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.

Electricity price forecasting is a critical aspect of energy market operations and investments, therefore, the development of new, more accurate forecast models is a continuous effort. However, the evaluation of forecasting models is typically limited to general accuracy metrics without a deep understanding of the underlying root causes of making these predictions, which limits their real world applicability in critical infrastructure. In this work, we extend two state-of-the-art data-driven forecast models to predict the day-ahead system prices of the NordPool market. The goal is to attribute the importance that the models give to the input features, and, with this, see if the cause-effect relationship learnt by the models is consistent with reality. When this is not the case, the reliability of these models in real-world applications, where forecasts inform downstream decision-making, cannot be trusted, despite their overall accuracy. The findings of this study indicate that, while generally precise, even state-of-the-art forecasting models face challenges in maintaining consistency with real-world conditions.

Hydropower systems consist of complex river systems making hydropower modelling computational expensive. The power output of hydropower stations are depending on the efficiency curve of a turbine, the head-height and the discharge through a turbine. The cascade ordering of reservoirs in river system results in further interdependencies and thus long simulations. Especially when simulating bigger energy system, e.g. the European system, a simplification is required. In earlier research, an 'equivalent model' is calculated by using a bi-level optimization problem with a particle swarm optimization (PSO) approach. The principle is to intelligently aggregate plants in one area into an equivalent model that mimics the behavior of the detailed model. Due to the simplifications, the risk of losing interdependencies is high. The equivalent model underestimates production in high price periods, and overestimates in low price periods respectively. This paper combines recently ideas and introduces two new adjustments that improves the equivalent performance.

Hydro power modelling is important to facilitate the integration of large amounts of variable renewable energy. However, appropriately modelling hydro power presents challenges due to its interconnections with both electricity and river systems. The aim of this paper is to investigate the performance of two selected open-source energy modelling tools for hydro power planning, SpineOpt and PyPSA, with a focus on user-friendliness, accuracy and execution time. In this study, a small river system with two hydro power plants is modelled to maximize the revenue using both tools. At the same time, this linear optimization problem is implemented by Gurobi directly, such that results and execution times from SpineOpt and PyPSA are compared with this baseline. In conclusion, both open-source tools can appropriately model the hydro power system, with SpineOpt having a unique graphical interface and PyPSA has a good performance in execution speed.

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.

This paper studies the problem of forecasting electricity prices in continuous short-term electricity markets, specifically focusing on the intraday volume-weighted average price of hourly products in the last three hours of trading. Two state-of-the-art recurrent neural network architectures, namely the Temporal Fusion Transformer and the DeepAR network, are compared against well-established statistical models, such as the Linear Regression-LR, ARX, and SARIMAX models, concerning their forecast accuracy. Historical electricity market and grid data from European Energy Exchanges were used to create a forecasting dataset and train and compare five different model structures stemming from traditional statistical methods or contemporary deep learning-based counterparts.

The increasing importance of cross-border trade has brought the topic of allocating cross-border transmission capacity under the limelight. In this paper, we focus on two different transmission capacity calculation methods, namely, Available Transfer Capacity and Flow-based Capacity allocation, in the continuous intraday electricity market. The effect of these two methods is studied on the continuous intraday electricity market through an agent-based model comprising various types of trading agents. It also models market operator agent and transmission system operator agent inspired by the Single Intraday Coupling project in Europe. The price-volume decisions for the orders posted by the market participants are determined by two different strategies where one of them adapts to changing market conditions and new information while the other is naive. The model accounts for ramping constraints which enables the analysis of the trading behavior and interaction of agents across multiple delivery products simultaneously. A switch parameter is availed in the trading timeline for the thermal and storage agents to change from a less conservative approach of ignoring the ramping and charging/discharging rate constraints respectively to considering them. An interplay between different switch parameters and cross-border transmission capacity calculation methods is studied.

Combined heat and power (CHP) plants are main generation units in district heating systems that produce both heat and electric power simultaneously. Moreover, CHP plants can participate in electricity markets, selling and buying the extra power when profitable. However, operational decisions have to be made with unknown electricity prices. The distribution of unknown electricity prices is also not known exactly and uncertain in practice. Therefore, the need of tools to schedule CHP units' production under distributional uncertainty is necessary for CHP producers. On top of that, a heating network could serve as a heat storage and an additional source of flexibility for CHP plants. In this paper, a distributionally robust short-term operational model of CHP plants in the day-ahead electricity market is developed. The model accounts for the heating network and considers temperature dynamics in the pipes. The problem is formulated in a data-driven manner, where the production decisions explicitly depend on the historical data for the uncertain day-ahead electricity prices. A case study is performed, and the resulting profit of the CHP producer is analyzed. The proposed operational strategy shows high reliability in the out-of-sample performance and a profit gain of the CHP producer, who is aware of the temperature dynamics in the heating network.

In a hydropower system, inflow is an uncertain stochastic process that depends on the meteorology of the reservoir's location. To properly utilize the stored water in reservoirs, it is necessary to have a good forecast or a historical inflow record. In the absence of these two pieces of information, which is the case in Ethiopia and most African countries, the derivation of the synthetic historical inflow series with the appropriate time resolution will be a solution. This paper presents a method of developing synthetic historical inflow time series and techniques to identify the stochastic process that mimics the behavior of the time series and generates inflow scenarios. The methodology was applied to the Ethiopian power system. The time series were analyzed using statistical methods, and the stochastic process that mimics the inflow patterns in Ethiopia was identified. The Monte Carlo simulation was used to generate sample realizations of random scenarios from the identified stochastic process. Then, three cases of inflow scenarios were tested in a deterministic simulation model of the Ethiopian hydropower system and compared with the actual operation. The results show that the generated inflow scenarios give a realistic output of generation scheduling and reasonable reservoir content based on the actual operation.