Generation Dispatch Research Articles

Mitigation strategies can alleviate power system vulnerability to climate change and extreme weather: a case study on the Italian grid

Abstract This study explores compounding impacts of climate change on power system’s load and generation, emphasising the need to integrate adaptation and mitigation strategies into investment planning. We combine existing and novel empirical evidence to model impacts on: (i) air-conditioning demand; (ii) thermal power outages; (iii) hydro-power generation shortages. Using a power dispatch and capacity expansion model, we analyse the Italian power system’s response to these climate impacts in 2030, integrating mitigation targets and optimising for cost-efficiency at an hourly resolution. We outline different meteorological scenarios to explore the impacts of both average climatic changes and the intensification of extreme weather events. We find that addressing extreme weather in power system planning will require an extra 5–8 GW of photovoltaic (PV) capacity, on top of the 50 GW of the additional solar PV capacity required by the mitigation target alone. Despite the higher initial investments, we find that the adoption of renewable technologies, especially PV, alleviates the power system’s vulnerability to climate change and extreme weather events. In fact, renewable energy sources are generally less vulnerable to the impacts of climate change, such as rising temperatures and shifting precipitation patterns, compared to thermal power and hydropower generation. Furthermore, enhancing short-term storage with lithium-ion batteries is crucial to counterbalance the reduced availability of dispatchable hydro generation.

Renewable DG allocation in a pre-scheduled grid power supply for different types of loads with and without distribution network reconfiguration

ABSTRACT This paper suggests a technique for dispatchable and non-dispatchable renewable distributed generation (DG) unit deployment under a constant quantity of power supply from the grid. It also accounts for seasonal fluctuations in load demand, solar and wind power generation. To analyse the scenario, the typical load flow approach is changed by inserting PQVδ and zero bus into the system. Three renewable DGs are used based on solar, wind and biomass to meet the load demand. Along with meeting the load demand, DG is operating to enhance the voltage profile and reduce the power loss. The research is conducted on a 33 and 69 bus distribution networks (DNRs) for constant power, constant current, constant impedance, composite and exponential or mixed load types. To reduce the loss even further, the networks are reconfigured in the presence of three different types of DGs. The suggested solution reduces loss by up to 54 % and allows for energy savings of up to 2.5546 × 10 7 kWh and 2.0719 × 10 7 kWh in 33 bus and 69 bus DNRs, respectively. Economic effectiveness is also analysed to indicate the profit earned from the DG installation. The uncertainty of solar and wind energy is also explored in this paper to see how the network responds to such a scenario.

Fair charging management of PHEVs in radial distribution networks with DG resources-a case study.

Considering the widespread use of PHEVs in advanced societies and the issues ahead, researchers' thinking has focused more on this issue. The important issue is that the use of EVs is increasing due to the advantages, but the necessary infrastructure for their charging stations in the distribution networks does not exist. The high penetration level of EVs can create a potential risk for the existing distribution network; the fair charging of EVs has a special value. This paper presents a new model for the fair charging management of EVs at the medium voltage level of a distribution network equipped with dispatchable and non-dispatchable distributed generation (DG) resources. A fuzzy controller is used to adjust the charging rate of EVs within the permissible periods for charging stations, At the same time, the voltage control and reactive power management tool is also available for the distribution network operator through DG resources that can be dispatched, such as diesel generators. Numerical studies are used on a 25-bus IEEE test distribution system in the presence and absence of DG resources. The simulation results show that the presence of DG resources and voltage control and reactive power management at the different buses along the feeder causes a larger number of electric vehicles in different charging stations of the distribution network can be provided their consumption energy from network. In addition, the time difference for EV charging is minimized, and only the number of EVs that can be charged at the various stations will be different. Volt/Var control tools through DSO cause an increase in the number of charged EVs at various stations.

Deep-learning-based multi-timescale load forecasting in buildings: opportunities and challenges from research to deployment

Electricity load forecasting for buildings and campuses is becoming increasingly important as the penetration of distributed energy resources (DERs) grows. Efficient operation and dispatch of DERs require reasonably accurate predictions of future energy consumption in order to conduct near-real-time optimized dispatch of on-site generation and storage assets. Electric utilities have traditionally performed load forecasting for load pockets spanning large geographic areas, and therefore, forecasting has not been a common practice by buildings and campus operators. Given the growing trends of research and prototyping in the grid-interactive efficient buildings domain, characteristics beyond simple algorithm forecast accuracy are important in determining the algorithm’s true utility for smart buildings. Other characteristics include the overall design of the deployed architecture and the operational efficiency of the forecasting system. In this work, we present a deep-learning-based load forecasting system that predicts the building load at 1-hour intervals for 18 hours in the future. We also discuss challenges associated with the real-time deployment of such systems as well as the research opportunities presented by a fully functional forecasting system that has been developed within the National Renewable Energy Laboratory’s Intelligent Campus program.

Optimum carbon tax rate for emission targets of electricity generation system by life cycle techno-economic-environmental optimization model

Reduction of greenhouse gas emissions from the electricity sector is of special interest for decarbonization of energy systems. Optimum carbon tax as a powerful policy tool is here studied by integrated techno-economic-environmental life cycle analysis in a unit commitment problem. The present model is formulated as a bi-level optimization problem to reach the optimum carbon tax rates for different emission targets. The total life cycle CO2 emissions are estimated to be 20333.90 tons/hr in a real case study of Iran's electricity sector for the peak demand hour. The carbon tax rate for a 2 % and a 3 % emissions reduction is equal to 15.87 and 24.65 $/ton, respectively. In the business-as-usual scenario, higher carbon taxes will be required each year to meet the same emission target. However, developing the electricity sector with cleaner technologies and strategic plans may decrease the carbon tax rate. The extent of this reduction depends on the type of development program implemented in the green revolution scenario. Therefore, policy makers play a direct role in ensuring the sustainable development of the electricity sector. The present model provides a robust framework to analyze the real carbon tax rates for the whole life cycle of electricity generation dispatch.

Linking Electricity and Air Quality Models by Downscaling: Weather-Informed Hourly Dispatch of Generation Accounting for Renewable and Load Temporal Variability Scenarios.

National models of the electric sector typically consider a handful of generator operating periods per year, while pollutant fate and transport models have an hourly resolution. We bridge that scale gap by introducing a novel fundamental-based temporal downscaling method (TDM) for translating national or regional energy scenarios to hourly emissions. Optimization-based generator dispatch is used to account for variations in emissions stemming from weather-sensitive power demands and wind and solar generation. The TDM is demonstrated by downscaling emissions from the electricity market module in the National Energy Model System. As a case study, we implement the TDM in the Virginia-Carolinas region and compare its results with traditional statistical downscaling used in the Sparse Matrix Operator Kernel Emissions (SMOKE) processing model. We find that the TDM emission profiles respond to weather and that nitrogen oxide emissions are positively correlated with conditions conducive to ozone formation. In contrast, SMOKE emission time series, which are rooted in historical operating patterns, exhibit insensitivity to weather conditions and potential biases, particularly with high renewable penetration and climate change. Relying on SMOKE profiles can also obscure variations in emission patterns across different policy scenarios, potentially downplaying their impacts on power system operations and emissions.

Resilience enhancement of cyber–physical distribution systems via mobile power sources and unmanned aerial vehicles

Mobile power sources (MPS) and unmanned aerial vehicles (UAV) are critical and flexible resources to enhance the resilience of cyber–physical distribution systems. However, they are usually independently planned and dispatched. In this paper, considering the cyber–physical interdependence, topology reconfiguration and planned distribution generator islanding, an allocation and dispatch strategy of MPSs and UAVs is proposed. Before an event, a two-stage stochastic optimization based allocation model is built to pre-position MPSs and UAVs considering the uncertainty of events. After the event, a dispatch model is proposed to identify routings of MPSs and UAVs to restore electricity services. Note that both the models are mixed-integer nonlinear three-dimensional (3D) problems. As the optimal service radius and height of a UAV are independent with other variables, these two models are decomposed into two parts, i.e., one part to calculate the optimal service radius and height, and the other to identify resilience enhancement strategy. Then the two models are transformed into a mixed-integer convex programming solved by Progressive Hedging algorithm and a mixed-integer second-order cone programming, respectively. The effectiveness is verified on the modified IEEE 33-node and 123-node test systems. Numerical results highlight the necessity of co-optimizing MPSs and UAVs on cyber–physical distribution systems.

Signal-devices management and data-driven evidential constraints based robust dispatch strategy of virtual power plant

Ensuring the safety and reliability of energy systems is very important in power grid operation. However, inherent intricacies and uncertainties of modern-day power systems, such as the mismatch between signal frequency and equipment response speed and the stochasticity associated with renewable energy and load forecasts, create significant challenges for generation dispatch planning. This study proposes a robust optimization approach with constraints based on signal-devices management and data-driven evidential distribution constraints. The first stage of this approach is to decompose and recombine the net power demand according to the Hilbert-Huang transform and device capability. The signal-device management constraints are then established based on this. The second stage formulates an evidential constraint based on data-driven evidential distribution and chance constraints in a distributionally robust optimization framework that caters to the stochasticity associated with renewable energy and load forecasts. The proposed model is validated on a virtual power plant to assess the approach’s efficacy for different dispatching scenarios. Penalty-based sensitivity analysis provides further insights into the proposed method’s performance for varying levels of CO2 emissions. Simulation results demonstrate that system flexibility becomes increasingly crucial for maintaining system stability and security as the penetration of renewable energy grows. Compared with chance constraint, the proposed data-driven evidential constraint effectively enables the optimization framework to handle stochasticity after sacrificing 0.62% more economic loss and 0.7% more environmental loss. Excessively high penalty parameters for CO2 do not promote economic development, resulting in 21.08% and 52.51% more economic and environmental losses without obvious environmental protection.

Data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on multi-head attention mechanism

Accurate and reliable daily reservoir inflow forecast plays an essential role in several applications involving the management and planning of water resources, such as hydroelectric generation, flood control, water supply, and basin ecological dispatching. Runoff usually exhibits strong non-linearity, high uncertainty, and spatial and temporal variability. Existing techniques fail to capture complete dynamics change processes effectively. A data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on a multi-head attention mechanism was developed, referred to as the GWOCS-VMD-CNN-Transformer (GCVCT). First, the model utilize Grey Wolf Optimizer coupled with Cuckoo Search (GWO-CS) algorithms to optimize parameters in variational mode decomposition model (VMD). This approach helps obtain highly correlated intrinsic mode function (IMF) components, enhancing the frequency resolution of the input dataset. The proposed method overcomes the bottleneck of other available methods by decomposing the time series to capture the main long-term and short-term properties of hydrological processes. Second, the convolution neural network and Transformer (CNN-Transformer) are based on a multi-head attention mechanism as the objective predictive method. Finally, six evaluation indicators verify the performance of the proposed approach. The approach’s reliability was evaluated using the historical daily reservoir inflow data from the Xiluodu (XLD) and Wudongde (WDD) reservoirs in the Jinsha River Basin, China. Several single and hybrid models were developed for comparative analysis. The results indicate that the proposed ensemble approach fits better than other developed model methods. The GCVCT model showed excellent performance in forecasting the inflows of XLD and WDD reservoirs, with NSE values of 0.985 and 0.984, respectively. Furthermore, the GCVCT framework forecast capacity for peak inflow was further verified through discussion and analysis of the 48 peak flows during the validation period, consistently outperforming other models in predicting peak flow for both study reservoirs. This framework provides an effective method for the scientific optimal scheduling of hydropower reservoirs, enabling more sustainable and efficient management practices. It also demonstrates the potential of powerful deep-learning models in intelligent hydrological forecasting.

A Communication‐Free Master–Slave Control of Cascaded‐Type DC Microgrids With Nondispatchable Generations

ABSTRACTThis paper addresses the challenge of integrating dispatchable and nondispatchable distributed generators (DGs) in cascaded‐type direct current (DC) microgrids and proposes a communication‐free master–slave control strategy. In the proposed method, all DGs are treated as voltage sources. A master dispatchable DG is primarily responsible for maintaining a constant DC voltage at the point of common coupling (PCC). The rest slave nondispatchable DGs adjust their output power based on a front maximum power point tracking (MPPT) controller. This communication‐free approach enhances system reliability and ease of implementation. Moreover, it ensures load voltage stability, providing a high‐quality power supply for users. Additionally, it enables nondispatchable DGs to achieve MPPT‐based output power and enables power curtailment operation. Subsequently, a small‐signal stability analysis confirms the efficacy of the proposed strategy. Finally, simulation and experiment results further validate its efficacy.

Short-Term Prediction Model of Wave Energy Converter Generation Power Based on CNN-BiLSTM-DELA Integration

Wave energy is a promising source of sustainable clean energy, yet its inherent intermittency and irregularity pose challenges for stable grid integration. Accurate forecasting of wave energy power is crucial for reliable grid management. This paper introduces a novel approach that utilizes a Bidirectional Gated Recurrent Unit (BiGRU) network to fit the power matrix, effectively modeling the relationship between wave characteristics and energy output. Leveraging this fitted power matrix, the wave energy converter (WEC) output power is predicted using a model that incorporates a Convolutional Neural Network (CNN), a Bidirectional Long Short-Term Memory (BiLSTM) network, and deformable efficient local attention (DELA), thereby improving the accuracy and robustness of wave energy power prediction. The proposed method employs BiGRU to transform wave parameters into power outputs for various devices, which are subsequently processed by the CNN-BiLSTM-DELA model to forecast future generation. The results indicate that the CNN-BiLSTM-DELA model outperforms BiLSTM, CNN, BP, LSTM, CNN-BiLSTM, and GRU models, achieving the lowest mean squared error (0.0396 W) and mean absolute percentage error (3.7361%), alongside the highest R2 (98.69%), underscoring its exceptional forecasting accuracy. By enhancing power forecasting, the method facilitates effective power generation dispatch, thereby mitigating the adverse effects of randomness on the power grid.

A novel frequency constrained unit commitment considering VSC-HVDC's frequency support in asynchronous interconnected system under renewable energy Source's uncertainty

The increasing share of renewable energy source (RES) poses a challenge to the frequency security of the power system. Frequency constrained unit commitment (FCUC) serves as an effective measure to address this challenge at the operational level. This paper introduces a novel FCUC model applicable to the asynchronous interconnected system connected by voltage source converter based HVDC (VSCHVDC), fully taking into account the frequency support capability of VSCHVDC in order to reduce the demand for synchronous generators' inertia and reserve while ensuring frequency security, thereby lowering operating costs. Additionally, the uncertainty of RES is also considered. The constraint expressions for three frequency indicators are derived based on a system frequency response model that includes frequency support from VSCHVDC. The proposed model optimizes unit commitment, generation and reserve dispatch and VSCHVDC transmission power and frequency response parameters, while addressing RES uncertainty using the distributionally robust chance constrained approach. In response to the highly nonlinear characteristics of the maximum frequency derivation (MFD) constraints, we propose an intelligent sampling-based support vector machine to convexify the MFD constraints and introduce a two-stage decomposition algorithm for solving the model. The effectiveness of the proposed model is demonstrated based on a modified IEEE RTS-79 system.

Practical solutions for microgrid energy management: Integrating solar forecasting and correction algorithms

Nowadays, the shift towards renewable energies is happening at an exponential pace. Microgrids are an integral part in integrating renewable energies into the global energy mix. Due to the volatility of renewables, such as solar or wind, proper energy management is needed to avoid generation or load curtailment. This issue is addressed throughout the literature using a wide variety of strategies, ranging from classical linear to nature inspired metaheuristic optimization algorithms. This paper goes beyond the simulation phase of different optimization algorithms and addresses the optimal energy management problem by proposing a novel framework to integrate optimization strategies into the daily operation of microgrids. The proposed framework showcases 3 practical methods for controlling the distributed generators, as well as a communication scheme which facilitates the data transfer between the machine running the optimization strategy and the energy management system of the microgrid. The framework is demonstrated on a small-scale islanded microgrid setup, located at the Technical University of Cluj-Napoca. The microgrid setup consists of a photovoltaic array, a battery energy storage system and two dispatchable generators. A two-stage optimization strategy is used, consisting of a day-ahead stage, which uses the solar forecast provided by the Global Forecast System, prior to each day, to issue a working plan for the dispatchable generators, and an intra-day stage, which uses hourly solar forecasts, issued during the day, to adjust the operation of the microgrid and minimize the error between the day-ahead solar forecast and the actual weather conditions. Mixed integer linear programming is used to solve the optimization problem and validate the correct operation of the proposed framework over two case studies. The results show that the microgrid works as intended, in both scenarios, as well as the benefits of using an intra-day correction stage.

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.

Power system benefits of simultaneous domestic transport and heating demand flexibility in Great Britain’s energy transition

The increasing share of variable renewables in power generation leads to a shortage of affordable and carbon neutral options for grid balancing. This research assesses the potential of demand flexibility in Great Britain to fill this gap using a novel linear optimisation model PyPSA-FES, designed to simulate optimistic and pessimistic transition pathways in National Grid ESO Future Energy Scenarios. PyPSA-FES models the future power system in Great Britain at high spatiotemporal resolution and integrates demand flexibility from both smart charging electric vehicles and thermal storage-coupled heat pumps. The model then optimises the trade-off between reinforcing the grid to align charging and heating profiles with renewable generation versus expanding dispatchable generation capacity. The results show that from 2030, under optimistic transition assumptions, domestic demand flexibility can enable an additional 20–30 TWh of renewable generation annually and reduce dispatchable generation and distribution network capacity by approximately 20 GW each, resulting in a total cost reduction of around £5bn yearly. However, our experiments suggest that half of the total system cost reduction is already achieved by only 25% of electric vehicles alone. Further, the findings indicate that once smart electric vehicle charging reaches this 25% penetration rate in households, minimal benefits are observed for implementing smart 12-hour thermal storages for heating flexibility at the national level. Additionally, smart heating benefits decrease by 90% across all metrics when only pre-heating (without thermal storages) is considered. Spatially, demand flexibility is often considered to alleviate the need for north–south transmission grid expansion. While neither confirmed nor opposed here, the results show a more nuanced dynamic where generation capacities are moved closer to demand centres, enhancing connectivity within UK sub-regions through around 1000 GWkm of additional transmission capacity.

EXPERIMENTAL ASSESSMENT OF CONSUMER UNBALANCE AND NONLINEARITY EFFECTS IN THREE-PHASE NETWORKS WITHOUT NEUTRAL CONDUCTOR

The present study proposes a feedforward angle droop strategy based on a discrete-time mathematical model for operation dispatchable distributed generation (DG) units connected directly or through voltage source converters (VSC) to the microgrid. These units should supply their local and common loads. Droop coefficients should be increased to improve power division between different DG units. Also, a time delay in systems’ state variables and controller input is a disruptive parameter for the control system's performance. These two parameters have negative effects on network stability. To ensure the stability of the closed-loop system, a predictive sliding mode controller can be used based on discrete-time systems. However, this strategy cannot reduce the chattering phenomenon. This paper discusses the effect of time delay and increases in droop angle coefficients on the whole system and how these impacts can be eliminated. So, a combination of integral sliding mode controller (ISMC), composite nonlinear feedback (CNF), and sliding mode learning control (SMC), which is called hybrid SMC, is used with an angle droop controller.

THE EFFECTS OF HYBRID SLIDING MODE LEARNING CONTROL AND FEEDFORWARD ANGLE DROOP CONTROLLER WITH HIGH DROOP GAIN IN HYBRID MICROGRID WITH LOAD UNCERTAINTY AND NONLINEARITY

The present study proposes a feedforward angle droop strategy based on a discrete-time mathematical model for operation dispatchable distributed generation (DG) units connected directly or through voltage source converters (VSC) to the microgrid. These units should supply their local and common loads. Droop coefficients should be increased to improve power division between different DG units. Also, a time delay in systems’ state variables and controller input is a disruptive parameter for the control system's performance. These two parameters have negative effects on network stability. To ensure the stability of the closed-loop system, a predictive sliding mode controller can be used based on discrete-time systems. However, this strategy cannot reduce the chattering phenomenon. This paper discusses the effect of time delay and increases in droop angle coefficients on the whole system and how these impacts can be eliminated. So, a combination of integral sliding mode controller (ISMC), composite nonlinear feedback (CNF), and sliding mode learning control (SMC), which is called hybrid SMC, is used with an angle droop controller.

Financial Viability of Thermal Power Generation Plants in the Transition to Renewable Energy

The energy transition will occur due to the natural decline in profitability of thermal power generation assets, and their place in the energy matrix will be taken over by renewable energies. Ensuring electric supply security, a cornerstone of the energy trilemma, is a priority for both developed and developing countries. Nations heavily reliant on hydrological resources employ thermal energy as a backup, making the exploration of financial feasibility for such projects pertinent. This study introduces an economic valuation model for a dispatchable thermal power generation plant in the Colombian market. Associated uncertainty is assessed based on revenue from market sales, bilateral contracts, and firm power remuneration – elements constituting the cash flow and converging into profitability and risk analysis. These are calculated through the Monte Carlo simulation methodology. Additionally, a sensitivity analysis is conducted, accounting for variables such as the "Reliability Payment" settlement price, capacity factor, and costs linked to coal supply. The results emphasize the need for firm power remuneration as a crucial incentive for the success of such projects. Furthermore, the criticality of dispatch level in relation to profitability is verified. Finally, the impact of coal taxes and supply negotiations on financial feasibility is assessed.

Security-constrained economic dispatch of power systems with line outage using firefly algorithm

The Security-constrained Economic Dispatch (SCED) problem is the problem of finding the optimal generation dispatch for a power system that minimizes the operating cost while ensuring the security of the system. The security of the system is defined as the ability of the system to withstand disturbances without violating any operational constraints. Line outage is a common disturbance that can affect the security of a power system. The Firefly Algorithm, inspired by the flashing behavior of fireflies in nature, is a metaheuristic optimization technique known for its effectiveness in solving complex and dynamic optimization problems. In this study, the FA is adapted to the SCED problem with a specific focus on enhancing grid resilience during line outage scenarios. The proposed method aims to simultaneously optimize the economic cost of power generation and the system's ability to withstand line outages. To demonstrate the effectiveness of the proposed approach, extensive simulations are conducted on standard IEEE test systems with varying degrees of complexity and network sizes. The results showcase the superior performance of the SCEDL-Firefly Algorithm compared to traditional optimization methods, as it provides a resilient dispatch solution that can effectively adapt to unexpected line outages while maintaining economic efficiency. Overall, this research contributes to the advancement of secure economic dispatch techniques and power system resilience by leveraging the Firefly Algorithm's capabilities. The findings offer valuable insights for power system operators and planners seeking to enhance grid reliability in the presence of line outages, ultimately promoting a more sustainable and resilient energy infrastructure.

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.