As the amount of active and reactive resources managed by microgrid operator (MGO) increases, those managed by the distribution system operator (DSO) are gradually decreasing. While the reactive power supply and voltage control of the distribution system are still managed by the DSO, these responsibilities should be assigned to the MGOs to promote the utilization of reactive power resources in microgrids. This paper proposes a pricing and transaction method for active and reactive power in microgrids considering the operational state of the distribution system. First, a bi-level framework and model of active and reactive power joint transaction for MGOs are constructed based on the distribution network needs. Then, using the equilibrium problem with equilibrium constraints (EPEC) approach, the bi-level model is transformed into a single model, which is further linearized to facilitate the solution. Finally, the proposed method is analyzed and verified by the modified 33-node distribution system with four microgrids. The proposed method can make microgrids freely choose to trade active and reactive resources considering the needs of the distribution system, and improve the benefit of microgrids without damaging the benefit of DSO.

The zonal market (ZM) adopted in Europe, in contrast to the nodal market (NM), reconciles the inconsistency between physical networks and administrative management. However, the growing integration of renewable energy sources (RESs) has introduced zonal supply-demand imbalances that exacerbate congestion and the need to re-dispatch. Furthermore, different clearing mechanisms between the day-ahead and real-time markets provide further opportunities for collusive bidding, decreasing total social welfare (TSW). Thus, this paper is the first to propose a long-term congestion management (CM) framework through a market zone (MZ) configuration approach with CM assessment considering collusive bidding in the joint spot markets. More specifically, a topology-based location division (TLD) method is proposed to partition optimal MZs, ensuring the minimum number of MZs based on critical branches. Then, a bi-level evolutionary model is developed to analyze the collusive bidding of producers in the day-ahead and ancillary service markets. Finally, the established framework is applied to a 20-bus test and simplified European systems. Our simulation on the 20-bus system shows that compared with the initial ZM and NM, the congestion cost of the optimized ZM decreased by 90% and 33%, respectively, while the TSW increased by around 13% and 1%, respectively.

This paper presents a novel automated tool developed in MATLAB for the rapid generation of Simulink models representing electrical networks. The increasing complexity of modern power systems, coupled with the integration of Distributed Energy Resources and smart grid technologies, necessitates advanced simulation tools capable of accurately modeling transient behaviors in real-time. Despite the availability of commercial power system analysis software, there remains a significant gap in tools that can efficiently generate Simulink models. The developed tool addresses this gap by providing a framework that standardizes network data representation and automates the Simulink model generation process. The tool implements a standardized intermediate format that accommodates various input data sources while ensuring consistency and completeness. The tool employs graph-based techniques to analyze network topology and constructs hierarchical Simulink models using a customized component library that accurately represents electrical behaviors. Evaluations conducted on test feeders demonstrate that the algorithm significantly reduces the model development time compared to manual approaches while maintaining simulation accuracy, avoiding possible errors due to wrong parameter settings due to the high number of branches present in the networks.

Logistic-energy coordination is an effective way to improve energy efficiency for electrified seaports. However, exiting works adopt fixed logistic power load models and seldom address renewable energy uncertainty adequately, resulting in low system flexibility and robustness. In this paper, a novel logistic-energy collaborative dispatch model is first proposed. The model integrates energy feedback from all-electric ships (AESs) and electric-powered cranes as well as automated guided vehicles (AGVs) battery swapping mode into entire logistic-energy coordination process. Logistic-side flexibility provision is significantly enhanced by variable bidirectional power flows and win-win battery swapping. Then a multi-objective multistage distributionally robust optimization (MMDRO) framework is established to address renewable energy uncertainty. This enables a trade-off between multiple objectives while ensuring solution non-anticipativity, economics and robustness via multistage distributionally robust optimization (DRO). The MMDRO is intractable due to multi-objectives, mixed-integer property and nested min-max-min optimization structure. To this end, an improved multi-objective stochastic dual dynamic integer programming (SDDiP) algorithm with controllable convergence process and two-step weight update is developed to effectively solve the model. Case studies demonstrate the superiority of our approach over existing methods.

From January 2021, the configuration of the electricity market zones in Italy has changed, which prompts us to question whether modifying the existing bidding zone structure would effectively contribute to achieving the goals of enhancing competition and efficiency in the market mechanism. To assess the influence of different Italian zonal configurations on the market performance, we have devised a set of evaluation indicators that encompass various aspects. These indicators are conceived to allow the comparison among different market dispatch mechanisms such as the "pure economic dispatch" and the "network constrained dispatch with nodal representation", as well as the "network constrained dispatch with zonal representation". By comparing the two Italian bidding zone configurations existing in 2020 and 2021, respectively, the performance of such configurations is analysed and compared. The calculation results reveal that the implemented reconfiguration has yielded increased social surplus and reduced congestion costs. In contrast to that of 2020, the configuration implemented in 2021 enables enhanced market efficiency and increased market liquidity, particularly during the summer.

The Mediterranean basin has been characterized by a net flow of fossil commodities from the North African shore to Southern Europe and the Middle East for decades; however, decarbonizing the energy system implies to substantially modify this situation, turning the current “black dialogue” into a “green dialogue” (i.e., based on the exchange of renewable electricity and green hydrogen). This paper presents a feasibility study conducted to estimate the potential green hydrogen production by electrolysis in three Tunisian sites. It shows and compares several plant layouts, varying the size and typology of renewable electricity generators and electrolyzers. The work adopts local weather data and technical features of the technologies in the computations, and accounts for site specific topographical and infrastructural constraints, such as land available for construction and local power grid connection capacities. It shows that configurations able to produce large quantities of green hydrogen may not be compliant with such constraints, basically nullifying their contribution in any hydrogen strategy. Finally, results show that the LCOH lies in the range 1.34 $/kgH2 and 4.06 $/kgH2 depending on both the location and the combination of renewable electricity generators and electrolyzers.

The rapid adoption of Electric Vehicles requires advancements in Electric Vehicle Supply Equipment to accommodate the demand for enhanced performance. Commercial chargers now enable vehicles to inject power into the grid. However, the operation of the chargers introduces also distortions, in particular when multiple chargers operate simultaneously at the same node. This paper investigates the interaction between two different DC charging stations: one unidirectional and the other bidirectional. Then the analysis is extended to the harmonic interaction between the chargers. The results demonstrate the significant influence of the charger operation on grid power quality and highlight the importance of managing harmonic distortion.

The accelerating adoption of electric vehicles and their integration into smart grids through Vehicle-to-Everything technologies present novel opportunities for enhancing energy efficiency and grid stability. This study explores the potential of leveraging electric vehicles within building energy systems, employing a digital twin model for comprehensive analysis and optimization. By simulating various scenarios, including standard and cloud-impacted photovoltaic production and different energy pricing strategies, the research assesses the operational and eco-nomic impacts of utilizing a V2G-enabled car park. The proposed formulation is cast as mixed-integer linear programming, tested through a digital twin approach, demonstrates improvements in energy management and cost efficiency. The findings suggest that, under certain conditions, the bidirectional energy flow capabilities of electric vehicles can effectively substitute for traditional stationary storage systems, offering a dual benefit of vehicle charging and grid support.

Cities play a pivotal role in achieving worldwide carbon neutrality due to their significant contribution to global energy consumption and carbon emissions. Therefore, planning effective strategies and guiding evidence-based policymaking at the city scale becomes even more crucial. Composite indices serve as a valuable tool for monitoring urban energy transition trends. This paper aims to present a novel approach, robust and flexible even under conditions of data scarcity, for tracking the energy transition trend of a city by means of a composite index (UETI). The Turin case study is introduced to test the applicability of the proposed approach. Additionally, to demonstrate the robustness of the composite index framework, the paper includes the findings of correlation and sensitivity analyses. This study reveals a significant improvement in Turin’s environmental and energy domains, while the socio-economic domain shows more modest improvement. Furthermore, the study highlights the need to address the shortage of urban data to enhance the accuracy and reliability of metric-based frameworks and to extend the assessment to a larger sample of cities.

The so-called “energy transition” aims at shifting the primary energy sources used especially for electricity production from fossil fuels to Renewable Energy Sources (RES) in order to pursue environmental sustainability. The increased share of RES into the electricity grid makes the planning and operation more difficult tasks and a correct modeling and simulation of such an emerging system, along with the interactions with its components, become increasingly important and also more arduous to carry out. In this perspective, this paper presents a Multi-Site Real-Time Co-Simulation (MS-RTCS) Laboratory, which is able to pool together geographically-distributed laboratories (from local, to national to intercontinental scale) for performing synchronous tests, through Internet-based communication protocols, sharing local expertise, as well as local HW and SW resources, The paper, after a bit of story of this initiative, frames the design and implementation of such laboratories, introduces the EnSiEL National Energy Transition Real-Time Laboratory (ENET-RTLab) as the Italian proposal in this challenge.

The decarbonization of the energy sector represents a challenge that requires new tools and approaches of analysis. This paper aims to demonstrate the fundamental role that geographical distributed real-time co- simulations (GD-RTDS) can play in this regard. To this end, three different case studies have been analyzed with GD-RTDS, covering a wide range of applications for the energy sector decarbonization: (a) implementation of Renewable Energy Communities for supporting the share increase of Renewable Energy Sources, (b) the integration and management of Onshore Power Supply, and (c) the integration of a forecasting tool for the management of the Electric Vehicle charging. The performed experiments included fully simulated components, together with (power) hardware-in-the-loop and software-in-the-loop elements. These components have been simulated in different laboratory facilities in Italy and Germany, all operating in a synchronized manner under the presented geographically-distributed setup. The results show that the proposed architecture is flexible enough to be used for modeling all the different case studies; moreover, they highlight the significant contribution that the GD-RTDS methodology can give in informing and driving energy transition policies and the fundamental role of power systems to spearhead the complete decarbonization of the energy sector.

Improper configuration of bidding zones can lead to market efficiency losses, hinder the integration of renewable energy sources (RESs), and reduce grid security. To evaluate the impact of different bidding zone configurations on market performance, we developed a multi-dimensional evaluation framework containing a series of indicators covering aspects of market efficiency, grid security, and sustainability. These indicators facilitate the comparisons among different market dispatch mechanisms. To validate the proposed framework, the reconfiguration of the Italian bidding zones has been applied to a simplified Italian grid model to compare the market performance under different bidding zone configurations. The simulation results indicate that the implemented reconfiguration has led to enhanced market efficiency and security in the Italian power system. However, the reconfiguration shows a comparatively lower reduction in greenhouse gas (GHG) emissions, suggesting a weaker sustainable performance.

The European cross-zonal day-ahead (DA) electricity market is transitioning to the flow-based market coupling model for market clearing. With the increasing integration of renewable energy sources (RESs), market participants have opportunities for collusive bidding, resulting in decreased social welfare (SW). Thus, this paper is the first to propose an approach to configure the market zone (MZ) considering collusive bidding among conventional generators (CGs) and RESs in the DA market. Specifically, a bi-level model is developed to analyze collusive bidding among the CGs and RESs. Then, multi-dimensional market performance indices are used to determine the critical branches (CBs), on which the configuration of MZs is based. Finally, test 6-bus and simplified European systems are used to demonstrate the validity and merit of the proposed approach. Our simulation on the 6-bus system shows that when compared with the initial zonal market (ZM), the SW of the optimized ZM increased by 18 %, while the re-dispatch surrogate cost decreased to 0. Also, the penetration of RESs improved by 12 %, which guarantees the development of RESs.

This paper proposes a new mode of cyber-physical attack based on injecting false commands, which poses an increasing risk to modern power systems as a typical example of Cyber-Physical Systems (CPS). Such attacks can trigger physical attacks by driving the system into vulnerable states. To address the critical issues arising from this new mode, we define an inverse-community (IC) in power flow distribution and evaluate it using inversemodularity. To identify the most vulnerable state of the IC that represents the inherent vulnerability of the system, we employ a full malicious power dispatch problem. We also analyze an example of the proposed mode, where a partial malicious power dispatch that maximizes inverse-modularity is combined with physical attacks aimed at disconnecting vulnerable IC boundary lines, making cascading failures highly likely. To demonstrate the potential impact of this coordinated cyber-physical attack, we use the IEEE-118 and IEEE-300 bus systems for simulation. The results show the effectiveness of this attack strategy and provide a new perspective to analyze cyber-physical security issues in modern power systems.

The energy transition and sustainability are the critical challenges of our time, and cities are at the forefront of this transformation. As key players, these urban centers are pioneering innovative strategies to reduce carbon emissions, enhance energy efficiency, and promote renewable energy sources. Central to the transition is the role of electricity, which acts as a cornerstone in the “cocktail” of sustainable solutions. The development of an efficient and resilient “Electricity City Grid” is essential to support this shift. This article explores the target functions and challenges associated with designing and operating the Smart Electricity City Grid of the future. It delves into the infrastructural costs required for this transition and examines the economic and technical hurdles that must be overcome. Finally, the article looks ahead to the future of research in this field, highlighting the areas that will be crucial for the continued evolution and success of smart urban grids. Through this comprehensive analysis, we aim to suggest a roadmap for cities striving to achieve sustainability through advanced electrical infrastructure.

The need for decarbonizing the entire energy system calls for new operational approaches in different sectors, currently (almost) fully dominated by fossil fuels, such as the transports. In particular, the decarbonization of the light-duty passenger transport, based on the implementation of Battery Electric Vehicles, may have a twofold benefit, because of (i) the reduction of local and global direct emissions, and (ii) the role that the Battery Electric Vehicles can have in supporting the operation of the power system in case of large share of non-dispatchable renewable energy sources. This paper aims to investigate, through a Power Hardware-In-the-Loop laboratory setup, the impacts of the Vehicle-to-Grid and Grid-to-Vehicle paradigms on a Low Voltage grid portion serving as grid infrastructure a car parking. The results show that the Low Voltage grid losses, if not taken into account, can cause a wrong evaluation of the expected impact on the grid of the Battery Electric Vehicles. Furthermore, the harmonics of current injected into the grid by several chargers could compromise the perceived power quality. Both the analyzed aspects must be hence carefully considered for properly evaluating pros and cons that the installation of several chargers may have on the grid side. The main contributions refer to the calculation of losses and to the evaluation of the power quality aspects through a Power Hardware-In-the-Loop configuration, enabling to take into account the interaction between charging stations and power grid. & COPY; 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Managing a large share of non-dispatchable renewable energy sources requires new approaches, which have to be integrated with existing and well-established control systems currently used to guarantee power system operation. In this context, the number of energy communities is expected to increase, and hence their effective integration is fundamental. This paper aims at assessing the role and impact of energy communities on the operation of a transition power system, still having a share of active traditional power plants. Simulation models and real hardware have been included in the experiment, based on a live real-time co-simulation. The results highlight both the significant contribution that the methodology (i.e., geographically-distributed real-time co-simulation) can give in informing and driving energy transition policies and the important role that energy communities may have in supporting the transition towards completely de-carbonized power systems.

Many countries are still characterized by high fossil demand, mainly satisfied by imports due to the scarcity of local resources. These countries are more vulnerable to deterioration of geopolitical stability of suppliers, with potential negative impacts on economic growth and welfare, making science-based methodologies crucial for policy makers to monitor the security of energy supply system. The proposed model aims to quantify the risk related to the external front of the supply system, by extending the classical risk analysis approach to the geopolitical dimension, coupling the geopolitical risk of supply corridors with their energy flows and spatial dimension. The proposed methodology allows to perform risk scenario analyses, including the variation of countries' geopolitical stability and supply interruptions, at single country level and considering different energy commodities delivered through captive and maritime corridors. Finally, the developed approach is applied to the security of the Italian crude oil supply system, by discussing and comparing different risk scenarios and possible countermeasures. The obtained results highlight some possible weaknesses of the system (e.g., in case of blockade of Turkish Straits) due to limited replaceability of certain crude types (according to their physical/ chemical properties), suggesting further diversification of crudes mix.

In the last decade, data analytics studies have covered a wide range of fields across the entire value chain in the electricity sector, from production and transmission to the electricity market, distribution, and load consumption. It is essential to integrate and organize the wide range of current scientific publications to effectively allow researchers and specialists to implement and progress cutting-edge methodologies in the future. Because of the electricity market's significance in the value chain of the electricity sector, in this study, we structure a systematic literature review of the data analytics-related works following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) framework to categorize the more common applications and approaches in the electricity market field. After refining the identified studies from the Web of Science database using the inclusion and exclusion criteria, 925 articles were chosen as the final pool of literature. Investigation of the extracted studies reveals that the application of data analytics in the electricity market can be clustered into four distinct groups: Prediction, Demand Side Management (DSM), Analysis of the market power, and Market simulation. Within the categorized applications, Prediction with 67% is the most frequent application of data analytics in the electricity market, followed by market simulation (14%), analysis of the market power (9%), DSM (7%), and other applications (3%).

In this paper, the short-, medium-, and long-term effects of the COVID-19 pandemic on the Italian power system, particularly electricity consumption behavior and electricity market prices, are investigated by defining various metrics. The investigation reveals that COVID-19 lockdown caused a drop in load consumption and, consequently, a decrement in day-ahead market prices and an increase in ancillary service prices.