Electric Grid Research Articles

Steps for the Realization of a Medium-power Photovoltaic Park

The design of photovoltaic parks has become in Romania, now, one of the most profitable and safe investments in the field of renewable energy sources, not only at present, but even more so in the future. Solar photovoltaic parks are favored by maximum exposure to solar radiation, a minimal impact on the environment, allowing the production of energy with a cost per kWp lower than that of other installations. The execution period of a photovoltaic park varies between 3-6 months depending on location and weather conditions. Romania has a high degree of solar radiation, suitable for investment in photovoltaic parks. There are three categories of documents required for the establishment of a photovoltaic park: those related of the land on which the park is build, the solution study for obtaining the technical approval for connection (ATR) to the electrical grid and obtaining the establishment authorization. The realization costs involved in building and designing a photovoltaic park are between 1.1-1.9 Euro/Watt. The quality of the equipment used in construction, the shape of the land, the costs of connection to the national electrical grid, are factors that can constitute the cost of realization. Another important factor in terms of income is the level of operational expenses, which is between 2% and 8% of the annual income and includes security, equipment and land maintenance, system monitoring. The paper will present a technical and economic calculation for a photovoltaic park with a power of 400 kW.

ОРГАНІЗАЦІЙНО-ЕКОНОМІЧНІ СКЛАДОВІ ПРОГРАМ РОЗВИТКУ ЕЛЕКТРИЧНИХ МЕРЕЖ

In the article researched the indicators that directly depend on the state of electrical networks. The first of them is the level of reliability of electricity supply, the indicators of which are the SAIDI and SAIFI indices. The domestic indicators of the specified indices significantly exceed the level in the countries of the European Union and to date have no signs of a decrease. Plans for reducing the indicator SAIDI have been analyzed in relation to the method of incentive tariffs for operators of distribution systems. The next considered indicator is high technological losses of electrical energy. It was analyzed that the volume of these losses has a growing trend and also significantly exceeds the average European level. The main reason for these phenomena is the outdated and worn-out condition of electrical grids, which, given the damage caused by missile strikes and shelling, tends to worsen. Proposals have been identified that in the future may help improve the condition of grids and eliminate a number of problematic phenomena Their main problems and approximate volumes of priority grids modernization were summarized. Possible practical measures aimed at helping to reduce technological losses of electricity are considered. The structure of the proposed traditional measures necessary to manage the development of networks and consumers were analyzed. It has also recently started in Ukraine development and approval of a set of documents which are directed to the implementation of the Energy Transition scenario and the implementation of the Smart Grid concept. The further need for the development of system and program documents at both the state and regional levels was identified. It is proposed to include in these documents a number of organizational and economic components which will most effectively contribute to the achievement of the set goals.

Utilities and the Future Grid

It is widely accepted among energy industry professionals that the industry is undergoing a significant and rapid transformation. There are increasing challenges facing the industry including extreme weather events, widespread aging of the electric grid infrastructure, rapidly increasing electricity demand, as well as the impact of climate change policy initiatives. This has spurred an innovation rebirth in an industry that has traditionally been viewed as outdated, and most importantly inaccessible for participation by average customers. However, as the challenges of the changing industry and effects of climate change are impacting their day‐to‐day lifestyles, energy customers are now demanding more visibility, choices, and flexibility from their energy providers, putting a spotlight on the roles and services of utility companies.

Smart Grid Management Enhanced by IoT Device Integration to Improve Energy Efficiency and System Reliability

As a potential way to improve energy economy and system stability, putting Internet of Things (IoT) devices into smart grid control has become clear. This study looks at the full effects of adding IoT devices to smart grid control, focusing on the good and bad things that might happen. The smart grid is an updated electricity grid that uses digital communication technologies to track and manage power flows. This makes energy supply more efficient, reliable, and long-lasting. The standard smart grid, on the other hand, has problems like not being able to see how the grid works, using energy inefficiently, and being easy to disrupt. IoT devices offer an answer by giving real-time information on how well the grid is working, which allows for proactive repair and improvement. A mixed-methods approach was used for this study, which includes both quantitative analysis of data on energy use and qualitative conversations with experts in the field. Machine learning techniques are used to predict how much energy IoT devices in a testbed smart grid will use. This lets us look closely at how adding IoT devices affects system stability and energy efficiency. The study finds that incorporating IoT devices into smart grid control greatly enhances energy economy, resulting in an average 15% drop in energy use. IoT devices also make systems more reliable by letting you see how the grid is doing in real time and responding quickly to problems. The results show that adding IoT devices could change how smart grids are managed, letting companies make better use of energy, make systems more reliable, and improve grid performance overall. But problems like data protection and communication still exist, which shows that this area needs more study and development.

A tutorial review on solid oxide fuel cells: fundamentals, materials, and applications

AbstractSolid oxide fuel cells (SOFCs) are recognized as a clean energy source that, unlike internal combustion engines, produces no CO2 during operation when H2 is used as a fuel. They use a highly efficient chemical-to-electrical energy conversion process to convert oxygen and hydrogen into electricity and water. They can provide smaller-scale power for transportation (e.g., cars, buses, and ships) and be scaled up to provide long-term energy for an electrical grid, making SOFCs a promising, clean alternative to hydrocarbon combustion. Conventional SOFCs faced challenges of high operating temperatures, high cost, and poor durability. Research into advanced cathode, anode, electrolyte, and interconnect materials is providing more insight into the ideal structural and chemical properties that enable the commercialization of highly stable and efficient intermediate temperature (IT) SOFCs. In this paper, we discuss the functional properties of the cathode, anode, electrolyte, and interconnectors for IT-SOFCs. The performance of SOFCs depends not only on the materials used but also on the optimization of operating conditions to maximize efficiency. The voltaic, thermodynamic, and fuel efficiency of SOFCs is presented.

Enhancing microgrid energy management through solar power uncertainty mitigation using supervised machine learning

This study addresses the inherent challenges associated with the limited flexibility of power systems, specifically emphasizing uncertainties in solar power due to dynamic regional and seasonal fluctuations in photovoltaic (PV) potential. The research introduces a novel supervised machine learning model that focuses on regression methods specifically tailored for advanced microgrid energy management within a 100% PV microgrid, i.e. a microgrid system that is powered entirely by solar energy, with no reliance on other energy sources such as fossil fuels or grid electricity. In this context, “PV” specifically denotes photovoltaic solar panels that convert sunlight into electricity. A distinctive feature of the model is its exclusive reliance on current solar radiation as an input parameter to minimize prediction errors, justified by the unique advantages of supervised learning. The performance of four well-established supervised machine learning models—Neural Networks (NN), Gaussian Process Regression (GPR), Support Vector Machines (SVM), and Linear Regression (LR)—known for effectively addressing short-term uncertainty in solar radiation, is thoroughly evaluated. Results underscore the superiority of the NN approach in accurately predicting solar irradiance across diverse geographical sites, including Cairo, Egypt; Riyadh, Saudi Arabia; Yuseong-gu, Daejeon, South Korea; and Berlin, Germany. The comprehensive analysis covers both Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), demonstrating the model’s efficacy in various solar environments. Additionally, the study emphasizes the practical implementation of the model within an Energy Management System (EMS) using Hybrid Optimization of Multiple Electric Renewables (HOMER) software, showcasing high accuracy in microgrid energy management. This validation attests to the economic efficiency and reliability of the proposed model. The calculated range of error, as the median error for cost analysis, varies from 2 to 6%, affirming the high accuracy of the proposed model.

Artificial intelligence computational techniques of flywheel energy storage systems integrated with green energy: A comprehensive review

In recent years, the operation of the electric power grid has become more efficient and resilient due to the integration of renewable energy sources (RESs). Solar and wind energy are being incorporated aggressively into the main grid, while other RESs like biomass and geothermal energy are also on the rise. However, the intermittent nature of these RESs necessitates the use of energy storage devices (ESDs) as a backup for electricity generation such as batteries, supercapacitors, and flywheel energy storage systems (FESS). This paper provides a thorough review of the standardization, market applications, and grid integration of FESS. It examines the components of FESS, including the electric motor/generator set, power converters, bearings, and control techniques. The paper also highlights the application of modern artificial intelligence (AI) methodologies in optimizing FESS operations, referencing over 240 recent publications in reputable journals. Metaheuristic optimizers, machine learning techniques, and well-matures software's are the main AI aspects discussed in this paper. Additionally, it explores the use of FESS in commercial sectors such as marine, space, and transportation, and its integration with RESs for participating in green energy. Finally, the paper emphasizes the role of AI in enhancing the synergy between FESS and RESs to contribute to a more sustainable and secure energy future.

Towards full electrification of local public transport: A tool to guide strategies for implementing the electric charging network

This paper describes a methodology and a Decision Support Tool (DST) for the assessment of the impacts of future scenarios of public electric mobility on the electric Distribution Network (DN) of a city, aimed to support the implementation strategies of the electric charging network for the Local Public Transport (LPT).The objective of the proposed tool is supporting the decision makers in estimating the effect of the future development of electromobility on the urban electric grid considering the evolution of both electric buses and their charging infrastructure.Some scenarios for the development of electric charging infrastructure for LPT were identified together with the local public transport operator and the energy provider. The results, which are of interest to both the local Public Transport Operator (PTO) and the local Distribution System Operator (DSO), have been quantified for the city of Turin (Italy), but the methodology can be adopted in any urban context: DSOs and PTOs of each city can use the proposed tool as a support to define their public transport electrification strategies.Some results from the case study examined in this paper (Turin, Italy) show that the energy demand related to the electrification of LPT on a typical weekday is about 45 MWh in 2030 (corresponding to 40 % e-bus), and about 90 MWh when the entire bus fleet is electric. It is also shown how more distributed recharging during the day (with intermediate recharging at the terminus) can dampen the energy/power peaks required at the depot compared to overnight recharging alone: a reduction of up to 27 % of energy demand at the depot is observed due to opportunity charging.

Solar Power Plant Optimization using Automatic Transfer Switch (ATS) and Low Voltage Disconnect (LVD)

The automatic system model uses a Solar Charge Controller (SCC) to turn off the solar panel system at the minimum battery point so that the battery is safe and durable and an Automatic Transfer Switch (ATS) to automatically transfer the electricity network from State Electricity Company or Grid or solar panels. In this study, Solar Panel Priority Grid electricity is used if the solar panel is insufficient and hybrid system with solar panel priority with 2 cut out battery usage. The results of this study are 450WP Solar Panel, 100A SCC MPPT, 12V/100Ah Battery, and 3000 Watt Modified Sine Wave Inverter, Miniature Circuit Breaker, Contactor, Relay Switch, Time Delay Relay and Indicators. The results of the switching process test between the Solar Power Plant source and grid with ATS control can run automatically in Grid Priority Mode, meaning Solar Power Plant as a backup, or Solar Power Plant Priority Mode where the grid source is used as a power backup system. In the Battery Capacity Optimization System, Low Voltage Disconnect (LVD) Protection can work well, namely it can cut off the voltage from the inverter if the battery is in a low voltage state at a rating below 10.8 Volts, Auto Cut Charging Protection testing can charge the battery up to 13.8 Volts and Cycle Use, namely the process of this system can work to store energy while releasing energy to run the load. The results of this study can be used as a reference for the best choice of the most efficient Solar Power Plant system.

Advanced mathematical modeling of mitigating security threats in smart grids through deep ensemble model

A smart grid (SG) is a cutting-edge electrical grid that utilizes digital communication technology and automation to effectively handle electricity consumption, distribution, and generation. It incorporates energy storage systems, smart meters, and renewable energy sources for bidirectional communication and enhanced energy flow between grid modules. Due to their cyberattack vulnerability, SGs need robust safety measures to protect sensitive data, ensure public safety, and maintain a reliable power supply. Robust safety measures, comprising intrusion detection systems (IDSs), are significant to protect against malicious manipulation, unauthorized access, and data breaches in grid operations, confirming the electricity supply chain’s integrity, resilience, and reliability. Deep learning (DL) improves intrusion recognition in SGs by effectually analyzing network data, recognizing complex attack patterns, and adjusting to dynamic threats in real-time, thereby strengthening the reliability and resilience of the grid against cyber-attacks. This study develops a novel Mountain Gazelle Optimization with Deep Ensemble Learning based intrusion detection (MGODEL-ID) technique on SG environment. The MGODEL-ID methodology exploits ensemble learning with metaheuristic approaches to identify intrusions in the SG environment. Primarily, the MGODEL-ID approach utilizes Z-score normalization to convert the input data into a uniform format. Besides, the MGODEL-ID approach employs the MGO model for feature subset selection. Meanwhile, the detection of intrusions is performed by an ensemble of three classifiers such as long short-term memory (LSTM), deep autoencoder (DAE), and extreme learning machine (ELM). Eventually, the dung beetle optimizer (DBO) is utilized to tune the hyperparameter tuning of the classifiers. A widespread simulation outcome is made to demonstrate the improved security outcomes of the MGODEL-ID model. The experimental values implied that the MGODEL-ID model performs better than other models.

Methods for addressing variable turbine operations during wind farm noise compliance monitoring

Post-construction wind farm noise measurements at dwelling locations are commonly required in Australia to verify compliance with noise limits. These are often required as soon as the wind farm begins operating. However, commissioning issues and electricity network restrictions are a common occurrence during initial stages of operation. As such, curtailed modes of operation and shutdowns are a regular occurrence, and large sections of a wind farm may only be operating intermittently. This presents challenges to obtaining noise measurement data that is representative of normal operation of the wind farm, and may ultimately limit the conclusions that are able to be reached from measurements during the early stages of operation. To address these challenges, it is necessary to identify and account for any periods when operational noise levels may be lower as a result of turbines being curtailed or shut down. Several methods have been developed for this purpose, and range from simple procedures that remove any data affected by curtailment or shutdown, through to more complex assessments of the magnitude and implications of the uncertainty introduced by variable wind turbine operations. These methods are presented and assessed in this paper with case studies of their application to operational projects.

Single and multi-threaded power flow algorithm for integrated transmission-distribution-residential networks

The rapid development of renewable sources has significantly increased interdependencies between electricity networks of all voltage levels, leading to bidirectional flows between transmission and distribution networks, and requiring analysis of Integrated Transmission-Distribution (ITD) network. The interconnectivity is further amplified by additional coupling with electricity, gas, heat and hydrogen networks, on both national and regional levels. Moreover, installation of solar, wind and storage on customers’ premises has indicated that residential networks need to be included in the overall integrated model, called Integrated Transmission-Distribution-Residential (ITDR) network. The main goal of the paper is to develop a general model of the ITD/ITDR networks and to solve the power-flow problem on large-scale networks in an efficient way. The general model includes three-phase transmission and multi-phase distribution models, as well as accurate control strategies for traditional and electronically coupled electricity resources. The proposed model is solved via novel single-threaded power flow procedure, which incorporates new network elements’ models and the developed algorithm for integrated power flow calculations in different domains. This is further improved by developing a multi-threaded approach. Analyses on a small-scale ITD network have shown that single- and multi-threaded approaches are, respectively, (1.5–4) and (2–5) times faster compared to the state-of-the-art procedures. As network size increases, the efficiency of the proposed multi-threaded procedure becomes more pronounced compared to the single-threaded one (up to 2.39 times).

Microgrid control under uncertainty

Microgrids – decentralized electrical grids that can function both in conjunction with wide area macrogrids and without – are a powerful tool to address energy resiliency and climate change mitigation. Microgrid control, however, remains a challenge; their bespoke nature and the existence of multiple sources of uncertainty lead to a control problem that traditional grid modeling and control techniques are ill-suited to handle. We build a microgrid interface to simulate microgrids under uncertainty and devise off-policy reinforcement learning algorithms to control microgrids. Our algorithms, which incorporate domain randomization and random network distillation for exploration and computational efficiency, achieve performance better than model predictive control and rule based control benchmarks under battery model uncertainty on seven of ten tested scenarios. Our model code is available at https://github.com/ahalev/Microgrid-Control-Under-Uncertainty and our microgrid simulator is available at https://github.com/ahalev/python-microgrid.

Novel optimized models to enhance performance forecasting of grid-connected PERC PV string operating under semi-arid climate conditions

This study focuses on modeling and forecasting the performance of a grid-connected photovoltaic (PV) string, aiming to enhance the accuracy of output power prediction, which is crucial for the effective management and stability of the electrical grid. While previous research has extensively examined PV modules, there is a notable lack of studies specifically targeting PV string behaviors, especially in harsh climates prevailing in semi-arid regions. This study addresses this gap by investigating the modeling and performance prediction of a Passivated Emitter Rear Cell (PERC) PV string under semi-arid climate conditions using analytical and predictive approaches based on both implicit and explicit models as Single Diode Model (SDM), Das Model (DM), Boutana et al. Model (BM) and Power Law Model (PLM). New mathematical models of DM and BM shape parameters versus temperature and irradiance along with novel analytical formulas giving DM shape parameters as a function of PV metrics have been introduced. The proposed approaches are validated using real measurements of a PERC PV string incorporating 12 JKM405M-72H PV modules operating at Green Energy Park (GEP) research facility in Morocco. The reliability of these approaches is assessed by comparing I-V curves, P-V curves and peak power generated and predicted by SDM, DM, BM and PLM to actual measurements. The comparison reveals that generated and predicted outcomes align well with measured data, with an average value of Normalized Root Mean Square Error (NRMSE) not exceeding 4.16 % for all models throughout the day. The findings indicate that the proposed approaches effectively predict the performance of the PERC PV string, accounting for climatic influences and providing insights into optimizing PV system performance, thereby contributing to improved grid stability in semi-arid regions.

Hydrogen underground storage for grid electricity storage: An optimization study on techno-economic analysis

This study performs a techno-economic analysis of hydrogen underground storage systems for grid electricity storage, evaluating their economic viability at the plant scale using dynamic optimization. It explores the feasibility of various system configurations and revenue models in the context of volatile electricity prices and the necessity for multiple revenue streams. The hypothesis tested is that large-scale hydrogen storage, despite its low round-trip efficiency, can be economically viable with the right mix of revenue streams. This study uses scenario-based analysis to assess the impacts of different system configurations, including engaging in time-shifting arbitrage, ancillary service markets and blending hydrogen with natural gas. Results indicate potential annual net cash flows of up to $1.5 million from ancillary services integration and $5.2 million from natural gas blending, contingent on specific system sizes. The study concludes that hydrogen underground storage for grid electricity storage can be profitable, and emphasizes that proper system design and precise electricity price forecasting are crucial for optimizing system performance and economic returns. This research sets the stage for further investigations into the scalability of hydrogen storage systems and their broader implications for grid electricity storage and energy market dynamics.

Strategic approach to accelerating regional bioenergy development: Bioelectricity for emission reduction and sustainability

The western Himalayas hold significant potential for generating bioelectricity from dry pine needle biomass. This approach provides multiple benefits, including forest fire prevention, electricity generation, and emission reduction. However, despite these advantages, the growth of this sector has fallen short of expectations. The projected electricity generation potential from pine needle biomass in this region could lead to a significant annual emission reduction of 1.7 MtCO2e through forest fire prevention and an additional 0.49 MtCO2e by displacing carbon-intensive grid electricity. Regional bioenergy development plays a crucial role in the global energy transition and aligns with SDG 13, "Climate Action," and SDG 7, "Affordable and Clean Energy." By focusing on bioenergy as a renewable and accessible energy source, local and regional communities can contribute to climate action while simultaneously ensuring affordable and clean energy for their communities. Therefore, this paper employs a hybrid SWOT-AHP analysis as a strategic planning tool to achieve emission reduction targets by stimulating regional bioenergy growth. Twenty-nine SWOT factors were identified under four variables, and then the AHP technique was employed to assign priority weights to both SWOT variables and factors. Combining SWOT with AHP analysis provides quantitatively determined priorities for the factors, enabling their objective comparison. Eventually, this study offers a comprehensive perspective that leads to policy recommendations and serves as a valuable resource for relevant stakeholders, policymakers, and researchers seeking to achieve emission reduction goals through bioelectricity generation.

Regulatory Barriers For Entrepreneurship and Start-Ups In Renewable Energy: Bibliometric Analysis

This article conducts a bibliometric analysis to investigate regulatory barriers faced by entrepreneurs and start-ups in the renewable energy sector. The research highlights how inconsistent and outdated regulations in many regions challenge the growth and scalability of renewable energy ventures. Key barriers include complex permitting processes, restrictive grid connection policies, and inadequate support mechanisms such as subsidies and tax incentives. The study underscores that although global investment in renewable energy technologies has grown, many emerging and developing economies lag behind due to high capital costs and regulatory hurdles. The methodology involves data collection from the Scopus database using specified keywords such as “entrepreneurship,” “start-ups,” “renewable energy,” and “regulatory barriers.” One hundred seven documents published between 2002 and 2024 were analysed through bibliometric techniques using tools like Biblioshiny and Excel. The study cleans and processes data by removing irrelevant terms and consolidating synonyms to improve the accuracy of the bibliometric analysis. Key research indicators, such as the h-index, g-index, m-index, and citation rates, are employed to evaluate the impact of publications and authors. The analysis also focuses on co-citation networks, clustering keywords, and tracking the evolution of themes over time. Results reveal that regulatory barriers to renewable energy entrepreneurship are widely studied but fragmented. Five main keyword clusters were identified: renewable energy technologies, policy and regulatory frameworks, business models, economic development, and environmental sustainability. A notable trend is the convergence of interdisciplinary research, combining economic, policy, and technological perspectives on overcoming barriers. The analysis highlights a significant publication growth in recent years, peaking in 2022 with 15 publications, which reflects an increasing academic interest in this area. The most cited papers often discuss solutions to regulatory challenges, such as streamlining administrative processes, improving grid access, and implementing supportive financial mechanisms. Case studies from leading renewable energy markets, like Germany and California, showcase successful strategies that have fostered innovation and allowed start-ups to thrive. However, the study emphasises that many emerging markets continue to struggle with outdated regulatory frameworks that hinder the commercialisation of new technologies, particularly in energy storage. The study concludes that future research should focus on detailed longitudinal studies and interdisciplinary approaches that combine legal, economic, and technological analyses. Policymakers are urged to adopt flexible, adaptive regulations to support innovation in renewable energy, ensuring that start-ups can leverage technological advancements and contribute to the global energy transition.

Achieving Off-Grid Refrigeration in Remote Areas: A Solar-Powered Vapor Compression Refrigerator Prototype with PCM Integration

The availability of vaccines, medicines, and perishable goods in remote or off-grid areas remains a formidable challenge. Integrating solar photovoltaic (PV) systems with refrigeration technology has emerged as a promising solution to address this critical need. This paper presents an autonomous solar-powered refrigerator prototype for off-grid refrigeration in remote areas utilising renewable energy. The system comprises a 160 W photovoltaic module, a 12/24 V DC compressor refrigerator, a lead-acid battery, and a Maximum Power Point Tracking (MPPT) controller. Its main feature is complete autonomy from the electricity grid, thanks to its standalone configuration. An experimental campaign evaluated the system's behaviour in the laboratory for 24 hours at different set-point temperatures. A water-based Phase Change Material (PCM) was implemented to improve its autonomy in severe outdoor conditions. A further experimental campaign emulated the functioning of the prototype while managing the temperature of a sample solution whose melting temperature was equal to – 21°C to ensure its liquid state during the tests. The target range for preserving the sample was defined at ± 1°C. Several real conditions have been considered, such as higher cooling loads realised through 21 litres of additional thermal mass in the refrigerator and pick and place conditions for simulating the opening and closing of the door. The results demonstrate that the solar refrigerator prototype achieves complete autonomy from the electricity grid, paving the way for solutions for preserving perishable goods such as medicines and food in unelectrified areas.

Extreme Space Weather Impacts on GNSS Timing Signals for Electricity Grid Management

AbstractExtreme space weather events can have serious impacts on critical infrastructure, including Global Navigation Satellite Systems (GNSS). The use of GNSS, particularly as sources of accurate timing signals, is becoming more widespread, with one example being the measurement of electricity grid frequency and phase information to aid grid management and stability. Understanding the likelihood of extreme space weather impacts on GNSS timing signals is therefore becoming vital to maintain national electricity grid resilience. This study determines critical intensity thresholds above which the complete failure of a GNSS based timing system may occur. Solar radio bursts are identified as a simple example to investigate in more detail. The probability of occurrence of an extreme space weather event with an intensity equal to or greater than the critical intensity is estimated. Both a power law and extreme value theory were used to evaluate recurrence probabilities based on historical event frequencies. The probability was estimated to be between 3%–12% per decade to cause the complete failure of any GNSS‐based timing system.

Optimal solution of multiobjective stable environmental economic power dispatch problem considering probabilistic wind and solar PV generation

The Environmental Economic Power Dispatch (EEPD) problem, a widely studied bi-objective nonlinear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This paper addresses these concerns by formulating and solving the Stable Environmental Economic Power Dispatch (SEEPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modeled using random variable generation techniques, applying Gaussian, Weibull, and log-normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic SEEPD problem extends to multiple periods by replicating the single-period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi-Objective Evolutionary Algorithms (MOEAs) have gained importance for solving complex nonlinear problems involving multi-objective functions. This paper applies the latest MOEAs to tackle the proposed SEEPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp-up and ramp-down constraints for thermal generators. A bidirectional coevolutionary-based multi-objective evolutionary algorithm is employed, integrating an advanced constraint-handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade-off between various conflicting objective functions compared to other state-of-the-art MOEAs.