Penetration Of Renewable Energy Sources Research Articles

The Clearing Strategy of Primary Frequency Control Ancillary Services Market from the Point of View ISO in the Presence of Synchronous Generations and Virtual Power Plants Based on Responsive Loads

Since the increase in penetration of renewable energy sources connected to the system reduces the inertia of power systems, the penetration of these sources leads to increase in the requirements of primary frequency control (PFC) services. Fortunately, with the expansion of network intelligence platforms, responsive loads (RL) can be effectively useful in ancillary services in the near future and can be used like traditional power plants. Since these equipment have a high rate of change of status, if they are visible in the market by aggregating (with virtual power plant (VPP)), they can compete with synchronous generations (SG). Because the response speed of the participants in the market can affect the decision independent system operator (ISO) in determining the winning units, therefore in this article, we have proposed a market framework to create competition between SGs and VPPs in providing ancillary services. In the proposed framework, ISO minimizes the weighted sum of power purchase costs from VPPs and SGs. The proposed weighting coefficients express the response speed of each unit. In fact, the desired objective function is affected by two terms, cost and speed. The presented model has been simulated on a test system including four SGs units and one VPP unit in matrix laboratory (MATLAB) software and checked under five different scenarios. The comparison of the obtained results indicates an increase in the possibility of accepting units with a smaller weighting factor and a higher response speed (the meaning of accepting units are market players, i.e. SGs and VPPs).

Innovative Microgrid Services and Applications in Electric Grids: Enhancing Energy Management and Grid Integration

Currently, microgrids are a reliable solution for integrating distributed energy resources and managing demand on electricity grids, serving as a pathway towards a responsible energy transition. However, the evolving needs of the sector require specialized approaches to enhance grid flexibility and support the increasing penetration of renewable energy sources and their rising demand. This article explores and characterizes various advanced and innovative services offered by microgrids to improve the resilience, security, and reliability of electricity grids. It analyzes technical advances and novel control methodologies that demonstrate the potential for microgrids beyond mere energy provision. These include ancillary services, services aligned with demand response programs, and advanced asset management and energy resource optimization services. A global case study is conducted to provide a framework for the services that microgrids can provide. The case study validates the efficiency and reliability of electric grids with microgrids and addresses challenges related to their stability and resilience. This research provides a comprehensive perspective on the benefits of implementing microgrids and proposes new guidelines for the deployment of these systems in both urban and rural areas within the framework of energy communities in the Colombian electricity system, emphasizing the need for collaboration among stakeholders to ensure sustainable energy solutions.

Probabilistic online learning framework for short-term wind power forecasting using ensemble bagging regression model

The increasing penetration of renewable energy sources, with a notable focus on wind power, within modern electricity grids requires computationally efficient and burden-free short-term wind power forecasting models. Traditional models generating prediction intervals are trained offline and thus deployed for prediction purposes. However, this approach cannot obtain interval forecasts from the most recent wind power observations. In contrast, combining multiple regression models through ensemble learning is recognised as a successful method for improving forecasting performance. By utilising the most recent observations and exploiting the strengths of multiple regression models, this article investigates an Online Ensemble Bagging Regression (OEBR) model for generating prediction intervals. Online gradient descent based optimisation algorithms capable of adaptive-depth calculation from a stream of observations are used here to address the problems with traditional batch learning frameworks. The proposed online learning framework is evaluated against other online learning frameworks using publicly accessible datasets. The results show the proposed model competes with the compared models regarding probabilistic metrics and energy estimations and outperforms computational time requirements for the same number of observations.

Distributional reinforcement learning-based energy arbitrage strategies in imbalance settlement mechanism

Growth in the penetration of renewable energy sources makes supply more uncertain and leads to an increase in the system imbalance. This trend, together with the single imbalance pricing, opens an opportunity for balance responsible parties (BRPs) to perform energy arbitrage in the imbalance settlement mechanism. To this end, we propose a battery control framework based on distributional reinforcement learning. Our proposed control framework takes a risk-sensitive perspective, allowing BRPs to adjust their risk preferences: we aim to optimize a weighted sum of the arbitrage profit and a risk measure (value-at-risk in this study) while constraining the daily number of cycles for the battery. We assess the performance of our proposed control framework using the Belgian imbalance prices of 2022 and compare two state-of-the-art RL methods, deep Q-learning and soft actor–critic (SAC). Results reveal that the distributional soft actor–critic method outperforms other methods. Moreover, we note that our fully risk-averse agent appropriately learns to hedge against the risk related to the unknown imbalance price by (dis)charging the battery only when the agent is more certain about the price.

Comparison of Advanced Flexible Alternating Current Transmission System (FACTS) Devices with Conventional Technologies for Power System Stability Enhancement: An Updated Review

The continuously growing penetration of renewable energy sources (RESs) in electrical networks provides increasing challenges and critical situations to be managed by worldwide system operators. Due to their features and variability, non-programmable RES power plants, whose increasing penetration reduces the inertia level of the power system, may determine the instability effects on the grids, especially from the frequency and voltage regulation standpoints. The present study focuses on the support that advanced FACTS (Flexible Alternating Current Transmission System) devices, such as STATCOMs (Static Synchronous Compensators), can provide to the power system operation in terms of system inertia improvement, frequency stability, and voltage stability. In particular, a review of the scientific literature and practice is performed, with the aim of benchmarking the ongoing evolution of these technologies, also comparing them with different options based on synchronous condensers, synchronous condensers integrated with flywheels, and STATCOMs with supercapacitors. The outcome of the analysis consists of an updated evaluation of the state-of-the-art technological development in the field and of a comparison between different FACTSs with the purpose of identifying the most suitable solutions for different practical situations, also taking account of synergies across various options. This study includes an updated overview regarding the status of STATCOM installation in the Italian power grid.

Survey of Reliability Challenges and Assessment in Power Grids with High Penetration of Inverter-Based Resources

Decarbonization is driving power systems toward more decentralized, self-governing models. While these technologies improve efficiency, planning, operations, and reduce the carbon footprint, they also introduce new challenges. In modern grids, particularly with the integration of power electronic devices and high penetration of Renewable Energy Sources (RES) and Inverter-Based Resources (IBRs), traditional reliability concepts may no longer ensure adequate performance due to systemic restructuring. This shift necessitates new or significantly modified reliability indices to capture the characteristics of the evolving power system. Ensuring converter reliability is essential for effective planning, which requires precise, component-to-system-level modeling, as different converters impact system performance indicators. However, the existing literature in this field faces a significant limitation, as most studies focus on a singular perspective. Some examine reliability at the device-level, others at the component-level, while broader reviews in power systems often emphasize system-level analysis. In this paper, we aim to bridge these gaps by comprehensively reviewing the interconnections between these levels and analyzing the mutual influence of power converter and system reliability. A key point to highlight is that, with the rapid evolution of modern power grids, decision-makers must adopt a multi-level approach that incorporates insights from all levels to enable more accurate and realistic planning and operational strategies. Our ultimate goal is to provide an in-depth investigation of studies addressing the unique challenges posed by modern power grids. Finally, we will highlight the gaps in the literature and suggest directions for future research.

Operation Analysis of Power Systems with HVDC Interconnections Using a Transient Stability Aware OPF Method

High-voltage direct current (HVDC) transmission systems are widely used today due to their several advantages over high-voltage alternative current (HVAC) systems. They are primarily utilized in connecting different large grids or smaller independent networks, especially in submarine interconnections. Many studies have been conducted globally on the optimal planning, operation, and control of these power systems. Understanding the transient stability of these systems during significant disturbances is also of great importance. This paper specifically focuses on power systems with HVDC connections and high penetration of renewable energy sources. Analytical models were developed for power system components like HVDC converters, DC lines, and full converter wind generators. A simulation of a test-case power system was conducted, including various significant disturbances such as HVDC line trips, short circuits, power unit failures, and major changes in load. The voltage and frequency stability of the system under specific operational scenarios was also examined. The results obtained indicate technical limitations and operating guidelines to avoid undesirable conditions and system failure. In conclusion, adopting short-term stability tests the optimal operation point of the system, and the limitations in the penetration by RES units can be decided.

Enhancing distance protection in transmission grids with high penetration of renewable energy sources through cooperative protection

AbstractThis article introduces innovative protection strategies, including cooperative protection, for power transmission grids amidst a significant shift towards renewable energy sources (RES) such as wind and solar power, as well as inverter‐based resources (IBRs). The method employs a global consensus algorithm to achieve cooperative protection efficiently. This scheme leverages consensus protocols to dynamically oversee distance relay decisions, ensuring efficient fault detection and localization. The decentralized nature of the proposed method enhances robustness and security, while its high‐speed operation is ensured through non‐iterative global consensus algorithms, which provide rapid fault detection and localization crucial for real‐time protection. By incorporating virtual leaders and leveraging existing communication infrastructure, the method achieves superior selectivity in identifying faulty lines, enhancing the reliability and stability of power transmission grids with high‐RES penetration. Notably, the method does not require learning and training processes, making it adaptable to varying power system topologies without the need for extensive retraining or adaptation periods. The proposed methodology enables simultaneous participation in multiple protection zones by establishing interaction rules between agents. Virtual leaders simplify the selection of protection areas, enhancing scalability and fault localization. Simulation results conducted on the IEEE 39‐bus test system validate the effectiveness of the proposed method.

Oscillation Suppression of Grid-Following Converters by Grid-Forming Converters with Adaptive Droop Control

The high penetration of renewable energy sources (RESs) and power electronics devices has led to a continuous decline in power system stability. Due to the instability of grid-following converters (GFLCs) in weak grids, the grid-forming converters (GFMCs) have gained widespread attention featuring their flexible frequency and voltage regulation capabilities, as well as the satisfactory grid-supporting services, such as inertia and damping, et al. Notably, the risk of wideband oscillations in modern power grids is increasingly exacerbated by the reduced number of synchronous generators (SGs). Thus, the wideband oscillation suppression method based on adaptive active power droop control of GFMCs is presented in this paper. First, the stability of the hybrid grid-forming and grid-following system is obtained according to the improved short circuit ratio (ISCR), where the GFMC is in parallel at the point of common coupling (PCC) of the GFLC. Then, an adaptive adjustment strategy of the active power droop control is proposed to enhance the oscillation suppression capability across the full frequency range, thereby mitigating the wideband oscillation caused by phase-locked loop (PLL) synchronization in the GFLCs. Additionally, a first-order inertia control unit is added to the active and reactive power droop controllers to mitigate frequency and voltage variations as well as suppress potential mid-to-high frequency resonance. Finally, the wideband oscillation suppression strategy is validated by the simulation and experimental results.

The Calibrated Safety Constraints Optimal Power Flow for the Operation of Wind-Integrated Power Systems

As the penetration of renewable energy sources (RESs), particularly wind power, continues to rise, the uncertainty in power systems increases. This challenges traditional optimal power flow (OPF) methods. This paper proposes a Calibrated Safety Constraints Optimal Power Flow (CSCOPF) model that uses the Improved Acceleration Coefficient-Based Bee Swarm algorithm (IACBS) in combination with the equivalent current injection (ECI) model. The proposed method addresses key challenges in wind-integrated power systems by ensuring preventive safety scheduling and enabling effective power incident safety analysis (PISA). This improves system reliability and stability. This method incorporates mixed-integer programming, with continuous and discrete variables representing power outputs and control mechanisms. Detailed numerical simulations were conducted on the IEEE 30-bus test system, and the feasibility of the proposed method was further validated on the IEEE 118-bus test system. The results show that the IACBS algorithm outperforms the existing methods in both computational efficiency and robustness. It achieves lower generation costs and faster convergence times. Additionally, the CSCOPF model effectively prevents power grid disruptions during critical incidents, ensuring that wind farms remain operational within predefined safety limits, even in fault scenarios. These findings suggest that the CSCOPF model provides a reliable solution for optimizing power flow in renewable energy-integrated systems, significantly contributing to grid stability and operational safety.

Power flow analysis using quantum and digital annealers: a discrete combinatorial optimization approach

Power flow (PF) analysis is a foundational computational method to study the flow of power in an electrical network. This analysis involves solving a set of non-linear and non-convex differential-algebraic equations. State-of-the-art solvers for PF analysis, therefore, face challenges with scalability and convergence, specifically for large-scale and/or ill-conditioned cases characterized by high penetration of renewable energy sources, among others. The adiabatic quantum computing paradigm has been proven to efficiently find solutions for combinatorial problems in the noisy intermediate-scale quantum (NISQ) era, and it can potentially address the limitations posed by state-of-the-art PF solvers. For the first time, we propose a novel adiabatic quantum computing approach for efficient PF analysis. Our key contributions are (i) a combinatorial PF algorithm and a modified version that aligns with the principles of PF analysis, termed the adiabatic quantum PF algorithm (AQPF), both of which use Quadratic Unconstrained Binary Optimization (QUBO) and Ising model formulations; (ii) a scalability study of the AQPF algorithm; and (iii) an extension of the AQPF algorithm to handle larger problem sizes using a partitioned approach. Numerical experiments are conducted using different test system sizes on D-Wave’s Advantage™ quantum annealer, Fujitsu’s digital annealer V3, D-Wave’s quantum-classical hybrid annealer, and two simulated annealers running on classical computer hardware. The reported results demonstrate the effectiveness and high accuracy of the proposed AQPF algorithm and its potential to speed up the PF analysis process while handling ill-conditioned cases using quantum and quantum-inspired algorithms.

A modified synchronverter for a weak grid with virtual power circles‐based PQ decoupling scheme

AbstractThe high penetration of renewable energy sources (RES) results in a low‐inertia, weak power grid. To mitigate this and restore system inertia, it has been widely proposed to operate the inverters of RES units to mimic synchronous generators; this technology is known as a virtual synchronous generator (VSG). In weak grids there is, however, strong coupling between active power () and reactive power (), and any VSG technique therefore requires decoupling in order to operate effectively. This article proposes a new decoupling technique based on the transformation of the power circle of a VSG connected to a weak grid: first, the power circle is translated from its designed position to that of a conventional synchronous generator (SG) connected to a strong grid, achieving partial decoupling. Then, to achieve full decoupling, the authors propose further to modulate the radius of the translated power circle; this is achieved using a series resistance‐capacitance–inductance () circuit which is virtually implemented in the VSG controller. The efficacy of the proposed scheme is validated using a modified synchronverter connected to the weak grid in representative loading scenarios. It is demonstrated that the technique achieves a decoupled control for the synchronverter connected in a weak grid. Moreover, the modified synchronverter is capable of supporting frequency and voltage regulation in the grid without inducing large transient grid currents during mild frequency and voltage variations.

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.

Optimal hydrogen-battery energy storage system operation in microgrid with zero-carbon emission

To meet the greenhouse gas reduction targets and address the uncertainty introduced by the surging penetration of stochastic renewable energy sources, energy storage systems are being deployed in microgrids. Relying solely on short-term uncertainty forecasts can result in substantial costs when making dispatch decisions for a storage system over an entire day. To mitigate this challenge, an adaptive robust optimization approach tailored for a hybrid hydrogen battery energy storage system (HBESS) operating within a microgrid is proposed, with a focus on efficient state-of-charge (SoC) planning to minimize microgrid expenses. The SoC ranges of the battery energy storage (BES) are determined in the day- ahead stage. Concurrently, the power generated by fuel cells and consumed by electrolysis device are optimized. This is followed by the intraday stage, where BES dispatch decisions are made within a predetermined SoC range to accommodate the uncertainties realized. To address this uncertainty and solve the adaptive optimization problem with integer recourse variables in the intraday stage, we proposed an outer-inner column-and-constraint generation algorithm (outer-inner-CCG). Numerical analyses underscored the high effectiveness and efficiency of the proposed adaptive robust operation model in making decisions for HBESS dispatch.

Analysis of non-uniform grid-forming control techniques for the HVDC connection of renewable energy

Grid-forming control techniques are required to achieve a high penetration of power electronics-based renewable energy sources for decarbonized electric systems. However, different grid-forming control strategies can operate in the same grid, and grid stability shall be warranted. This paper analyses the use of different grid-forming control strategies for diode rectifier-based wind power plants. The analysed grid-forming controllers are the well-known droop control, an advanced droop control, and the virtual synchronous machine control. The controllers’ analysis validates the three grid-forming controllers’ interoperability, identifying each control parameter’s contribution to the stability of each system state variable. Furthermore, the analysis allows a better tuning of the control parameters. Additionally, a fault-ride-through strategy that improves the system restoration after faults is proposed and validated. The proposed fault-ride-through strategy achieves a soft restoration of the active power.

The role of hydropower in decarbonisation scenarios

An increased penetration of renewable energy sources is essential for the energy transition. A major role will be played by wind and solar, as they are widely available. Hydropower is another crucial resource, currently covering large shares of power generation (e.g., Norway, Italy, Brazil). Despite little expected growth, in a context of increasing electrification, improved integration of hydropower can play a critical role thanks to programmable operation. This work addresses the modelling of hydropower flexibility in energy system models and analyses the impact of hydropower operation on CO2 emission-constrained scenarios. To implement the study, a detailed dataset of the Italian programmable hydroelectric plants is created, using open-source information, covering location, rated power, and storage capacity. Inflow timeseries are derived from historical operational data. These new sets of data are employed in OMNI-ES (a multi-node, multi-sector, and multi-vector energy system model) to study optimal configurations and operation of the Italian energy system in decarbonisation scenarios, such as net-zero-CO2 and Fit-for-55 targets. Considering different operational strategies and multiple historical reference years (impacting the inflow), results demonstrate significant changes in hydropower behaviour and highlight its relevance as zero-carbon resource in terms of both power and energy output, influencing the installation of other technologies.

Real-Time Optimization of Ancillary Service Allocation in Renewable Energy Microgrids Using Virtual Load

The stability of global economies relies heavily on power systems (PS) that have sufficient operating reserves. When these reserves are insufficient, power systems become vulnerable to issues such as load shedding or complete blackouts. Maintaining grid stability becomes even more challenging with a high penetration of renewable energy sources (RES). However, RES, connected through power electronic devices, offer significant potential as ancillary service (AS) sources. Renewable energy-based microgrids (MG), which aggregate various RES resources and have substantial load control potential, further enhance the capability of AS provision from RES. The presence of diverse AS resources raises the question of how to dispatch ancillary service signals optimally to all resources. Most of the previous research work related to AS allocation relied on single-bus MG models. This paper proposes a detailed MG model for the optimal dispatching of ASs among the resources using Virtual Load, along with an optimization procedure to achieve the best results. The model incorporates voltage profiles and power losses for AS dispatching, and a comparative analysis is conducted to quantify the significance of grid modeling. The model and proposed procedure are tested using the CIGRE microgrid benchmark model. The results indicate that detailed modeling of MG can impact the results by 11%, compared to single-bus modeling, which qualifies detailed MG modeling for all future research work and shows the impact that modeling can have on technical and economic indicators of MG operation.

Chaos quasi-opposition crayfish based modified new controller designed for hybrid deregulated power environment considering cyber-attack

Frequency security is critical to the stability and dependability of the electrical systems. The integration of renewable energy resources presents new challenges for power networks and frequency security. In order to provide a more reliable control mechanism for improved frequency regulation in multi area deregulated environments with penetrations of renewable energy sources like Solar photovoltaic and wind system has been included considering along with its intermitance nature. Additionally, to get more realistic approach and its analysis, nonlinearities such as generation rate constant and governor dead band has been taken into consideration for the anticipated Load Frequency Control (LFC). A novel modified cascaded Dual-Loop Linear Active Disturbance Rejection based Tilted controller has been proposed for the suggested LFC paradigm. A modified version of the Chaotic Quasi-Opposition Crayfish Optimisation algorithm (CQOCOA) has been offered as a novel optimization approach for optimal parameters of the proposed controller. ITSE has been taken as objective function for optimization purpose. Moreover, a comprehensive detailed examination for the proposed LFC system has been investigated and successfully implemented. The performance has been examined under a variety of operating scenarios, including multiple-step, random load disturbances and Distributed Generation (DG) analysis along with its technical core advancements. This study has been investigated and validated over large multi area power system i.e., IEEE-118 bus system. This work also put forward the deep analysis and implementation of learning-based attack detection and mitigation method for the grid's frequency regulation with cyber-physical model. The effectiveness of the proposed controller has been compared and verified over previous published literature work of international repute. The compressive results analysis provide strong evidence in favour of effectiveness and efficacy of the proposed control methodology. Furthermore, this work also suggests its potential to implement in hybrid multi area power system for enhancing the performance and stability in deregulated power structure.

Multi-objective coordinated optimization of low-carbon building energy systems based on high renewable energy penetration

In the context of China's targets to achieve a "carbon peak" by 2030 and "carbon neutrality" by 2060, the operation and maintenance of buildings with low-carbon and zero-energy consumption emerge as a critical technology for accomplishing the "dual carbon" goals in the building sector. As a novel energy supply system, low-carbon building energy systems can provide low-carbon, efficient, and economical energy supplies. Accordingly, this paper initially constructs a low-carbon building energy system based on a high penetration of renewable energy sources. Subsequently, considering the benefits of economic efficiency, carbon emissions, and renewable energy utilization, a multi-objective cooperative optimization model for low-carbon building energy systems with high renewable energy penetration is established. Finally, a multi-objective particle swarm optimization algorithm is applied to solve the model, and the system is studied and analyzed using a large domestic park as a case study. The results indicate that the low-carbon building energy system with high renewable energy integration, aiming for economic efficiency, carbon reduction, and increased renewable energy consumption, achieves supply energy costs of $ 19.2/m2 and carbon emissions of 49.1 kg/m2. These values represent reductions of $ 7.4/m2 and 60.9 kg/m2 compared to separate supply systems. Lastly, the study of the low-carbon building energy system provides theoretical references for renewable energy integration and energy transition in the building sector.

Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation

AbstractThe economic‐environmental power dispatch (EEPD) problem, a widely studied bi‐objective non‐linear 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 article addresses these concerns by formulating and solving the economic environmental and stable power dispatch (EESPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modelled 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 EESPD 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 prominence for solving complex non‐linear problems involving multi‐objective functions. This article applies the latest MOEAs to tackle the proposed EESPD 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.