Generator Output Research Articles

Aligning the Western Balkans power sectors with the European Green Deal

Abstract Located in Southern Europe, the Drina River Basin is shared between Bosnia and Herzegovina, Montenegro, and Serbia. The power sectors of the three countries have an exceptionally high dependence on coal for power generation. In this paper, we analyse different development pathways for achieving climate neutrality in these countries and explore the potential of variable renewable energy (VRE) and its role in power sector decarbonization. We investigate whether hydro and non-hydro renewables can enable a net-zero transition by 2050 and how VRE might affect the hydropower cascade shared by the three countries. The Open-Source Energy Modelling System (OSeMOSYS) was used to develop a model representation of the countries' power sectors. Findings show that the renewable potential of the countries is a significant 94.4 GW. This potential is 68% higher than previous assessments have shown. Under an Emission Limit scenario assuming net zero by 2050, 17% of this VRE potential is utilized to support the decarbonization of the power sectors. Additional findings show a limited impact of VRE technologies on total power generation output from the hydropower cascade. However, increased solar deployment shifts the operation of the cascade to increased short-term balancing, moving from baseload to more responsive power generation patterns. Prolonged use of thermal power plants is observed under scenarios assuming high wholesale electricity prices, leading to increased emissions. Results from scenarios with low cost of electricity trade suggest power sector developments that lead to decreased energy security.

Light‐Assisted Polyproton Dissociated PAAm‐PA Hydrogel‐Based Moisture‐Driven Electricity Generator with a Broad Operating Range

AbstractDue to its widespread availability and spontaneity, moisture electricity generation (MEG) holds unique advantages in self‐powered systems. However, it faces challenges, including the limitations of relying on a single kind of power generation and insufficient output performance. Inspired by the mechanisms of water absorption of plants, this paper explores a light‐moisture coordinated electricity generating hydrogel (L‐MEGH) device with flexible, scalable, and highly efficient energy conversion performance, which is obtained through the UV polymerization of hydrophilic acrylamide (AAM) and phytic acid (PA) in the presence of photosensitizers. The obtained hydrogel demonstrates superior moisture absorption and remarkable electricity generation stability across a range of humidity conditions. Notably, the open‐circuit voltage (Voc) of the L‐MEGH increased from 0.675 to 0.838 V after the addition of photosensitizers (Erythrosin B, E) (the significant enhancement, up to 24%), and the short‐circuit current (Isc) reaching 635.543 µA. This L‐MEGH can realize stable electrical output even under extreme temperatures, producing 0.5 V at −20 °C for 45 h. The scalable L‐MEGHs (connected on‐demand in series/parallel) can power various commercial electronics, including nighttime illumination, mobile phones, and health monitoring sensors. This work pioneers a sustainable power generation pathway capable of enhancing performance through the hybrid collection of multiple natural energy sources.

Decomposition prediction fractional-order active disturbance rejection control deep Q network for generation control of integrated energy systems

The number of distributed renewable energy sources of integrated energy systems (IESs) are increasing. The volatility of renewable energy exacerbates active power imbalances and increase frequency deviations in power systems. This work proposes a decomposition prediction fractional-order active disturbance rejection control deep Q network (DPFOADRCDQN) to control each generator of IESs more accurately and to reduce systemic frequency fluctuation. According to the fluctuation size of frequency deviation, the sub-signal obtained after the prediction of the modal decomposition of frequency deviation is classified into big fluctuating signals and small fluctuating signals; fractional-order active disturbance rejection control (FOADRC) controls small fluctuating signals; deep Q network (DQN) controls big fluctuating signals. Frequency deviation signals in complex power systems with a large percentage of renewable energy have higher smoothness after modal decomposition, FOADRC can efficiently follow the small fluctuating signals, and DQN can learn the rich data features in the big fluctuating signals and give precise control commands. The DPFOADRCDQN, proportional-integral-derivative, Q-learning, fuzzy control, and the previous state-of-the-art reinforcement learning are simulated in IESs where the renewable energy generators output is 0 %, 20 %, 40 %, 65 %, and 80 % of all generators output, the results show the average frequency deviation, total generation cost, and carbon emission cost are at least 42.7 %, 0.92 %, and 0.93 % smaller than the comparison algorithm, respectively.

Coordinated Planning for Multiarea Wind-Solar-Energy Storage Systems That Considers Multiple Uncertainties

As the scale of renewable energy sources (RESs) expands, it is essential to optimize the configuration of wind, solar, and storage resources across different areas. Nevertheless, the unavoidable uncertainties associated with both energy supply and demand present significant challenges for planners. This study aims to address the challenge of coordinated planning for multiarea wind-solar-energy storage systems considering multiple uncertainties. First, uncertainties related to future peak demand, thermal generation output boundaries, demand variability, and stochastic unit production are analyzed and modeled on the basis of robust optimization and stochastic programming techniques. Then, a hierarchical coordinated planning model that incorporates both system-wide (SW) and local area (LA) planning models is proposed. The SW planning model is designed to manage the optimal capacity configuration of RESs and energy storage systems (ESSs) within each LA, as well as the operational boundary of LAs. The LA planning models aim to further optimize the capacities of RESs and ESSs and minimize the economic cost within each LA on the basis of local resource characteristics. To achieve the optimal solution, the analytical target cascading (ATC) algorithm is integrated with the column-and-constraint generation (C&CG) algorithm. The simulation results validate the effectiveness and reasonableness of the proposed coordinated planning model, which not only outperforms independent planning approaches but also effectively manages the uncertainties.

Boosting self-powered wearable thermoelectric generator with solar absorber and radiative cooler

The electrical output of wearable thermoelectric generators (wTEGs) has traditionally been constrained by small temperature differentials when powering microelectronics. In this study, we innovatively combine photothermal and radiative cooling mechanisms within a single wTEGs system, enabling substantial, uninterrupted power generation. Specifically, we designed a multilayer selective solar absorber (m-SSA) composed of flexible dielectric-metal stacks. This absorber demonstrates exceptional solar absorption efficiency of 93 % and significantly low thermal emissivity of 10 %. In practical outdoor conditions, it achieves a temperature increase of up to 108 °C under solar irradiation. Concurrently, we developed a flexible hierarchically porous radiative cooler (HP-RC), which reflects 96 % of solar energy and emits 97 % of thermal energy, achieving a cooling differential of up to 10 °C, even at ambient temperatures of 42 °C. Integration of the m-SSA and HP-RC with wTEGs allows for the simultaneous harvesting of heat from solar, cold space, and earth (robots or human body). This novel energy capture mechanism yielded a notable power density of 198 mW/m² for human body and 52 mW/m2 for steel robots in outdoor wearable applications. This significant advancement promotes the field toward high-performance, integrated green power technologies and holds promise for next-generation wearable self-powered devices.

An acoustic nanogenerator based on porous PDMS/ZnO/PVDF-TrFE structure for self-powered intermediated-frequency sound monitoring

Abstract Sound energy is widely present in natural environment, nowadays predominantly collected using voluminous and heavy rigid devices, limiting their portability. Till now, there are few reports about using flexible materials to collect sound energy, and it is usually susceptible to environmental humidity. This study combines flexible polymer polydimethylsiloxane (PDMS), piezoelectric material polyvinylidene difluoride trifluoroethylene (PVDF-TrFE), nano zinc oxide (ZnO), and conductive carbon nanotubes (CNTs) to fabricate a porous nano ZnO/PVDF-TrFE/CNTs PDMS sound-driven nanogenerator (PZPCP SNG). It amalgamates frictional and piezoelectric effects: its porous structure enables efficient collection of vibration and frictional energy generated by sound waves; the piezoelectric effect of PVDF-TrFE and nano ZnO facilitates the conversion of acoustic mechanical energy, collectively enhancing the generator's output. Experimental optimization yields the best production conditions, achieving optimal outputs of 481.1 mV (open-circuit voltage), 209.13 nA (short-circuit current) under 400 Hz/125 dB sound stimuli, with a surface power density of 9.1 µW/m² (volumetric power density of 2.28 mW/m³). And it can convert sound ranging from 63-3000 Hz. With hardware circuitry, up to 5 series-connected light-emitting diodes (LEDs) can be illuminated within the circuit, demonstrating a certain degree of sound recognition capability. The proposed PZPCP SNG design is simple, effective, and lightweight, enabling flexible and stable intermediated-frequency acoustic energy harvesting. Its hydrophobic structural characteristics render it adaptable to various humidity, presenting a new approach toward widespread low-power sound detection and self-powering applications.

Experimental evaluation and optimization of the heat pipes integrated thermoelectric generator using response surface methodology

In this study, integration of heat pipes with thermoelectric generators is proposed for the recovery of waste heat. Heat energy is transferred from a heat source to thermoelectric elements via heat pipes, enabling energy conversion. To dissipate excess heat from the system after conversion, additional heat pipes are positioned on the cold sides of thermoelectric elements. Cooling of the condenser sections of these heat pipes is facilitated using air flow provided by a fan. A novel approach in this study involves the innovative integration of heat pipes with thermoelectric generators. This integration provides a new pathway for optimizing waste heat recovery systems by combining advanced thermal management techniques with thermoelectric technology. Experiments conducted at different power inputs (12–42 W) and air velocities (0.4, 0.8, 1.2 m/s) determine the performance of the thermoelectric generator. Furthermore, a power usage efficiency analysis was conducted to discuss the applicability of the proposed method in electronics. At low air velocities, values below the ideal value of 1 were achieved. Experimental data were analyzed using response surface methodology to assess the impact of varying input parameters on the power output and efficiency of the thermoelectric generator. Optimization studies highlighted the importance of effectively cooling the cold side of thermoelectric elements. The highest power output and efficiency are achieved under conditions of 42 W power input, 1.2 m/s air velocity, and use of 4 heat sinks, yielding approximately 0.8 W and 2 %, respectively. Furthermore, the response surface methodology analysis indicated that the most significant effect on system outputs was the power input. Moreover, the results of this study lay the groundwork for future strategic advancements in thermoelectric generators, enhancing their role in sustainable energy solutions.

Active power balance control of wind-photovoltaic-storage power system based on transfer learning double deep Q-network approach

IntroductionThis study addresses the challenge of active power (AP) balance control in wind-photovoltaic-storage (WPS) power systems, particularly in regions with a high proportion of renewable energy (RE) units. The goal is to effectively manage the AP balance to reduce the output of thermal power generators, thereby improving the overall efficiency and sustainability of WPS systems.MethodsTo achieve this objective, we propose the transfer learning double deep Q-network (TLDDQN) method for controlling the energy storage device within WPS power systems. The TLDDQN method leverages the benefits of transfer learning to quickly adapt to new environments, thereby enhancing the training speed of the double deep Q-network (DDQN) algorithm. Additionally, we introduce an adaptive entropy mechanism integrated with the DDQN algorithm, which is further improved to enhance the training capability of agents.ResultsThe proposed TLDDQN algorithm was applied to a regional WPS power system for experimental simulation of AP balance control. The results indicate that the TLDDQN algorithm trains agents more rapidly compared to the standard DDQN algorithm. Furthermore, the AP balance control method based on TLDDQN can more accurately manage the storage device, thereby reducing the output of thermal power generators more effectively than the particle swarm optimization-based method.DiscussionOverall, the TLDDQN algorithm proposed in this study can provide some insights and theoretical references for research in related fields, especially those requiring decision making.

Virtual energy dispatching method for hybrid microgrid demand response based on Lyapunov optimization

Abstract The hybrid microgrid (HMG) possesses power generation, storage, and distribution capabilities, serving as an integrated foundational structure for advanced localized distribution networks. However, due to significant discrepancies in load demands and energy consumption among different users, the photovoltaic (PV) generation output and end-user load requirements exhibit unpredictability, resulting in relatively low self-sufficiency ratio (SSR) and self-consumption ratio (SCR) of the HMG PV system. To address this issue, this study proposes a virtual energy dispatching method for hybrid microgrid demand response based on Lyapunov optimization. Initially, a bidirectional virtual energy dispatching system model based on energy provider/buyer classification is constructed, optimizing load demands and energy management of both the load side and the main utility grid (MUG) while considering demand response. Subsequently, a Lyapunov optimization-based bidirectional virtual energy dispatching algorithm (LOBVED) is introduced, optimizing queueing systems in stochastic networks based on drift-plus-penalty technique to resolve the dynamic interactions among stability, performance, and energy self-sufficiency in the HMG environment, aiming to maximize the PV SSR and SCR of the HMG while ensuring the reliability of battery energy storage (BES) energy queues. Finally, the effectiveness of the proposed method is validated through MATLAB simulations.

Analysis of the Influence of Variation in Medical Waste Heating Value on the Electrical Energy Output of Generator in Medical Waste Cogeneration Incinerator System using Gatecycle Solver

Abstract Medical waste incinerator is a technology capable of processing and exterminating hazardous medical waste through combustion at temperatures exceeding 800°C. Medical waste exhibits a range of heating values depending on its material composition. The heating value represents the potential heat energy released when a substance undergoes complete combustion. A cogeneration incinerator is designed to treat medical waste while also harnessing the heat of the flue gas that emanates from the combustion process. The flue gas acts as a hot fluid that transfers its heat, causing the water temperature inside the boiler to increase and transform into high-temperature steam. This steam drives a steam turbine connected to a generator to generate electrical energy. The amount of electrical energy generated is influenced by the steam quality produced through the boiler’s water-heating process. This study aims to investigate the influence of variation in the heating value in medical waste on the electrical energy output of the generator in a medical waste cogeneration incinerator system. The range of heating value variations used in the testing varies from 25 MJ/kg to 35 MJ/kg. The variation is tested through thorough simulations using GateCycle software to obtain data on the electrical energy generated. To achieve a maximum electrical energy output of 200 kW, fuel with a total heating value of 32.25 MJ/kg is required. This study is beneficial to be implemented as renewable energy sources based on biomass powerplant systems.

The effect of wind turbines with low rotor power density on power fluctuations

Increasing penetration of variable renewable energy, particularly wind power, necessitates improved grid integration strategies. One option that has received little attention to date is to adapt the design of the turbines in the direction of lower rotor power density. By simulating various turbine models with different specifications at selected locations, the effects on several performance indicators are investigated. Since no single suitable performance indicator exists, a comparison of widely used indicators is made, whereby the introduction of new parameters proves to be useful. Results show that lowering rotor power density through reduced generator output significantly mitigates power fluctuations in the 10-minute range. Considering an entire wind farm instead of an individual turbine, total annual production is slightly decreased, while the required connected load to the grid is substantially reduced.

Design optimization of inner and outer‐rotor PMSGs for X‐ROTOR wind turbines

AbstractThis article presents an analytical design approach for the inner and outer rotor Permanent Magnet Synchronous Generators (PMSGs) that are a key component of wind turbines. By developing the analytical model, sensitivity analysis has been performed to determine the effect of some important geometrical and electromagnetic parameters on the generator's output characteristics. The GA optimization algorithm was used to find optimized designs in terms of efficiency, weight, cost, and temperature rise. In order to find the global optimization solution within the allowed variable range and in accordance with the constraints, a combined objective function has also been defined. In order to validate the analytical model, ANSYS electromagnetic and thermal simulations have been used. With this approach, the designer is provided with a good understanding of the input parameters and constraints in light of the application requirements.

Robust fractional-order load frequency control for hybrid power system concerning high wind penetration

Hybrid power systems concerning wind farms have witnessed significant dynamic characteristic changes due to unpredicted generation output and integration mode conversion between frequency-sensitive wind plants and non-scheduled ones. This work presents a robust fractional-order load frequency control (LFC) method for a multi-area hybrid power system in the presence of wind penetration uncertainties. To characterize the injection effect of wind farms, the power system is described by a model with an uncertain wind penetration factor in a pre-estimated range. Then, an enhancement of Levy’s identification method is developed to convert this model into a fractional-order reduced structure to design the load frequency controller, which is in a form of fractional-order PID with a filter for ease of engineering implementation. The closed-loop stability is analyzed by the stability boundary locus (SBL) method under different wind penetration levels. It is shown that superior frequency response performance and system robustness can be obtained if the controller is designed in terms of the model with the worse-case wind penetration. Illustrative examples including a three-area hybrid power system under a variety of wind penetration variations are given to show the effectiveness of the proposed method.

Hybrid piezo-triboelectric wind energy harvesting mechanism with flag-dragging the cantilever beam vibration

Hybrid power generators will improve the extraction of electrical energy from the wind to develop sustainable and eco-friendly self-powered wireless sensors. A novel piezo-triboelectric flag wind energy harvesting nanogenerator (P-FTENG) is proposed that consists of a piezoelectric cantilever beam and a triboelectric flag. The flag flutters in the wind, inducing vibration in the cantilever beam, thereby converting wind energy into electrical power with piezo and triboelectric effects. Based on the aerodynamic mechanism, a nonlinear flutter theoretical model is derived to evaluate the drag force and pressure of fluid separation on the piezo-triboelectric structure. Furthermore, this paper comprehensively elaborates on the transverse motion of the flag flutter caused by the airflow. The force from the transverse motion excites the cantilever beam. The power output of P-FTENG generation depends on various factors. The optimized flag-shaped configuration may activate the piezoelectric cantilever beam vibration and decrease the crucial flutter wind speed, according to test results. P-FTENG generates a voltage of around 17.8 V when the wind speed is 8.5 m/s. The cantilever beam energy harvester's PEH reaches 32 mW/m2, while Flag-TENG's output power density reaches 37 μW/m2, which charges capacitors and lights the array of LEDs. The hybrid piezo-triboelectric flag nanogenerator fluttering in the wind is promising for powering the function of a calculator or electronic thermometer/hygrometer sensor.

Physical model learning based false data injection attack on power system state estimation

The cyber security of power system state estimation (PSSE) is crucial, and its robustness against evolving false data injection attacks (FDIA) is being rigorously assessed to develop advanced countermeasures. Existing FDIA methods have achieved satisfactory success rates but often fail to align with practical constraints such as the assumption of partial or complete knowledge of the power system network by the attacker, modifications in generator output measurements, and the sparsity of the attacks. This work proposes a near practical, stealthy approach using a deep generative adversarial network-long short-term memory autoencoder (GAN-LSTMAE) learning based sparse FDIA method against AC PSSE, leveraging only measurement data. To evade the bad data detection (BDD) mechanism effectively, an LSTMAE-based PSSE mimic is proposed, further optimizing the GAN-based attack generator to embed the physical laws of the system along with measurement residuals and temporal dependencies of states to the generated false data. The proposed modified training data preparation algorithm, coupled with the attack sub-graph method, defines the optimal attack region while keeping generator output measurements intact. The generated attack is validated extensively using IEEE 14 and 118 bus test benchmarks against various defense techniques, demonstrating high success rates.

Influence of Temperature on Brushless Synchronous Machine Field Winding Interturn Fault Severity Estimation

There are numerous methods for detecting interturn faults (ITFs) in the field winding of synchronous machines (SMs). One effective approach is based on comparing theoretical and measured excitation currents. This method is unaffected by rotor temperature in static excitation SMs. However, this paper investigates the influence of rotor temperature in brushless synchronous machines (BSMs), where rotor temperature significantly impacts the exciter excitation current. Extensive experimental tests were conducted on a special BSM with measurable rotor temperature. Given the challenges of measuring rotor temperature in industrial machines, this paper explores the feasibility of using stator temperature in the exciter field current estimation model. The theoretical exciter field current is calculated using a deep neural network (DNN), which incorporates electrical brushless synchronous generator output values and stator temperature, and it is subsequently compared with the measured exciter field current. This method achieves an error rate below 0.5% under healthy conditions, demonstrating its potential for simple implementation in industrial BSMs for ITF detection.

Electricity contract design and wholesale market outcomes in Australia's National Electricity Market

AbstractThe emergence of variable renewable energy (VRE) technologies has created a range of different energy contracting techniques. Within Australia's National Electricity Market (NEM), Run‐of‐Plant (RoP) Power Purchase Agreements (PPAs) became the most common form of contract with purchasers of wind and solar energy agreeing to pay a fixed price for energy irrespective of when it is produced and, therefore, its actual value to the market. In November 2023, the Commonwealth Government adopted a 32 GW RoP PPA Contract‐for‐Difference (CfD) underwriting policy that aims to effectively shield the generator from market price risk. This article discusses different contract structures and their impact on participant behaviour during periods of material oversupply and negative prices. We find that embedded solar PV exports into the distribution network, which are not required to dynamically participate in the wholesale market, have increased wholesale energy supply, enabling profit maximising vertically integrated renewable firms to drive prices lower in a manner that partially strands the output of RoP PPA CfD generators with a $0/MWh price floor. A key conclusion from our analysis is that requiring embedded solar PV to effectively participate in the wholesale market appears to be a pre‐condition for the efficacy of government‐initiated RoP PPA CfDs.

A security-aware dynamic hosting capacity approach to enhance the integration of renewable generation in distribution networks

Hosting capacity (HC) describes the electricity network’s ability to accommodate distributed generation (DG) without deteriorating electrical performance indicators. Distribution system operators typically express their networks’ HC as a single threshold, called static hosting capacity (SHC). SHC is determined via conservative regulatory criteria, increasing connection costs and time. This paper explores the potential for additional energy injection into the network via dynamic hosting capacity (DHC). A network node’s DHC is derived from the hourly operation of the network, accounting for the time variability of existing distributed generation (DG) output and demand. The methodology considers the network assets’ N-1 contingencies and their probabilities, defining the security-aware DHC (SDHC). The SDHC definition is technologically neutral. Through a case study of a radial medium voltage distribution network, the paper highlights the significant limitations of SHC due to conservative calculation criteria mandated by regulators. Annual injectable energy is increased by 62% to 76% when comparing DHC to SHC. Variations between average DHC and SDHC are below 0.01% due to low N-1 probabilities. This finding points out the potential of dynamic hosting capacity definitions, allowing more efficient use of the existing network and facilitating the integration of new DG capacity with reduced connection costs and time.

A Study the optimisation of smart grid power flow

Optimal Power Flow (OPF) is a fundamental concept in the management and operation of modern electrical power systems, particularly within the context of smart grids. As the demand for electricity continues to grow and the energy landscape evolves towards greater sustainability, the need for efficient, reliable, and adaptive power systems has become more critical than ever. The primary objective of OPF is to determine the most efficient way to operate the power system while satisfying various technical and operational constraints. This involves the optimization of several variables, including generator outputs, voltage levels, and power flows, to minimize costs, reduce losses, and enhance system reliability. Smart grids can enhance their resilience to disturbances, support the efficient use of renewable energy, and provide reliable electricity to consumers.

The Virtual Power Plant Bidding Strategy Model based on Multi-stage Semi-anticipativity Distributionally Robust Optimization

For virtual power plants (VPPs), the strength of their market competitiveness largely depends on their ability to dispatch various distributed resources. An efficient bidding strategy model that can provide superior decision-making solutions is the key to enhancing this competitiveness. This paper focuses on price-making VPP and constructs an efficient three-period VPP bidding strategy model (3P-VPP) with a bi-level optimization framework for the day-ahead energy market by introducing three-period generator output constraints and generator ramping constraints. Furthermore, to address the uncertainty of renewable resources, we introduce the theory of distributionally robust optimization (DRO) and the concept of semi-anticipativity, refining the 3P-VPP model into a multi-stage DRO VPP bidding strategy model based semi-anticipativity (SADRO-VPP). The model possesses dynamic adaptation capabilities, tailoring the optimal decision-making solutions based on the accuracy of the forecast data. When faced with unreliable prediction data, the model will offer a more robust decision-making plan to guard against potential extreme scenarios. Conversely, when the forecast data is credible, the model adeptly utilizes this information to provide more economical decision-making strategies. The experimental results demonstrate that the 3P-VPP model exhibits higher computational efficiency compared to traditional model, while the SADRO-VPP model provides effective scheduling solutions and bidding strategies for VPP.