Generation Units Research Articles

A distributed coordinated control strategy for isolated AC microgrids based on consensus algorithm considering communication delay

AbstractIn the multi‐paralleled converter microgrid system, the traditional hierarchical control strategy can eliminate the bus voltage amplitude and frequency deviation from the rated value. However, isolated AC microgrids may face extreme scenarios such as communication delays and interruptions in data transmission due to the use of low‐bandwidth communication (LBC) lines. Additionally, the inconsistent line impedance of each distributed generation (DG) unit may result in the inaccurate division of reactive power in multi‐paralleled converter systems, thus affecting system stability. To address these issues, this paper presents a distributed coordinated control strategy for isolated AC microgrids based on the consensus algorithm. The proposed strategy first replaces LBC lines with a filter to alleviate the effects of communication delays. A small‐signal model is established in its state space, and stability of the microgrid system under the proposed control strategy is verified through eigenvalue analysis. Furthermore, based on the above theoretical analysis, a consensus algorithm is introduced, and a distributed control strategy for isolated AC microgrids based on the consensus algorithm is proposed to solve the issue of inaccurate equalization of the system's reactive power. Finally, factors that influence the dynamic convergence performance of the consensus algorithm are analyzed through simulation in PLECS software. Also, the maximum tolerable communication delays of the microgrid system under different communication topologies are also compared, and the system's robustness is evaluated under the condition of sudden communication interruption in a DG unit and sudden weather variation. These analyses confirmed the robustness of the proposed strategy against communication delays, output power fluctuation, and communication interruptions.

A hybrid chaotic bat algorithm for optimal placement and sizing of dg units in radial distribution networks

Appropriate location and sizing of distributed generation (DG) units in a radial distribution system (RDS) play a significant role in the improvement of its overall performance. Indeed, proper DG placement and sizing help in maintaining the balance between power supply and demand, decreasing energy losses and enhancing voltage profile. Within this context, this paper presents an effective approach for the determination of the optimal location and the appropriate capacity of a photovoltaic distributed generation (PVDG) unit in RDSs. In the suggested approach, the best position of the PVDG is selected using the loss sensitivity factor (LSF). Meanwhile, a new optimization technique incorporating chaos, bats’ self-adaptive compensation, and Doppler effect into the original bat algorithm (BA) is developed to find the optimal size of the PVDG unit. The optimal PVDG size is optimally determined in such a way that total active power losses of the RDS is reduced and voltage profile is enhanced. The performance of the proposed optimization technique, symbolized by (CSA-DC)BA, is evaluated using various benchmark functions. Moreover, the applicability and effectiveness of the suggested approach are verified on the IEEE 33-bus and the IEEE 69-bus RDSs. Obtained results revealed that the proposed (CSA-DC)BA outperforms the comparison optimization techniques.

Reliability enhancement through optimal placement of photovoltaic power plant and battery energy storage in distribution system

Integration of distributed generation (DG) units could reduce the duration of power outage for a certain number of consumers in distribution networks after a network failure. Prerequisites are that i) the DG unit has the technical characteristics that enable its islanded operation, the degree of automation of the distribution network allows it and ii) it is in accordance with the current technical regulations of the country. The extent to which integration of distributed sources can improve reliability depends on the DG size, type, and the point of common coupling. Besides, an energy storage system could be installed along with DG unit, so that energy supply availability during fault periods can be secured. This paper proposes an algorithm based on mixed-integer linear programming (MILP) approach for optimal placement of photovoltaic power plant (PVPP) and battery energy storage system (BESS). The optimal location is determined so that expected non supplied energy is minimized. The uncertainties in the estimation of production of PVPP, load and BESS state of charge (SoC) were taken into account by Monte Carlo simulations (MCS).

Comprehensive evaluation of a new integrated ORC-VCR system with a thermoelectric generator unit combining sustainable energies for hydrogen production

Recently, there has been a growing emphasis on the efficient generation of energy from sustainable sources. This study aims to evaluate a new combined Organic Rankine Cycle-Vapor Compression Refrigeration (ORC-VCR) system from energy, exergy, economics, and environmental perspectives. The system has been designed to cogenerate power, heat, cooling, and hydrogen using solar and biomass energy. To enhance efficiency, a Thermoelectric Generator (TEG) unit has been integrated into the system. The power produced by the TEG is used to generate hydrogen in the Proton Exchange Membrane Electrolyser (PEME). To ensure the continuous operation of the solar collector, especially under conditions of unstable and intermittent solar radiation, a thermal storage tank and a biomass-fired boiler utilizing four different types of biomass (wood, paper, municipal solid waste, and sawdust) have been incorporated. The primary objective of this research is to investigate the impact of employing various biomass types on the system's performance. The results indicate that in terms of energy and exergy, municipal solid waste yields the highest system efficiency. However, from an environmental viewpoint, paper incurs lower pollutant emissions. The proposed hybrid system can produce 145.5 kW of cooling, 513.6 kW of heat, 37.93 kW of electricity, and 0.756 kg/h hydrogen with energy and exergy efficiencies of 32.73% and 14.18%, respectively. The estimated total equipment cost is 7.67$/h, with a CO2 emission of 0.0029 kg/s.

Constant power adaptive control of photovoltaic generator unit considering equivalent charge state

Constant power adaptive control of photovoltaic generator unit considering equivalent charge state

A dispatch model coordinated power and heating system in electricity market considering resource aggregation and decomposition

Abstract With the high proportion of installed capacity of renewable energy, the stability and robustness of the new power system need to be improved. The integration of the power system and heating system is an orientation to promote the ability of wind power accommodation. This thesis first introduces the integrated model considering power and heating systems. However, the integrated model requires the topology in the heat network and the parameters of heat devices, which is difficult to access and complicated to solve. Under this situation, the heat devices are aggregated into an equivalent generation unit in the power network. A coordinated dispatched model based on resource aggregation and decomposition is then proposed. In simulation, the economy, flexibility, and security of a given formulation are investigated through marginal price and units’ behavior.

Dynamic optimal allocation of energy storage systems integrated within photovoltaic based on a dual timescale dynamics model

Energy storage systems (ESSs) operate as independent market participants and collaborate with photovoltaic (PV) generation units to enhance the flexible power supply capabilities of PV units. However, the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood. This study introduces a dual-timescale dynamics model that integrates a spot market clearing (SMC) model into a system dynamics (SD) model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems (IESSs) with that of an ESS integrated within a PV (PV-ESS). Furthermore, this study aims to ascertain the optimal allocation of the PV-ESS. First, SD and SMC models were set up. Second, the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem, a dual-timescale model was constructed. Finally, a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation. Additionally, negative electricity prices were considered during clearing processes. The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS, helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs.

Islanded microgrid: hybrid energy resilience optimization

To maintain a dependable and sustainable power supply in a microgrid system, it is crucial to combine renewable energy sources with hybrid energy backup. To achieve maximum output power from solar source, a high gain DC-DC boost converter is managed using the dual Kalman filter based perturb and observe approach. A sliding mode current controller at a three phase inverter with an LC filter is intended to follow an undisturbed reference voltage. To effectively manage the power flow and optimize the utilization of available resources, a robust power management algorithm is required. The novel power management algorithm for a solo operated renewable distribution generation unit with hybrid energy backup in a microgrid is introduced. The algorithm aims to dynamically allocate power among various sources, storage systems, and loads, considering their characteristics and the overall system constraints. The algorithm utilizes sliding mode control techniques to regulate the current flow from the renewable generator and effectively manage the power allocation among different energy sources, storage systems, and loads.

A new fault detection and relay coordination technique for low voltage DC microgrid

Integration of various distributed generation units in the DC distribution network creates an outsized impact on magnitude and direction of fault current. The specific behaviour of fault current during a short circuit fault demands quick and reliable detection and isolation of fault, which poses a significant challenge in developing an effective protection and coordination scheme for DC microgrids. In this regard, a new fault detection and relay coordination algorithm is proposed for a Low voltage DC (LVDC) microgrid, which utilizes inverse time relay characteristics based on variance of fault current. While the relay responds and picks up during both internal and external faults, its operation is automatically limited upon operation of the downstream circuit breaker without the need for any communication channel. The efficacy of the proposed strategy is evaluated by modelling a mesh-connected DC microgrid network using PSCAD/EMTDC software. The protection coordination problem is developed as a constraint non-linear optimization problem to determine optimal relay settings. The proposed approach has been tested by simulating various types of faults at different fault locations and fault resistances along with various microgrid operating modes. The results suggest that the proposed strategy stands out by optimizing relay operating time and providing backup protection while upholding relay coordination. A comparative assessment of the proposed technique with existing ones proves its effectiveness in terms of swift relay tripping time, better immunity against noise, and independency against microgrid operating mode and topology.

ANALYSIS OF THE DESTRUCTION OF UKRAINIAN NUCLEAR FUEL CYCLE ENERGY FACILITIES AS A RESULT OF RUSSIAN MILITARY OPERATIONS

Problem statement. Due to Russia's ongoing terrorist attacks on Ukraine's energy facilities, the energy system was damaged, resulting in blackouts and rolling blackouts across the country. The destruction of power capacities resulted in the unstable operation of certain critical infrastructure units for electricity generation at thermal, nuclear and hydroelectric power plants and the introduction of forced hourly blackout schedules. Seizure of the Chornobyl Nuclear Power Plant, occupation and full control of the Zaporizhzhia Nuclear Power Plant and the Zaporizhzhia Thermal Power Plant (TPP). Complete destruction of the Kakhovka hydroelectric power station (HPP), partial destruction of the Dnipro HPP, Dniester HPP and Kaniv HPP. During the hostilities, Kryvyi Rih and Prydniprovia TPPs were damaged, Luhansk TPP (90–100 %), Vuhlehirsk TPP (90–100 %), Zmiiv TPP (up to 90 %) were completely destroyed, Burshtyn TPP (80–90 %) and Ladyzhyn TPP (70–95 %) were almost completely destroyed. The purpose of the article is to identify and analyse the damage to Ukrainian TPPs, HPPs and NPPs caused by the Russian military actions against Ukraine and to determine possible consequences for the nuclear fuel cycle of our country and possible prospects for the transition to new nuclear reactors of European and global fuel cell manufacturers, to improve equipment and stabilise the energy market in the context of military operations. Conclusion. In the course of the analysis of potential hazards at radiation hazardous facilities (RHF) in Ukraine, it was determined that the main threat is accidents at RHF with release of radiation into the atmosphere, lithosphere and hydrosphere. In the light of recent events regarding the military seizure of the Chornobyl NPP and Zaporizhzhya NPP, measurements using standard methods of research, screening and monitoring are not possible, and access of IAEA representatives is limited, which is why the latest measurement methods should be used with the use of autonomous remote-controlled ground, surface and air drones in automatic object or sample measurement of radiation parameters at RNS. It is the use of autonomous vehicles (drones) that is currently the most pressing issue of radiation safety not only in the nuclear power industry but also in the entire nuclear fuel cycle as part of the environmental safety of Ukraine. In case of seizure of a nuclear facility or limited access to it, research methods should be changed. If the facility is partially seized or access is available, then express methods of radiation contamination registration or sampling can be used and the method of laboratory research can be used in the future. However, if a nuclear power plant or a facility that is part of the nuclear fuel cycle of Ukraine is captured and access to it is impossible, then only methods of remote measurement of the radiation situation at and around nuclear facilities using ground and airborne autonomous vehicles remain. For further research, it is necessary to identify technical possibilities for using Ukraine's own mineral resources and switching from Soviet RBMK-1000, VVER-440 and VVER-1000 reactors with fuel elements supplied to us from the Russian Federation to newer types of European and American reactors with appropriate fuel elements.

A novel balanced teaching-learning-based optimization algorithm for optimal design of high efficiency plate-fin heat exchanger

Plate-fin heat exchangers (PFHE) are crucial for engineering applications. Pursuing high performance in PFHE design through optimal methods is common, yet challenging for traditional optimization approaches. Meta-heuristic algorithms (MAs) continuously achieve significant breakthroughs in this problem. This paper proposes a novel balanced teaching-learning-based optimization (NBTLBO) algorithm for PFHE optimal design, which optimizes entropy generation units and heat transfer area using objective functions derived from thermal modeling. NBTLBO introduces two phases, as straightforward as that of TLBO, that collaborate to balance exploitation and exploration, offering substantial superiority over TLBO. This addresses the limitations of existing TLBO studies that involve complex multiple phases and neglect phase interactions. The experiments reveal that NBTLBO achieves the best ranking in CEC 2014 and 2020 benchmarks compared with many popular MAs and even their enhanced variants. It also yields the best results across all four PFHE design examples, showing significant improvement over TLBO and other existing methods. A non-dominated sorting NBTLBO algorithm is developed for multi-objective PFHE design, achieving improved Pareto results compared to existing methods. It verifies that NBTLBO, despite its simplicity akin to TLBO, effectively balances exploitation and exploration, thereby achieving notably enhanced performance, rendering it a preferred option for tackling global optimization challenges.

On the role of sliding friction effect in nonlinear tri-hybrid vibration-based energy harvesting

This work aims to develop an experimental investigation into the effectiveness of the sliding-mode approach for hybrid vibration-based energy harvesting. A proposed sliding-mode triboelectric-electromagnetic-piezoelectric energy harvesting model involves a cantilever beam with a tip mass exposed to magnetic and frictional forces. The experimental findings indicate that the system can achieve its peak inter-well oscillation output within a low-frequency range of 4Hz–6Hz. Friction has a lesser impact on the open-circuit voltage output at an excitation acceleration of 1.5g compared with 1g. The distribution of tri-stability changes with the presence of friction. This model provides a deeper understanding of the influence of the dry friction coefficient (0.2–0.5) on the interactive behaviors of different generator units.

Strategic Integration and Configuration of Distributed Generators Units in Distribution Networks: An Overview

Strategic Integration and Configuration of Distributed Generators Units in Distribution Networks: An Overview

Local Energy Community to Support Hydrogen Production and Network Flexibility

This paper deals with the optimal scheduling of the resources of a renewable energy community, whose coordination is aimed at providing flexibility services to the electrical distribution network. The available resources are renewable generation units, battery energy storage systems, dispatchable loads, and power-to-hydrogen systems. The main purposes behind the proposed strategy are enhancement of self-consumption and hydrogen production from local resources and the maximization of the economic benefits derived from both the selling of hydrogen and the subsidies given to the community for the shared energy. The proposed approach is formulated as an economic problem accounting for the perspectives of both community members and the distribution system operator. In more detail, a mixed-integer constrained non-linear optimization problem is formulated. Technical constraints related to the resources and the power flows in the electrical grid are considered. Numerical applications allow for verifying the effectiveness of the procedure. The results show that it is possible to increase self-consumption and the production of green hydrogen while providing flexibility services through the exploitation of community resources in terms of active and reactive power support. More specifically, the application of the proposed strategy to different case studies showed that daily revenues of up to EUR 1000 for each MW of renewable energy generation installed can be obtained. This value includes the benefit obtained thanks to the provision of flexibility services, which contribute about 58% of the total.

Trade-off between frequency stability and renewable generation – Studying virtual inertia from solar PV and operating stability constraints

The significant increase in solar PV and wind capacity worldwide has raised growing concerns about power system frequency stability in several countries. One reason for this is that these renewable technologies offer almost no inertial response. However, there are some options available to address this issue. Renewables could now offer virtual inertia. Also, power system operators could impose frequency stability constraints as part of the generation Unit Commitment (UC). These options could potentially have a large impact on renewable generation and hence also on energy costs and system emissions. In this paper, we study two key options improving stability, inertia from solar PV generators supplied by virtual synchronous machines, and frequency stability constraints as part of the UC. We assess N-1 generation contingencies for the interconnected power systems of Chile, Argentina, and Peru. We found that the option of virtual inertia reduces the violation of the RoCoF in hours of high solar generation, without affecting renewable generation. On the other hand, frequency stability constraints ensure non-violation of all stability parameters but reduce renewable generation by 12 %. This reduction increases total operating costs by about 35 %, even when the constraints are combined with virtual inertia and battery support.

Subsea DC power cable modelling and computational validation

The growing number of applications involving the transmission of electrical energy from AC sources for the purpose of interconnecting onshore/offshore AC networks has motivated several investigations to define the most appropriate topological and parametric structures for the purposes in question. In this scenario, the issue of electrical supplies for platforms for oil extraction arises to reduce greenhouse gases produced by local generation units. The strategies used may consist of AC or DC transmission systems, being the latter and attractive from the point of view of technical and operational feasibility. A DC interconnection complex comprises several units, among which the submarine cables stand out. For carrying out performance studies of the system as a whole, the performance assessments require, in addition to the sending and receiving converter stations, DC interconnection cable reliable models. To this end, together with propositions of equivalent circuits and their representative parameters, this article carries out a comparative computational analysis of the calculated parameters with those commercially available.

Optimization of the cold‐side heat exchanger design to improve the performance of the motorcycle exhaust thermoelectric generator

AbstractThis study optimizes the main parameters of the cold‐side heat exchanger (CHE) longitudinal fin, including fin quantity (Nf), fin thickness (Tf), and fin height (Hf), to enhance the performance of motorcycle exhaust thermoelectric generator units (TGUs) utilizing the computational fluid dynamics approach. The investigation shows that these parameters significantly affect the dissipation area of the CHE heat and the outside air velocity distribution in fin gaps, resulting in the fluctuation of TGU output power. The output power increases with respect to Hf; nevertheless, it first increases as Nf and Tf increase but decreases dramatically when Nf or Tf becomes too large. Besides, Hf significantly affects output power, and its impact is almost independent of Tf and Nf, and vice versa; meanwhile, Tf and Nf have a strong relation. This study proposes two Hf of 40 and 60 mm along with the optimal Tf of 1 mm and Nf of 29, providing significantly high output power, low weight, and compact size for TGU. This work contributes insight into the effect of CHE parameters on the TGU performance, and it is a crucial case study for selecting suitable heat sink parameters for TGU, considering practical requirements and conditions.

Ambient Moisture-Driven Self-Powered Iontophoresis Patch for Enhanced Transdermal Drug Delivery.

Iontophoretic transdermal drug delivery (TDD) devices are known to enhance the transdermal transport of drugs. However, conventional transdermal iontophoretic devices require external power sources, wired connections, or mechanical parts, which reduce the comfort level for patients during extended use. In this work, a self-powered, wearable transdermal iontophoretic patch (TIP) is proposed by harvesting ambient humidity for energy generation, enabling controlled TDD. This patch primarily uses moist-electric generators (MEGs) as its power source, thus obviating the need for complex power management modules and mechanical components. A single MEG unit can produce an open-circuit voltage of 0.80V and a short-circuit current of 11.65µA under the condition of 80% relative humidity. Amplification of the electrical output is feasible by connecting multiple generator units in series and parallel, facilitating the powering of certain commercial electronic devices. Subsequently, the MEG array is integrated with the TDD circuit to create the wearable TIP. After 20 min of application, the depth of drug penetration through the skin is observed to increase threefold. The effective promotion effect of TIP on the transdermal delivery of ionized drugs is corroborated by simulations and experiments. This wearable TIP offers a simple, noninvasive solution for TDD.

Characterizing multi-pollutant emission impacts of sulfur reduction strategies from coal power plants

Fuel combustion for electricity generation emits a mix of health- and climate-relevant air emissions, with the potential for technology or fuel switching to impact multiple emissions together. While there has been extensive research on the co-benefits of climate policies on air quality improvements, few studies have quantified the effect of air pollution controls on carbon emissions. Here we evaluate three multi-pollutant emission reduction strategies, focused on sulfur dioxide (SO2) controls in the electricity sector. Traditional ‘add-on’ pollution controls like flue gas desulfurization (FGD) reduce SO2 emissions from coal combustion but increase emissions of nitrogen oxides (NO X ), volatile organic compounds (VOCs), fine particulate matter (PM2.5), and carbon dioxide (CO2) due to heat efficiency loss. Fuel switching from coal to natural gas and renewables potentially reduces all pollutants. We identified 135 electricity generation units (EGUs) without SO2 controls in the contiguous US in 2017 and quantified the unit-level emission changes using pollution control efficiencies, emission rates, fuel heat input, and electricity load. A cost-benefit analysis is conducted, considering pollution control costs, fuel costs, capital and operation and maintenance (O&M) costs, the monetized health benefits from avoided multi-pollutant, and the social cost of carbon as the benefit for carbon reduction. We find that add-on SO2 controls result in an average annual net benefit of $179.3 million (95% CI: $137.5-$221.0 million) per EGU, fuel switching from coal to natural gas, $432.7 million (95% CI: $366.4-$498.9 million) per EGU; and fuel switching from coal to renewable energy sources, $537.9 million (95% CI: $457.1-$618.9 million) per EGU. Our results highlight multi-pollutant emission reduction strategy as a cost-effective way to synergistically control air pollution and mitigate climate change.

Adaptive extreme learning machine using soft computing fuzzy propositions—Validating operating state of solar energy system

In this paper soft computing fuzzy proposition/rule based adaptive extreme learning is proposed. This article illustrates the impact of agricultural solar panel (AgSP) and its operating condition/state during the farming seasons and hence safety of energy system sustained. Extreme learning machine (ExLM) used as learning machine (LM) for artificial intelligence (AI) training to predict the operating state of applied AgSP green energy unit. soft computing Fuzzy rules are framed for every instant of leaning machine to identifying the lower mismatch conditions and avoid the false prediction. Higher and lower limits of adaptive resonance theory (ART) support LM (ART-LM) is properly used by providing unusual dust formation (UnDFR) data and usual/normal dust formation (UsDFR) data for AI training. Outcome of AI neural net is determined by resonant neuron net (RSNN). Predicted variable from ART-LM is calculated with higher and lower limits of dataset captured from applied energy common node (CN). Many test conditions of dust formation on solar photovoltaic (PV) has been validated to predict the state of applied energy system. For any UnDFR confirmation, entire AgSP based distributed generator (DG) unit will isolate from common node/utility grid. Proposed ART-LM based AI algorithm has been validated even for charging state events of electrical vehicle (EV) during the abnormal conditions.