Electric Grid Research Articles

Commercial EV fleet smart charging for cost reduction and renewables integration: A case study in Germany

The transition to electric mobility reduces transportation carbon emissions but presents challenges in integrating growing electricity demand with renewable electricity generation and power systems. Smart charging emerges as a key technology to minimize operational costs and support higher utilization of green electricity for electric vehicles (EVs). Market analysis indicates a trend towards dynamic electricity tariffs, incentivizing EV charging at times favourable to the electricity grid, yet existing literature fails to address smart charging technology in sync with times of lower electricity prices and higher renewables integration. This work examines different smart charging strategies for a commercial EV fleet in Germany, considering a Real-Time Pricing (RTP) tariff indexed to day-ahead market prices and renewable electricity integrations. HOMER Grid is used to model these smart charging strategies and generate new EV load profiles. Direct and indirect CO2 emissions and renewable electricity usage ratios are calculated based on detailed electricity grid and self consumption data. The resulting Levelized Cost of Energy (LCOE) is minimized, by adjusting solar PV and battery capacities. The adoption of an RTP tariff decreases the LCOE from €0.530 to €0.314 per kWh. A smart charging strategy designed to reduce peak demand and schedule charging at lower price times further reduces the LCOE to €0.283 per kWh. The lowest LCOE of €0.281 per kWh is achieved with 34.4 kWp of solar PV capacity and no batteries. The carbon impact is minimized with larger solar PV and battery capacities. The performed work indicates a favourable framework for EV charging integrators to maximize cost savings and renewable energy integration for an EV fleet charging use case.

Open-Source Hardware Design of Modular Solar DC Nanogrid

The technical feasibility of solar photovoltaic (PV) direct current (DC) nanogrids is well established, but the components of nanogrids are primarily commercially focused on alternating current (AC)-based systems. Thus, DC converter-based designs at the system level require personnel with high degree of technical knowledge, which results in high costs. To enable a democratization of the technology by reducing the costs, this study provides a novel modular plug-and-play open-source DC nanogrid. The system can be customized according to consumer requirements, enabling the supply of various voltage levels to accommodate different device voltage needs. The step-by-step design process of the converter, controller, data logger, and assembly of the complete system is provided. A time-domain simulation and stability analysis of the designed system were conducted in MATLAB/Simulink (version 2024b) as well as experimental validation. The results show that transforming the nanogrid from a distribution network to a device makes it suitable for various user-specific applications, such as remotely supplying power to campsites, emergency vehicles like ambulances, and small houses lacking grid electricity. The modular DC nanogrid includes all the features available in a DC distribution network, as well as data logging, which enhances the user experience and promotes the use of solar-powered DC grid systems.

Impacts of Natural Gas Pipeline Congestion on the Integrated Gas–Electricity Market in Peru

This paper investigates the impact of natural gas pipeline congestion on the integrated gas–electricity market in Peru, focusing on short-term market dynamics. By simulating congestion by reducing the primary natural gas pipeline’s capacity, the study reveals significant patterns in production costs and load flows within the electrical network. The research highlights the critical interdependencies between natural gas and electricity systems, emphasizing how constraints in one network can directly affect the other. The findings underscore the importance of coordinated management of these interconnected systems to optimize economic dispatch and ensure the reliability of both gas and electricity grids. The study also proposes strategic public policy interventions to mitigate the financial and physical impacts of pipeline congestion, contributing to more efficient and resilient energy market operations.

Multi-objective optimal coordination of electric vehicle charging, power grid, energy storages and renewables

Considering that the grid connection of variable renewable energies (VREs) and the disorderly charging loads of large-scale electric vehicles (EVs) will adversely affect the power grid stability, the optimization strategy of EV charging and grid-connected scheduling are investigated, in which energy storage system is added to balance the demand and supply of the power grid. First of all, considering the profit of EV charging station, the charging cost of EV users and power loss, a multi-objective optimal scheduling model of EV charging, power grid, pumped hydro-storage (PHS) and wind farm is constructed, which is improved from the aspect of wind farm wake. Then, the multi-objective optimization algorithm based on genetic algorithm is used to solve the model to realize the optimal charging of EVs. Finally, the IEEE-13 power grid is used to verify the effectiveness of the proposed multi-objective optimal scheduling model. Combined with a specific example for simulation analysis, the results show that the model can achieve orderly charging of EVs, ensuring the safety of the power grid and promote the use of VREs. In addition, the optimization algorithm can further improve the profit, and reduce the charging cost and power loss.

New Approaches in Finite Control Set Model Predictive Control for Grid-Connected Photovoltaic Inverters: State of the Art

Grid-connected PV inverters require sophisticated control procedures for smooth integration with the modern electrical grid. The ability of FCS-MPC to manage the discrete character of power electronic devices is highly acknowledged, since it enables direct manipulation of switching states without requiring modulation techniques. This review discusses the latest approaches in FCS-MPC methods for PV-based grid-connected inverter systems. It also classifies these methods according to control objectives, such as active and reactive power control, harmonic suppression, and voltage regulation. The application of FCS-MPC particularly emphasizing its benefits, including quick response times, resistance to changes in parameters, and the capacity to manage restrictions and nonlinearities in the system without the requirement for modulators, has been investigated in this review. Recent developments in robust and adaptive MPC strategies, which enhance system performance despite distorted grid settings and parametric uncertainties, are emphasized. This analysis classifies FCS-MPC techniques based on their control goals, optimal parameters and cost function, this paper also identifies drawbacks in these existing control methods and provide recommendation for future research in FCS-MPC for grid-connected PV-inverter systems.

Data-driven two-stage scheduling of multi-energy systems for operational flexibility enhancement

The multi-energy system, encompassing electricity networks, district heating networks (DHNs), and hydrogen-enriched compressed natural gas (HCNG) networks, provides an alternative for enhancing operational flexibility. This paper investigates a data-driven two-stage scheduling strategy of multi-energy system to promote uncertain renewable energy integration and improve economic benefits. Dynamic models for DHNs and HCNG networks are established, and the flexibility of multi-energy system is quantified through distribution-level power aggregation. To compromise flexibility enhancement and operational cost reduction, the multiobjective day-ahead scheduling optimization is conducted based on adaptive batch-ParEGO method. Taking day-ahead scheduling as a baseline, a data-driven real-time dispatch method is proposed based on the deep deterministic policy gradient algorithm, which adaptively modifies the energy management scheme in smaller temporal dimension to address uncertainties on both the load and source sides. The performance of the proposed two-stage scheduling method is demonstrated through numerical tests.

Short-term PV energy yield predictions within city neighborhoods for optimum grid management

The global shift towards the exploitation of renewable energy sources is a key aspect in the transition to carbon-free societies. Although harnessing of PV energy disclosed the numerous environmental and economic benefits of PV systems, the ever-increasing share of PV systems in power generation has revealed their adverse effect on the electricity grid due to their dependence on weather conditions. Uncertain environmental parameters, especially cloud cover, can adversely affect the stability of the energy generated by a PV system, causing dynamic changes in PV power generation in a given future time period. Therefore, accurate predictions of PV power output are a major challenge, as they can contribute to the optimal management and flexibility of the power grid, and to the improvement of the operating efficiency of PV power stations, thus enabling the development of flexible green power electricity grids across cities. In addition, precise forecasting can provide system/grid operators with information needed for efficient capacity management and scheduling and ensure grid stability.Most forecasting models provide promising results in terms of error performance; however, they need weather variables or satellite information as inputs that make prognosis significantly challenging. Hence, the current work utilizes a data-driven method for predicting PV power generation from distributed PV systems. The spatio-temporal model Sparse Gaussian Conditional Random Fields was considered. Although this model was used extensively for wind forecasting, in this work, it was used for solar PV power output predictions. Moreover, the effect of varying spatial distribution for aggregated multiple PV systems was investigated on the forecast model performance. The main objective was to provide the solar PV energy sector with important information for building a smart grid for cities or regions.Continuous data from a grid-connected dense network of 21 PV systems was used within a region of 38.485 km2 in Nicosia, Cyprus. It should be noted that the test set includes data only from winter months. The results showed that the predicted power output signal responds to the fluctuation and follows the trend of the actual power signal, even on days with large variations in actual PV power. The average nRMSE of all PV systems in each dataset studied ranges from 0.119 to 0.146, proving that the proposed method delivered significant results that can be compared with existing results from similar studies. Overall, the proposed method can provide important information that can assist in decision-making for the management, optimization, and visualization of grids across cities.

Intra-hour photovoltaic power point-interval prediction using a dual-view deep neural network

Photovoltaic (PV) power is intermittent, fluctuating, and uncertain due to meteorological factors, which makes it challenging to integrate PV power into electrical grids. Overcoming this problem requires an accurate and reliable PV power prediction model; thus, a dual-view deep neural network (Dv-DNN) is proposed. The model fuses a gate recurrent unit (GRU) neural network and a temporal convolutional network (TCN) in parallel, enabling end-to-end dual-view deep prediction for PV power. Using data from solar systems in the Yulara region of Australia, three sets of experiments—single-step, multi-step, and interval prediction—are conducted to explore the performance of two recurrent and convolutional deep neural networks in PV power prediction. For point prediction, the experimental results show that the Dv-DNN has better forecasting performance than other models as the prediction horizon increases. Regarding interval prediction, at confidence levels of 97.5%, 95%, 90%, and 85%, the method based on kernel density estimation (KDE) is superior to the method based on maximum likelihood estimation (MLE) according to the coverage width-based criterion (CWC).

GIS-based method for assessing the viability of solar-powered irrigation

To attain food security, sub-Saharan Africa needs to increase its use of irrigation to improve agricultural productivity and resilience to climate change-induced droughts and erratic rain patterns. Stand-alone solar-powered irrigation systems are a promising technology to power irrigation where grid electrification is unavailable. However, these systems’ economic viability and sustainability are affected by spatially varying factors such as climate, water availability, and solar potential, making it challenging to plan their roll-out. This study addresses the planning challenge by developing a novel open-source reproducible geospatial methodology to determine irrigation water demand, peak power demand, life cycle cost, and locations where solar-powered irrigation systems would be cheaper than alternative off-grid irrigation technologies. The method considers spatial–temporal data for meteorological conditions, soil quality, water distance and depth, solar resource potential, technology efficiency and local equipment and fuel costs. Maize cultivation in Kenya is taken as a case study to demonstrate the method. We find the median peak irrigation water demand in Kenya to be 65,000 l/ha/day and the median peak power demand to be 2.6 kW/ha. The levelized cost of solar-powered irrigation systems ranges from US$ 0.09/kWh to US$ 0.25/kWh, making solar irrigation cheaper than diesel-powered irrigation in all Kenyan regions. The results from the method offer valuable insights to governments, farmers and investors on where water use regulations are required for sustainable water use, where subsidies and innovative financial models are needed for the affordability of solar irrigation and where alternative uses of energy from the solar systems beyond irrigation are possible.

Economic and environmental considerations for the deployment of industrial very high temperature heat pumps in European markets

Very high temperature heat pumps (VHTHP) have begun to emerge on the market during the past decade, providing an alternative to fuel-fire boilers for generating industrial process heat up to 200 °C. Large temperature lifts are common for VHTHPs, resulting in a reduced coefficient of performance (COP) compared to traditional heat pumps. Thus, the economic feasibility of operating VHTHPs becomes more dependent on local price conditions, and the environmental impact is affected by increased electricity usage. Both the economics and the environmental impact of operating industrial VHTHPs in place of fossil fuel-fired boilers in Europe are studied in this article. Discounted payback periods are estimated for the varying price conditions in Europe, and life cycle assessment tools are used to quantify the environmental impact of operating VHTHPs. Finland, Sweden, and Denmark were found to be ideal countries for VHTHP deployment, as they showcase both favorable economic conditions as well as generally small environmental impacts in the endpoint categories Human health, Climate change, and Resource usage, if local grid electricity is used. The effect on the endpoint category Ecosystem impacts was generally larger for most countries, including the aforementioned three, mostly due to the use of biomass in the electricity grid.

High-Resolution Monitored Data Analysis of EV Public Charging Stations for Modelled Grid Impact Validation

As electric vehicle adoption grows, understanding the impact of electric vehicle charging on electricity grids becomes increasingly important. Accurate grid impact modelling requires high-quality charging infrastructure data. This study examined the electric vehicle recharging infrastructure and usage patterns in a region of the Italian Alps over a three-year period from 2021 to 2023. The primary objectives were to analyze the growth and distribution of electric vehicle charging stations, assess energy consumption, and evaluate charging behaviours across various recharging points. The research involved collecting empirical data from 411,800 recharging sessions and simulated data using the emobpy tool to model energy consumption and charging behavior. Key findings reveal a substantial increase in the number of recharging points, from 673 in 2021 to 970 in 2023, with the total energy delivered increasing from 938 MWh in 2021 to 4133 MWh in 2023. The data showed distinct temporal trends: AC points were primarily used during the day, while DC points saw higher usage during morning and late afternoon peaks, aligning with travelling times. The study’s validation of simulation results against empirical data emphasized the importance of high-quality input for accurate grid impact assessments. These findings suggest the necessity for strategic placement of recharging infrastructure and provide practical insights for policymakers, urban planners, and utility companies to support sustainable electric vehicle integration.

Numerical Investigation of the Thermal Performance of Air-Cooling System for a Lithium-Ion Battery Module Combined with Epoxy Resin Boards

Lithium-ion batteries (LIBs) have the lead as the most used power source for electric vehicles and grid storage systems, and a battery thermal management system (BTMS) can ensure the efficient and safe operation of lithium-ion batteries. Epoxy resin board (ERB) offers a wide range of applications in LIBs due to its significant advantages such as high dielectric strength, electrical insulation, good mechanical strength, and stiffness. This study proposes an air-cooled battery module comprised of sixteen prismatic batteries incorporating an ERB layer between the batteries. To compare the performance of the ERB-based air-cooling system, two other air-cooling structures are also assessed in this study. Three-dimensional numerical models for the three cases are established in this paper, and the heat dissipation processes of the battery module under varying discharge rates (1C, 2C, and 5C) are simulated and analyzed to comprehensively evaluate the performance of the different cooling systems. Comparative simulations reveal that incorporating ERB into the battery assembly significantly reduces battery surface temperatures and promotes temperature uniformity across individual batteries and the entire pack at various discharge rates. Notably, under 5C discharge conditions, the ERB-based thermal management system achieves a maximum battery surface temperature increase of 16 °C and a maximum temperature difference of 8 °C between batteries. Additionally, this paper also analyzes the impact of battery arrangement on air-cooling system performance. Therefore, further optimization of the structural design or the integration of supplementary cooling media might be necessary for such demanding conditions.

Risk-return analysis of clean energy grid project investment based on integrated ISM and Monte Carlo model

To solve the problem of complex and difficult to quantify factors affecting investment returns and risks in clean energy power grids, this study comprehensively applies the interpretive structural model and Monte Carlo model to the analysis of investment risk-returns in clean energy power grid projects. The interpretive structural model is utilized to analyze project investment returns, while the Monte Carlo model is used to analyze project investment risks. The project investment risk is based on the factor analysis of project investment returns, and key risk factors are identified through 1000 simulations, and the impact of these risks on project returns is quantified. By combining the two, the investability of the project is analyzed. The results showed that grid electricity prices, kilowatt hour subsidies, technology learning rates, total annual sunshine hours, and system power generation efficiency were key factors driving investment returns. The average expected value of investment return was about 20%, and the probability of investment return below 6% was close to 0. The overall project is worth investing in. From this, it can be seen that the research designed investment risk-return analysis methods for clean energy grid projects can effectively distinguish the main factors affecting investment returns and risks, and pre simulate the risk situation of returns. This study can provide reference for investor decision-making.

A probabilistic approach for optimal integration of EVs and RES using artificial hummingbird algorithm in distribution network

AbstractThe adoption of electric vehicles (EVs) is crucial for reducing pollution from traditional automobiles. Strategic placement of electric vehicle charging stations (EVCS) is needed to meet demand while minimizing impacts on the electrical grid. This article outlines a practical method to identify optimal EVCS locations within the IEEE 69 bus system. The transition to EVs affects the electrical distribution network, requiring consideration of voltage regulation, power loss, stability, reliability, and energy loss costs when deploying EVCS. To manage increased energy demands, the article recommends integrating solar distributed generation (SDG) units at strategic points in the network, creating a self‐sustaining system. The study explores the resilience of the distribution system with EVCS and SDGs through eight case studies (CS), examining EVCS deployment scenarios with and without SDG integration. The impact of slow and fast EV charging on system objectives is also analysed. The artificial hummingbird algorithm is used to solve the allocation problem, with results compared to other optimization methods. Notably, active power loss decreased from 224.67 kW (CS1) to 53.35 kW (CS8), and reactive power loss was reduced by 71.4% in CS8 compared to CS1.

Large Eddy Simulations of a Francis Turbine Operating at Ultra-Low Load to Overload Conditions

Due to the increasing global power demand, hydropower plants face the challenge of operating at flow rates far from their best efficiency point to comply with the electrical grid. Consequently, turbine units experience unstable high swirling flows under different load conditions, reducing the life of system components. Understanding the mechanisms behind the onset and sustenance of these instabilities in the swirling flow leaving the turbine runner is crucial for improving turbine performance. To achieve this, large-eddy simulations (LES) were conducted to characterize the unsteady flow inside an industrial-sized Francis turbine. The turbine was studied under a wide range of flow rates, 100, 80, 60, 40, and 120% of the design flow rate. A high swirl velocity was introduced as the flow rate deviated from 100% load, causing significant changes in the flow structure. An organized structure of vortex filaments in a helical pattern is seen inside the draft tube at 80 and 60% load, and the 40% load case showed an unorganized collection of small-scale vortices. A straight vortex cavity is seen in the draft tube center at 120% excess load. At the best design point, the magnitude of the swirl velocity was 0.1 times the tip speed. However, as the flow rate dropped to 40% of the design flow rate, the magnitude increased to 0.4 times the tip speed near the wall. In addition, the 40% load case had the highest magnitude of pressure fluctuations, over 25 times the peak design case near the runner. This showed a very unsteady flow field could damage the system or cause unstable operation at ultra-low partial loads. Generated power dropped dramatically as the flow rate decreased, and more noise was present in the power signal.

ENGINEERING CHALLENGES AND SOLUTIONS IN SMART GRID INTEGRATION WITH ELECTRIC VEHICLES

This paper explores the critical engineering challenges and innovative solutions for successfully integrating intelligent grids and electric vehicles (EVs), emphasizing the increasing need for a resilient and adaptive electrical grid as global EV adoption accelerates. It comprehensively examines the technological, infrastructural, and regulatory obstacles that must be addressed to ensure seamless integration, focusing on advanced energy management systems, grid stability amidst fluctuating demand, and incorporating renewable energy sources. The study delves into the infrastructural requirements, including the expansion of charging networks, upgrades to transmission and distribution systems, and the implementation of vehicle-to-grid (V2G) technologies, while also analyzing the necessary regulatory and policy frameworks, stressing the importance of clear standards, incentives, and public-private collaboration. The paper offers a forward-looking perspective on overcoming current challenges by reviewing recent advancements in innovative grid technology—such as high-capacity energy storage and artificial intelligence (AI) use for predictive maintenance and load balancing. It highlights the need for interdisciplinary collaboration among engineers, policymakers, and industry leaders to develop a cohesive strategy for future energy distribution while underscoring the role of AI in optimizing grid performance, predicting energy consumption patterns, and enhancing overall efficiency. Ultimately, the paper provides a comprehensive analysis of the current state of smart grid and EV integration, offering actionable insights for stakeholders and concluding with recommendations for future research and development priorities, with a strong emphasis on continued innovation and cooperation to achieve a sustainable and resilient energy future.

Virtual active power sensor for eolic self-consumption installations based on wind-related variables

Abstract Green energy production is expanding in individual and large-scale electricity grids, driven by the imperative to reduce greenhouse gas emissions. This research performs a comparative analysis of several linear and non-linear regression models, intending to identify the most effective method to estimate the active power produced for a mini wind turbine using meteorological variables, looking for a reliable virtual sensor. The modeling process followed a feature selection step before applying eight machine learning techniques whose results were statistically analysed to determine the best performance. The implemented virtual sensor accurately estimated the active power, being an interesting tool for anomaly detection, maintenance management or decision-making.

Impact of Artificial Intelligence on the Planning and Operation of Distributed Energy Systems in Smart Grids

This review paper thoroughly explores the impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids. With the rapid advancement of artificial intelligence techniques such as machine learning, optimization, and cognitive computing, new opportunities are emerging to enhance the efficiency and reliability of electrical grids. From demand and generation prediction to energy flow optimization and load management, artificial intelligence is playing a pivotal role in the transformation of energy infrastructure. This paper delves deeply into the latest advancements in specific artificial intelligence applications within the context of distributed energy systems, including the coordination of distributed energy resources, the integration of intermittent renewable energies, and the enhancement of demand response. Furthermore, it discusses the technical, economic, and regulatory challenges associated with the implementation of artificial intelligence-based solutions, as well as the ethical considerations related to automation and autonomous decision-making in the energy sector. This comprehensive analysis provides a detailed insight into how artificial intelligence is reshaping the planning and operation of smart grids and highlights future research and development areas that are crucial for achieving a more efficient, sustainable, and resilient electrical system.

Decentralized distributionally robust chance-constrained operation of integrated electricity and hydrogen transportation networks

Hydrogen energy offers a promising pathway to decarbonizing future energy systems. Renewable energy-based hydrogen production and road transportation-based hydrogen delivery have strengthened the coupling between the power distribution network (PDN) and the hydrogen transportation network (HTN). In this article, we propose a decentralized distributionally robust chance-constrained (DRCC) method for the integrated electricity and hydrogen transportation network (IEHTN) to foster synergies between PDN and HTN. First, an optimal scheduling framework is developed to coordinate hydrogen production and transportation in the IEHTN, where multiple operational states of water electrolyzers and the time-constrained tube trailer routing are modeled. Second, a data-driven DRCC approach is proposed to model the uncertain behaviors of renewable power generation and hydrogen demand. A conditional value-at-risk approximation is then employed to convert the DRCC problem to a tractable mixed-integer linear program that can be solved by the off-the-shelf solver. Third, an alternating direction method of multipliers (ADMM) based solving scheme is designed to solve the problem in a distributed manner with limited information exchange between PDN and HTN. To address the non-convexity brought by binary variables in the ADMM, a tractable alternating optimization procedure (AOP) strategy is utilized to guarantee the convergence of solution. Finally, case studies are conducted on an IEHTN composed of the modified IEEE 33-bus PDN and 52-node HTN to verify the validity of the proposed method.

Operation of coupled power-gas networks with hydrogen refueling stations, responsive demands and storage systems: A novel stochastic framework

Because of factors such as low natural gas prices, low greenhouse gas emission and high efficiency has increased the penetration of natural gas-fired generators in electric power systems. Large gas consumption of gas-fired generators has caused intense interdependence of electricity and gas systems. Most often, the operational planning of power and gas systems is conducted through a co-optimisation model in which the total cost of electricity and gas systems is minimised and the constraints of both systems are considered. On the other hand, due to environmental concerns, the usage of fuel cell vehicles (FCVs) is increasing. Hydrogen refueling stations (HRSs) are needed for refueling FCVs. This paper puts forward a stochastic model for co-optimisation of wind-integrated power and gas systems, hosting HRSs, electric storage systems (ESS's) and responsive electricity and gas demands. The effect of ESS's, responsive demands, contingencies, wind uncertainties and demand response on operation of power-gas nexus is scrutinized. An incentive-based demand response program has been used for both electricity and gas demands. The case study is the Belgian gas network connected to the a 24-bus power system. The findings indicate that responsive electric demands reduce electric system cost by 9.4 %, while responsive gas demands reduce the gas network cost and total operation cost by 1 %. The contingency analysis on the power-gas nexus shows that single line outage contingencies in power system does not result in any demand shed and does not increase the operation cost of either electricity network or gas network.