Flexible Energy System Research Articles

Development of feed-forward model predictive control for applications in biomass bubbling fluidized bed boilers

In order to accommodate more intermittent renewable energy sources, biomass fueled combined heat and power plants (bio-CHPs) can contribute towards sustainable and flexible energy systems. However, the varying properties of biomass, such as moisture contents and heating values, can clearly affect the combustion in boilers, which further affects the flexibility provided by bio-CHPs. In order to achieve better control, this paper proposes a feed-forward model predictive controller (FF MPC) to handle the variation of biomass properties. A dynamic model was built in Dymola to simulate the performance of a bubbling fluidized bed boiler, which was validated against the real operation data. Based on the simulation, the key manipulated variables were optimized for the given controlled variables. The advantages of the proposed FF MPC were demonstrated through comparisons with proportional–integral (PI), FF PI and MPC. The results of FF MPC show the best performance, such as the lowest magnitude of fluctuations for 3 outputs (thermal load, steam and fluidized bed temperature), and the most stable operation. Consequently, FF MPC can potentially increase the electricity generation and further lead to an economic benefit. Using one week in winter as an example, compared to PI, FF PI and MPC, FF MPC can generate more electricity and improve revenues by 14.77 MWh/590 €, 4.1 MWh/164 € and 5.03 MWh/211.2 € respectively.

Recent progress and prospects in the electrode materials of flexible sodium-ion battery

Flexible electrochemical energy systems (EES) have gained huge market value for powering flexible and wearable electronics from roll-up displays to implantable medical devices. Flexible EES should possess high flexibility under mechanical deformation (bending, folding, twisting and stretching), high energy and power density and simple design at low cost. Sodium-ion battery (NIB) is considered as a optimistic candidate to replace the flexible lithium-ion battery, due to low cost and large-scale availability. Here, we give a brief introduction to the electrode (anode and cathode) of flexible NIB. The article focused detailing on carbon-based (graphene, carbon nanofiber, and heteroatom doped material), metal composite-based material for the anode and layered transition metal oxide, polyanionic for the cathode. Among these categorized materials, anode with metal supported by carbon framework exhibits the highest charge capacity with 612.5 mAhg −1 and cathode with polyanionic material on carbon substrate shows promising electrochemical activity. The electrical conductivity and the mechanical stability cum flexibility of carbon support plays a vital role in battery performance by overcoming the formation of a solid-electrolyte interface. Metal incorporated in such support will render high energy and power capacity to the battery. The review also covers, a general perspective on flexible NIB with regards to current and future industrial scope in continents like Asia, Europe and American are covered. • Flexible sodium ion battery is a potential alternate to flexible lithium-ion battery. • It is used from roll-up display to bio-medical devices. • Metal composite on heteroatom doped carbon framework anode material exhibits high capacity with long cyclic stability. • Carbonaceous based polyanionic cathode material provide excellent mechanical flexibility with high-rate performance.

Economic Optimization Dispatch Model of a Micro-Network with a Solar-Assisted Compressed Air Energy Storage Hub, with Consideration of Its Operationally Feasible Region

Using a variety of renewable energy sources can significantly improve energy system flexibility and efficiency. Energy hubs, which have the function of generating, converting, and storing energy in various forms, are vital facilities in micro-energy networks (MENs). In this paper, we present a Solar-Assisted Compressed Air Energy Storage (SA-CAES) hub which can accommodate and flexibly supply multi-energy by being connected to a power distribution network (PDN) and a district heating network (DHN). We formulate economic dispatch models of the SA-CAES hub, the PDN, and the DHN, respectively. The economic dispatch model is formulated as a mixed-integer linear programming problem (MILP) that can be solved by commercial solvers. Further, the operationally feasible region of the SA-CAES hub is explored by thermodynamic analysis. The results indicate that the operation costs have been reduced by 4.5% in comparison with conventional MENs.

Electrolytic water technology based on transformable and amorphous liquid metal electrodes

AbstractHydrogen has important values in industrial manufacture, medical health care, and diverse energy application areas. Among the many possible approaches, convenient and safe hydrogen production from water electrolysis has always been a preferred in situ hydrogen supply technology. So far, many of the electrodes thus involved are based on rigid metals or conductive items which may encounter inconvenience or insufficiency sometimes. To achieve targeted release of hydrogen, appropriate administration of suitable flexible hydrogen evolution electrodes is the key. In recent years, gallium‐based liquid metals have emerged in the field of flexible and biological electrodes. Starting from this point, liquid metal is proposed here as a transformable amorphous electrode for hydrogen evolution in electrolytic water which may help mold new conceptual electrolysis and energy utilization ways. The electrochemical performances and flexible behaviors lying behind are investigated and clarified. According to the experiments, the catalytic activity of the liquid metal itself generally is not remarkable, but it would be significantly improved after being composited with certain active materials such as nickel. Further, the controllability, flexibility, and adaptability of liquid metal‐based amorphous materials which are unmatched by other electrode materials were evaluated. Overall, the motions of such shape transformable liquid metal electrodes could be controlled by electric or magnetic fields, and their deformations are compliantly adaptive to different spaces. This might easily expand the area of hydrogen evolution and lead to multisite in situ hydrogen production. The present study demonstrates the value of liquid metals in flexible energy systems and opens up more application possibilities for electrolytic hydrogen production.

Optimization of the operation plan taking into account the flexible resource scheduling of the integrated energy system

Under the high proportion of renewable energy access in the integrated energy system, the insufficient total allocation of flexible resources and the unreasonable spatial allocation of resources are important causes of wind abandonment and load shedding. The fundamental solution to this problem is to increase the flexibility of the system to meet demand, so it is important to study multiple types of flexible resources, the state of supply and demand for system flexibility and the optimization of flexibility resource scheduling. This paper studies the flexibility of the integrated energy system. First, it analyzes the basic characteristics of the flexible resources of the system and gives the basic structure of the integrated energy system;Then, the typical flexible resources are modeled and analyzed. Based on the time-of-use price, the minimum daily operation cost of the system is optimized as the objective function, and the particle swarm optimization algorithm is introduced to solve it, so as to explore the flexible adjustment of the system with the participation of flexible resources;Finally, the model established in this paper is used to verify the rationality of the scheduling scheme, and the relationship between the access effect of flexible resources and the location of access nodes is explored. The research on the modeling of integrated energy system and system flexibility in this paper not only provides a tool to reflect the fluctuation of system supply and demand state, but also provides an important basis for system reasonable scheduling, ensuring energy supply quality and improving operation flexibility.

Predictive control of a combined heat and power plant for grid flexibility under demand uncertainty

In recent years, the increasing penetration of non-programmable renewable energy technologies has made energy system flexibility essential for ensuring power grid stability, in terms of balance between power demand and supply. In particular, cogeneration plants can provide flexibility services by making a defined amount of power available at a given schedule, upon request. The grid operator can request it or not depending on the actual dispatch management. In the presence of this uncertain request, producers need smart controllers to meet thermal demand and, at the same time, to maximize profit while fulfilling the needs of the grid. This work presents a hierarchical predictive control architecture for optimal management of combined heat and power production sites equipped with a thermal energy storage tank supplying district heating networks. The feasibility of the proposed approach is investigated by simulating several scenarios with different degrees of uncertainty about the actual power request from the grid operator. It is shown that the controlled system is able to comply with the requirements to the maximum extent when the exact power dispatch is known a few hours in advance, also giving a profit increase as well as environmental benefits. While the grid operator request for flexibility is met up to 100%, the reduction in carbon emissions ranges from 3% to 7% and the operating margin is increased by 20%. This can be realized when the power availability is scheduled at periods of both maximum and minimum heat demand. Thus, the method is promising also in improving production planning of future integrated energy systems from the perspective of grid flexibility.

Techno-economic analysis of Power-to-Gas plants in a gas and electricity distribution network system with high renewable energy penetration

Distributed generation, based on the exploitation of Renewable Energy Sources (RES), has increased in the last few decades to limit anthropogenic carbon dioxide emissions, and this trend will increase in the future. However, RES generation is not dispatchable, and an increasing share of RES may lead to inefficiencies and even problems for the electricity network. Flexible resources are needed to handle RES generation in order to support the delicate electricity generation and demand balance. Energy conversion technologies (P2X, Power to X) allow the flexibility of energy systems to be increased. These technologies make a connection between different energy sectors (e.g., electricity and gas) possible, and thus create new synergies within an overall multi-energy system. This paper analyzes how the P2G technology can be used at the distribution network level (both gas and electricity) to optimize the use of RES. In fact, in order to coordinate P2X resources, it is necessary to take into account the whole multi-energy scenario, and not just the electrical side: it therefore becomes fundamental to recognize the pros and cons that Balancing Service Providers (BSPs), composed of a number of P2G plants (representing the Balancing Responsible Providers, BRPs), may have when offering services to an electricity network. Moreover, the convenience of the decarbonization of the gas grid has been evaluated through the calculation of the levelized cost of Synthetic Natural Gas (LCSNG) for cost scenarios for the years 2030 and 2050, considering different assumptions about the cost of the surplus utilization of RES. The results show that LCSNG may vary from 47 to 319 €/MWh, according to the different configurations, i.e., only in the best-case scenario is the SNG cost comparable with the cost of natural gas, and hence does the P2G technology result to be profitable.

A Generic Framework for the Definition of Key Performance Indicators for Smart Energy Systems at Different Scales

The growing integration of intermittent renewable energy sources (RESs) and the increasing trend of shutting down fossil-fuel-based power plants has brought about the need for additional flexibility in energy systems. This flexibility can be provided in various forms, including controllable generation and consumption, storage, conversions, and exchanges with interconnected systems. In this context, an increasing focus is placed on the development of smart energy systems (SESs) that combine different types of distributed energy resources (DERs), information and communication technologies (ICTs), demand side management (DSM), and energy conversion technologies. The utilization of SESs can lead to multiple benefits for the stakeholders involved; therefore, the assessment of their performance is a primary concern. Due to their multidisciplinary nature, there are no known or universally accepted standards for assessing the performance of SESs. Previous efforts only define key performance indicators (KPIs) for individual homogeneous subsystems, focusing on a specific SES type and application area. This paper focuses on the development of a novel comprehensive KPI framework that can be applied to any type of SES, regardless of the application area. The proposed framework consists of four layers that specify the application area, the main SES requirements, and the involved stakeholders’ objectives. Next, the KPIs are identified for each of the stakeholders’ objectives. The proposed KPI framework is applied to the use case of a European research project with different application areas, to demonstrate its features. Finally, a repository of KPIs is identified for each use case with respect to the aforementioned SES requirements.

Modeling flexibility in energy systems — comparison of power sector models based on simplified test cases

Model-based scenario analyses of future energy systems often come to deviating results and conclusions when different models are used. This may be caused by heterogeneous input data and by inherent differences in model formulations. The representation of technologies for the conversion, storage, use, and transport of energy is usually stylized in comprehensive system models in order to limit the size of the mathematical problem, and may substantially differ between models. This paper presents a systematic comparison of nine power sector models with sector coupling. We analyze the impact of differences in the representation of technologies, optimization approaches, and further model features on model outcomes. The comparison uses fully harmonized input data and highly simplified system configurations to isolate and quantify model-specific effects. We identify structural differences in terms of the optimization approach between the models. Furthermore, we find substantial differences in technology modeling primarily for battery electric vehicles, reservoir hydro power, power transmission, and demand response. These depend largely on the specific focus of the models. In model analyses where these technologies are a relevant factor, it is therefore important to be aware of potential effects of the chosen modeling approach. For the detailed analysis of the effect of individual differences in technology modeling and model features, the chosen approach of highly simplified test cases is suitable, as it allows to isolate the effects of model-specific differences on results. However, it strongly limits the model’s degrees of freedom, which reduces its suitability for the evaluation of fundamentally different modeling approaches.

Quantifying demand flexibility of building energy systems under uncertainty

Quantification of demand flexibility is of key importance to decision makers at the planning level, when designing demand response programs and determining capacity requirements for new battery storage installations, and, at the operation level, when assessing the demand-side potential for providing grid services. While different frameworks have been proposed for demand flexibility quantification, the literature still lacks a framework that considers uncertainty sources, affecting building energy control problems, when directly quantifying the demand flexibility potential. We propose a novel framework based on stochastic model predictive control for direct demand flexibility quantification in a bottom-up manner that can account for uncertainty. The proposed framework is utilized to quantify the flexibility potential of a building under uncertainty arising from weather forecasts and the available reduced-order model. The results are compared with those obtained using an existing deterministic approach. We demonstrate that the deterministic approach tends to overestimate the flexibility potential on account of ignoring uncertainty, and the occupants’ thermal comfort may be jeopardized if the grid asks for the flexibility estimated with the deterministic approach. The study accentuates the importance of developing an accurate model for model-based demand flexibility quantification owing to the significant effect of the modeling uncertainty.

Investigation and evaluation of building energy flexibility with energy storage system in hot summer and cold winter zones

The dramatically increasing energy demand of building air-conditioning in hot summer and cold winter zones fluctuates greatly, especially during the period of cooling and heating in summer and winter, which exerts strict operation burden and challenge on power-grid system stability. In order to realize building energy flexibility, this paper proposes a type of energy utilization system including electricity chiller, gas-boiler, electricity heater, cold and thermal energy storage tank (case II), on the basis of electricity chiller and gas-boiler for cooling and heating demand (case I), which is commonly used as cold and heat source in hot summer and cold winter zones. The total cost of the whole life period and annual carbon emission of energy systems are served as objective function is optimized. Furthermore, the system capacity confirmation and load demand management control are analyzed and discussed, the characteristics of energy-flexibility with and without energy storage are presented, the load coefficient is introduced to evaluate the impact of case II on building energy flexibility. Compared with case I, with the deployment of cold and thermal energy storage, the cost-effectiveness of case II is 16.5% and carbon emission is reduced by 30.9% compared with case I .In case II, the load coefficient of electricity chiller and electricity heater is 83.34% and 30.65% respectively, which means that energy storage system enables to promote building energy flexibility improvement and grid-friendliness enhancement. This paper provides methods and theoretical guidance for the planning-operation of building energy system and building energy flexibility in hot summer and cold winter zones.

Optimal design of a hybrid renewable energy system with grid connection and comparison of techno-economic performances with an off-grid system: A case study of West China

Reliable and sustainable power supply for remote rural areas is of vital importance for development of these areas. In this paper, the techno-economic feasibility and flexibility of hybrid renewable energy systems are demonstrated under both off-grid and on-grid modes for rural electrification, where a case study of a village in West China is presented. According to simulation and optimization results as well as sensitivity analysis, the most cost-competitive configuration is found by Hybrid Optimization of Multiple Energy resources (HOMER). Comparison of the off-grid and on-grid modes of the hybrid system has been carried out. The results demonstrate that a hybrid renewable energy system which comprises solar, wind and biomass is reliable and cost-effective regarding sustainable rural electrification. Moreover, environmental and social benefits can also be acquired by this system. The high capability of the proposed flexible design is demonstrated over conditions for both off-grid and on-grid modes.

An overview of machine learning applications for smart buildings

The efficiency, flexibility, and resilience of building-integrated energy systems are challenged by unpredicted changes in operational environments due to climate change and its consequences. On the other hand, the rapid evolution of artificial intelligence (AI) and machine learning (ML) has equipped buildings with an ability to learn. A lot of research has been dedicated to specific machine learning applications for specific phases of a building's life-cycle. The reviews commonly take a specific, technological perspective without a vision for the integration of smart technologies at the level of the whole system. Especially, there is a lack of discussion on the roles of autonomous AI agents and training environments for boosting the learning process in complex and abruptly changing operational environments. This review article discusses the learning ability of buildings with a system-level perspective and presents an overview of autonomous machine learning applications that make independent decisions for building energy management. We conclude that the buildings’ adaptability to unpredicted changes can be enhanced at the system level through AI-initiated learning processes and by using digital twins as training environments. The greatest potential for energy efficiency improvement is achieved by integrating adaptability solutions at the timescales of HVAC control and electricity market participation.

A Flexible Nuclear Energy System Using Cryptoassets as Enablers: Economic Assessment

A Flexible Nuclear Energy System Using Cryptoassets as Enablers: Economic Assessment

Multifunction Battery Energy Storage System for Distribution Networks

Battery Energy Storage System (BESS) is one of the potential solutions to increase energy system flexibility, as BESS is well suited to solve many challenges in transmission and distribution networks. Examples of distribution network’s challenges, which affect network performance, are: (i) Load disconnection or technical constraints violation, which may happen during reconfiguration after fault, (ii) Unpredictable power generation change due to Photovoltaic (PV) penetration, (iii) Undesirable PV reverse power, and (iv) Low Load Factor (LF) which may affect electricity price. In this paper, the BESS is used to support distribution networks in reconfiguration after a fault, increasing Photovoltaic (PV) penetration, cutting peak load, and loading valley filling. The paper presents a methodology for BESS optimal locations and sizing considering technical constraints during reconfiguration after a fault and PV power generation changes. For determining the maximum power generation change due to PV, actual power registration of connected PV plants in South Cairo Electricity Distribution Company (SCEDC) was considered for a year. In addition, the paper provides a procedure for distribution network operator to employ the proposed BESS to perform multi functions such as: the ability to absorb PV power surplus, cut peak load and fill load valley for improving network’s performances. The methodology is applied to a modified IEEE 37-node and a real network part consisting of 158 nodes in SCEDC zone. The simulation studies are performed using the DIgSILENT PowerFactory software and DPL programming language. The Mixed Integer Linear Programming optimization technique (MILP) in MATLAB is employed to choose the best locations and sizing of BESS.

Techno-economic assessment of hybrid energy flexibility systems for islands’ decarbonization: A case study in Italy

Geographical Islands have been considered a challenging environment to test renewable energy integration strategies as well as cutting-edge technologies due to the alternation between actual grid-connected and island mode. Energy system flexibility is foreseen as a strategy to integrate higher renewable share and reduce adverse events to the Power Grid. At the same time, the storage options for supporting high Renewable Energy Systems (RES) share remain still just an option in many countries, especially for Europe. In this research, the Catania city is considered for the case study in the Mediterranean climate region from Sicily Island in Italy. HOMER software is used to simulate the economic and technical use of photovoltaic panels/wind turbines/fuel cell/electrolyzer and hydrogen tank to meet the demand for 2 MW energy. It is pointed that photovoltaic panels and wind turbines can complement each other in some months of the year for production power. Photovoltaic panels provided 72% of the total energy and have higher efficiency than wind turbines and fuel cells. The results show that to meet the needs of 2 MW of the region, a capital cost of 4.85 million euros should be spent, which return of investment (ROI) is 2.59 million euros in the estimated 25-year life of the project.

Maximum production point tracking method for a solar-boosted biogas energy generation system

Low biogas yield in cold climates has brought great challenges in terms of the flexibility and resilience of biogas energy systems. This paper proposes a maximum production point tracking method for a solar-boosted biogas generation system to enhance the biogas production rate in extreme climates. In the proposed method, a multi-dimensional R–C thermal circuit model is formulated to analyze the digesting thermodynamic effect of the anaerobic digester with solar energy injection, while a hydrodynamic model is formulated to express the fluid dynamic interaction between the hot-water circulation flow and solar energy injection. This comprehensive dynamic model can provide an essential basis for controlling the solar energy for digester heating to optimize anaerobic fermentation and biogas production efficiency in extreme climates. A model predictive control method is developed to accurately track the maximum biogas production rate in varying ambient climate conditions. Comparative results demonstrate that the proposed methodology can effectively control the fermentation temperature and biogas yield in extreme climates, and confirm its capability to enhance the flexibility and resilience of the solar-boosted biogas generation system.

Impact of the COVID-19 Pandemic to the Sustainability of the Energy Sector

In order to control the COVID-19 pandemic, the governments of the world started to implement measures regarding social distance and social contacts, including closures of cities, work and study relocations, and work suspension. The epidemical situation and the lockdown of the economy by governments in various countries caused changes in production, changes in the habits of energy consumers and other energy-related changes. This article analyses the impact of the global pandemic on the energy sector and the relationship with the progress to the sustainability of the energy sector. The systematic literature review was performed in the Web of Science (WoS) database. The research follows recommendations of the SALSA (Search, Appraisal, Synthesis and Analysis) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approaches. A total of 113 relevant articles were selected for the analysis. All selected articles were categorized according to their application and impact areas. The five main impact areas of the COVID-19 pandemic to the sustainability of the energy sector were identified: consumption and energy demand; air pollution; investments in renewable energy; energy poverty; and energy system flexibility. Based on the current research findings and perception of the problem, the main insights for future research in the field are provided.

Computationally efficient integrated design and predictive control of flexible energy systems using multi‐fidelity simulation‐based Bayesian optimization

AbstractWe present a multi‐fidelity black‐box optimization approach for integrated design and control (IDC) of constrained nonlinear systems in the presence of uncertainty. The IDC framework is becoming increasingly important for the systematic design of next‐generation (flexible) manufacturing and energy systems. However, identifying optimal solutions to realistic IDC problems is intractable when (i) the dynamics occur on much shorter timescales than the system lifetime, (ii) the uncertainties are described by continuous random variables with high variance, and (iii) operational decisions involve a mixture of discrete and continuous variables. Instead of aggressively simplifying the problem to improve tractability, we develop a simulation‐based optimization procedure using high‐quality decision rules that map information that can be measured online to optimal control actions. In particular, we rely on the Bayesian optimization (BO) framework that has been shown to perform very well on noisy and expensive‐to‐evaluate objective functions. We also discuss how BO can be extended to take advantage of computationally cheaper low‐fidelity approximations to the high‐fidelity IDC cost function. Three major low‐fidelity approximation strategies are described in this work, which are related to the simplification of the system simulator, decision rule solution method, and time grid. Lastly, we demonstrate the advantages of multi‐fidelity BO on the design of a solar‐powered building heating/cooling system (with battery and grid support) under uncertain weather and demand conditions with hourly variation over a year‐long planning horizon.

Analysis of taxation and framework conditions for hybrid power plants consisting of battery storage and power-to-heat providing frequency containment reserve in selected European countries

Flexible energy plants are one of the key requirements for future energy systems with high levels of fluctuating renewable energy. In the course of the transition to sustainable energy systems, regulatory frameworks and tax systems should promote carbon-reduced flexible power plants in a timely manner.This paper considers hybrid systems consisting of battery energy storage systems (BESS) and Power-to-Heat (PtH) modules which can contribute to a more flexible energy system by providing Frequency Containment Reserve (FCR). Contrary to many papers, this contribution explicitly focuses on taxes for FCR providing power plants, which are incurred annually or based on energy consumption. Additionally, regulatory frameworks are investigated, meaning requirements for FCR provision and conditions for energy trading. The effects of these factors on the economic efficiency of hybrid power plants providing FCR are analysed.The regulatory framework conditions and tax systems from three countries are analysed: Germany, France and Austria. For each of these countries four scenarios are simulated in which the net present values (NPV) are calculated considering the corresponding national tax systems and framework conditions. Additionally, operational strategies using the degrees of freedom (DoF) are examined regarding their influence on the economic performance.The comparison shows a huge influence of taxes on the profitability of the hybrid system. Framework conditions mostly play a minor role in this context. Compared to a benchmark scenario with uniform framework conditions and without taxes, on average the NPV decreases more rapidly considering taxes (−107 k€ in France to −710 k€ in Austria) than considering country specific framework conditions (−122 k€ in France to −308 k€ in Austria). Since framework conditions mostly determine the size of the battery capacity, they primarily affect the investment costs. Additionally, the longer the time slices and the earlier the gate closure is, the more often the hybrid systems violate requirements for FCR provision.