Search Results (23)

This study presents the design and techno-economic optimization of a hybrid renewable energy system (HRES) for a coastal hotel in Manavgat, Türkiye. The system integrates photovoltaic (PV) panels, wind turbines (WT), pumped hydro storage (PHS), hydrogen storage (electrolyzer, tank, and fuel cell), batteries, a fuel cell-based combined heat and power (CHP) unit, and a boiler to meet both electrical and thermal demands. Within this broader optimization framework, six optimal configurations emerged, representing grid-connected and standalone operation modes. Optimization was performed in HOMER Pro to minimize net present cost (NPC) under strict reliability (0% unmet load) and renewable energy fraction (REF > 75%) constraints. The grid-connected PHS–PV–WT configuration achieved the lowest NPC ($1.33 million) and COE ($0.153/kWh), with a renewable fraction of ~96% and limited excess generation (~21%). Off-grid PHS-based and PHS–hydrogen configurations showed competitive performance with slightly higher costs. Hydrogen integration additionally provides complementary storage pathways, coordinated operation, waste heat utilization, and redundancy under component unavailability. Battery-only systems without PHS or hydrogen storage resulted in 37–39% higher capital costs and ~53% higher COE, confirming the economic advantage of long-duration PHS. Sensitivity analyses indicate that real discount rate variations notably affect NPC and COE, particularly for battery-only systems. Component cost sensitivity highlights PV and WT as dominant cost drivers, while PHS stabilizes system economics and the hydrogen subsystem contributes minimally due to its small scale. Overall, these results confirm the techno-economic and environmental benefits of combining seawater-based PHS with optional hydrogen and battery storage for sustainable hotel-scale applications. Full article

This paper proposes a comprehensive framework for strategic power system decarbonization planning that integrates the William Newman method (diagnosis–options–forecast–decision) with a multi-objective Mixed-Integer Linear Programming (MILP) model. The approach simultaneously minimizes (i) generation cost, (ii) expected cost of energy not supplied (Value of Lost Load, VoLL), (iii) demand response cost, and (iv) CO₂ emissions, subject to power balance, technical limits, and binary unit commitment decisions. The methodology is validated on the IEEE RTS 24-bus system with increasing demand profiles and representative cost and emission parameters by technology. Three transition pathways are analyzed: baseline scenario (no environmental restrictions), gradual transition (−50% target in 20 years), and accelerated transition (−75% target in 10 years). In the baseline case, the oil- and coal-dominated mix concentrates emissions (≈14 ktCO₂ and ≈12 ktCO₂, respectively). Under gradual transition, progressive substitution with wind and hydro reduces emissions by 15.38%, falling short of the target, showing that renewable expansion alone is insufficient without storage and demand-side management. In the accelerated transition, the model achieves −75% by year 10 while maintaining supply, with a cost–emissions trade-off highly sensitive to the carbon price. Results demonstrate that decarbonization is technically feasible and economically manageable when three enablers are combined: higher renewable penetration, storage capacity, and policy instruments that both accelerate fossil phase-out and valorize demand-side flexibility. The proposed framework is replicable and valuable for outlining realistic, verifiable transition pathways in power system planning. Full article

In response to the challenge of multi-objective optimal scheduling and efficient solution of hydropower stations under large-scale renewable energy integration, this study develops a multi-objective optimization model with the dual goals of maximizing total power generation and minimizing the variance of residual load. Four complementarity evaluation indicators are used to analyze the wind–solar complementarity characteristics. Building upon this foundation, Hyper-dominance Evolutionary Algorithm (HEA)—capable of efficiently solving high-dimensional problems—is introduced for the first time in the context of wind–solar–hydropower integrated scheduling. The case study results show that the HEA performs better than the benchmark algorithms, with the best mean Hypervolume and Inverted Generational Distance Plus across nine Walking Fish Group (WFG) series test functions. For the hydro-wind-solar scheduling problem, HEA obtains Pareto frontier solutions with both maximum power generation and minimal residual load variance, thus effectively solving the multi-objective scheduling problem of the hydropower system. This work provides a valuable reference for modeling and efficiently solving the multi-objective scheduling problem of hydropower in the context of emerging power systems. This work provides a valuable reference for the modeling and efficient solution of hydropower multi-objective scheduling problems in the context of emerging power systems. Full article

Amid the global transition toward sustainable energy, regional power grids with high wind power penetration are increasingly emerging. The implementation of frequency control is critically essential for enhancing the frequency support capability of grid-connected devices. However, existing studies indicate this may induce ULFOs (ultra-low frequency oscillations). Current research on ULFOs have been predominantly concentrated on hydro-dominated power systems, with limited exploration into systems where thermal power serves as synchronous sources—let alone elucidation of the underlying mechanisms. This study focuses on regional power grids where wind and thermal power generation coexist. Eigenvalue analysis reveals that frequency regulation control of doubly-fed induction generators (DFIGs) can trigger ULFOs. Leveraging common-mode oscillation theory, an extended system frequency response (ESFR) model incorporating DFIG frequency control is formulated and rigorously validated across a range of operational scenarios. Moreover, frequency-domain analysis uncovers the mechanism by which inertia control affects ULFO behavior, and time-domain simulations are conducted to validate the influence of DFIG control parameters on ULFOs. Full article

This document explores short-term hydrothermal economic dispatch (HTED) while explicitly modeling the valve-point effect of thermal units as a factor that adds complexity to power system optimization. Two nature-inspired optimizers, the Bat Algorithm (BAT) and the Artificial Bee Colony (ABC) algorithm, were used on a 24 h horizon for nine unit power plants (five thermal, four hydro). After 30 independent runs, BAT produced the lowest daily operating cost at USD 307,952.44, whereas ABC obtained USD 311,457.48, a 1.14% saving (USD 3.5 k) in favour of BAT. However, ABC converged almost twice as fast, stabilizing after around 40 iterations, while BAT required around 80 iterations. The results demonstrate that BAT offers a modest but measurable economic advantage, whereas ABC provides faster convergence, which is important when real-time computational limits dominate. These quantitative findings confirm that meta-heuristic techniques are practical tools for HTED and highlight the trade-off between cost minimization and computational speed. Full article

With the increasing presence of large-scale new energy sources, such as wind and photovoltaic (PV) systems, integrating traditional hydropower with wind and PV power into a hydro–wind–PV complementary system in economic dispatch can effectively mitigate wind and PV fluctuations. In this study, Markov chains and the Copula joint distribution function were adopted to quantize the spatiotemporal relationships among hydro, wind and PV, whereby runoff, wind, and PV output scenarios were generated to simulate their uncertainties. A dual-objective optimization model is proposed for the long-term hydro–wind–PV co-scheduling (LHWP-CS) problem. To solve the model, a well-tailored evolutionary multi-objective optimization method was developed, which combines multiple recombination operators and two different dominance rules for basic and elite populations. The proposed model and algorithm were tested on three annual reservoirs with large wind and PV farms in the Hongshui River Basin. The proposed algorithm demonstrates superior performance, with average improvements of 2.90% and 2.63% in total power generation, and 1.23% and 0.96% in minimum output expectation compared to BORG and NSGA-II, respectively. The results also infer that the number of scenarios is a key parameter in achieving a tradeoff between economics and risk. Full article

In a hydropower-dominated power grid, the primary frequency regulation (PFR) capability of hydropower units is typically compromised to suppress ultra-low frequency oscillations (ULFOs). However, as renewable wind power is further integrated, a practicable solution to damp ULFOs has emerged, which is to adjust the frequency control parameters of wind turbine (WT) units. Driven by the goals of overall damping enhancement and ULFO suppression, this paper first establishes an extended unified frequency model (EUFM) of a hydro–wind power sending system. Based on EUFM, the damping torque of the hydro–wind power sending system is derived, and the specific impact of WT control parameters on ULFOs and PFR characteristics is investigated. Then, a novel optimization objective function considering damping in the ultra-low frequency band and PFR is formulated and solved using an intelligence algorithm. By optimizing the parameters of the WT to suppress ULFOs, the PFR capability of hydropower units can be released. Finally, simulation results verify that the optimized WT parameters can simultaneously address the ULFO problem and guarantee PFR performance, thereby enhancing the frequency dynamic stability of the sending system. Full article

Streamflow forecasting plays a crucial role in the operational planning of hydro-dominant power systems, providing valuable insights into future water inflows to reservoirs and hydropower plants. It relies on complex mathematical models, which, despite their sophistication, face various uncertainties affecting their performance. These uncertainties can significantly influence both short-term and long-term operational planning in hydropower systems. To mitigate these effects, this study introduces a novel Bayesian model averaging (BMA) framework to improve the accuracy of streamflow forecasts in real hydro-dominant power systems. Designed to serve as an operational tool, the proposed framework incorporates predictive uncertainty into the forecasting process, enhancing the robustness and reliability of predictions. BMA statistically combines multiple models based on their posterior probability distributions, producing forecasts from the weighted averages of predictions. This approach updates weights periodically using recent historical data of forecasted and measured streamflows. Tested on inflows to 139 reservoirs and hydropower plants in Brazil, the proposed BMA framework proved to be more skillful than individual models, showing improvements in forecasting accuracy, especially in the South and Southeast regions of Brazil. This method offers a more reliable tool for streamflow prediction, enhancing decision making in hydropower system operations. Full article

To make clear the influence of renewable energy on ultra-low-frequency oscillation (ULFO) in a hydro-dominant system via an high voltage direct current (HVDC) transmission system, this paper studies the damping characteristics when wind power is integrated into the sending power system. The damping torque method is applied for the mechanism study and risk evaluation of ULFO. The study of the inner cause and outer performance proves that ULFO belongs to frequency oscillation rather than conventional power oscillation. According to the theoretical analysis, the suggested control mode is identified to reduce the risk of ULFO. Moreover, a robust controller is designed for wind units based on mixed H₂/H_∞ robust control theory, and the control method makes use of the fast response ability of the converter without compromising the primary frequency regulation ability. Finally, a simulation model of a hydro-dominant sending power system with wind integration is established in PSCAD Version 4.6 software. The simulations verify the proposed control can suppress ULFO effectively. Full article

The rapid fall in the cost of solar photovoltaics and wind energy offers a pathway to the deep decarbonization of energy at an affordable price. Off-river pumped hydro energy storage and batteries provide mature and large-scale storage to balance variable generation and demand while minimizing environmental and social impacts. High-voltage inter-regional interconnection and dispatchable capacity (existing hydro and geothermal) can help balance supply and demand. This work investigates an Indonesian energy decarbonization pathway using mostly solar photovoltaics. An hourly energy balance analysis using ten years of meteorological data was performed for a hypothetical solar-dominated Indonesian electricity system for the consumption of 3, 6 and 10 megawatt-hours (MWh) per capita per year (compared with current consumption of 1 MWh per capita per year). Pumped hydro provides overnight and longer storage. Strong interconnection between islands was found to be unnecessary for Indonesia, contrary to findings from similar modelling in countries at higher latitudes. Storage requirements for power and energy were found to be smaller than three kilowatts and 30–45 kilowatt-hours per person, respectively. Introducing gas turbines (burning hydrogen or synthetic methane) contributing around 1% of annual generation reduced the levelized cost of electricity (LCOE) by 14% and halved the storage requirements by allowing the system to ride through prolonged cloudy periods at lower cost. This work showed that Indonesia’s vast solar potential combined with its vast capacity for off-river pumped hydro energy storage could readily achieve 100% renewable electricity at low cost. The LCOE for a balanced solar-dominated system in Indonesia was found to be in the range of 77–102 USD/megawatt-hour. Full article

In the power system, economic power dispatch is a popular and fundamental optimization problem. In its classical form, this problem only considers thermal generators and does not take into account network security constraints. However, other forms of the problem, such as economic emission dispatch (EED), are becoming increasingly important due to the emphasis on minimizing emissions for environmental purposes. The integration of renewable sources, such as solar, wind, and hydro units, is an important aspect of EED, but it can be challenging due to the stochastic nature of these sources. In this study, a multi-objective algorithm is developed to address the problem of economic emission power dispatch with the inclusion of these renewable sources. To account for the intermittent behavior of solar, wind, and hydro power, the algorithm uses Lognormal, Weibull, and Gumbel distributions, respectively. The algorithm also considers voltage limitations, transmission line capacities, prohibited areas of operation for thermal generator plants, and system restrictions. The multi-objective real coded non-dominated sorting genetic algorithm II (R-NSGA-II) is applied to the problem and includes a procedure for handling system restrictions to meet system limitations. Results are extracted using fuzzy decision-making and are analyzed and discussed. The proposed method is compared to other newer techniques from another study to demonstrate its robustness. The results show that the proposed method despite being older is cost-significant while maintaining the same or lower emissions. These results were observed consistently and did not happen by chance, detailed explanation of why and how is discussed. Full article

Access to inexpensive, clean energy is a key factor in a country’s ability to grow sustainably The production of electricity using fossil fuels contributes significantly to global warming and is becoming less and less profitable nowadays. This work therefore proposes to study the different possible scenarios for the replacement of light fuel oil (LFO) thermal power plants connected to the electrical network in northern Cameroon by renewable energy plants. Several scenarios such as the combination of solar photovoltaic (PV) with a pumped hydro storage system (PHSS), Wind and PHSS and PV-Wind-PHSS have been studied. The selected scenarios are evaluated based on two factors such as the system’s total cost (TC) and the loss of load probability (LOLP). To achieve the results, metaheuristics such the non-dominated sorting whale optimization algorithm (NSWOA) and non-dominated sorting genetic algorithm-II (NSGA-II) have been applied under MATLAB software. The optimal sizing of the components was done using hourly meteorological data and the hourly power generated by the thermal power plants connected to the electrical grid. Both algorithms provided satisfactory results. However, the total cost in the PV-PHSS, Wind-PHSS, and PV-Wind-PHSS scenarios with NSWOA is, respectively, 1%, 6%, and 0.2% lower than with NSGA-II. According to NSWOA results, the total cost for the PV-Wind-PHSS scenario at LOLP 0% is 4.6% and 17% less than the Wind-PHS and PV-PHSS scenarios, respectively. The profitability study of all three scenarios showed that the project is profitable regardless of the scenario considered. Full article

This study proposes a new optimization technique, known as the eagle strategy arithmetic optimization algorithm (ESAOA), to address the limitations of the original algorithm called arithmetic optimization algorithm (AOA). ESAOA is suggested to enhance the implementation of the original AOA. It includes an eagle strategy to avoid premature convergence and increase the populations’ efficacy to reach the optimum solution. The improved algorithm is utilized to fine-tune the parameters of the fractional-order proportional-integral-derivative (FOPID) and the PID controllers for supporting the frequency stability of a hybrid two-area multi-sources power system. Here, each area composites a combination of conventional power plants (i.e., thermal-hydro-gas) and renewable energy sources (i.e., wind farm and solar farm). Furthermore, the superiority of the proposed algorithm has been validated based on 23 benchmark functions. Then, the superiority of the proposed FOPID-based ESAOA algorithm is verified through a comparison of its performance with other controller performances (i.e., PID-based AOA, PID-based ESAOA, and PID-based teaching learning-based optimization TLBO) under different operating conditions. Furthermore, the system nonlinearities, system uncertainties, high renewable power penetration, and control time delay has been considered to ensure the effectiveness of the proposed FOPID based on the ES-AOA algorithm. All simulation results elucidate that the domination in favor of the proposed FOPID-based ES-AOA algorithm in enhancing the frequency stability effectually will guarantee a reliable performance. Full article

As we move towards electrical networks with a growing presence of renewable generation, the representation of the electrical components becomes more important. In hydro-dominated power systems, modelling the forbidden zones of hydro plants becomes increasingly challenging as the number of plants increases. Such zones are ranges of generation that either should be avoided or are altogether unreachable. However, because representing the forbidden zones introduces a substantial computational burden, hydrothermal unit-commitment problems (HTUC) for large systems are usually formulated ignoring the forbidden zones. Nonetheless, this simplification may demand adjustments to the solution of the HTUC, because the generation of the hydro stations may fall in forbidden zones. In practice, the adjustments are usually performed based on the experience of system operators and, then, can be far from an optimal correction. In this paper, we study the impact of explicitly representing the hydro-generation forbidden zones in a large-scale system with more than 7000 buses, 10,000 lines, and 700 hydro units. Our findings show that the simplified model that is current used can deviate significantly from the model with forbidden zones, both in terms of the generation of hydro plants, as well as the generation of thermal plants and the system marginal costs. Full article

The main challenge with the penetration of variable renewable energy (VRE) in thermal-dominated systems has been the increase in the need for operating reserves, relying on dispatchable and flexible resources. In the case of hydro-dominated systems, the cost-effective flexibility provided by hydro-plants facilitates the penetration of VRE, but the compounded production variability of these resources challenges the integration of baseload gas-fired plants. The Brazilian power system illustrates this situation, in which the development of large associated gas fields economically depends on the operation of gas-fired plants. Given the current competitiveness of VRE, a natural question is the economic value and tradeoffs for expanding the system opting between baseload gas-fired generation and VRE in an already flexible hydropower system. This paper presents a methodology based on a multi-stage and stochastic capacity expansion model to estimate the optimal mix of baseload thermal power plants and VRE additions considering their contributions for security of supply, which includes peak, energy, and operating reserves, which are endogenously defined in a time-varying and sized in a dynamic way as well as adequacy constraints. The presented model calculates the optimal decision plan, allowing for the estimation of the economical tradeoffs between baseload gas and VRE supply considering their value for the required services to the system. This allows for a comparison between the integration costs of these technologies on the same basis, thus helping policymakers and system planners to better decide on the best way to integrate the gas resources in an electricity industry increasingly renewable. A case study based on a real industrial application is presented for the Brazilian power system. Full article