High Penetration Levels Research Articles

Empowering microgrids for sustainable transportation electrification: A comprehensive methodology for resource adequacy and grid resilience

Transportation electrification has emerged as a pivotal solution to mitigate carbon emissions from the transportation sector. However, this shift towards electric vehicles (EVs) presents significant challenges for municipalities and utilities, particularly in ensuring the electrical grid's capacity to meet the increased demand. In this sense, this work introduces a novel methodology to address these challenges by 1) quantifying the impact of EVs on the hosting capacity (HC) of residential microgrids (MGs), and 2) proposing a novel control strategy to meet grid resource adequacy requirements under high levels of transportation electrification. For this, first, a simplified approach to quantify the overall demand in residential MGs, accounting for the influence of EVs and typical demand profiles based on real-world data from municipalities and utility systems is developed. Second, a new frequency controller is introduced, utilizing Distributed Phasor Measurement Units (D-PMU) and mobile energy sources (MES) to ensure compliance with resource adequacy requirements under high levels of EVs penetration. The proposed controller takes advantage of the low latency and high-resolution capabilities of D-PMU technology to enable the harnessing of MES, preventing critical frequency nadir events, and improving the overall system resource adequacy performance under both transient and steady-state analysis. Real-world data from Seattle, WA, USA, is used in the developed case studies, and comparative analysis between traditional SCADA, state-of-the-art D-PMU-based controller, and the proposed controller is presented. The obtained results indicate that significant improvements are achieved by the proposed controller, empowering utilities to ensure reliable operations amidst transportation electrification challenges imposed on resource adequacy.

Insurance market development and economic growth in Africa: Contingencies and thresholds of structural transformation

Despite insurance contributions to economic growth, the intervening role of structural transformation on the insurance-growth nexus remains scarce, leaving a gap in the literature. Motivated by the 2030 Sustainable Development Agenda, which envisages higher structural transformation, financial development and economic progress, this study examines the threshold effects and the joint impact of insurance and structural transformation on economic growth in Africa. The study employed the generalised method of moments and dynamic panel threshold estimations on 38 countries covering 2008 to 2020. The following findings emerge: First, insurance and structural transformation promote economic growth. Second, the results indicate that structural transformation enhances the impact of insurance on economic growth. This suggests that structural transformation matters in driving insurance market development, ultimately promoting economic growth. Third, the empirical evidence further reveals nonlinearities in the relationship and presents that a high level of insurance penetration stimulates growth and vice versa. More so, the synergistic effect of insurance and structural transformation on economic growth is more substantial at higher threshold levels. This implies that though insurance spurs economic growth, its impact is more effective through the intervening role of a strong structural transformation. Therefore, this study calls for government intervention and policies to improve structural transformations and form insurance synergies to promote growth.

Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control

The integration of autonomous vehicles into urban traffic systems offers a significant opportunity to improve traffic efficiency and safety at signalized intersections. This study provides a comprehensive evaluation of how different autonomous vehicle driving behaviors—cautious, normal, aggressive, and platooning—affect key traffic metrics, including queue lengths, travel times, vehicle delays, emissions, and fuel consumption. A four-leg signalized intersection in Balgat, Ankara, was modeled and validated using field data, with twenty-one scenarios simulated to assess the effects of various autonomous vehicle behaviors at penetration rates from 25% to 100%, alongside human-driven vehicles. The results show that while cautious autonomous vehicles promote smoother traffic flow, they also result in longer delays and higher emissions due to conservative driving patterns, especially at higher penetration levels. In contrast, aggressive and platooning autonomous vehicles significantly improve traffic flow and reduce delays and emissions. Mixed-behavior scenarios reveal that different driving styles can coexist effectively, balancing safety and efficiency. These findings emphasize the need for optimized autonomous vehicle algorithms and signal control strategies to harness the potential benefits of autonomous vehicle integration in urban traffic systems fully, particularly in terms of improving traffic performance and sustainability.

A Standardized Sky Condition Classification Method for Multiple Timescales and Its Applications in the Solar Industry

The deployment of photovoltaic (PV) systems has increased globally to meet renewable energy targets. Intermittent PV power generated due to cloud-induced variability introduces reliability and grid stability issues at higher penetration levels. Variability in power generation can induce voltage fluctuations within the distribution system and cause adverse effects on power quality. Therefore, it is essential to quantify resource variability to mitigate an intermittent power supply. In this study, we propose a new scheme to classify the sky conditions that are based on two common variability metrices: daily clear-sky index and normalized aggregate ramp rates. The daily clear-sky index estimates the cloudiness in the sky, and ramp rates account for the variability introduced in the system generation due to sudden cloud movements. This classification scheme can identify clear-sky, highly variable, low intermittent, high intermittent and overcast days. By performing a Chi-square test on the training and test sets, we obtain Chi-square statistic values greater than 3 with p-value > 0.05. This indicates that the distribution of the training and test clusters are similar, indicating the robustness of the proposed sky classification scheme. We have demonstrated the applicability of the scheme with diverse datasets to show that the proposed classification scheme can be homogenously applied to any dataset globally despite their temporal resolution. Using various case studies, we demonstrate the potential applications of the scheme for understanding resource allocation, site selection, estimating future intermittency due to climate change, and cloud enhancement effects. The proposed sky classification scheme enhances the precision and reliability of solar energy forecasts, optimizing system performance and maximizing energy production efficiency. This improved accuracy is crucial for variability control and planning, ensuring optimal output from PV plants.

Ultra-short-term global horizontal irradiance forecasting based on a novel and hybrid GRU-TCN model

The need for energy is increasing globally due to a several factors, including population growth and economic development. Achieving this energy demand in the face of global warming and the depletion of fossil fuels requires the use of renewable energy. Photovoltaic energy is one of the renewable energy sources that is widely used in many nations across the world. Photovoltaic (PV) energy integration into the grid has significant benefits for the environment and economy, but at high penetration levels, its intermittent nature makes system stability difficult to maintain. Accurate ultra-short-term global horizontal irradiance forecasting is necessary in order to guarantee the most optimal use of photovoltaic power production sources. For GHI forecasting, a novel GRU-TCN-based model is proposed in this paper. It is composed of two neural networks: a temporal convolutional network and a gated recurrent unit. After extracting the temporal features from time-series solar irradiance data using GRU, the spatial features are obtained from the correlation matrix of different meteorological variables of the target and its neighbor position using TCN. Univariate and multivariate GRU-TCN models have been used for GHI ultra short-term forecasting. This paper compares the univariate and multivariate GRU-TCN models with TCN, LSTM, and GRU models based on three evaluation metrics in order to investigate how different combinations of variables affect the accuracy of the GRU-TCN models for one-step forecasting. The findings indicate the adoption of a univariate model using historical data of GHI is suitable to obtain reliable forecasting with 23.02 (W/m2) in MAE as opposed to the best multivariate model that achieved 25.67 (W/m2) in MAE. According to the results, the proposed model outperforms the other models assessed and offers a practical alternative for ultra-short-term GHI forecasting.

Terminal ballistics performance of 9 × 19mm cartridges in 10% ballistic gelatin: FBI Protocol based analysis of Brazilian-made law enforcement ammunition.

Understanding that a projectile entering the human body can cause damage or destruction to live tissues through a variety of wounding mechanisms - permanent cavity, temporary cavity, and fragmentation - is crucial for researching terminal ballistics and understand the patterns of gunshot wound configuration. The present work tested four different types of ammunition in caliber 9 × 19mm (Full Metal Jacketed, Gold Hex, Copper Bullet Tactical and Bonded), using ballistic gelatin at 10% as soft tissue surrogate. The tests were based on the Federal Bureau of Investigation Protocol and included shots through bare gelatin, heavy clothing, plywood, steel sheets and auto glass. As a comparison parameter, the American-made Federal™ HST, used by several law enforcement agencies in the USA, was also tested in the same conditions. The Full Metal Jacketed cartridge had a uniform performance throughout the experiment, showing high penetration levels and no expansion, as expected. Gold Hex demonstrated a strong tendency to fragment with low levels of penetration and weight retention. Copper Bullet Tactical did not achieve the 12" minimum penetration in the soft barrier phases but expanded aggressively. Finally, Bonded only failed to achieve the 12" mark of penetration in phase 5 (auto glass), the hardest barrier in the whole Protocol. Tested for comparison purposes, Federal HST showed aggressive expansion in the initial phases (over 100%), after surpassing the 12" threshold. The study concluded that heavier projectiles (CBC Bonded and Federal HST) performed better than lighter and faster bullets in terms of terminal ballistics.

Consumer Theory-Based Primary Frequency Regulation in Multi-Microgrid Systems within a P2P Energy Management Framework

This paper presents a novel primary frequency regulation strategy for multi-microgrid (MMG) systems, utilizing consumer theory within a peer-to-peer (P2P) energy management framework. By coordinating photovoltaic (PV) systems and energy storage systems (ESS), the proposed method ensures a rapid and effective response to frequency deviations. Unlike conventional approaches, this strategy minimizes the curtailment of renewable energy sources by prioritizing the use of ESS, allowing excess energy to charge the ESS for later use during under-frequency events. This not only enhances energy efficiency but also maximizes renewable energy utilization. Simulations demonstrate that the proposed scheme achieves lower frequency deviations and faster stabilization compared to traditional droop and virtual inertia methods. These results highlight the potential benefits of integrating consumer theory-based models into primary frequency regulation, significantly enhancing system stability and efficiency in power systems with high levels of renewable energy penetration.

Fuzzy logic-based estimation model of distribution transformers aging

Currently, we are moving towards more ecological modes of transport. As a result, the demand for electric vehicles is growing exponentially, and new loads, related to charging, can negatively affect the operation of electrical grids and their elements. Concurrent and uncontrolled charging of electric vehicles can cause a blackout. Considering the stochastic nature of the additional load from electric cars, it is difficult to predict such a load by analytical methods. The fuzzy logic approach can be used to formalize the tasks with ambiguity. In this paper, a fuzzy-based block diagram of the algorithm was developed in the MATLAB-Simulink environment for evaluating the impact of the charging load of electric vehicles on the aging of transformers, as well as a model was built and the effect of various penetrations of electric cars was evaluated. The fuzzy logic-based model includes the effects of ambient temperature, power quality, and overloads. It contains a diagnostic part that warns the dispatcher of the power distribution network about possible problems, providing up-to-date information about the state of the transformer. In addition, the efficiency of photovoltaic generators to increase the service life of distribution transformers was analyzed. The results show that photovoltaic power plants are not effective enough for high levels of electric vehicle penetration, and more advanced strategies should be used.

Deep Low-Carbon Economic Optimization Using CCUS and Two-Stage P2G with Multiple Hydrogen Utilizations for an Integrated Energy System with a High Penetration Level of Renewables

Integrating carbon capture and storage (CCS) technology into an integrated energy system (IES) can reduce its carbon emissions and enhance its low-carbon performance. However, the full CCS of flue gas displays a strong coupling between lean and rich liquor as carbon dioxide liquid absorbents. Its integration into IESs with a high penetration level of renewables results in insufficient flexibility and renewable curtailment. In addition, integrating split-flow CCS of flue gas facilitates a short capture time, giving priority to renewable energy. To address these limitations, this paper develops a carbon capture, utilization, and storage (CCUS) method, into which storage tanks for lean and rich liquor and a two-stage power-to-gas (P2G) system with multiple utilizations of hydrogen including a fuel cell and a hydrogen-blended CHP unit are introduced. The CCUS is integrated into an IES to build an electricity–heat–hydrogen–gas IES. Accordingly, a deep low-carbon economic optimization strategy for this IES, which considers stepwise carbon trading, coal consumption, renewable curtailment penalties, and gas purchasing costs, is proposed. The effects of CCUS, the two-stage P2G system, and stepwise carbon trading on the performance of this IES are analyzed through a case-comparative analysis. The results show that the proposed method allows for a significant reduction in both carbon emissions and total operational costs. It outperforms the IES without CCUS with an 8.8% cost reduction and a 70.11% reduction in carbon emissions. Compared to the IES integrating full CCS, the proposed method yields reductions of 6.5% in costs and 24.7% in emissions. Furthermore, the addition of a two-stage P2G system with multiple utilizations of hydrogen further amplifies these benefits, cutting costs by 13.97% and emissions by 12.32%. In addition, integrating CCUS into IESs enables the full consumption of renewables and expands hydrogen utilization, and the renewable consumption proportion in IESs can reach 69.23%.

Accurate short-term GHI forecasting using a novel temporal convolutional network model

Global energy demand is on the rise, driven by factors such as population growth and economic development.Utilizing renewable energy is crucial to meeting this energy demand in light of global warming and the depletion of fossil resources.One of the renewable energy sources that is extensively utilized in multiple countries worldwide is photovoltaic energy.While integrating photovoltaic (PV) energy into the grid offers substantial economic and environmental advantages, its intermittent nature presents challenges to maintaining grid stability at high penetration levels. Accurate solar energy estimations are essential for photovoltaic (PV) based energy facilities to enable early participation in energy markets and efficient resource planning. This study proposes and evaluates a Temporal Convolutional Network (TCN) model that can rapidly predict global horizontal irradiance (GHI) using univariate and multivariate approaches. To our knowledge, this is the first study to employ a TCN model for GHI forecasting. The univariate model solely considers GHI, while the multivariate time series models incorporate a combination of six variables: global horizontal irradiance, active power of a module plant, PV module temperature, wind speed, air temperature, and air pressure. The proposed model’s effectiveness is validated by comparing its performance to benchmark models, including MLP, RNN, GRU, and LSTM. The prediction models’ performance is evaluated using root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). The results demonstrate that the effectiveness of univariate TCN models is evident in their performance for 5, 10, 15, and 20 min ahead short-term GHI forecasting, its mean absolute error (MAE) values achieved are 22.935 W/m2 33.47 W/m2, 36.9994 W/m2, and 41.9946 W/m2, respectively. The multivariate model, incorporating additional variables, yielded higher MAE values : 37.928 W/m2, 55.88 W/m2, 66.61 W/m2, and 74.82 W/m2, respectively. These results suggest that the TCN model’s capability to capture both long-term and short-term dependencies within the data contributes to its accuracy for GHI forecasting compared to other models.

Artificial intelligence-based approach for islanding detection in cyber-physical power systems

Modern power grids, functioning as a cyber-physical system (CPS), accommodate high penetration level of distributed generations (DGs) to ensure sustainability. Achieving sustainability through grid-scale DGs, however, increases the likelihood of islanding occurrences, which jeopardizes a major parameter of CPS implementation: security. This paper proposes an artificial intelligence-based approach for detecting islanding in modern power grids. The method utilizes measured voltage and frequency to develop a fuzzy center of gravity (COG)-based equivalent model of the system. The model is derived by combining data collected from phasor measurement units (PMUs) with fundamental dynamical equations that govern power system dynamics. In this model, the system is represented using a set of fictitious reactances calculated using goal programming, which are then utilized to connect the COG to the local centers of inertia (COIs). By having a set of fictitious reactances for various operating points fed into a fuzzy model, one could develop an online model to calculate the fictitious reactances with high accuracy and speed at specific snapshots. By incorporating the maximum allowable phase differences between areas into the COG model to ensure transient stability, one could enhance the developed model to be robust against cyber-physical contingencies and cyber-attacks, albeit at the expense of a slight reduction in accuracy. The calculated fictitious reactances, represented in terms of local frequencies throughout the system, serve as valuable indicators for detecting islanding. By classifying the calculated fictitious reactances using support vector clustering over a period of time, islanding could be detected with high accuracy. Furthermore, incorporating the COG concept with the clustering based on fictitious reactances makes it possible to detect false data injection in an area of the system. The efficacy of the proposed method is assessed using simulated data from the renewable integrated 73-bus IEEE test system.

Multi-stage framework for optimal incorporating of inverter based distributed generator into distribution networks

Recently, hydrogen-based distributed generators (DG) have gained significant attention for modern energy generation systems. These modem DGs are typically outfitted with power electronics converters, resulting in harmonic pollution. Furthermore, increasing the growth of modern nonlinear loads may result in exceeding the harmonic beyond the permitted level. This research proposes a framework for optimal incorporation of inverter-based distributed generation (a fuel cell connected to an AC distribution system) for minimizing power losses, enhancing the voltage profile, and limiting both total and individual harmonic distortion according to the IEEE-519 standard. In addition, for accounting system sustainability, the proposed framework considers load variation and the expected rise in demand. Therefore, the suggested framework comprises three stages, which include fundamental and harmonic power flow analysis. The first stage identifies the optimal size and location of the DG in relation to the base load operating condition. While, with the optimal DG of the first stage, the amount of harmonic pollution may violate the limits during a high level of nonlinear load penetration, as a result, the second stage resizes the DG, considering the connection bus of the first stage, to mitigate the harmonics and optimize the system at a higher level of nonlinear load penetration. Both the first and second stages are performed off-line, while the third stage optimizes the system operation during run time according to loading conditions, harmonic pollution, and the available DG capacity of the previous stages. DG’s harmonic spectrum is represented according to recently issued IEEE 1547-2018 for permissible DG’s current distortion limits. The suggested approach is applied and evaluated using an IEEE 33-bus distribution system for various combinations of linear and nonlinear loads. For run-time operation throughout the day, the presented framework reduces the energy losses from 5.281 to 2.452 MWh/day (about 53.57% energy savings). This saving is associated with voltage profile enhancement without violating the permissible standard levels of harmonics and other system constraints.

A new hybrid approach for cold load restoration using deep learning

After an extended outage in a power system, restoration of feeders with high penetration levels of thermostatically controlled loads (TCLs) will be a challenging problem. By restoring these feeders, the demand will increase several times the normal conditions which is called the cold load pick-up (CLPU) phenomenon. This can lead to overload of equipment such as power transformers in transmission substations. In order to increase the reliability of network load supply, in transmission substations, usually several transformers are used which can be operated in parallel by closing circuit breakers on the secondary side called bus section (BS). When the restoration process is long and the loads are cold, more loads can be restored by closing the BSs and paralleling the transformers. On the other hand, it is not possible to close all the BSs simultaneously because paralleling all the transformers at the same time increases the short circuit level on the secondary side. Furthermore, in power systems, some loads are voltage-dependent and by reducing the voltage, their demand can be decreased and therefore the restoration process is expedited. Due to the increase in demand caused by CLPU, additional stress is applied to the power system, which can lead to operational constraints violation and even long-term voltage instability. Therefore, at the time of restoration of each feeder, the settings of voltage control devices are updated to avoid the violation of operational constraints and the occurrence of wider events. In this paper, in order to reduce cold load restoration time, closing the bus sections and voltage reduction are used simultaneously. Also, short circuit and operational constraints are considered. The cold load restoration problem is formulated as a mixed integer nonlinear programming and is solved using the pattern search algorithm. Since there are many decision variables and solving the problem is time-consuming, a combination of deep learning networks, pattern search algorithm and parallel computing is used to accelerate the computations.

Space Division and WGAN-GP Based Fast Generation Method of Practical Dynamic Security Region Boundary

Fast and accurate transient stability analysis is crucial to power system operation. With high penetration level of wind power resources, practical dynamic security region (PDSR) with hyper plane expression has outstanding advantages in situational awareness and series of optimization problems. The precondition of obtaining accurate PDSR boundary is to locate sufficient points around the boundary (critical points). Therefore, this paper proposes a space division and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) based fast generation method of PDSR boundary. First, the typical differential topological characterizations of dynamic security region (DSR) provide strong theoretical foundation that the interior of DSR is hole-free and the boundaries of DSR are tight and knot-free. Then, the space division method is proposed to calculate critical operation area where the PDSR boundary is located, tremendously compressing the search space to locate critical points and improving the confidence level of boundary fitting result. Furthermore, the WGAN-GP model is utilized to fast obtain large number of critical points based on learning the data distribution of the small training set aforementioned. Finally, the PDSR boundary with hyperplanes is fitted by the least square method. The case study is tested on the Institute of Electrical and Electronics Engineers (IEEE) 39-bus system and the results verify the accuracy and efficiency of the proposed method.

Multi-objective optimization of solar resource allocation in radial distribution systems using a refined slime mold algorithm

The integration of distributed generation resources in power systems offers various advantages, such as peak load management and reduced transmission line congestion. However, it also introduces challenges related to voltage stability. This paper presents a novel multi-objective model for optimizing the allocation of solar resources in radial distribution systems. The model aims to achieve an optimal voltage profile, minimize losses, and maximize penetration levels. To address the conflicting nature of these objectives, a refined multi-objective slime mold algorithm (MOSMA) is proposed. This algorithm demonstrates exceptional capabilities in finding Pareto fronts, avoiding local optima, and effectively solving multi-objective problems compared to other optimization methods. Additionally, the corrected social hierarchy method is integrated to enhance performance. The proposed method is evaluated using a standard system under various operational conditions, showing superior results in terms of maintaining an acceptable voltage profile and significantly reducing losses. The study reveals that while losses decrease for penetration levels ranging from low to medium, they start to increase for levels exceeding 100 %. Notably, the proposed method achieves approximately 12 % system efficiency improvement, as measured by the voltage profile, at a penetration level of 300 %. These findings highlight the effectiveness of the proposed method, even at high penetration levels, surpassing other optimization approaches based on the inverse generation distance parameter.

Studying the Optimal Frequency Control Condition for Electric Vehicle Fast Charging Stations as a Dynamic Load Using Reinforcement Learning Algorithms in Different Photovoltaic Penetration Levels

This study investigates the impact of renewable energy penetration on system stability and validates the performance of the (Proportional-Integral-Derivative) PID-(reinforcement learning) RL control technique. Three scenarios were examined: no photovoltaic (PV), 25% PV, and 50% PV, to evaluate the impact of PV penetration on system stability. The results demonstrate that while the absence of renewable energy yields a more stable frequency response, a higher PV penetration (50%) enhances stability in tie-line active power flow between interconnected systems. This shows that an increased PV penetration improves frequency balance and active power flow stability. Additionally, the study evaluates three control scenarios: no control input, PID-(Particle Swarm Optimization) PSO, and PID-RL, to validate the performance of the PID-RL control technique. The findings show that the EV system with PID-RL outperforms the other scenarios in terms of frequency response, tie-line active power response, and frequency difference response. The PID-RL controller significantly enhances the damping of the dominant oscillation mode and restores the stability within the first 4 s—after the disturbance in first second. This leads to an improved stability compared to the EV system with PID-PSO (within 21 s) and without any control input (oscillating more than 30 s). Overall, this research provides the improvement in terms of frequency response, tie-line active power response, and frequency difference response with high renewable energy penetration levels and the research validates the effectiveness of the PID-RL control technique in stabilizing the EV system. These findings can contribute to the development of strategies for integrating renewable energy sources and optimizing control systems, ensuring a more stable and sustainable power grid.

MAINTAINING GAS RECOVERY IN LOW-PERMEABILITY FORMATIONS

The purpose of this work was to ensure efficient gas production in low-permeability and heterogeneous formations by numerically modeling the distribution of reservoir pressure drop around production and injection wells. To investigate this goal, we used a simulation based on the combined finite-element-difference method for nonstationary Leibenson piezoelectric conductivity problems. The method is based on the combination of elements of the already known finite difference and finite element methods for the nonstationary piezoelectric conductivity problem. The study found that there are many factors that affect the process of maintaining gas production in poorly permeable areas of a gas-bearing formation. Among them, first of all, after increasing the permeability of the corresponding section of the reservoir where production is created. Another important factor for maintaining gas recovery is the ability to naturally or artificially maintain gas phase infiltration within the affected reservoir area. This aspect is especially important for ensuring stable gas recovery over a long period of operation. During the intensive and long-term operation of the working area of the gas-bearing formation, additional injection wells were installed in the complex of production wells to avoid its depletion. It is important to note that provided a high level of gas phase penetration at the beginning of field operation, other factors of the production process, such as additional production wells and infiltration within the working area, have a lesser impact on the overall pressure distribution in this area. Thus, the study shows that with the passage of time and prolonged operation of the working section of the reservoir, the influence of its penetration decreases, but the importance of injected substances in the injection wells and gas phase infiltration within this section of the reservoir increases.

An enhanced stochastic optimization for more flexibility on integrated energy system with flexible loads and a high penetration level of renewables

Full utilization of the potential regulation ability of flexible loads in an integrated energy system (IES) and expanding its structure for more flexibility via integrating auxiliary devices is the key to realizing its high-efficiency operation and high-proportion renewable consumption. The present paper builds the flexible regulation models of thermal and electrical loads and introduces them to the structure expansion planning of IES. A two-stage stochastic probability optimization method integrating the uncertain operation of introducing flexible loads is then proposed to balance the additional costs of device integration and the benefits of performance promotion. Herein, an enhanced sample average approximation based on a stochastic hierarchy scenario generation method is developed to solve the optimization. The regulation mechanisms of flexible loads and their influence on expansion planning are then analyzed by comparing the optimized results. The results show that the path to decreasing the cost via introducing flexible thermal and electrical loads into optimization is increasing renewable consumption and coordinating an increment in renewable consumption and average electrical efficiency, respectively. The regulation between them has synergism on cost reduction and saturation on increasing renewable consumption. The synergism can reduce the cost by 0.62 %. Besides, there is a synergism between electric boiler integration and flexible loads, which further reduces the total cost of the expanded IES by 24.86 %.

Towards sustainable urban living: A holistic energy strategy for electric vehicle and heat pump adoption in residential communities

Electric vehicles (EVs) and heat pumps (HPs) are key in reducing carbon emissions from transportation and domestic heating, yet their adoption may increase peak load demands on electrical networks. One of the aims of this research is to assess the potential impact of uncontrolled EV charging on community-scale distribution networks, exploring how this could stress the existing electrical infrastructure. It also explores the role of EVs in Vehicle-to-Grid (V2G) and smart charging applications, aiming to enhance community distribution systems. The study investigates the maximum stabilisation level achievable under various scenarios, highlighting the importance of smart energy management in integrating renewable energy and addressing uncertainties in the modelling process. Additionally, this study discusses the proposed systems' scalability, consumer behaviours' impact on the suggested energy solutions, and the potential implications of recent technological advancements for simulated communities. The research employs a sophisticated, integrative approach, combining stochastic methods with several robust energy software. Key findings suggest that uncontrolled EV charging can lead to grid capacity issues at high EV penetration levels, particularly during colder months. While smart charging and V2G technologies can moderate peak loads in many scenarios, achieving 100 % sustainable technology integration requires enhanced energy management or increased network capacity, especially in winter. Wind and solar power integration demonstrates strategic complementarity, particularly in winter, enhancing the reliability and stability of the community grid. It is also observed that peak solar generation hours misalign with the community's highest demand times, posing challenges for solar energy utilisation in EV charging in residential-based areas.

Insights from a Comprehensive Capacity Expansion Planning Modeling on the Operation and Value of Hydropower Plants under High Renewable Penetrations

This paper presents a quantitative assessment of the value of hydroelectric power plants (HPPs) in power systems with a significant penetration of variable renewable energy sources (VRESs). Through a capacity expansion planning (CEP) model that incorporates a detailed representation of HPP operating principles, the study investigates the construction and application of HPP rule curves essential for seasonal operation. A comparative analysis is also conducted between the proposed rule curve formulation and alternative modeling techniques from the literature. The CEP model optimizes installed capacities per technology to achieve predefined VRES penetration targets, considering hourly granularity and separate rule curves for each HPP. A case study involving twelve reservoir hydropower stations and two open-loop pumped hydro stations is examined, accounting for standalone plants and cascaded hydro systems across six river basins. The study evaluates the additional generation and storage required to replace the hydropower fleet under high VRES penetration levels, assessing the resulting increases in total system cost emanating from introducing such new investments. Furthermore, the study approximates the storage capabilities of HPPs and investigates the impact of simplified HPP modeling on system operation and investment decisions. Overall, the findings underscore the importance of reevaluating hydro rule curves for future high VRES penetration conditions and highlight the significance of HPPs in the energy transition towards carbon neutrality.