Flexible Operation Strategy Research Articles

Optimal capacity and multi-stable flexible operation strategy of green ammonia systems: Adapting to fluctuations in renewable energy

The potential of power-to-ammonia is increasingly recognized as a large-scale renewable electrical energy storage technology in the energy-transition landscape. Unlike conventional, continuously operating Haber-Bosch production processes, power-to-ammonia processes face operational challenges stemming from fluctuations in renewable power supply. Systematic strategies that can adapt to these fluctuations are required to improve the operational flexibility and stability of power-to-ammonia processes for industrial applications. This study proposes a multi-stable flexible ammonia operation strategy, which is practical and feasible in engineering. To maximize net profit, a design scheduling coordination optimization model is established and solved using a two-stage algorithmic framework of particle swarm and mixed integer linear programming. Additionally, three flexible scenarios are designed for the simulation analysis of a grid-connected green ammonia system. Compared with the traditional constant inflexible scenario, the multi-stable flexible scenario increases annual net profit by 5.05 M$ and reduces carbon emission intensity by 1.61 kg CO2/kgNH3. Furthermore, compared with the continuous flexible scenario, the volatility of the ammonia production load is reduced by 79.89 %. The multi-stable flexible operation strategy balances the green ammonia system’s economic, safety, and low-carbon attributes. Sensitivity analysis reveals that wind and photovoltaic complementary power generation reduces green ammonia costs and carbon emissions. When the selling price of ammonia exceeds 550 $/t, ammonia production and sales should be prioritized, but at lower ammonia prices, selling electricity to the grid to optimize economic efficiency is advised. This study provides novel insights into the flexible operation of the power-to-ammonia process and is expected to offer guidance for constructing and operating green ammonia systems.

A complete dynamic modeling of two-by-one gas-steam combined cycle unit for coordinated control design

The high penetration of renewable energy can potentially change the role of combined cycle gas turbines (CCGT) from load suppliers to flexible suppliers. However, the slow process in the dynamic modeling of the CCGT complete system restricts the design of its best flexible operation strategy. Therefore, this paper adopts the composite modeling method to establish a complete nonlinear dynamic model of a 2 × 1 CCGT including two gas turbines, two three-pressure heat recovery steam generators (HRSG), one steam turbine, and one heat exchanger. This model can reflect the dynamic characteristics of pressure and flow in the bottom cycle due to the changes in operating mode, which can guide the design of the flexible operation control strategy of the 2 × 1 CCGT. To verify the model’s accuracy, the simulated data were compared with the field data from a 900 MW cycle with the same configuration. The results show that the established model can accurately reflect the changing trend of variables during mode switching. The range errors of essential variables such as gas turbine (GT) power is 3.8 %, GT outlet temperature is 1.4 %, high-pressure drum pressure is 4.5 %, and the range errors of other variables are below 5 %, proving the fidelity of the established control model. The dynamic model established in this paper will be the basis for model-based controller design which can handle multi-objective optimization and constraints in flexible operations. Moreover, this model will be used to guide the design of multi-mode control and energy management strategies.

An Optimal Operation Strategy for Collaborative Flexibility Provision of a Carbon Capture and Utilization Process With Wind Energy

Improving power system flexibility by responsive demand is essential for integrating wind energy with a high level of variability in power systems. Carbon dioxide-based chemical processes as energy-intensive industrial loads may offer a vast potential of new forms of flexible operation due to their existing control infrastructure and storage capabilities. However, a collaborative decision model is needed for optimal energy sharing among the chemical plant and the grid under the variations and uncertainties of wind power. This study develops an optimal two-stage stochastic programming model for a novel flexible operation strategy of the chemical process coupled with wind turbines. In the proposed control scheme, a small-scale wind farm provides the power input of a chemical plant. Wind turbines are connected to the grid and actively participate in the day-ahead energy and reserve markets, considering the chemical plant as a source of flexibility. An equivalent scenario-based model of the proposed optimization problem is suggested using the Group Method of Data Handling (GMDH) for a data-driven prediction of stochastic variables. Simulation results demonstrate the effectiveness and significance of the proposed approach for an optimal and collaborative contribution in ancillary market of a carbon dioxide-based chemical plant supplied by wind energy.

Strategies for flexible operation of power-to-X processes coupled with renewables

Power-to-X (PtX) is a large-scale, long-term energy storage approach for converting renewable electrical energy into various chemical fuels. Unlike conventional fossil fuel production processes that operate continuously, PtX processes encounter operational challenges due to fluctuations in renewable power supply. To improve the operational flexibility of PtX processes for industrial applications, it is crucial to develop systematic strategies that can adapt to these fluctuations, which will increase PtX's energy and cost efficiencies. This study comprehensively explores various strategies for flexible operation of renewable-powered PtX processes, which include the use of additional energy buffers and grid connections. Power and material allocation models are developed for PtX processes based on islanded; grid-assisted; and grid-assisted, bidirectional connections by using three possible energy buffers (i.e., hydrogen intermediate storage, Li-ion batteries, and Carnot batteries). Surrogate model-based optimization is performed to establish an economically optimal design for each case. A power-to-methanol process implemented in Kramer Junction, California is studied to investigate the effects of flexible operation strategies on process performance in terms of production cost, renewable penetration, and energy efficiency. The findings of this study offer new insights into the operational flexibility of PtX processes and provide general recommendations for improving the flexibility and performance of PtX, particularly with respect to the establishment of a grid connection and the selection of an appropriate energy buffer.

Development of Energy Flexible and Sustainable Operation Strategies of Air Conditioning Systems for Industrial Production Environments

The increasing expansion of renewable energies and the shortage of fossil fuels lead to rising and increasingly fluctuating energy prices. To benefit from these significant fluctuations and to become less dependent on fossil energy sources, this paper shows how the electricity demand of cooling supply systems for industrial room air conditioning can be flexibly adapted to the volatile energy supply. For this purpose, an energy-flexible air-conditioned climate room with a smart chiller and thermal storage units was built as a demonstrator at the ETA research factory at the Technical University of Darmstadt. To determine the energy flexibilization potential and to develop flexible operating strategies, a thermal simulation model of the climate room was also created in the simulation environment Dymola and experimentally validated. In addition, an energy-flexible operation of the climate room was validated on the real system and the economic potential was determined as well as the impact on sustainability was derived. The demonstrator shows that in the application scenario of air conditioning of cold storage facilities 26.3 % of electricity costs and 20.6 % of CO2 emissions can be saved by the developed energy-flexible operating strategies. The findings can be transferred and scaled to different cooling supply systems for industrial air conditioning.

Two-Layer Coordinated Energy Management Method in the Smart Distribution Network including Multi-Microgrid Based on the Hybrid Flexible and Securable Operation Strategy

With the advent of smart grid theory, distribution networks can include different microgrids (MGs). Therefore, to achieve the desired technical and economic objectives in these networks, there is a need for bilateral coordination between their operators. In the following, by defining an energy management problem for them, it is predicted that the mentioned goals can be achieved. Therefore, this paper presents the hybrid flexible-securable operation (HFSO) of a smart distribution network (SDN) with grid-connected multi-microgrids using a two-layer coordinated energy management strategy. In the first layer, the microgrid (MG) operator is coordinated with sources, storages, and demand response operators. This layer models the HFSO method in the grid-connected MGs, which is based on minimizing the difference between the sum of operating cost of nonrenewable distributed generations and cost of energy received from the SDN, and the sum of flexibility and security benefits. It is constrained to AC optimal power flow, flexibility and voltage security constraints, operation model of sources and storages, and demand response. The second layer concerns coordination between the MG operators and the SDN operator. Its formulation is the same as that of the first layer, except that the HFSO model is used in the SDN according to MGs power daily data obtained from the first layer problem. The strategy converts the mixed-integer nonlinear programming to linear one to obtain the optimal solution with low calculation time and error. Moreover, stochastic programming models the uncertainties of load, energy price, and renewable power. Eventually, numerical results confirm the capability of the scheme to improve technical and economic indices simultaneously. As a result, by expecting the optimal operation for sources, storage, and responsive loads, it succeeded to enhance energy loss, voltage profile, and voltage security of the mentioned networks by up to 30%, 22%, and 5%, respectively, compared to power flow studies. In addition, there was enhancement in economic and flexibility status of the SDN and MGs.

Energy Management and Pricing Strategy of Building Cluster Energy System Based on Two-Stage Optimization

With the application of advanced information and communication technology in building cluster energy system (BCES), energy management based on two-way interaction has become an effective method to improve its operation efficiency. BCES can quickly respond to the mismatch between supply and demand by adjusting flexible load and system operation strategy, which can improve operation reliability and reduce energy cost. This paper proposes an energy management and pricing framework of BCES based on two-Stage optimization method. First, on the basis of profit-seeking modeling of energy service provider (ESP) and building clusters (BCs), a dynamic pricing decision-making framework for energy management in a hierarchical energy market is proposed, which considers both ESP’s energy supply income and BCs’ comprehensive benefit. The dynamic pricing problem is formulated as a discrete finite Markov decision process (MDP), and Q-learning algorithm is adopted to solve the MDP problem. Moreover, an operation optimization model of the BCES based on the obtained optimal price decision is established, and the established model is solved by the alternating direction multiplier method algorithm (ADMM). Through numerical simulation case studies, it is demonstrated that the proposed method can achieve the optimal pricing decision-making closer to the psychological needs of ESP and BCs, and can significantly reduce the cost of BCs and improve the operational efficiency of BCES.

Flexible operation strategy for formic acid synthesis providing frequency containment reserve in smart grids

The demand-side contribution to grid frequency regulation is becoming increasingly important due to the growing penetration of renewable energy in the power system. Among energy-intensive industrial loads, chemical plants have a high potential to offer grid balancing services due to their existing control infrastructure and storage capabilities. However, applying fast ramp rates and providing time-critical grid services is not straightforward due to strict constraints and the nonlinear dynamics of chemical systems. Therefore, adaptive operating approaches are required to increase the process’s flexibility and facilitate the fast demand response operation. This work proposes a flexible operating strategy for the cooperative operation of a Polymer Electrolyte Membrane (PEM) electrolyser and multi-stage compression systems in a chemical process to provide Frequency Containment Reserve (FCR). This strategy aims to realise the desired power regulation dynamics on the grid side while maintaining the reactor’s optimal operating conditions, i.e., temperature, pressure and flow rate ratio. A techno-economic analysis is performed to obtain optimal operating points. The techno-economic analysis shows that operating the process at a baseload of 73% while offering the remaining capacity as a power reserve can create additional revenue and improve the economic profit by around 10%. The proposed approach is validated by dynamic simulations of a Carbon Capture and Utilisation (CCU) process for formic acid production. The results show that the proposed strategy can enhance the process’s operational flexibility and enable FCR provision with a limited impact on reactor efficiency (<1%).

Flexible operation strategy of an integrated renewable multi-generation system for electricity, hydrogen, ammonia, and heating

Sustainable development and efficient integration of different resources are imperative for future energy systems. To solve the uneven temporal–spatial distribution of renewables, green hydrogen and ammonia can be used as short- and long-term energy storage mediums, respectively. The synergistic effect of introducing these two media into an integrated renewable multi-generation system was examined in this study. The system provided a constant 10 MW electrical baseload and green ammonia when excess hydrogen was available to satisfy the electricity needs for 24 h. The effects of energy storage capacity and baseload electricity were considered. The performance of the system and strategy was thermodynamically investigated, and its generality and feasibility were dynamically evaluated based on the hourly weather data of 10 cities in China in 2020. The analysis revealed that the maximum overall energy and exergy efficiencies were 50% and 73.1%, respectively. The maximum energy efficiency in each subsystem varied from 33.7% to 73.8%, whereas the maximum exergy efficiency changed from 43.5% to 60.9%. In the baseline condition, the total energy outputs of solar, wind, and backup power subsystems were 33.3, 48.5, and 16.4 GWh, respectively. The hydrogen and ammonia yields were 994.5 and 7,201 t·y−1, respectively. The higher hydrogen and ammonia yields of Shenyang, Shanghai, Kunming, and Harbin demonstrate great potential for the development of this system.

Nonlinear Model Predictive Control of Post-combustion CO2 Capture Process for Flexible Operation

A large amount of energy requirement for solvent regeneration is a major barrier to the widespread adoption of amine-based post-combustion CO2 capture (PCC). Flexible operation is one of the ways to lower the energy penalty by responding to changes in economic factors like the energy price. However, for effective implementation of flexible operation strategies, it is necessary to identify the most economic operating condition under various potential scenarios and to establish an appropriate control strategy to operate the process. As flexible operation will inherently involve a large operating envelope, we investigate the use of nonlinear model predictive control (NMPC) technology. To circumvent the problem of solving a large-scale nonlinear programming problem online, a simpler NARX model is identified and used. With the NARX model, an offset-free NMPC is designed and simulated under various dynamic scenarios. The developed NARX-based NMPC shows satisfactory control performance, stabilizing the CO2 capture rate faster than LMPC by 60-100 min.

Optimal day-ahead dispatch of an alkaline electrolyser system concerning thermal–electric properties and state-transitional dynamics

Green hydrogen is viewed as a promising energy carrier for sustainable development goals. However, it has suffered from high costs hindering its implementation. For a stakeholder who considers both renewable energy and electrolysis units, it is important to exploit the flexibility of such portfolios to maximize system operational revenues. To this end, an electrolyser model that can characterize its dynamic behavior is required in both electric and thermal aspects. In this paper, we develop a comprehensive alkaline electrolyser model that is capable of describing its hydrogen production properties, temperature variations and state transitions (among production, stand-by and off states). This model is further used to study the optimal dispatch of an electrolyser based on a real-world hybrid wind/electrolyser system. The results show the model can effectively capture the coupling between thermal–electric dynamics and on–off performance of an electrolyser. The flexible operation strategy based on this model is proven to significantly increase daily revenues under different spot price conditions for electricity. Comparing the model with the ones derived from conventional modeling methods reveals this model offers more operating details and highlights several operational features, such as the preference for working at partial load conditions, although at the expense of more computing resources. It is suggested to use this model in studies related to energy integration, operation planning and control scheme development, in which the multi-domain dynamic properties of electrolysers in electricity/gas/heat need to be properly characterized. A sensitivity analysis on key parameters of such electrolyser system is also introduced to connect the daily operation with long-term planning.

Discussion on the Flexible Manufacturing and Operation Strategy Model for a Machine Tool Component Company in Taiwan

Recently, many manufacturing industries have been facing challenges such as rising material costs, small-volume and large-variety products, shortened production cycles, increased labor costs and longer after-sales service times, which is a very tough challenge for most small and medium-sized component manufacturing suppliers. In addition to the current hot topics in the manufacturing industry - Smart Manufacturing (Industry 4.0) and lean production management, if small and medium-sized enterprises are not able to adjust the pace of manufacturing timely and find a suitable production model, they will soon be overwhelmed by the torrent of the era of speed and accuracy. In the face of the dramatic changes in the industry structure, the company can deploy the global expansion of overseas customers in advance, and adjust to apply and implement a flexible manufacturing model system through the introduction of the Industrial Internet of Things and flexible manufacturing production management. In order to meet the market needs, the manufacturing industry is gradually oriented towards customized production and the rapid development of new products. To meet such stringent requirements, flexible manufacturing becomes one of the necessary ways for enterprises to consider their development models. Therefore, the efficiency and reliability of work can be improved through the Industrial Internet of Things that facilitates machine-to-machine communication, cloud-based big data and learning and imitations of smart robots. This study is an in-depth study of a company that is currently in the process of digital transformation, collecting relevant information and reviewing the analysis to find a suitable smart manufacturing solution for the company and to explore the impact of the COVID-19 pandemic on the strategic development of the company. The findings can provide a significant reference for homotypic companies in the development of their business strategies.

Episodic Retail Settings: A Sustainable and Adaptive Strategy for City Centre Stores

The fact that an already damaged retail industry is being challenged by a pandemic makes the industry’s survival a matter of urban resilience. Sustainable and adaptive strategies are needed to reverse the negative development of the retail sector, and in this conceptual paper, a new perspective is suggested based on episodic retail settings. Such a perspective can increase a physical store’s attraction and may serve as a flexible operation strategy for urban retailers and give added value to urban consumers as they shape an ongoing dramatological discourse and facilitate social interaction in a way that traditional fixed-store formats are unable to compete with. By applying the scientific circle of enquiry (SCE), the authors develop an interdisciplinary perspective cutting across the sustainability, service science, and urban studies fields. On this ground, they present a set of conceptual premises and a tripartite conceptual framework delineating how to effectively design episodic retail settings that are adaptive and sustainable. The paper concludes with suggestions for research questions to further advance this field of study.

Simulating the Impact of Climate Change with Different Reservoir Operating Strategies on Sedimentation of the Mangla Reservoir, Northern Pakistan

Reservoir sedimentation reduces the gross storage capacity of dams and also negatively impacts turbine functioning, posing a danger to turbine inlets. When the sediment delta approaches the dam, further concerns arise regarding sediments passing through turbine intakes, blades abrasion due to increased silt/sand concentration, choking of outlets, and dam safety. Thus, slowing down the delta advance rate is a worthy goal from a dam manager’s viewpoint. These problems can be solved through a flexible reservoir operation strategy that prioritize sediment deposition further away from the dam face. As a case study, the Mangla Reservoir in Pakistan is selected to elaborate the operational strategy. The methodology rests upon usage of a 1D sediment transport model to quantify the impact of different reservoir operating strategies on sedimentation. Further, in order to assess the long-term effect of a changing climate, a global climate model under representative concentration pathways scenarios 4.5 and 8.5 for the 21st century is used. The reduction of uncertainty in the suspended sediments concentration is achieved by employing an artificial neural networking technique. Moreover, a sensitivity analysis focused on estimating the impact of various parameters on sediment transport modelling was conducted. The results show that a gradual increase in the reservoir minimum operating level slows down the delta movement rate and the bed level close to the dam. However, it may compromise the downstream irrigation demand during periods of high water demand. The findings may help the reservoir managers to improve the reservoir operation rules and ultimately support the objective of a sustainable reservoir use for the societal benefit.

Design and analysis of a dual-receiver direct steam generator solar power tower plant with a flexible heliostat field

The direct steam generator (DSG) solar power tower plant concept is receiving more attention due to its ability to provide superheated steam without any intermediate heat exchanger and heat transfer fluid. However, the literature on the time-dependent or quasi-static performance evaluation of these systems is limited. In this study, a thermoeconomic assessment of a DSG solar tower with a dual receiver mechanism integrated with a regenerative steam Rankine cycle is carried out. A quasi-static analysis of the system is provided to study the performance of a DSG solar power tower under different operating conditions, i.e. ambient temperature and solar irradiation. To do so, a flexible operation strategy is developed for the heliostat field at which the number of mirrors directed toward the evaporator and superheater is varied to maximize the output of the DSG system. The developed control strategy provides flexibility and improvement in the system’s performance in off-design conditions. Thermoeconomic analysis showed that a 50 MWe plant LCOE is 127.7 US$/MWh with an annual solar share of 42.3%. The plant LCOE is reduced to 114.6 US$/MWh by increasing the plant capacity to 150 MWe.

Real-Time Active-Reactive Optimal Power Flow with Flexible Operation of Battery Storage Systems

In this paper, a multi-phase multi-time-scale real-time dynamic active-reactive optimal power flow (RT-DAR-OPF) framework is developed to optimally deal with spontaneous changes in wind power in distribution networks (DNs) with battery storage systems (BSSs). The most challenging issue hereby is that a large-scale ‘dynamic’ (i.e., with differential/difference equations rather than only algebraic equations) mixed-integer nonlinear programming (MINLP) problem has to be solved in real time. Moreover, considering the active-reactive power capabilities of BSSs with flexible operation strategies, as well as minimizing the expended life costs of BSSs further increases the complexity of the problem. To solve this problem, in the first phase, we implement simultaneous optimization of a huge number of mixed-integer decision variables to compute optimal operations of BSSs on a day-to-day basis. In the second phase, based on the forecasted wind power values for short prediction horizons, wind power scenarios are generated to describe uncertain wind power with non-Gaussian distribution. Then, MINLP AR-OPF problems corresponding to the scenarios are solved and reconciled in advance of each prediction horizon. In the third phase, based on the measured actual values of wind power, one of the solutions is selected, modified, and realized to the network for very short intervals. The applicability of the proposed RT-DAR-OPF is demonstrated using a medium-voltage DN.

Optimal configuration of modular cogeneration plants integrated by a battery energy storage system providing peak shaving service

Combined heat and power (CHP) generation is a fundamental practice to reduce primary energy consumptions and mitigate the related greenhouse gas emissions. Cogeneration plants are even more important in the Hospital sector, as they give the opportunity to operate regardless of the electric power grid, so ensuring energy availability also during grid faults while providing significant cost savings to the public healthcare system. The integration of a battery energy storage with the CHP system could further increase the expected advantages by enabling more flexible operating strategies, further energy reliability and lower operating costs. Therefore, this research paper addresses the development of a specific methodology for the energetic and economic assessment of a grid connected CHP plant assisted by a battery energy storage. More specifically, an evaluation algorithm has been developed and then coupled to a genetic evolutionary algorithm. Then, a vector optimization problem has been solved to find the optimal configurations of a modular cogeneration plant (i.e. size and number of CHP gas engines; battery size) which maximize the primary energy saving while minimizing the payback period. Results based on electricity and heat demand of a hospital facility demonstrate how the battery energy storage system allows the shifting of the Pareto frontier towards better economic results if compared with Pareto solutions found when the optimization problem does not consider a battery storage supporting the CHP plant. The proposed methodology provides an effective and flexible procedure for the optimal configuration and detailed analysis of CHP plants integrated by an electrochemical based energy storage system. In fact, the effects of a high number of interacting energetic and economic parameters are considered together with the technical constraints required to extend battery lifetime in real applications.

Multi-timescale, multi-period decision-making model development by combining reinforcement learning and mathematical programming

This study focuses on the linkage between decision layers that have different time scales. The resulting expansion of the boundary of decision-making process can provide more robust and flexible management and operation strategies by resolving inconsistencies between different levels. For this, we develop a multi-timescale decision-making model that combines Markov decision process (MDP) and mathematical programming (MP) in a complementary way and introduce a computationally tractable solution algorithm based on reinforcement learning (RL) to solve the MP-embedded MDP problem. To support the integration of the decision hierarchy, a data-driven uncertainty prediction model is suggested which is valid across all time scales considered. A practical example of refinery procurement and production planning is presented to illustrate the proposed method, along with numerical results of a benchmark case study.

Flexible Operation Strategies for Coal- and gas-CCS Power Stations under the UK and USA Markets

The increased penetration of the intermittent renewable energy has increased the demand for flexible electricity supply. In this work, we evaluate four distinct strategies for flexible operation of CCS power plants: load following, solvent storage, exhaust gas by-pass and variable solvent regeneration (VSR) for coal- and gas-CCS power stations. With the aim to decoupling the power and capture plants in order to maximize profits, a multi-period dynamic optimisation problem was formulated and solved in the context of UK- and US-type markets. It was found that whilst the flexible operation strategies are strongly affected by the different markets, in all cases the variable solvent regeneration strategy was found to be the most profitable.

Optimisation and evaluation of flexible operation strategies for coal- and gas-CCS power stations with a multi-period design approach

Thermal power plants are increasingly required to balance power grids by compensating for the intermittent electricity supply from renewable energy resources. As CO2 capture and storage is integrated with both coal- and gas-fired power plants, it is vital that the emission mitigation technology does not compromise their ability to provide this high-value service. Therefore, developing optimal process operation strategies is vital to maximise both the value provided by and the profitability of these important assets. In this work, we present models of coal- and gas-fired power plants, integrated with a post-combustion CO2 capture process using a 30wt% monoethanolamine (MEA) solvent. With the aim to decoupling the power and capture plants in order to facilitate profit maximising behaviour, a multi-period dynamic optimisation problem was formulated and solved using these models. Four distinct scenarios were evaluated: load following, solvent storage, exhaust gas by-pass and variable solvent regeneration (VSR). It was found that for both coal- and gas-fired power plants, the VSR strategy is consistently the most profitable option. The performance of the exhaust by-pass scenario is a strong function of the carbon prices and is only selected at very low carbon prices. The viability of the solvent storage strategy was found to be a strong function of the capital cost associated with the solvent storage infrastructure. When the cost of the solvent tanks has been paid off, then the solvent storage scenario is 3.3% and 8% more profitable than the baseline for the pulverised coal and gas-fired power plants, respectively. Sensitivity analyses showed that, for all strategies, the flexibility benefit declined with reduced carbon and fuel prices, while a “peakier” electricity market, characteristic of one with significant quantities of intermittent renewables deployment, more significantly rewarded flexible operation.