, a sensitivity analysis provides insights regarding market design and policy implications.

In this paper, an hourly dispatch model was developed to analyze the system balancing and wind power curtailment challenges in the future of the Ethiopian electric power grid system. The developed model was validated using historical data and was used for the analysis of the grid system in 2030 with different scenarios. The model was used to examine the impacts of transmission capacity, regulation reserve requirement, and daily minimum generation of hydropower for irrigation with three cases of wind annual energy share of 14.5%, 17.8%, and 25.2%. Thus, the curtailment was found to be below 0.2%, 1.1%, and 9.8% for each case, respectively. The cost of wind energy increases in proportion to the percentage of curtailment and the increase in transmission line capacity. Reducing the minimum hydropower generation results in smaller wind power curtailment and better generation–consumption balancing.