Power control systems (PCSs) can exploit low-carbon technologies (LCTs) to provide grid ancillary services. This work develops a bilevel mixed-integer linear programming PCS of photovoltaics (PVs), electric vehicles (EVs), heat pumps (HPs), and battery energy storage systems (BESS), for providing automatic frequency restoration reserves (aFRR) with energy arbitrage, PV self-consumption, and customers’ thermal and charging comfort. The contribution of the BESS and the flexible loads is evaluated under different seasons, grid types and sizes, and energy/reserve prices. Validating against a PCS solely for minimum grid energy cost (energy arbitrage), the findings demonstrate the increased cost savings when a PCS participates in the reserve market with BESS and EV combined. The cost of solely energy arbitrage was found consistently higher than 100% (e.g., 40€ compared to only 19€ with aFRR provision). These benefits have become more important recently in 2023, with the higher energy prices, and much higher reserve prices compared to 2018 (up to 540% increase). While the always present BESS is able to contribute more to ancillary services compared to the uncertain EV fleets, the contribution of EVs increased to a notable 38.5% of the total provided aFRR energy share at larger grids. Finally, mixed nodes that comprise both residential-commercial buildings and home-public chargers have a higher potential for ancillary services provision, demonstrating a 5x and 12x higher potential compared to residential and commercial nodes, respectively. Overall, this work highlights the importance of PCSs in large grids or with a variety of loads to provide ancillary services for enhanced savings.

Power management systems (PMS) of low-carbon technologies (LCTs) such as PV generation, electric vehicles (EVs), and heat pumps (HPs) battery energy storage systems (BESS) are becoming increasingly important for energy arbitrage and ancillary services provision. In this work, a PMS is developed which is based on optimal dispatch to maximize cost savings, perform direct load control (DLC) for congestion management, and offer frequency regulation reserves while simultaneously optimally allocating BESS capacity in a distribution grid. Results showed that a BESS installation is not economically viable only with energy arbitrage but becomes highly profitable when ancillary services such as frequency regulation are provided. Furthermore, it was shown that a centralized BESS at the closest node to the main grid is the most beneficial solution due to lower power losses, although that also depends on the price profiles and the different flexibilities of the nodes. The importance of a BESS installation and the benefits of its synergy with flexible loads for both system-wide frequency regulation, as well as for direct load control for congestion management, were also proven, increasing DLC and load shifting success rates to always above 90%. In this regard, commercial nodes that are characterized by low flexibility are in the greatest need of a BESS installation if they participate in ancillary services provision.

Lithium-ion batteries (LIB) are widely used in various applications. The LIB degradation curve and, most significantly, the knee-point and End-of-life (EoL) point identification are critical factors for the selection of the appropriate application, such as electric vehicles and stationary energy storage systems, due to their effect on performance and lifespan, safety, and environmental footprint. Linear degradation models can be inaccurate in capturing the highly nonlinear behavior of LIB degradation caused by multiple simultaneous degradation mechanisms. Hence, this work first analyzes the main different mechanisms, their causes, and their interrelations. Secondly, the various single- and multi-mechanism physics-based (PB) and data-driven (DD) models for LIB degradation and knee-point identification are summarized and compared regarding their prediction performance on degradation and transition from stabilized to saturated aging. While single-mechanism PB models can be effective in the LIB first-life prediction, they can seriously undermine the knee-point and saturated aging. Moreover, the modeling of the different aging mechanisms can significantly increase the complexity of the multi-mechanism PB models. Finally, while DD models for LIB degradation have been developed, a DD model focused on knee-point identification and LIB second-life is still missing from the literature.

Distributed generation, such as photovoltaics (PVs), and electrification of heating and transportation with heat pumps (HPs) and electric vehicles (EVs) will play a major role in the energy transition. However, these low-carbon technologies (LCTs) do not come without side effects such as voltage violations, power loss increase, component overloading, higher energy consumption, power peaks, and power quality issues, e.g., harmonics and phase unbalance. This work constitutes a review analysis and summary of all the important findings concerning the various grid impact issues that can appear due to the grid integration of these 3 LCTs. The work also encapsulates various research characteristics such as grid topology, seasons, simultaneous operation under various LCT combinations, penetration levels, etc. Moreover, it incorporates a qualitative analysis of the impact level of the most investigated grid issues and quantitative comparisons between the different grid types and LCTs. It has been shown that the combined integration of PVs-EVs and PVs-HPs can result in mitigation effects without extra solutions. Moreover, voltage deviations and unbalance affect more the rural grids while component overloading is more hazardous for suburban grids. Finally, proposed mitigation solutions, such as energy storage, smart charging, etc., are correlated with their respective grid impact issues.

This paper proposes a shared multi-stakeholder PV system for traction substations and nearby residential loads to reduce the need for storage, AC grid exchange, and curtailment. The residential stakeholders offer both the base electrical load and the solar panels installation space needed by the traction stakeholder, who brings the peak load and investments to the former. Two case studies were conducted for one year in the city of Arnhem, The cy=Netherlands, using comprehensive and verified simulation models: A high-traffic and a low-traffic substation. The results showed a positive, synergetic benefit in reducing the PV system's excess energy and size requirement for any type of traction substations connected to any number of households. In one detailed example, the multi-stakeholder system suggested in this paper is shown to reduce curtailment by up to 80% in moments of zero-traction load. Generally, the direct load coverage of a PV system is increased by as much as 7 absolute percentage points to the single-stakeholder system when looking at energy-neutral system sizes. This multi-stakeholders system offers then an increase in the techno-economic feasibility of PV system integration in urban loads.

Power control of flexible loads will play a significant role in energy transition. This work has developed a mixed-integer linear power control (MILP) model that manages electric vehicle (EV) chargers, heat pumps (HPs), and PV rooftops. The power control was tested with and without vehicle-to-grid (V2G) capabilities in different grid types, namely residential, commercial, and mixed grids. Moreover, the effect of different seasons and charger efficiencies was investigated. It was shown that grid characteristics such as EV parking times and building occupations can affect significantly the power control, e.g. the amount of imported and V2G power. Moreover, while V2G power is rarely used due to current V2G round-trip efficiency, future efficiency improvement can lead to a significant increase in V2G use. Finally, the seasonal effect had also a significant impact with Summer being characterized by higher exported and lower imported energy due to the high and prolonged PV power availability.

The large Electric Vehicle (EV) fleet penetrations can provoke several grid impact issues if no EV smart-charging is implemented. However, many EV smart-charging works assume an accurate prediction of input data, such as the EV driving patterns, which are highly uncertain. This paper addresses the impact and potential management of several uncertainties related to EV smart charging, such as photovoltaic (PV) generation, load demand, arrival state-of-charge (SOC), requested energy, and arrival and departure time of the EVs. The application of different levels of uncertainty budgets is proposed to account for the gradual impact of every uncertainty on smart charging performance. Moreover, potential uncertainty management is investigated with the use of robust optimization (RO) in predictive receding-horizon EV smart charging under the worst-case uncertainty level, and the ''price of robustness"is calculated. The results show that the EV driving uncertainties are more hazardous for the provided charging energy. In contrast, PV generation and load demand uncertainties have a significant impact mostly on the charging cost. Moreover, the price of robustness is very low for EV charging under every uncertainty case.

An important aspect of the energy transition is the expected grid impact due to the abrupt increase of distributed electric generation and electric load demand. A part of this impact is going to be inflicted by the electrification of heating with heat pumps (HPs). Therefore, it is essential that the future power consumption of electric heating is estimated. This work develops a power estimation model without the use of heating demand data, needing only weather data and building heat pump specifications. Moreover, it is characterized as a risk-averse estimation since it uses no optimal control and utilizes the heat pump output capacity curves giving simultaneous priority to the customers' thermal comfort. Finally, it also estimates the power savings of electric heating due to future buildings' new insulation and energy label norms, revealing their importance.

Low Carbon Technologies (LCTs), such as Photovoltaics (PVs), Electric Vehicles (EVs), and Heat Pumps (HPs), are expected to cause a huge electric load in future distribution grids. This paper investigates the grid impact in terms of over-loading and nodal voltage deviations in different distribution grids due to increasing LCT penetrations. The major objectives are the identification of the most severe LCT, grid impact issue, seasonal effect, and vulnerable distributional area, considering the physical models of the LCTs. It is concluded that Winter is the most hazardous for the future grid impact, characterized by nearly 3 times higher over-loading and 2.5 times higher voltage deviations during high HP penetrations, while suburban areas are the most vulnerable. Moreover, while HPs seem to have, in general, a greater impact compared to EVs, EVs cause more prolonged violations. While this work follows a bottom-up approach, using detailed physical models, aggregated national data has also been acquired, which is often used by top-down approaches. Different grid impact issues have been compared for the two approaches in terms of magnitude and duration. While bottom-up approaches generate more pessimistic results regarding the magnitude of the violations, results about the duration of the violations can be contradictory.