Operational Flexibility Research Articles

Low-carbon environment-friendly economic optimal scheduling of multi-energy microgrid with integrated demand response considering waste heat utilization

- The integration of multi-energy sources in microgrids offers a promising approach to address the challenges of energy efficiency and bolster environmental stewardship. This paper presents a novel optimization scheduling model for multi-energy microgrids (MEMG) with carbon capture and storage (CCS) technology in various renewable energy scenarios. The model effectively coordinates integrated demand response (IDR) and the flexible operation of waste heat utilization, enhancing energy utilization efficiency while ensuring user satisfaction. Furthermore, introducing flexible operation combined with ladder-type carbon trading minimizes operating costs and carbon emissions. The improved particle swarm optimization (PSO) algorithm is employed to solve the eco-economic economic scheduling model. Simulation analysis shows that integrating CCS into MEMG can reduce carbon emissions by 74.4%, while lowering the overall cost by 37.9%. These findings not only improve MEMG's low-carbon and economic efficiency but also pave the way for environmentally responsible energy management practices.

Robust planning upon unit commitment

Introducing operational flexibility in power system planning has motivated the development of planning models to be built upon and validated by unit commitment. While handling uncertainty, these models form a holistic robust optimization that makes three groups of decisions in a single model, namely investment, commitment, and dispatch. A dilemma arises when aligning commitment with other decisions, either on the same layer with investment or with dispatch—the former makes the model easy to solve but violates the decision-making process in reality, while the latter creates a triple-layer optimization with a mixed-integer inner layer that is harder to solve. A tractable routine is proposed for the latter with a nested decomposition algorithm to contribute a technique that splits the investment and commitment decisions for power system planning under uncertainty, which is well suited to the real-world infrastructure investment decision process.

Microgrid-Based Small Modular Reactor for a High-Renewable-Energy Penetration Grid in Ghana

Achieving the necessary energy balance entails the capacity to adapt both power supply and demand, which is known as flexible operation. At present, the Ghana National Commission on Culture depends on a combination of well-coordinated measures designed to uphold the system’s integrity when confronted with abnormal system conditions stemming from significant disturbances within the system. The set of coordinated measures comprises manual (controlled load shedding) and automatic (Special Protection Schemes, Under-Frequency Load Shedding, Over-Frequency Control Scheme, scheme against voltage collapse, etc.) steps to control important system parameters to keep the power system stable and cascading effects leading to major blackouts. Ghana’s system suffers from voltage and frequency degradation as a result of a mismatch of power supply and demand; to maintain grid stability and avoid power outages, a flexible operation is required for real-time supply and demand balancing. SMRs, with their load-following capability, can adjust their output to match the change in demand. This research proposes a microgrid-based design for a high-renewable-energy penetration grid in Ghana with the inclusion of an SMR for voltage stability.

Environmental Protection tax and diversified transition of heavily polluting enterprises: Evidence from a quasi-natural experiment in China

A comprehensive evaluation of the impact of green taxes on enterprises contributes to expediting the economic transition towards environmental sustainability. This study explores the effect of environmental protection tax (EPT) on corporate diversification by leveraging the quasi-natural experiment of EPT law implementation in China. Empirical evidence reveals that, compared to non-heavily polluting enterprises, EPT increases the level of diversification of heavily polluting enterprises by affecting corporate profitability, debt capacity, and risk. Moreover, this facilitative influence is notably pronounced among non-state-owned enterprises (non-SOEs) and enterprises operating within intensely competitive environments. Further analysis shows that diversification helps heavily polluting enterprises shift to green. This transition has benefits for companies in terms of environmental penalties and operational flexibility, which improves firm viability. From the perspective of corporate diversification, this research enhances our understanding of the influence of green taxes, thus providing empirical evidence for evaluating the effectiveness of EPT.

Coordinated scheduling of integrated electricity, heat, and hydrogen systems considering energy storage in heat and hydrogen pipelines

The increasing interdependency of electricity, heat, and hydrogen sectors in recent years provides a growing incentive to make use of the possible synergies obtained when coordinating these energy sectors from the perspective of an integrated energy system (IES). However, coordinated scheduling frameworks remain poorly developed for integrated electricity, heat, and hydrogen systems, and the energy storage potentials in heat and hydrogen pipelines have not been quantitatively analyzed. The present work addresses these issues by separately analyzing the energy storage value provided by district heat systems (DHSs) and hydrogen transmission systems (HTSs). These storage potentials are applied to develop an operation model that considers the delay characteristics of heat pipelines and hydrogen pipeline storage. Then, we develop a coordinated scheduling model for a power–heat–hydrogen IES. The analysis is facilitated by quantifying the value of energy storage in heat pipelines and hydrogen pipelines in terms of operation costs. Finally, a case study is conducted to verify the necessity of accounting for the dynamic energy storage potentials of the DHS and HTS during IES optimization. Numerical results verify the economic value of comprehensive consideration of heat and hydrogen pipeline storage, which results in higher operational flexibility of IES with lower operating cost and reduced wind power curtailment.

Design Space and Control Strategy for the Manufacturing of Wet Media Milled Drug Nanocrystal Suspensions by Adopting Mechanistic Process Modeling.

Wet media milling is a fully industrialized technology for the manufacturing of drug nanocrystal suspensions. This work describes the development of an advanced control strategy and an associated design space for a manufacturing process at a commercial scale. Full-scale experiments and mechanistic process modeling have been used to establish a physically reasonable control strategy of factors relevant to the quality attributes of the nanocrystal suspension. The design space has been developed based on a mature mechanistic process model of the wet media milling procedure. It presents the process-product attribute relationship between a multidimensional range of measured process parameters and a range of the product-quality attribute mean particle sizes. The control strategy allows for simple, robust, and sound scientific process control as well as the operational flexibility of the suspension batch size. This is an industrial case study of control strategy and design-space definition with the crucial contribution of mechanistic process modeling for an intended commercial manufacturing process.

Impact of locational pricing on the roll out of heat pumps in the UK

This paper investigates the impact of locational pricing on the roll out of heat pumps in the UK. Qualitative assessment of proposals set out for electricity market reform in the UK identified locational pricing as potentially having an impact on heat pump running costs. Energy system modelling was used to assess the economics of both individual heat pumps and system-wide heat pump roll out under both unified pricing and locational pricing for the UK for 2020 and 2035. PyPSA-GB, a future power system model, was employed to simulate unified and locational wholesale prices and the Octopus Agile tariff was used to calculate indicative retail tariffs applicable to domestic heat pumps. The research highlights that locational pricing can create market conditions which better reflect the true cost of generating and delivering electricity, however, it can also lead to higher heat pump operating costs in regions with projected high heat demand. Key findings reveal that locational pricing could result in significant geographical disparities in heat pump operating costs due to varying electricity prices across different zones. Further work is required to develop policy to alleviate high operating costs and to promote flexible operation of heat pumps.

Novel renewable seawater desalination system using hydrogen as energy carrier for self-sustaining community

This study proposes a novel renewable seawater desalination system in which hydrogen is utilized as an energy carrier to balance the fluctuating electricity supply and demand and provide heat to the desalination system. The proposed system employs a water‑hydrogen nexus framework to achieve an efficient operation for providing electricity, potable water, and green hydrogen. The optimal system configuration that considers seasonal variations in renewable energy production is determined by using a heuristic algorithm. It can sustain flexible and standalone operations with less curtailment. The results demonstrate that the system has an average efficiency of 38.9 %, which is higher than the results reported in previous studies. The daily differences in system efficiency are primarily caused by the solar and water electrolysis systems. Curtailment rates of 5 % to 14 % during the operation are owing to the energy storage capacity and supply-demand balance. A higher utilization rate of water electrolysis and fuel cell systems is beneficial for profitability. However, the present utilization rate ranges from 40 %–60 % for water electrolysis systems and <20% for fuel cell systems. Thus, an integrated net-zero system of water electrolysis and fuel cells is proposed, which can be used as a substitute for traditional fossil-based desalination plants.

Energy flexibility quantification of a tropical net-zero office building using physically consistent neural network-based model predictive control

Building energy flexibility plays a critical role in demand-side management for reducing utility costs for building owners and sustainable, reliable, and smart grids. Realizing building energy flexibility in tropical regions requires solar photovoltaics and energy storage systems. However, quantifying the energy flexibility of buildings utilizing such technologies in tropical regions has yet to be explored, and a robust control sequence is needed for this scenario. Hence, this work presents a case study to evaluate the building energy flexibility controls and operations of a net-zero energy office building in Singapore. The case study utilizes a data-driven energy flexibility quantification workflow and employs a novel data-driven model predictive control (MPC) framework based on the physically consistent neural network (PCNN) model to optimize the building energy flexibility. To the best of our knowledge, this is the first instance that PCNN is applied to a mathematical MPC setting, and the stability of the system is formally proved. Three scenarios are evaluated and compared: the default regulated flat tariff, a real-time pricing mechanism, and an on-site battery energy storage system (BESS). Our findings indicate that incorporating real-time pricing into the MPC framework could be more beneficial to leverage building energy flexibility for control decisions than the flat-rate approach. Moreover, adding BESS to the on-site PV generation improved the building self-sufficiency and the PV self-consumption by 17% and 20%, respectively. This integration also addresses model mismatch issues within the MPC framework, thus ensuring a more reliable local energy supply. Future research can leverage the proposed PCNN-MPC framework for different data-driven energy flexibility quantification types.

A Stochastic MPC-Based Flexibility Scheduling Strategy for Community Integrated Energy System Considering Multi-Temporal-Spatial-Scale and Inertia Components

The network trend of isolated communities adds urgency to accelerate the deployment of community integrated energy systems (CIES). CIES effectively combines and optimizes multiple energy systems, leveraging their complementarity for efficient utilization and economical energy supply. However, the escalating intricacies of coupling multiple energy sources and the rising system uncertainties both pose challenges to flexibility scheduling of energy supply and demand. Additionally, the potential flexibility of building thermal inertia and pipeline gas linepack in diverse CIES, encompassing residential, commercial, and industrial communities, remains unexplored. To tackle these issues, a stochastic model predictive control (SMPC) based multi-temporal-spatial-scale flexibility scheduling strategy considering multiple uncertainty sources and system inertia components is proposed. First, the optimization model of CIES is formulated to improve operational flexibility and efficiency, resolve energy discrepancies and expand the capacity for renewable energy utilization. Then, the SMPC-based framework embedding an auto-regressive model and scenario generation method are established to make real-time corrections to the day-ahead scheduling stage and offset the prediction errors of uncertainty sources economically. Furthermore, thermal inertia of the aggregated buildings with different envelopes and linepack in gas pipelines are both leveraged to enhance the flexibility and synergy of CIES. Finally, a case study is executed to verify the effectiveness and applicability of the proposed strategy. The simulation results unequivocally demonstrate that this strategy successfully coordinates and harnesses complementary advantages from various energy sources, fostering a balanced energy supply-demand equilibrium across multiple temporal and spatial scales.

Nonlinear Model Predictive Control for Doubly Fed Induction Generator with Uncertainties

Doubly fed induction generators (DFIG) find extensive application in variable-speed wind power plants, providing notable advantages such as cost-effectiveness, operational flexibility across varying speeds, and enhanced power quality. This research focuses on the control of DFIGs employed in variable-speed wind turbine configurations. A suitable mathematical model is chosen for representative systems following a comprehensive review of contemporary research. Subsequent analysis reveals the instability of the open-loop time response of the system. To address this instability, the initial approach involves the implementation of the conventional model predictive controller (MPC). However, the outcomes indicate that this controller falls short of delivering satisfactory performance despite the enhanced stability. In the subsequent phase, efforts are made to mitigate the impact of wind input variability by utilizing the Kalman filter, given its effectiveness in handling high variability. Following this, a novel methodology is introduced, which combines nonlinear MPC with the Lyapunov function. This method is based on the nonlinear model of the system. By using the Lyapunov function in the nonlinear MPC structure, the stability of the designed controller is guaranteed. To validate the proposed control approach, the results are compared with PID based controller in MATLAB/Simulink. The simulation results showed that the output variables of the modeled DFIG system achieve stability within a reasonable timeframe applying the input.

Safety and efficacy of one-hole split endoscope technique for surgical treatment of thoracic ossification of the ligamentum flavum

Surgical intervention is typically recommended for thoracic ossification of the ligamentum flavum (TOLF). This study aimed to evaluate the efficacy and safety of a novel non-coaxial one-hole split endoscope (OSE) technique for treating TOLF. We performed OSE procedure on 13 patients with TOLF from June 2022 to July 2023. The mean operative time was 117.5 ± 15.4 min. VAS scores for lower limbs decreased from 6.5 ± 0.8 preoperative to 1.6 ± 0.4 at the last follow-up (P < 0.001). ODI scores improved from 62.4 ± 5.7 preoperative to 18.6 ± 2.2 at the last follow-up (P < 0.001), and mJOA scores increased from 5.1 ± 1.6 preoperative to 8.4 ± 1.5 at the latest follow-up (P < 0.001). All patients achieved ASIA scale grade D or E at the final follow-up, except for two patients remained residual limb numbness. None of the thirteen patients suffered from severe perioperative complications. The OSE technique proves to be a safe and effective procedure for treating TOLF or even with dura mater ossification, characterized by minimal surgical trauma, relatively smooth learning curve and flexible operation.

Revealing instantaneous gas-particle segregation in bubbling and turbulent fluidized beds through fiber optic sensing

Bubbling and turbulent fluidized beds (BFB and TFB) are widely used in the chemical and energy industries, owing to their enhanced heat transfer and improved operation flexibility. In BFB and TFB, the gas-particle suspension is featured with a dilute phase (bubbles) and a dense phase. The gas exchanges between the bubbles and the dense phase often through an intermediate region, further enhancing the gas-particle contact. However, few comprehensive studies have been conducted on the intermediate region and the dense phase. In this work, the instantaneous solids holdup was obtained in BFB and TFB through optical fiber sensing. Subsequently, through the use of statistical signal analysis, solids holdup thresholds were proposed to discriminate between the bubbles, the intermediate region (named as ‘mid-phase’) and the dense phase. Finally, gas-particle segregation details were further revealed through the quantification of the different phases to advance the development of future modelling, design and scale-up processes.

Design of Multi-Input Multi-Output Non-linear Model Predictive Control for Main Steam Temperature of Super Critical Boiler

Flexible operation of coal-fired power plants is becoming increasingly necessary for successful integration of large-scale renewable power generation into the power grid. The maximum ramp rate and the number of load cycles are generally limited by the thermal stress experienced by the boiler pressure parts, turbine metallurgy and creep and fatigue of critical thick-walled components Main steam temperature is a critical operating parameter that must be controlled within acceptable limits for safe operation. Main steam temperature deviation beyond acceptable limit has impact on boiler pressure parts and turbine material of construction due to creep and fatigue effect. Base load operating units do not require steep ramp rate and hence recommended ramping rates are kept low within the safe operating zone in comparison to the flexible operation of the units with wide range load change width. Thermal stresses are caused by the temperature changes inside the thick-walled components and turbine steam admission parameters. Hence, the quality of main steam temperature control plays a vital role in flexible operation of the coal fired units. Conventional cascaded PID temperature control loop architecture performs well at steady state condition within a limited variation of load change at low ramp rate but it acts slowly and performs poorly at transient operating conditions of flexible operation of the boiler turbine with wide range load variation and load cycle with high ramp rate and remains far from rated conditions. In this paper, a Multi-Input Multi-Output (MIMO) Non-linear Model Predictive Control (MPC) design for regulation of the main steam temperature of a Once-Through supercritical Boiler is proposed. The controller is based on a non-linear dynamic model which incorporates dynamics of the variables of interest. It has the capability to operate effectively across a wide load range while maintaining main steam temperature within acceptable limits. A notable advancement in this design of MPC is the incorporation of coal flow demand and feedwater flow demand as additional control inputs alongside primary and secondary spray flows. In simulation test cases, the MPC controller demonstrates satisfactory performance and computational efficiency.

Pattern-based integration of demand flexibility in a smart community network operation

The proliferation of utilizing smart devices and electric vehicles (EVs) has engendered new opportunities for local distribution operators (LDOs) to flexibly energize these loads, given customers’ preferences. In this context, this paper proposes an integrated scheduling model allowing an LDO to efficiently incorporate and optimally reschedule potential demand flexibilities. Given the full knowledge of the LDO over the grid conditions and availability of its owned assets, we argue that an LDO can better reshape the load profiles of the customers than they could do. The proposed model couples the branch flow-based AC power flow model in distribution network with potential demand flexibilities classified into four generic types. The LDO is assumed to operate multiple solar PV plants, energy storage systems (ESS), and EV parking lots with smart charging stations. The practicality of the proposed model is investigated through two case studies implemented on the modified IEEE 33-node distribution test system. The simulation results reveal that the total operation cost of the community can be reduced by ∼10% without sacrificing customers’ comfort if the potential demand flexibilities are optimally rescheduled.

Energy management system based on economic Flexi-reliable operation for the smart distribution network including integrated energy system of hydrogen storage and renewable sources

This paper presents the energy management of smart distribution network including integrated system of hydrogen storage and renewable sources. Objective is to assess economic, operation, flexibility, and reliability goals of the distribution system operator. Objective function minimizes costs of operation, reliability, energy losses, and network flexibility. Scheme is constrained by AC optimal power flow equations, network reliability limitation, and integrated system model. Scheme utilizes scenario-based stochastic optimization to model of uncertainties, such as load, parameters of renewable resources, energy prices, and the availability of network equipment. Novelty is modeling and performance evaluation of the proposed integrated system as a type of flexibility resource in the operation of distribution systems proportional to the economic, operation, flexibility, and reliability objectives of the network operator. Numerical results demonstrate energy management capabilities of the discussed integrated energy system, which contribute to enhancing economic and technical conditions of distribution network. The inclusion of hydrogen storage in the renewable integrated energy system has been found to enhance the economic viability, operational efficiency, and reliability of the distribution network by around 46.8%, 41%–53%, and 95%, respectively, when compared to network power flow. Aforementioned integrated system demonstrates 100% flexibility through the utilization of hydrogen storage.

Production of α-olefins from biomass gasification: Process development and multi-objective optimization for techno-economic and environmental goals

Efficient utilization of biomass as a substitute for fossil fuels holds great promise for meeting future greener chemical demands in a sustainable manner. This study presents a process simulation and multi-objective optimization for an innovative process of biomass-based Fischer-Tropsch synthesis of value-added linear α-olefins. Optimal design and operation solutions with heat-exchange network integration taken into account are obtained that minimize total annual costs (TAC), generalized energy consumption (GEC), greenhouse gas emissions (GHG), and maximize product yields (η). Compared to the benchmark case, this optimization leads to a 9.93 % reduction in TAC, a 21.76 % reduction in GEC, a 19.23 % reduction in GHG, and increases η by 1.41 %. Additionally, a comprehensive factor analysis of the optimal design and operation parameters in the Pareto front helps to discover patterns related to energy consumption, environmental impact, or economy. This allows to maximize a specific key process performance metric while minimizing the loss in the other, thus enabling flexible process design and operation under diverse manufacturing environments.

Addressing Hydrogen Sulfide Corrosion in Oil and Gas Industries: A Sustainable Perspective

In the oil and gas industry, the corrosion attributed to hydrogen sulfide (H2S) is one of the most significant challenges. This review paper systematically investigates the diverse facets of H2S corrosion, including its sources, corrosion locations, mechanisms, and resultant corrosion products. Understanding different forms of H2S corrosion, such as stress-oriented hydrogen-induced cracking (SO-HIC), sulfide stress cracking (SSC), and hydrogen-induced cracking (HIC), provides a thorough comprehension of these phenomena. The paper discusses critical factors influencing H2S corrosion, such as temperature, flow rate, pH, and H2S concentration, highlighting their implications for sustainable practices in the oil and gas sector. The review emphasizes the significance of monitoring and mitigation strategies, covering continuous monitoring, applying corrosion inhibitors, selecting materials, and conducting thorough data analysis and reporting. Furthermore, the role of training in fostering a sustainable approach to H2S corrosion management is highlighted. This exploration advances the overarching goal of sustainable development in the oil and gas industries by providing insights into understanding, monitoring, and mitigating H2S corrosion. The findings presented here offer a foundation for developing environmentally conscious strategies and practices to guarantee the long-term viability and flexibility of refinery operations.

Distributionally robust decarbonizing scheduling considering data-driven ambiguity sets for multi-temporal multi-energy microgrid operation

As concerns about environmental sustainability continue to grow, the demand for effective low-carbon energy management becomes increasingly pressing. This study presents a novel framework for multi-temporal multi-energy microgrids (MMGs), integrating advanced low-carbon technologies to meet this imperative. The framework ensures flexible operations to navigate uncertainties stemming from renewable energy sources (RES) and fluctuating energy demand. Facilitating multi-energy transactions, encompassing gas and power exchanges in both markets, the model accommodates uncertainties from RES and demand fluctuations. Objectives include reducing carbon emissions and improving economic efficiency. To address uncertainties in the MMG system, a data-driven distributionally robust optimization (DRO) method is employed. Day-ahead scheduling utilizes a two-stage three-level approach, deploying the column-and-constraints generation (C&CG) algorithm, showcasing the efficiency of DRO in minimizing energy waste and carbon emissions while remaining cost-effective. Practicality is demonstrated through real-time intra-day scheduling using the model predictive control (MPC) algorithm, building upon hourly day-ahead results. The effectiveness of both strategies is evaluated using empirical data from an MMG based on the IEEE 33-bus test system. This cost-saving framework not only achieves a significant carbon reduction of 10.6 % but also provides reliable and adaptable solutions, effectively addressing real-world variations in renewable energy and mitigating potential risks.

Modeling of self-oscillating flexible circuits based on liquid crystal elastomers

Deformable and movable flexible circuits are characterized by simple construction, precise control, flexible and versatile morphology, and various functional forms, which can be used to build dynamic circuit systems, achieve the integration of drivers and controllers, and automated control of circuits, etc. Limited to conventional materials, challenges remain in designing novel self-oscillating flexible circuit. In this paper, a concept for a self-oscillating flexible circuit based on electrothermally responsive liquid crystal elastomers (LCEs) is presented, which is capable of generating periodic continuous oscillation under steady applied voltage. The governing equation of the self-oscillating LCE-based flexible circuit is formulated, and numerically calculated by fourth-order Runge-Kutta method with Matlab software. The numerical calculations reveal that the self-oscillating flexible circuit involves two motion patterns, i.e., static pattern and self-oscillation pattern. The self-oscillation arises from the coupling between the electrothermally driven deformation of the LCE film and its motion, and the electrothermal input compensates for the damping dissipation. Moreover, it is found that system parameters affect the self-oscillation of flexible circuit and that there are critical values of these parameters that trigger self-oscillation. Based on the self-oscillating flexible circuit, we conceptualized an automatic control device and a disturbance monitoring device, realizing automatic control of electrical appliances without human intervention and automatic monitoring of external disturbances. The proposed self-oscillating LCE-based flexible circuit features convenient operation, simple construction, flexibility, lightweight, and noiseless operation, rendering it suitable for constructing dynamic circuit systems, achieving actuator-controller integration, and facilitating circuit automation applications.