Wind power is one of the key energy sources for promoting emission reduction in power systems. Data centers, due to their specific electricity consumption characteristics, exhibit relatively high carbon intensity in power consumption. To explore approaches for increasing renewable energy penetration in data centers, this study takes a wind-powered data center as a representative case and performs a multidimensional evaluation of a hybrid energy storage system (HESS), considering full life-cycle carbon emissions. To mitigate the conflict between the intermittency of wind power and the stable operation requirements of data centers, an adaptive sliding window smoothing strategy is employed to smooth wind power fluctuations. Meanwhile, the power allocation and capacity sizing of storage units in the HESS are optimized using the Variational Mode Decomposition (VMD)-Hilbert transform. A consequential life cycle assessment (CLCA) is then performed to calculate the life-cycle carbon emissions of both the HESS and the integrated wind-storage power system. The results are analyzed in multiple dimensions, including storage configuration ratios, fluctuation mitigation effectiveness, and peak shaving and valley filling rates. The results show that, compared with thermal power peak-shaving regulation, the life-cycle carbon emissions of the wind-storage system are significantly lower than those of the combined wind-and-coal-fired regulation mode. Among different HESS configurations, the lithium iron phosphate (LFP) battery combined with supercapacitors under the adaptive sliding window smoothing strategy achieves the most effective emission reduction and demonstrates superior performance in fluctuation smoothing. Overall, this research promotes the low-carbon configuration and evaluation of data centers and HESS. 

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 €100, 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 growing integration of renewable energy sources and electric vehicles (EVs) into the grid has increased the importance of energy storage systems (ESS). As many different types of ESS presently exist, the theory of Ragone plots can aid in evaluating the energy-power relationship of these storage devices. Until now, much of the research has focused on analytical models of ideal batteries or experimental data from batteries to develop Ragone plots. However, there are often discrepancies between Ragone plots created from analytical studies and actual experimental data. A research gap exists in developing analytical models for batteries where studies often neglect two factors: (1) actual operational constraints like temperature, voltage, and current limits, and (2) aging characteristics. The research presented here aims to bridge this gap by implementing a practical Ragone plot method through analytical studies with measured constraints. In addition, this model incorporates aging characteristics to determine the trajectory of the Ragone plot as the cell ages. Through this inclusive approach, we aim to bridge the gap between analytical models and the operational reality of battery performance assessment.

Rapid expansion of solar photovoltaic (PV) installations worldwide has increased the importance of electromagnetic compatibility (EMC) of PV components and systems. This has been highlighted by interference reported from PV installations (PVI) in the Netherlands, the United States, Sweden, etc. Significant research and development efforts are seen in the domain of enhancing efficiency and economic viability of PVI, whereas the EMC aspects have received less attention and are mainly focused on the PV system acting as a victim of lightning and a victim of changing grid impedance. This article presents a review of the important EMC aspects of PVI as a disturbance source. It has the following main parts: (a) reported cases of emissions and interference from PV installations; (b) modeling and analysis of PV subcomponents from an EMC perspective; and (c) the main standards related to the topic. Mitigation techniques for improving EMC aspects of PVI are also described, along with suggested directions for future research. The compilation brings together wide-ranging sources, both for EMC engineers who want to understand the EMC context of PV systems and for PV system designers seeking to improve EMC performance.

With the emergence of cyber-physical systems (CPSs) in utility systems like electricity, water, and gas networks, data collection has become more prevalent. While data collection in these systems has numerous advantages, it also raises concerns about privacy as it can potentially reveal sensitive information about users. To address this issue, we propose a Bayesian approach to control the adversarial inference and mitigate the physical-layer privacy problem in CPSs. Specifically, we develop a control strategy for the worst-case scenario where an adversary has perfect knowledge of the user’s control strategy. For finite state-space problems, we derive the fixed-point Bellman’s equation for an optimal stationary strategy and discuss a few practical approaches to solve it using optimization-based control design. Addressing the computational complexity, we propose a reinforcement learning approach based on the Actor-Critic architecture. To also support smart meter privacy research, we present a publicly accessible “Co-LivEn” dataset with comprehensive electrical measurements of appliances in a co-living household. Using this dataset, we benchmark the proposed reinforcement learning approach. The results demonstrate its effectiveness in reducing privacy leakage. Our work provides valuable insights and practical solutions for managing adversarial inference in cyber-physical systems, with a particular focus on enhancing privacy in smart meter applications.

Considering inverter as the source of electromagnetic emission signals in a photovoltaic (PV) plant, a comprehensive set of measurements of conducted emissions at the input and output of the inverter in a 10 kW PV plant are presented. These are particularly relevant on the backdrop of (a) ban of products in the EU market due to non-compliance and (b) the increased switching frequency in the inverters ( 100s of kHz) in near future. Specifically, the common mode (CM) and differential mode (DM) currents and voltages are measured, and their frequency domain behavior is studied. It is suggested that conducted emissions from PV can be classified into three zones: viz., extremely low frequency (ELF) zone, power frequency zone, and switching frequency zone. Important observations from this exercise are measurement of harmonic contents of current with total rated current distortion (TRD), imbalance in the output voltage and low frequency ripples in the DC voltage. Frequency domain behavior of the CM quantities is studied which throws light on important points like relation between input and output CM quantities, relation between CM voltage and CM current.

To mitigate the ongoing climate changes and promote a power grid that widely utilizes the production of renewable electricity and flexibility services, synergy with the users is of interest. However, it is not certain that critical actors such as governmental bodies or the industry perceive the users and their motivations correctly, and misconceptions might hinder the implementation of green technologies in society. In this paper, we investigate how different types of users in Sweden differ in their ability to create revenues using green technologies and flexibility. We investigate if views held by, e.g., governmental bodies or the industry could be detrimental to necessary future policy changes.

The nature of common mode (CM) parasitic impedance (ZPV) of photovoltaic (PV) panels is investigated by experimentation. Measurements are done by two methods: (a) time domain signal recording using a signal generator and an oscilloscope (b) frequency sweep using an LCR meter. It is shown that ZPV is not purely capacitive for the frequency range from 50 Hz to 1 MHz.  It is also shown that the total common mode impedance in a PV installation is affected by the nature of ZPV.  The frequency spectrum of CM current would be different if ZPV is not purely capacitive. This would affect the design of CM filters on the DC and AC side and also equipment like PV emulators, DC line impedance stabilization network, etc.

To promote the development of renewables, this article evaluates the life cycle greenhouse gas (GHG) emissions from hybrid energy storage systems (HESSs) in 100% renewable power systems. The consequential life cycle assessment (CLCA) approach is applied to evaluate and forecast the environmental implications of HESSs. Based on the power system of Sweden, different HESS combinations, which include energy storage (ES) technologies: pumped hydro ES, hydrogen ES, lithium-ion (Li-ion) batteries, lead-acid (PbA) batteries, vanadium redox (VR) batteries, supercapacitors (SCs), and flywheels, are discussed. The results show that for Sweden and similar large-scale utility applications, the cradle-to-gate GHG emissions from the HESS contribute to a major share of the life cycle GHG emissions due to the under-utilization of the cycle life. Among the HESSs compared in this study, the Pumped hydro+Li-ion+Flywheel combination exhibits the least life cycle GHG emissions. Moreover, the phasing out of nuclear power brings a severe challenge to the carbon reduction target. However, the introduced HESS manages to reduce GHG emissions from a 100% renewable power system.

Electrification of private transportation and residential heating is a potential action to decrease significantly carbon emissions. However, the lack of coordination of such emerging loads can increase stress on the power distribution grid. To this end, this paper proposes a two-stage energy management strategy, to enhance the flexibility of energy communities. First, an optimization algorithm-based model predictive control (MPC) is developed for home energy management systems (HEMS) to schedule electric vehicles (EVs) for charging and discharging in cooperation with heat pump and thermal energy storages. The objective is to reduce electricity bills and EV battery degradation costs while maintaining thermal demand. Second, the predictive coordination based on the nonlinear AC optimal power flow approach is applied to enhance the grid flexibility under a community energy management system scheme. To validate the performance of the proposed method, we develop a co-simulation framework by implementing the optimization algorithm and simulation scenarios in a Python Environment and modelling the electricity grid in the PowerFactory-DIgSILENT, which are linked together. Extensive numerical simulations are carried out under the active demand response program-based hourly electricity pricing and different weather data in Sweden. The results show that the proposed method can reduce operating costs and enhance the energy flexibility of the energy community. Moreover, it can decrease the stress on the community power grid and prevent load-shedding events compared to the non-predictive coordinated method.