Optimal Operating State Research Articles

Distribution network voltage control considering virtual power plants cooperative optimization with transactive energy

The integration of massive distributed energy resources (DERs) brings new challenges for the distribution networks (DNs) operation control. To control DERs effectively, virtual power plants (VPPs) are introduced into DNs. Conventionally, the control strategy of VPPs is formulated as an optimization model without considering voltage control and transactive energy (TE) in DNs. In this article, multiple VPPs cooperative optimization with TE is studied and cast as a nonlinear programming model. It aims to optimize the voltage control of DNs and the operation profits of VPPs. To dynamically capture the optimal operation state of VPPs in DNs, the distribution locational marginal pricing and DN partition are incorporated into the model. In general, this type of model is computationally challenging and mainly solved by approximate algorithms with low efficiency. Recently, multi-agent deep reinforcement learning (MADRL) is emerging as a scalable and promising data-driven approach. We propose an augmented MADRL solution to this problem. In the training stage, one soft actor-critic learning agent augmented by parameter sharing strategy is employed to simultaneously capture the state-action patterns of multiple VPPs. The prioritized experience replay technique is applied to improve learning efficiency and speed. Numerical test results demonstrate the superiority of the proposed method over benchmark methods.

Performance optimization and techno-economic analysis of a novel geothermal system

A novel geothermal system that has high efficiency and economic benefits was proposed in this study. The proposed geothermal system includes an organic Rankine cycle, absorption refrigeration subsystem, and heating/hot water subsystem. The selection of working fluid, performance optimization, and techno-economic analysis of the geothermal system were conducted by establishing mathematical models. The thermodynamic performance was enhanced by using evaporators in series and the separation-flash process. And the thermodynamic performance of the proposed system was investigated with five working fluids. The ratio of mass flow rate, separate pressure, and flash pressure were optimized using the stepwise multi-objective optimization technology. The results show that R245fa was the optimal working fluid. The system can reach its optimal operating state when the ratio of mass flow rate is 2.5, separate pressure is 2500 kPa and flash pressure is 1000 kPa. The largest power generation, thermal efficiency, exergy efficiency, and the lowest irreversible loss of the optimized system are 962.5 kW, 13.64 %, 35.74 %, and 4248 kW, respectively. The techno-economic analysis shows that the total investment cost of the novel geothermal system is 8.173–9.132 million dollars, the annual revenue is 2.451–2.608 million dollars per year, and the static payback period is 3.134–3.726 years.

Chaos-Based NMPC as a Novel MPPT Approach for Organıc Photovoltaıcs

Environmental degradation, alongside the dire consequences of climate change such as the melting of glaciers and rising sea levels, highlight the severe challenges associated with reliance on fossil fuels. As such, the shift towards sustainable and clean renewable energy sources, like photovoltaic systems, is becoming increasingly critical. This paper introduces a novel approach leveraging a chaotic-based nonlinear model predictive control to maximize the power output from organic photovoltaic cells. This method is distinguished by its rapid tracking capabilities and its effectiveness in enhancing the distribution network's performance under fault conditions. Utilizing a feedback-driven recursive control strategy, this approach efficiently predicts and adjusts to the optimal operating state, thereby minimizing its cost function. It comprises two primary phases: estimating the reference point and subsequently adjusting the operating point in alignment with this reference. In this process, the Lagrange function plays a pivotal role in optimizing the estimator's performance, while a chaotic neural network model predictive controller manages the boost converter's operation. Implementing this chaos-based nonlinear model predictive controller has successfully reduced overvoltage incidents by more than 1.3%. Notably, in the absence of such control methods, the penetration of OPV panels leads to voltage fluctuations beyond acceptable limits. The findings demonstrate that, alongside a decrease in network losses, there is an increase in the capacity of distribution feeders and a notable enhancement in the system's overall efficiency.

Thermodynamic and thermoeconomic analysis and optimization of a renewable-based hybrid system for power, hydrogen, and freshwater production

To address environmental pollution effectively, it is crucial to promote the increased utilization of renewable energy sources. Furthermore, an appealing opportunity arises from enhancing the efficiency of renewable-based power plants while diversifying their product output. This study introduces a hybrid system that revolves around renewable resources, with a primary focus on evaluating power generation, hydrogen production, and freshwater extraction. The designed system seamlessly integrates a flash-binary geothermal power plant with a solar system, incorporating cutting-edge phase-change material storage technology. Hydrogen is generated through a combination of steam-methanol reforming and pressure swing adsorption processes. Freshwater is procured utilizing humidification-dehumidification and multi-effect desalination units. In terms of power generation, the system leverages the capabilities of the geothermal power plant alongside a modified Kalina cycle. The performance of this integrated system is rigorously evaluated through a combination of thermodynamic and thermoeconomic approaches. An exergy-economic optimization scenario is employed to determine the most efficient operational mode. The results of this comprehensive analysis reveal that the system can produce 0.0224 kg/s of hydrogen, 8.017 kg/s of freshwater, and generate 215.9 W of net power. Impressively, it achieves an exergy efficiency of 58.3% at a unit cost of $32.23/GJ in the base mode. Furthermore, the optimal operating state boosts efficiency to 60.59%, with a unit cost of $32.22/GJ. Notably, adjustments in the selling price of hydrogen have a significant impact on the system's financial metrics. As the price of hydrogen rises from $5 to $6/kg, the payback period reduces from 4 to 3 years, and the net present value surges from $5.81 million to $10.16 million.

EVSE Effectiveness in Multi-Unit Residential Buildings Using Composite Optimization and Heuristic Search

The effectiveness of electric vehicle supply equipment (EVSE) is a very important factor in multi-unit residential buildings (MRBs) when planning to invest in an electric vehicle (EV). However, the expected benefits depend on not only technology but also need many non-functional requirements such as building facilities, electrical infrastructure, operating costs, and energy demand management of EVSE. We discuss the functional and non-functional constraints in utilizing EVSEs in MRBs and addresses them by constructing a composite optimization problem with a heuristic boundary selection. The proposed model urges the use of electrical safety codes –to mitigate the hazard risk– and promotes deploying a flexible and scalable energy management system (EMS) to schedule, reserve, and tune the charging sessions. This article provides an effective EMS for mitigating the energy demand growth caused by EV charging on MRBs, by finding the equilibrium number of EVSEs and their energy usage through a heuristic search. The proposed EMS is reinforced by embedding machine learning tools such as k-means, silhouette scoring, heuristic search, and autoregressive model to adjust the optimal operational state of EVSEs. We delineated the proposed framework and the outcomes using two case studies with real data. The results showed that more EVSEs can be utilized in MRBs and 100% EVs can be fully charged with a lower capital cost and no additional energy demand cost.

Low-carbon economic operation of energy hub integrated with linearization model and nodal energy-carbon price

Energy hub has positive significance in absorbing renewable energy, improving energy utilization efficiency and reducing carbon emissions. Considering both linearization energy hub model and nodal integrated energy-carbon price, a low-carbon economic operation model of energy hub is constructed by taking advantage of the bi-level optimization structure. First, in order to simplify the modeling procedures and improve the solving efficiency, reversibility judgment criteria and linearization processes in energy hub modeling are improved. Then, a nodal integrated energy-carbon pricing strategy is proposed and calculated according to flow tracing method and carbon emission flow method. Additionally, the bi-level optimization model and solution procedure are presented to obtain the optimal operating states of energy hubs. At last, for the simulation results, the characteristics and the influence of nodal price on energy consumption behaviors of energy hubs are analyzed, the proposed model is effective to reduce the carbon emissions and operating costs.

Multi-working condition performance assessment based on knowledge extraction of optimal operating states for continuous annealing processes

Performance assessment is a key to strip quality improvement and energy consumption reduction of Continuous Annealing Processes (CAP). However, existing methods focus on performing the assessment under a single working condition, and the assessment accuracy must be improved. This study proposes a new multi-working-condition performance assessment method based on the knowledge extraction of the optimal operating states for CAP. First, a mechanism–data fusion-based assessment index construction method is proposed for the key parameter selection. Second, a knowledge extraction strategy for the optimal operating states under multiple working conditions is proposed to construct a benchmark library. Third, a knowledge-enhanced assessment model is built to achieve qualitative performance evaluation and quantitative non-optimal traceability. The experiment based on the process data shows the effectiveness of assessing the operating performance, providing decision guidance for strip quality improvement and energy consumption reduction.

Optimization of operating conditions in the steam turbine blade cascade using the black-box method

Water droplets cause corrosion and erosion, condensation loss, and thermal efficiency reduction in low-pressure steam turbines. In this study, multi-objective optimization was carried out using the black-box method through the automatic linking of a genetic algorithm (GA) and a computational fluid dynamics (CFD) code to find the optimal values of two design variables (inlet stagnation temperature and cascade pressure ratio) to reduce wetness in the last stages of turbines. The wet steam flow numerical model was used to calculate the optimization parameters, including wetness fraction rate, mean droplet radius, erosion rate, condensation loss rate, kinetic energy rate, and mass flow rate. Examining the validation results showed a good agreement between the experimental data and the numerical outcomes. According to the optimization results, the inlet stagnation temperature and the cascade pressure ratio were proposed to be 388.67 (K) and 0.55 (−), respectively. In particular, the suggested optimal temperature and pressure ratio improved the liquid mass fraction and mean droplet radius by about 32% and 29%, respectively. Also, in the identified optimal operating state, the ratios of erosion, condensation loss, and kinetic energy fell by 76%, 32.7%, and 15.85%, respectively, while the mass flow rate ratio rose by 0.68%.

Study and implementation of a novel high‐efficiency piezoelectric energy harvesting circuit

SummaryThis paper proposes a self‐powered SSHI piezoelectric energy extraction circuit combined with Modified FOCV‐MPPT technology, which analyzes the electrical characteristics of S‐SSHI operation. The paper demonstrates that the determination of the optimal operating state of S‐SSHI can be determined independently of the open‐circuit voltage. Based on the key parameters obtained from the theoretical analysis, the FOCV‐MPPT strategy is modified, and the corresponding functional circuit is designed. This strategy serves as an intermediate stage between S‐SSHI and the load, addressing the dependence of S‐SSHI output power on the load while avoiding the power supply interruption issue of the traditional FOCV‐MPPT strategy. In addition, the higher output power provided by S‐SSHI improves the utilization of MPPT technology. The correctness of the investigations is verified through the simulation and experiments. By using self‐powered S‐SSHI, the proposed circuit increases the output power of PEH by a factor of 2.46. In the open‐circuit voltage range of 2–7 V, the average maximum power tracking efficiency achieved 90.1%. The maximum tracking efficiency can reach 98%.

Event-triggered reactive power tracking optimization for second-level power fluctuations of renewables and stochastic loads

The power fluctuations of renewable energy sources and stochastic loads (RESL) makes it difficult for system operators to achieve optimal control. This paper proposes an event-triggered reactive power optimization strategy based on the event-triggered algorithm to rapidly track the optimal operating states for accommodating the second-level power fluctuations of RESL, with less computation and communication. Firstly, on a longer temporal scale, event-triggered multi-stage global optimization (MGO) is performed on a rolling basis to obtain the time-interval optimal operating states according to chance constraint programming, and the system operation security is improved by narrowing the voltage-feasible regions. Next, at each moment within the time interval, event-triggered local tracking optimization (LTO) is performed at each controller of RESL by rapidly regulating reactive power to adapt to its second-level active power fluctuations. The LTO is completed at the local level by using the sensitivity information from MGO, without running the optimal power flow program, to relieve the computation and communication burdens. Then, event-triggered LTOs performed at all controllers synchronously form the system-level real-time optimization. The unnecessary MGO and LTO are skipped by using the event-triggered algorithm when small power fluctuations are occurring. Finally, a case study on the IEEE-39 bus power system validates the feasibility of the proposed strategy.

Multiscale analysis and cost optimization of a reversible solid oxide cell system with coordinated degradation and efficiency considering Ni-particle coarsening

In H2-supported microgrids, the electrochemical performance deterioration of reversible solid oxide cell (rSOC) is primarily caused by the coarsening of Ni particles. Specifically, there are still various unresolved questions from microdegradation mechanisms to macroperformance optimization of rSOC systems that require further investigation and solutions. In this study, a novel multiscale-multiphysics field model consisting of a modified Ni coarsening model incorporated into the rSOC system model is used to analyze the effects of operating modes and conditions on degradation behavior. The issue of coordination between degradation and efficiency is settled for the first time by introducing minimization of the total operating cost of an rSOC system. The results indicate that the degradation rate (rdeg) is higher in the fuel cell mode, and this is related to the partial pressure ratio pH2/pH2O within H2-electrode pores. Furthermore, rdeg is found to be most affected by the PEN temperature, followed by the fuel utilization and fuel molar fraction. The minimum total cost is approximately 0.49 $/kWh taken at a low system intake (including H2O, H2, and air) with higher temperatures. The optimization weight shifts gradually toward efficiency owing to the higher gas supply cost. These findings provide a valuable reference for the selection of the optimal operating state for the rSOC system considering degradation.

Energy and Exergy Analysis of Hydrogen-Based Fluidized Bed Direct Reduction towards Efficient Fossil-Free Ironmaking

Hydrogen-based fluidized bed direct reduction (H-FBDR) is an important and promising route for fossil-free ironmaking. In this study, to achieve the optimal operation state of energy use and exergy efficiency, the influences of the metallization process and the ratios of H2 injected on the energy and exergy flows in the H-FBDR process are studied. The results show that the thermodynamically designed two-stage reduction process (first: Fe2O3→FeO; second: FeO→Fe) requires a smaller H2 quantity than other metallization processes. According to the mass, energy, and exergy balance analyses, variations in the H2 consumption, exergy destruction, and energy/exergy losses of the overall system, iron ore preheater (F1), fluidized bed reactor system (R), heat exchanger (E), and gas preheater (F2) with different ratios of H2 injected (η) are derived. The total H2 consumption, total exergy destruction, and energy/exergy losses rise with increasing η, and sharp increases are observed from η = 1.3 to η = 1.8. The exergy efficiencies (φ) can be ranked as φR > φE > φF1 ≈ φF2, and the exergy destruction in components F1 and F2 is mainly caused by the combustion reaction, whereas physical exergy destruction dominates for components R and E. The performances of components F1, E, and F2 degrade from η = 1.0 to η = 1.8, and significant degradation arises when η exceeds 1.3. Thus, considering the H2 consumption, thermodynamic efficiency, and energy/exergy losses, the ratio of H2 injected should be set below 1.3. Notably, although the energy loss in the H-FBDR system is 2 GJ/h at η = 1.3, the exergy loss is only 360 MJ/h, in which the recycled gases from component E occupy 320 MJ/h, whereas the total exergy destruction is 900 MJ/h. Therefore, improving the performance of operation units, particularly the components F1 and F2, is as important as recovering the heat loss from component E for optimizing the H-FBDR process.

Boundary Guidance Strategy and Method for Urban Traffic Congestion Region Management in Internet of Vehicles Environment

Accelerated urbanization has increased regional traffic congestion. To alleviate traffic congestion in a homogeneous road network, combined with the advantage of real-time traffic information obtained by the Internet of Vehicles (IoVs), a boundary guidance strategy for traffic congestion region is proposed. The strategy considers the optimal operation state of traffic congestion region and is divided into two categories according to different destinations of traffic demands. Meanwhile, a method for the boundary guidance strategy is presented in which the macroscopic fundamental diagram (MFD) is used to determine the optimal accumulation, a traffic flow equilibrium model is established to calculate the real-time accumulation, and a fuzzy adaptive PID control algorithm is designed to calculate the optimal traffic inflow of the traffic congestion region. Furthermore, an example is selected for simulation. The results show that the boundary guidance strategy can effectively improve the operational state of the road network and alleviate traffic congestion. Finally, the influence of connected vehicle penetration rate on the strategy is discussed. The simulation results show that the strategy can improve the operation state of the road network under mixed traffic flow, and the higher the penetration rate, the more significant its effect on alleviating traffic congestion.

An innovative biomass-driven multi-generation system equipped with PEM fuel cells/VCl cycle: Throughout assessment and optimal design via particle swarm algorithm

This work proposes a new, efficient, economically, and environmentally viable approach for developing cutting-edge energy systems and assisting the anticipated global green transition with maximal renewable integration. The cogeneration of hydrogen and power is driven by biomass, which in turn drives the vanadium chloride cycle and the proton exchange membrane fuel cells. A cooling absorption unit is powered by waste heat recovered using a passive energy improvement technique to improve performance and cut costs. Energy, exergy, exergo-economic, exergo-environmental impacts, and CO2 emission rate of the suggested renewable-based model are analyzed using an engineering equation solver tool. Parametric analysis is also used to assess the impact of key operational factors on main performance indicators. With machine learning, a particle swarm method is implemented in MATLAB to find the optimal operating state with high precision and low computing cost. The results show the importance of multi-objective optimization by pointing out a conflicting change in the performance metrics from different angles by picking up the biomass moisture content and fuel cell current density. According to the optimization results, an acceptable total cost, environmental damage effectiveness, and exergy efficiency of 5 $/h, 0.86, and 55% are achieved through the integration of particle swarm optimizer and artificial neural network method. The results further reveal that the gasification temperature is not sensitive; however, changing the fuel cell utilization factor significantly impacts the system's performance from all sides. Finally, the chord diagram of the irreversibility rate indicates that the fuel cell and gasifier have the highest destruction of 6.4 kW and 2.6 kW under the optimum condition, owing to mixing and chemical reactions. As for the environmental aspect, by optimizing the system, the system's CO2 emission are greatly reduced.

Computational and experimental research on the performance of ECRIT ion source with nitrogen propellant

Electron cyclotron resonance ion thruster (ECRIT) with a diameter of 2 cm has the characteristics of no hot cathode and high specific impulse, which is suitable for the air-breathing electric propulsion system. In order to adapt to the atmospheric composition characteristics of nitrogen and oxygen in low orbit, the computational and experimental research on the performance of the ECRIT ion sourse with nitrogen propellant is an important basis for analyzing the feasibility of applying ECRIT to the air-breathing electric propulsion system. In this paper, the global model of the nitrogen ECRIT ion source with a diameter of 2 cm is established to calculate its performance. Then, the computational results are compared with the experimental results to analyze the difference. The research results show that when the input power of the ion source is 8 W and the gas flow rate is 2 ml/min, the computational and experimental results of the extracted ion beam current and thrust reach the maximum with the extracted beam current of 16.2 and 12.5 mA and the thrust of 476.6 and 368 μN, respectively. When the input power is 8 W and the gas flow rate is 0.6 ml/min, the computational and experimental results of the specific impulse are 2 095.8 and 1 855.6 s, both reaching the maximum value. The relative errors between the computational and experimental results of the extracted ion beam current, thrust and specific impulse all range from 2% to 32%. When the input power and gas flow rate used are 8 W and 1 ml/min in calculation, and 8 W and 0.8 ml/min in experiment, the ion source is on the optimal operating state. At this situation, the computational and experimental propellant utilization efficiencies with 17.8% and 16.2% respectively are high, and the ion energy loss with 443.9 and 596.2 W/A respectively is low.

A unified optimization control of wind farms considering wake effect for grid frequency support

This paper proposed a unified active power optimization control of wind farms under wake effect. It takes the sum of the kinetic energy variation and pitch angle variation as the optimization objective and used the particle swarm algorithm to achieve the optimization results. The main feature of the proposed method is that it unifies the kinetic energy optimization under a low wind speed area, the pitch angle and kinetic energy trade-off optimization under a medium wind speed area, and the pitch angle optimization under a high wind speed area. Combined with the de-loaded power constraint, it can flexibly reach various optimal operating states of the wind farm. The simulation results show that the proposed method optimizes the rotor speed and pitch angle in different wind speed areas, and releases kinetic energy and/or increases the output power of the wind farm to provide frequency support by switching the operating states.

Super-Resolution Structured Illumination Microscopy for the Visualization of Interactions between Mitochondria and Lipid Droplets

Visualizing the dynamics of fine structures in cells requires noninvasive, long-duration imaging of the intracellular environment at high spatiotemporal resolution and low background noise. With modularized hardware and polarized laser modulation, we developed a nanoscale super-resolution structured illumination microscope (SR-SIM) imaging technique. Combined with a reliable image reconstruction algorithm and timing synchronization of all devices, the super-resolution (SR) images in hepatocytes reached 134.95 nm spatial resolution and 50 fps temporal resolution. This imaging system was able to maintain the optimal operation state over hundreds of time points due to less exposure and low phototoxicity. In hepatocytes, interactions between mitochondria and lipid droplets (LDs) underpin many crucial physiological processes, ranging from cellular metabolism to signaling. In this study, we pioneered the use of the SIM system for imaging the interaction characteristics between mitochondria and LDs. More than 200 hepatocytes were counted and recorded effectively. We found that LDs in an unstable state were divided under mitochondrial contact and fused without it. Among 200 LDs, more than 69% were surrounded by mitochondria that tended to wrap LDs. The SR-SIM imaging technique was demonstrated to break the limitations of conventional imaging methods in spatial-temporal resolution and imaging duration in the field of the dynamic study between mitochondria and LDs.

Network flexibility regulation by renewable energy hubs using flexibility pricing-based energy management

This article presents the energy management of electrical and thermal networks in the presence of renewable energy hubs to regulate the flexibility of these networks based on flexible pricing services. Hubs consist of renewable sources and bio-waste units in addition to energy storage and responsive loads. The bio-waste unit produces electrical and thermal energy simultaneously. The proposed scheme minimizes the difference between the network's energy cost and the hub's flexibility income. It is subject to the optimal power flow equations and flexibility limitation in networks, operation model of resources, storage, and responsive loads in form of energy hub, and hub flexibility model. Unscented transformation-based stochastic optimization models uncertainties of load, renewable power, and energy price. Finally, the numerical results demonstrate the capability of the proposed scheme in extracting the optimal state of flexibility, operation, and economic situation of electric and thermal networks with optimal energy management of renewable hubs based on flexible pricing services. The inclusion of renewable resources, storage generators, and responsive loads into a renewable hub improves the economic situation by 32.9% and the status of operation indicators of the energy networks by 22.4–42% in condition of 100% flexibility.

Performance evaluation and multi-objective optimization of an innovative solar-assisted multigeneration energy storage system for freshwater/O2/H2 generation

A novel multigeneration energy system encompassing a two-stage water purification unit, high-temperature solid oxide electrolysis cell (SOEC), heliostat solar field, and power generation cycles including organic and two-stage steam Rankine cycles is introduced in this study. The two-stage desalination system comprises humidification, dehumidification, and reverse osmosis units. A thermoelectric generator is also utilized to convert dissipated heat into electricity. The proposed system is comprehensively analyzed from energy, exergy, economic, and sustainability standpoints. The effect of design parameters such as heliostat concentration ratio, view factor, desalination mass flow ratio, SOEC operating temperature, and current density on the hydrogen, oxygen and freshwater generation rate, sustainability index, total cost rate, and overall performance of the energy system are discussed. Further, multi-objective optimization based on non-dominated sorting genetic algorithm (NSGA-II) is implemented to identify the optimal operating state of the system. Comparative assessment of the system in the actual condition in the Middle East region showed that the plant has higher performance in June compared to December. Also, optimization revealed that by choosing C = 988, P7 = 1500 kPa, P8 = 300 kPa, MFD = 2.5 and Tcell = 1250 K, the highest obtainable exergy efficiency and minimum total cost rate in the optimal operating state would be 11.32 % and 78.18 $/h, respectively.

The integrated modeling of microgrid cyber physical system based on hybrid automaton

To fully reveal the interplay of the cyber system and physical system in the microgrid, this paper proposes a generic hierarchical modeling framework for cyber-physical integration modeling of microgrid, including two layers: physical device layer and controller layer. Each layer includes two parts: the continuous part (characterizing the physical system in the microgrid) and the discrete part (characterizing the cyber system in the microgrid). In this paper, firstly, detailed mathematical expressions describing the dynamic characteristics of each layer (including continuous characteristics and discrete characteristics) and describing the interplay of the continuous part and discrete part in each layer are given, which contributes to a better understanding of the interplay of the information flow and energy flow in the microgrid. Secondly, the cyber-physical integration modeling of each unit in the proposed framework is established using hybrid automaton, which can clearly present the operation state of each unit of the microgrid and its state transfer process, which is beneficial to design the optimal operation state trajectory of the system. The proposed modeling framework is generic and can be extended to any dynamic system with cyber-physical integration. Finally, a typical microgrid system is taken as an example to verify the feasibility of the proposed modeling approach.