Generation Dispatch Research Articles

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.

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.

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.

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.

Assessment of Colombian renewable energy auctions policy: Enabler or barrier for concentrating solar power plants

Auctions are an alternative for introducing renewable energy (RE) projects into electricity markets at competitive prices. Colombia added 2.2 GW capacity of RE through this mechanism. Nevertheless, 94% of the awarded generation is allocated between 12 a.m. and 5 p.m. because of a lack of low-cost storage. Integrating concentrating solar power plants (CSP) could be relevant in tackling this issue. Therefore, a preliminary step is to determine if Colombian instruments benefit the deployment of CSP and trigger the discussion about its future in the country. In this work, we evaluate how auction mechanisms and fiscal incentives (FI) influence the Levelized Cost of Electricity (LCOE) and Internal Rate of Return (IRR) for two CSP configurations. The results indicate that FIs reduce capital expenditures between 17.11% and 19.45%, reaching similar LCOEs to those reported internationally. However, energy prices higher than USD 150/MWh are required to achieve an adequate IRR. Regarding the auction design, it is observed that a “security criterion” leads to a significant loss of competitiveness for CSP as it neglects its possibility to support demand at night. Policy suggestions are provided for a level playing field where low-cost solutions can coexist with dispatchable renewable power generation.

From 30- to 5-minute settlement rule in the NEM: An early evaluation

Australia’s National Electricity Market has shifted from 30- to 5-minute settlement (5MS)— a major regulatory reform since its inception. Using a Bayesian structural time-series model over the period 1 October 2021 to 31 May 2022, we demonstrate that despite having no immediate impact on price dynamics, 5MS eventually exerts upward pressure on spot prices over time. Price volatility remains essentially unchanged. Moreover, dispatch-weighted prices (DWPs) for flexible battery generators increase. DWPs for aggregated gas generators barely change, while DWPs for incumbent black coal-fired generators unexpectedly increase. These findings support the notion that the market is gradually adapting to 5MS. For a more effective adaptation, energy policies must be in place to lower entry barriers for flexible, dispatchable generation capacity and reduce market concentrations.

Development of a Cost-Driven, Real-Time Management Strategy for e-Mobility Hubs Including Islanded Operation

The installation of electric vehicle supply equipment (EVSE) increases demand and peak loads, potentially straining existing energy distribution infrastructure. Dispersed and inadequately planned placement of charging points (CPs) can disrupt the electrical grid, surpass contracted demand thresholds, and require infrastructure upgrades, thereby incurring unfeasible costs for Distribution System Operators (DSOs). In this context, it is necessary to recognize the role of business models in enabling effective electrification of the transportation sector. In response to these challenges, this paper introduces a novel e-mobility hub management strategy, tailored for implementation in the Brazilian context. The proposed strategy revolves around a microgrid configuration encompassing dispatchable and photovoltaic generation, a battery energy storage system (BESS), EVSE infrastructure, and local loads. Moreover, this centralized controller facilitates the implementation of dynamic pricing and demand-response mechanisms, integral to business models seeking to integrate EVSE into the distribution grid. To validate the efficacy of the proposed solution, hardware-in-the-loop (HIL) simulations of the microgrid system are conducted. These simulations, incorporating the centralized controller, serve as a tool for assessing system performance and viability before on-site equipment deployment. Finally, this paper concludes with the insights gleaned from test analysis and its discussion through a selection of the most expressive scenarios, including islanded and connected operation modes.

A Study on Hybrid Power Plant (Solar & Biomass)

We have designed an on-grid hybrid power plant that combines solar and biomass energy sources. The solar plant has a capacity of 6 MW, and the biomass plant has a capacity of 2 MW. The hybrid solar-biomass power plant is still a recent renewable technology. It is an ongoing investigation that not only proposes dispatchable power generation but also benefits from having a flexible input source, including solar and bioenergy. We aimed to successfully design a hybrid project using proper analysis, to reduce the cost of power generation through a grid-connected hybrid system, while also reducing greenhouse gas emissions. We used PVsyst software to simulate the solar plant and created the PV mounting structure and Single Line Diagram (SLD) using AutoCAD software. We have used Microsoft Excel software to carry out the cost analysis. We also calculated how much biomass waste and gas the biomass plant will need. We have been able to successfully design our hybrid power plant and, at the same time, reduce the power generation cost comparatively. The hybrid system design has made it possible to ensure the most convenient arrangement of electricity.

Optimal charging of electric vehicles in smart stations and its effects on the distribution network using meerkat optimization algorithm

The proliferation of electric vehicles (EVs) presents a significant challenge and opportunity for the energy sector. This study proposes a novel approach for optimizing EV charging within smart stations, considering its impact on the distribution network. Leveraging the Meerkat Optimization Algorithm (MOA), we address the complex optimization problem of balancing EV charging demands with grid constraints. Navigating distribution grid energy management complexities, including renewable resources and dynamic demand, is challenging. We introduce a sophisticated optimization model tailored for grid operations, featuring meticulous formulations for energy management. The model optimizes battery usage, EV energy management, compensator utilization, and distributed generation dispatch. Through extensive simulations, we demonstrate the approach's effectiveness in minimizing charging costs, reducing grid congestion, and enhancing overall system performance. The multi-objective function minimizes energy losses, power purchases, load curtailment, distributed generation, and battery/EV expenses over 24 h. Simulations validate a significant reduction in the distribution grid's operating cost. This research highlights the potential of advanced optimization techniques in smart charging infrastructure to facilitate widespread EV adoption while ensuring grid reliability and efficiency. Incorporating electric vehicles (EVs) into the system yields significant improvements across performance indicators compared to scenarios without EVs. Results indicate a 19 % reduction in the objective function value, with a notable 74 % decrease in energy purchase and a 60 % reduction in energy losses. Additionally, load shedding decreases by approximately 75 %, while voltage deviation decreases by around 44 %. Importantly, no PV or WD curtailment is observed with EV integration, showcasing its compatibility with renewable energy generation profiles and emphasizing its potential to enhance system efficiency, reliability, and sustainability.

Dispatchable generation analysis and prediction by using machine learning: A case study of South Africa

South African power sector has a total installed capacity of about 58,095 MW. However, in the past five years, the nation's energy sector has been struggling to generate a daily average power of about 23,526 MW. Therefore, the use of machine learning technique is crucial to observing the short-term forecast of South African dispatchable power generation and its effects on the nation's economic growth. The choice of SARIMAX as the forecasting tool in this study is due to its flexibility, simplicity, accuracy, and robustness. This study has carried out time series modeling of the South African dispatchable power generation between the period: 2019 and 2023 by using the seasonal auto-regressive moving average with exogenous variables (SARIMAX). The accuracy of the model was measured by the root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), mean bias error (MBE), and mean absolute percentage error (MAPE). When subjected to ARIMA analysis, the predictive model produced the following metrics: RMSE of 669.05 MW, MAE of 575.07 MW, R2 value of 0.937, MBE of -2.45, and MAPE of 0.023. However, upon employing the SARIMAX method, notable improvements were observed, with the metrics indicating RMSE of 469.92 MW, MAE of 330.80 MW, R2 of 0.969, MBE of -0.09 MW, and MAPE of 0.015. The improved accuracy provided by the SARIMAX predictive method confirms the importance of considering seasonal effects in dispatchable generation prediction models. Dispatchable power generation prediction is an important route to achieving sustainable energy for a nation's development, social well-being, and security of life and properties.

Scenarios on future electricity storage requirements in the Austrian electricity system with high shares of variable renewables

This paper presents three scenarios (policy, renewables and electrification and efficiency) for transitioning to a 100 % renewable electricity sector in Austria, based predominantly on wind and photovoltaics, alongside sector-specific electrification. Considering renewable expansion targets and three distinctive weather years from an overall system perspective, the core objective is to minimize variable costs of electricity storage and dispatchable power plants. The model developed determines their optimal dispatch for meeting the underlying electricity demand each hour. Within the scenarios for renewable expansion, a special focus lies on integrating short-duration (batteries), medium-duration (pumped storage hydro) and long-duration (hydrogen) energy storage. Our analysis reveals the significant impact of weather patterns on renewable electricity generation, particularly the differences between winter and summer generation quantities. This necessitates seasonal balancing and the mitigation of extremes like low wind power events, which require corresponding backup capacities. This contrast is particularly evident when comparing the years 2030–2050, wherein in the latter, certain dispatchable generators are only utilized in one of the three underlying weather years during extreme weather conditions. In our paper, we demonstrate how, especially for hydrogen production and storage, weather conditions influence production levels and the re-electrification demand. The results indicate the feasibility of achieving a fully decarbonized energy system in Austria through suitable policy measures and expanded renewable generation, with long-duration storage playing a crucial role in seasonal balance and compensating for the absence of fossil fuel generation. Strategic planning is essential to aligning the expansion of renewable energy generation with the necessary flexibility.