Search

Variable renewable energy-based power is expected to increase toward a sustainable society, although the power cannot be dispatched effectively due to its intermittent nature. To accelerate renewable energy implementation, we simulated energy flows of paper mills installing wind energy and evaluated their potentials to function as energy hubs for appropriate early-stage design. We targeted 39 Japanese paper mills assumed to have thermal energy storage, wind–thermal energy converters and regional woody biomass installed, and analyzed the amount of power and heat selling and life cycle greenhouse gas emissions. The results for the paper mills were compared with conventional mills to examine effective conditions for greenhouse gas reduction. The amount of power or heat selling depended on the capacity of the paper mill and the installed equipment. Most paper mills could reduce greenhouse gas using thermal energy storage, wind energy, and woody biomass. The paper mill with the highest life cycle greenhouse gas reduction, compared with conventional mills, could achieve a 190% reduction. To function as a cogeneration energy hub, the equipment installed in paper mills should be designed according to the conditions of the paper mills and their regions. • Paper mills have potential as energy hubs by integrating variable renewable energy. • Thermal energy storage and wind–thermal energy converters were studied. • Energy flows and greenhouse gas emissions of paper mills were simulated. • Most mills could provide stable power and/or heat supplies and reduce emissions. [ABSTRACT FROM AUTHOR]

This work reports the charge storage ability of Ni x Co 1-x (OH) 2 nanosheet-carbon nanofoam paper (CNFP) composites prepared by electrodeposition for high areal capacity supercapacitor electrodes. The Ni x Co 1-x (OH) 2 film grew through instantaneous nucleation, uniformly covered CNFP, and displayed a porous morphology composed of a nanosheet percolation network. The electrochemical response of the Ni x Co 1-x (OH) 2 films was tuned by varying the Co to Ni ratios. The optimized Ni x Co 1-x (OH) 2 stoichiometry resulted in Ni 0.33 Co 0.67 (OH) 2 -CNFP electrodes displaying areal capacity/capacitance values of 1.52 C cm −2 /2.03 F cm −2 at 2.1 mA cm −2 , resulting from the contribution of the double layer capacity of CNFP with the redox capacity of Ni x Co 1-x (OH) 2 . A two electrode cell composed of CNFP as negative electrode and Ni x Co 1-x (OH) 2 -CNFP as positive electrode presented a high areal capacity value of 0.73 C cm −2 at 7.2 mA cm −2 . [ABSTRACT FROM AUTHOR]

Deep Learning (DL) models, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), have been widely used to predict the intermittency of wind power; however, the non-linear activation functions and backpropagation mechanisms in DL models increase computational complexity and energy consumption. This paper proposes a prediction model based on Green Learning (GL) to reduce energy consumption. The proposed GL model replaces the feature extraction of activation functions with a hybrid feature extraction approach combining categorical and numerical features. We also employ cluster centroids and quantile regression forest for classification/regression to eliminate the need for backpropagation in optimizing hyperparameters. Using Taiwan as a case study, this paper evaluates the risk of fluctuations in regional wind power generation in 2030. In simulations, the proposed GL model achieved excellent accuracy with energy consumption significantly lower than that of DL models. Our analysis also revealed that by 2030, fluctuations in wind power generation during the winter will exceed 40% of the peak supply capacity in the central region, indicating the need to enhance the resilience of regional power systems. • This is the first study to apply green learning to wind power prediction. • This study discriminated seasonal climate patterns at the regional level. • This study assessed the fluctuation risk under large-scale wind power integration. • The maximum hourly variability in wind power is projected to exceed 6,010 MWh by 2030. • We recommend the continued deployment of regional energy storage systems. [ABSTRACT FROM AUTHOR]

This paper proposes a distribution network fault emergency power supply recovery strategy based on 5G base station energy storage. This strategy introduces Theil's entropy and modified Gini coefficient to quantify the impact of power supply reliability in different regions on base station backup time, thereby establishing a more accurate base station's backup energy storage capacity model that can fully utilize the base station's energy storage resources to participate in the emergency power supply of distribution network faults. First, the optimal Copula function in each cycle is determined through the Akaike information criterion and squared Euclidean distance to establish a wind power-photovoltaic output scenario. Secondly, the traditional Gini coefficient is modified using the weighted average node degree and influence rate indicators. A comprehensive vulnerability model is established through the modified Gini coefficient and Theil's entropy indicators. Based on the comprehensive vulnerability model, a backup energy storage time model and a modified backup energy storage capacity model of the base station affected by power supply reliability are established. Thus, the callable energy storage capacity of base stations in different areas is obtained. Finally, a two-stage robust optimization model is introduced to minimize system operating costs to solve the volatility of 5G base station communications and wind-solar output, thereby establishing an emergency power supply recovery model for base station energy storage and wind-solar output. Simulated with the improved IEEE-33 node model, the results show that the proposed base station's energy storage model improves the utilization of the base station energy storage resources and, at the same time, effectively reduces the loss of load during the fault phase of the distribution network and improves the absorption of the PV output. • Modeling of 5G base station backup energy storage. Aiming at the shortcomings of existing studies that ignore the time-varying characteristics of base station's energy storage backup, based on the traditional base station energy storage capacity model in the paper [ 18 ], this paper establishes a distribution network vulnerability index to quantify the power supply reliability of the distribution network nodes by adopting the voltage Theil's entropy and the modified Gini coefficient. Based on the power supply reliability of power grid nodes and combined with load level weights, a model for the backup energy storage time of base stations affected by power supply reliability is established. • The model of 5G base station energy storage participating in power supply recovery In view of the impact of changes in communication volume on the emergency power supply output of base station energy storage in distribution network fault areas, this paper introduces a two-stage robust optimization model to deal with the uncertainty of communication volume. Based on the base station energy storage capacity model established in contribution (1), an objective function is established to minimize the system operating cost in the fault area, and the base station energy storage owned by mobile operators is used as an emergency power source to participate in power supply restoration. Establish sub-objective functions of the loss cost of base station energy storage charging and discharging, the subsidy cost of base station energy storage charging and discharging, and the base station energy storage operating cost to supplement the shortcomings of existing research. [ABSTRACT FROM AUTHOR]

The current single energy storage approach and market incentives do not fully exploit the potential of diverse energy storage methods and market incentives. Additionally, the traditional operation method often results in a poor alignment between energy supply and demand. Moreover, the conventional solution algorithm struggles to address complex problem-solving involving multiple variables. To address these issues, this paper proposes an improved carbon-neutral energy system (ICNES). Firstly, ICNES enhances residual energy utilization by implementing dual-layer residual power and heat treatment. Secondly, to enhance the effectiveness of market incentives, ICNES incorporates an improved approach that combines a dual-layer carbon capture drive, step carbon penalty, carbon reward, and green certificate trading. Thirdly, to improve the supply–demand matching, ICNES introduces a dynamic hybrid operation approach (DFHL). Finally, the paper proposes a honey badger-pattern search algorithm (HB-PSA) to optimize the configuration of ICNES for the DFHL multivariate scenario. Results indicate that compared to conventional carbon-neutral energy systems, ICNES reduces annual operating costs by 6.686%, CO 2 emissions by 9.538%, primary energy consumption by 12.826%, and energy waste rate by 17.465%. Furthermore, HB-PSA increases the optimization rate from 48.693% to 53.88% of the local optimum. • Dual-layer residual electric and residual heat treatment system is proposed. • Dual-layer carbon treatment for carbon neutrality and carbon profitability. • Carbon markets and green certificate are used in carbon-neutral energy system. • Dynamic hybrid operation to improve the matching of supply and demand. • A honey badger-pattern search algorithm is proposed for solving. [ABSTRACT FROM AUTHOR]

The solar-assisted heat pump (SAHP) systems are increasingly gaining attention for their potential feasibility in various fields due to their energy saving benefits. However, the available research shows that SAHP still has potential for improvement in drying filed, especially in terms of combining vapor compression heat pumps with solar energy. Consequently, this paper aims to summarize the development of SAHP technology and provide guidance for the application of SAHP technology in the drying field. The development of the SAHP systems is reviewed in this paper, including four ways to enhance the performance of SAHP systems are discussed: photovoltaic/thermal systems, collector optimization, energy storage and ejector enhancement. The technology of energy storage and ejector enhancement will inspire the development of SAHP technology. Additionally, research on optimizing the performance of hybrid heat source heat pump systems is also discussed, and a review of alternative refrigerants that could be used in SAHP systems. It could be found that R134a is the most widely used in SAHP systems currently, but it needs to be replaced soon for its high GWP. HFOs refrigerants are a good alternative. Furthermore, the application prospect of SAHP systems in drying field is also analyzed. Finally, based on the current research status of SAHP technology, the future development trend and research directions of solar-assisted heat pump drying (SAHPD) are proposed: developing suitable refrigerants for heat pump systems; researching high-efficiency heat storage technology; developing multi-heat source assisted heat pump systems; and optimizing the operation strategy of SAHPD using artificial intelligence. • Four ways to enhance SAHP systems are reviewed. • Study on optimization of hybrid heat source heat pump systems is summarized. • The alternative refrigerants in SAHP systems is investigated. • The application prospects of SAHP systems for drying field are analyzed. • The future development trend of SAHPD systems is proposed. [ABSTRACT FROM AUTHOR]

With the rapid development of demand-side management, battery energy storage is considered to be an important way to promote the flexibility of the user-side system. In this paper, a Stackelberg game (SG) based robust optimization for user-side energy storage configuration and basic electricity price decisions is proposed. Firstly, this paper put forward a two-stage energy management framework considering the interactive relationship between the supplier-side system and the user-side system. Secondly, based on the two-part electricity price mechanism, a bi-level optimal sizing of user-side energy storage is established in which robust dispatching is considered to deal with the uncertainty of renewable energy. Thus, a three-layer optimization model of "pricing on the power supply side–basic scenario configuration on the user side–worst-case scenario scheduling on the user side" is formulated. Through relaxing the state variables of energy storage in the configuration and scheduling models and combining Karush-Kuhn-Tucher conditions, the user-side model is transformed into a single-layer problem. A distributed algorithm based on the method of bisection is used to solve the two-stage SG problem. The simulation results demonstrate the basic electricity price and energy storage configuration suggestions and prove the superiority of the proposed method. [Display omitted] • A Stackelberg game-based three-layer optimization method is established for power pricing. • Demand declaration in the worst scenario is considered in the user-side energy storage configuration. • A bisection-based distributed algorithm and binary variable relaxation method are applied. • The proposed model improves the supplier's economy and reduces the user's peak load. [ABSTRACT FROM AUTHOR]

A dynamic model of a compressed gas energy storage system is constructed in this paper to discover the system's non-equilibrium nature. Meanwhile, the dynamic characteristics of the CO 2 binary mixture (i.e., CO 2 /propane, CO 2 /propylene, CO 2 /R161, CO 2 /R32, and CO 2 /DME) based system are first studied through energy and exergy analyses. Performance indicators are considered, including round trip efficiency, exergy efficiency, energy density, discharge time, and average power output. The effects of tank volume and ambient temperature on the system performance are analyzed. According to the results, although the round trip efficiency of the CO 2 mixture system is reduced by about 0.68%–11.97% compared to pure CO 2 , the compressor performance and the system pressure stability are improved. When the tank volume increases from 10,000 m3 to 15,000 m3, the system round trip efficiency increases by 0.16%–0.33%. A further improvement of round trip efficiency for the CO 2 mixture is found compared to pure CO 2 at higher ambient temperature. This paper demonstrates the dynamic operational characteristics of the compressed gas energy storage system when using different CO 2 binary mixtures, providing a reference for optimizing related systems. Potential scenarios and applications for the CO 2 binary mixture are discussed for future research. • A dynamic model of Compressed CO 2 binary mixture Energy Storage is constructed. • The effects of different CO 2 binary mixtures on dynamic performance are compared. • The effects of storage pressure and ambient temperature on performance are analyzed. • The RTE of CO 2 mixture system is reduced by 0.68%–11.97% compared to pure CO 2. • A 1 K increase in ambient temperature results in a 5% increase in storage density. [ABSTRACT FROM AUTHOR]

This paper presents a two-stage scheduling framework for a virtual power plant (VPP) to address uncertainties in wind power plants (WPP), photovoltaic (PV), load, and price in the power system. The framework consists of a day-ahead (DA) scheduling stage and an intraday adjustment stage. The DA scheduling stage formulates the VPP's internal controllable resource scheduling strategy and the energy market's bidding plan. The intraday adjustment stage provides flexibility to adjust the scheduling strategy of controllable resources in the VPP to enhance power generation stability. To address profit distribution issues, the paper proposes a profit distribution strategy based on scheduling costs of each component unit of the VPP. The VPP considers various factors, such as battery capacity degradation, carbon credit trading, flexibility of electric vehicles (EVs), and novel demand response (DR) strategies. The case study demonstrates that accounting for battery capacity degradation reduces the utilization of energy storage systems (ESSs) and EVs, while considering controllable load and EVs increases the VPP's flexibility and decreases carbon emissions. Furthermore, the proposed two-stage scheduling strategy effectively handles multiple uncertainties. Finally, the implementation of new DR strategies leads to a reduction in cost by 6.83% (time-of-use) and 8.1% (real-time price) for the VPP. • A novel two-stage scheduling framework of VPP is proposed. • VPP consists of traditional DGs, ESSs, WPP, PV, EVs and controllable loads. • The scheduling approach reduces the impact of multiple uncertainties on VPP. • A profit distribution strategy of the VPP is proposed. [ABSTRACT FROM AUTHOR]

Energy storage system (ESS) has been considered as a pivotal technology enjoying a wide range of applications in different levels of power systems. However, the investment of ESS is still relatively high. Thus, shared energy storage (SES) is consequently recognized as a promising business model. Considering the dynamic virtual energy storage characteristics of heat network, this paper proposes a power-thermal heterogeneous SES model to meet the practical need of participants. Firstly, a quasi-dynamic model of heat energy transport is conducted to analyze the virtual energy storage potential of heat network. Then, the power-thermal heterogeneous SES model is developed to describe the formation mechanism of ESS supply and demand with heat network as the schedulable energy storage resource. Furthermore, a pricing method of electric-thermal heterogeneous SES is proposed. The upper-level problem of it targets at maximizing the benefits of the SES operator, while the lower-level problem aims to minimize the costs of users. Simultaneously, the service price and user purchase capacity can be obtained as optimization variables. Finally, case studies are adapted to verify the rationality and validity of the proposed model. • This paper develops a novel electric-thermal heterogeneous shared energy storage framework. • This paper presents a business model of shared energy storage. • This paper proposes a bilevel Stackelberg game optimization pricing method for the SES the SES service. • A 33-bus electricity network coupled with a 44-bus heat network has been employed to test the effectiveness of this method. [ABSTRACT FROM AUTHOR]

Under the background of "carbon neutrality" and "carbon peak" concepts, China desires to develop a new power system based on renewable energy sources (RES), which will be the primary energy support in prospective China. Thus, it is imperative to improve the peak shaving capability of power system to address the problem of random fluctuation and intermittency of RES by developing various energy storage systems. An innovative approach to enhance the flexibility of the conventional thermal power unit (TPU) through the utilization of flywheel energy storage array (FESA) is presented, simulated, and evaluated in this paper. Using the relatively practical and specific model of TPU and FESA, modified frequency modulation and automatic generation control (AGC) instruction tracing operation scenario models are constructed. A comprehensive control strategy of FESA and a well-designed operation coordinated control method for TPU with FESA are proposed and applied. The simulation results are distinctively processed and evaluated by the methods in "two documents", aiming to assess the real-world operation performance of the model proposed in this paper. The comprehensive analysis of the indicators demonstrates that the FESA can improve the flexibility and economy of the units. This research may guide the usage of FESA to improve the operational flexibility of TPU. • Composition and Control Methods of the flywheel energy storage array are provided. • A coordinated control scheme for the thermal power unit with flywheel energy storage array is proposed. • Frequency modulation and AGC instruction tracking scenario models are constructed and simulated. • AGC regulation indicators are conducted and analyzed to evaluate the unit's performance. [ABSTRACT FROM AUTHOR]

The utilization of energy hubs (EHs) in the power system and simultaneous energy management of several energy networks is of high importance to increase energy efficiency. Therefore, this paper focuses on the energy management of grid-connected flexible energy hubs. Scheme includes electrical and heating networks, where renewables and storage devices are in connection using the hub as a coordinator. Problem minimizes the expected operating cost of the mentioned networks subject to networks optimal power flow model and the mathematical representation of flexible EHs. Uncertainties related to the amount of demand, price of electrical and heating energy, and renewable power have also been considered in the problem formulation. The unscented transformation method has been adopted to reduce the volume of the problem to obtain the optimal solution in low computation time, along with the precise modeling of flexibility and modeling of the aforementioned uncertainties. Modeling the flexibility of EHs in the presence of renewable sources, modeling bio-waste energy unit for the simultaneous generation of electric and heat energy, and modeling the various uncertainties of load using the unscented transformation are among the contributions of the paper. The obtained numerical results demonstrate the capability of the proposed scheme in improving the economic and operating status of electrical and heating networks, along with improving the flexibility of EHs in such a way that energy storages in renewable hubs can extract 100% flexibility conditions. In these conditions, the hubs are able to reduce energy costs, energy losses, voltage drop, and temperature by about 25%, 24%, 60%, and 59%, respectively, compared with the power flow studies of the networks under study. Also, based on sensitivity analysis results, the increase in load and energy price leads to an increase in the operating cost of the networks, while the increase in the size of renewable resources and storage devices reduces the networks' energy cost. • Utilizing various flexibility sources besides renewables, to establish a FEH by considering flexibility modeling. • Modeling performance of bio-waste unit with the CHP technology, which can generate electrical and thermal energy. • Modeling uncertainties of load, energy price, and renewable power based on the unscented transformation method. • Using unscented transformation method to accurately model the flexibility and reduce the computation time. [ABSTRACT FROM AUTHOR]

This paper proposes a decentralized multiple-Direct-Current-Microgrid (multi-DCMG) system to supply affordable load demands while addressing challenges posed by Hybridized-Energy-Storage-Systems (H-ESS) limitations, consumption/generation complexities, and renewables volatility. The paper's contributions include a system feasibility assessment for isolated users and a demonstration of the effectiveness of the control strategies adopted. To improve system resiliency and reliability, the proposed system adopts a high-control level for energy/power balances, using a Mamdani 50 rule-based Fuzzy Logic energy management system (FL-EMS) to supervise State-of-Charge (SoC) recovery. The low-control level manages/supervises DC-DC power converters' powers adopting Proportional-Integral (PI), Hysteresis-Current-Controller (HCC), and Linear-Quadratic-Regulator (LQR) in closed-Control-loops, besides an advanced low-pass-filtering (A-LPF) for load frequency decoupling. The results show that the proposed H-ESS outperformed single-ESS systems in dynamic load changes and renewables' uncertainty, and supercapacitors improved load supply, voltage regulation, and current tracking. However, expensive costs and slow restoration of H-ESS banks from critical SoCs are major drawbacks. The global system assessment demonstrated promising results through proper FL-EMS setpoint computation, stable Bus voltage with 0.55–6.9% deviations due to robust controllers, accurate SoC recovery of HESS batteries at critical SoCs (<10% and >90%), fast and accurate convergence with 3.35–3.37% mismatch, and 99.3% supply efficiency at minor power losses of 0.7–1.55%. • Decentralized multi-DCMGs based-PV/DG/H-ESS exposed to power/energy exchange balances. • Tow-level energy/power management of multi-DCMGs under regular/distrubing scenarios. • FL-EMS and A-LPF restore SC's SoC, optimize H-ESS energy usage, and extend lifcycles • Accurate DC-Bus voltage regulation and H-ESS/DG currents tracking using PI with LQR-I. • Accelerate voltage response, SoC recovery, and load convergence due to SC's dynamics. [ABSTRACT FROM AUTHOR]

In the configuration of distributed energy systems, the impact of ambient temperature on gas turbines is often overlooked, leading to inaccuracies and suboptimal performance. Previous research, which have used fixed operation strategies and electric cooling ratios, also leaves room for improvement. This paper addresses these issues by finely considering the mathematical model of micro gas turbines (MGT) in distributed energy systems with multiple energy storage (DES-MES), and by taking into account ambient temperature and operating power to determine the electrical efficiency of MGT throughout the year. This paper employs a two-layer co-optimization method to configure device capacity and find the optimal variable operation strategy and electrical cooling ratio. This study improves the annual total cost saving rate, carbon dioxide emissions saving rate, and primary energy consumption saving rate by 35.4%, 67.6%, and 64.7%, respectively, compared to the separate production system. By accounting for the effect of ambient temperature on MGT, the annual performance of DES-MES is improved by 24.38% compared to fixed electrical efficiency MGT model. Additionally, the annual performance of DES-MES using variable operation strategy and electric cooling ratio is optimized by 23.89% compared to fixed operation scheme. • Micro gas turbine model influenced by ambient temperature. • Integrated energy system with variable operation scheme. • A two-layer co-optimization approach considering both configuration and operation. • Combination of improved honey badger algorithm and Cplex solver. [ABSTRACT FROM AUTHOR]

The paper presents a new mathematical model of the processes of charging and discharging a thermochemical energy storage (TChES) reactor with a high potential for effective application in a residential building. The model is an intermediate approach between lumped-element models and 2D/3D spatially resolved models. The paper also contains the results of the numerical implementation of the developed model. The efficiency of the storage process is one of the key parameters describing the operation of the system, and the study of the impact of individual factors on its value is one of the main goals of the work carried out. A system for heat accumulation using solar collectors and a fixed bed thermochemical reactor was proposed for the analysis. The K 2 CO 3 hydration/dehydration reaction was selected for analysis purposes, but the model is also valid for other reversible reactions. The following parameters describe the considered reactor operations: specific humidity and temperature of the moist air in the reactor, degree of transformation of K 2 CO 3 ·1.5H 2 O into K 2 CO 3 and bed temperature across the reactor. Their temporal and spatial variations in the reactor enable finding the amount of energy accumulated in the reactor during the charging process (or the amount of heat rejected to the moist air in the reactor during the discharging process). Therefore, the efficiency of the storage process (or the efficiency of the discharging process) can be determined. The simulation results provide insight into the multi-physical phenomena occurring during heat accumulation and release processes. Therefore, they help identify the factors with the most significant impact on the proposed reactor's operation. It was found that the species conversion process and amount of heat stored are affected by the bed geometry (ratio of the inner and outer radius of the reactor, bed and particles porosity, and diameter of the bed particles) as well as reactor's diffusive properties. The developed model and presented results might facilitate the creation of valuable experimental setups and save the time and resources spent on unsuccessful experimental attempts. • A thermochemical heat storage system single-family building was analysed. • The K 2 CO 3 hydration/dehydration reaction was selected for analysis. • A mathematical model of charging/discharging process was developed. • The influence of most important factors on reactor's operation was investigated. • The inlet parameters of heat transfer fluid and bed geometry showed the greatest impact. [ABSTRACT FROM AUTHOR]

This paper presents the concept of compressed air energy storage (CAES) system with Partial Oxidation Gas Turbine (POGT) technology. In this type of turbine fuel is oxidized with an excess air coefficient λ of less than 1.0. Therefore, a given value of the turbine inlet temperature (TIT) can be reached at significantly lower air consumption. POGT products are electric power and combustible process gas that can be utilised in various ways. In this paper there is assumed a bottoming process, which consists of low pressure combustion and a secondary turboexpander. The system is considered to be functioning in power-to-power mode. However, POGT creates also other opportunities that can help improve flexibility of energy market. As the paper presents an initial study of possibilities the proposed system has been compared against the existing plant in Huntorf (Germany). In this way main differences as well as advantages and limitations of the proposed solution are compared against the existing conventional first generation CAES facility. The study is focused mainly on key thermodynamic parameters and potential performance improvement of the CAES process. The results revealed considerable round-trip efficiency gains. • CAES system with partial oxidation gas turbine (POGT) is proposed. • Performance of the system has been compared against the Huntorf CAES plant. • Round-trip energy efficiency gains are demonstrated. • Opportunities for system integration of CAES system with POGT are outlined. [ABSTRACT FROM AUTHOR]

This paper is an Editorial for the special issue (SI) of Energy Reviews (RSER) dedicated to the 10th International Conference on Sustainable Energy and Environmental Protection SEEP, held in Bled-Slovenia. During the conference more than 300 papers were presented, around 25% of these papers were accepted for this special issue. Papers discussed different topics related to renewable energy, energy storage systems and some environmental issues related to transportation. During this conference Prof Olabi presented a keynote talk on the relation between circular economy and renewable energy, case studies about implementing this concept in Scotland were included in his presentation. This editorial will give a short description on the relation between circular economy and renewable energy, then it will be followed by addressing the accepted papers in this special issue. [ABSTRACT FROM AUTHOR]

Integrated energy systems (IES) have become a popular direction in the field of energy research due to their economic, efficient and environmental friendly advantages. Among them, multi-region integrated energy systems (M-RIES) are more valuable than single-region integrated energy systems (S-RIES) due to their low energy loss, high economy and reliability. At present, there is a lack of an optimisation method that integrates station-network synergy, inter-station interaction, shared energy storage configuration, overall planning of equipment configuration and multi-timescale rolling for the multi-faceted performance enhancement of M-RIES. Therefore, this paper proposes an M-RIES with station-storage interaction and inter-station interaction under the consideration of station-network synergy, and conducts a study on the optimal configuration of M-RIES from the viewpoint of economy and environmental protection. The final analysis is based on a northern region. The results show that the system achieves an energy efficiency of 1.07, a 16.9% reduction in total station equipment configuration, a 3.87% reduction in cost and a 0.76% increase in new energy consumption rate compared to a stand-alone operating system. Taking one of the energy stations as an example, under the operating conditions of 10% intra-day and 5% real-time error, the multi-timescale rolling optimisation method significantly improves the energy supply rate compared to the day-ahead dispatching method, where the error rates for the three typical days of transition, winter and summer are only 0.43%, 2.56% and 0.06%, corresponding to a reduction of 5.02%, 2.49% and 5.06%, and a smaller cost error than the ideal solution The cost error rates for the three typical days are only 1.99%, 3.60% and 3.46%. In summary, the collaborative autonomous planning and operation method proposed in this paper has great advantages in terms of economy, reliability, energy efficiency and environmental protection. • A multi-area collaborative integrated energy system with shared energy storage is proposed. • Day-ahead collaborative, intra-day autonomous multi-timescale rolling optimisation method. • The system has advantages in terms of economy, energy efficiency and the rate of new energy consumption. • The multi-time-optimised rolling method has advantages in terms of both meeting demand and economy. [ABSTRACT FROM AUTHOR]

The traditional cogeneration system of cooling, heating and electricity still has problems such as high greenhouse gas emissions, high consumption of fossil fuels and low output of products. A novel combined cooling, heating, and power (CCHP) coupled with solar thermochemical (STC) process and energy storage unit is proposed in this paper. The system realizes the multi-energy supplementation of renewable energy and fossil energy, and actively regulates outputs through energy storage unit. This paper makes a comprehensive evaluation of the system from four perspectives of energy, exergy, exergoeconomics and environment (4 E). The results demonstrate that the total energy and exergy efficiencies of the new system reach up to 80.30% and 55.33%, respectively. The system product has an average specific cost of 0.122 $/kWh. The system's specific CO 2 emission and fossil energy saving ratio are 286.21 g/kWh and 55.96%, respectively. The analysis of a typical day shows that the energy storage unit enables the system to respond the changes of the cooling, heating and power outputs in time to meet the user's load demand. The results show that the new system improves energy efficiency, reduces CO 2 emission, saves methane, and realizes the active regulation of system energy input and output through energy storage component. • A novel combined cooling: heating, and power system coupled with solar thermochemistry and energy storage unit is proposed. • The total energy efficiency and exergy efficiency of the new system are 80.30% and 55.33%, respectively. • The average exergetic product cost and the specific CO 2 emission of new system are 0.122 $/kWh and 286.21 g/kWh, respectively. • The new system is more efficient, economical and cleaner than the traditional system. [ABSTRACT FROM AUTHOR]

More frequent extreme weather events due to climate change impose significant challenges on the post-disaster restoration of distribution systems with significant penetration of renewable generations. To address this challenge, this paper investigates a restoration scheme for distribution networks integrated with renewable generations, and transportable energy storage systems moving along a transportation network, such as railway or road network, are used to support the network restoration after the fault event. To achieve this, for given fault conditions, topological reconfiguration is first facilitated via the soft open point (SOP) technology, and local renewable generators and loads are rapidly regrouped to form microgrids for post-disaster restoration. Then, assisted with transportable energy storage, the resources within the microgrids can be dispatched more efficiently, facilitating a more efficient post-disaster recovery process. To enable a cost-effective restoration of the distribution network, an economic model is formulated with the aim of minimizing the economic losses during the post-disaster restoration process. Case studies considering a significant number of failure scenarios show that the proposed transportable energy storage-assisted restoration scheme can effectively minimize costs during the post-disaster period considering various load types across different areas. This scheme not only achieves cost-effective post-disaster restoration but also ensures compliance with the constraints of the electrical networks, achieving up to 93.3% recovery cost reduction and 74.25% increase in critical load restoration. • A post-disaster restoration scheme considering TESS, SOP and WTG is established. • Diverse failure scenarios are designed to validate the proposed scheme. • Restoration of loads with various priorities and types is comprehensively considered. [ABSTRACT FROM AUTHOR]

Integrated energy systems within communities play a pivotal role in addressing the diverse energy requirements of the system, emerging as a central focus in contemporary research. This paper contributes to exploring optimal scheduling in a smart community featuring multiple smart buildings equipped with a substantial share of distributed photovoltaic sources, shared energy storage, and controllable loads. The study formulates a two-stage scheduling optimization model incorporating multiple stakeholders, explicitly examining the game dynamics between the smart community operator and the customer load aggregator. The weights assigned to each optimization objective are determined using an analytic hierarchy process-entropy weight method to establish a balanced and nuanced approach. The ensuing optimal scheduling encompasses unit output and energy trading considerations, focusing on enhancing the system's economic viability, safety measures, and environmental sustainability. A comprehensive evaluation is conducted to validate the proposed model by comparing its performance with alternative operational strategies. The results show that the designed strategy can reduce the operating cost by 40.22% and increase the level of PV consumption by 22.57% compared to the traditional heat-based strategy, which is effective in mitigating power fluctuations, smartly responding to dispatch peak demand, enhancing new energy integration, and ensuring the security of grid operation. • Energy optimization dispatch model considering demand side response. • Shared energy storage in smart communities. • Multi-subject game to maximize benefits. • Reduce energy costs and improve operational safety. • Ensure photovoltaic output and realize thermoelectric decoupling. [ABSTRACT FROM AUTHOR]

Modular Gravity Energy Storage (M-GES) systems are emerging as a pivotal solution for large-scale renewable energy storage, essential for advancing green energy initiatives. This study introduces innovative capacity configuration strategies for M-GES plants, namely Equal Capacity Configuration (EC) and Double-Rate Capacity Configuration (DR), tailored to optimize energy storage efficiency and stability. Through comprehensive MATLAB/Simulink simulations, we demonstrate each configuration's distinct power characteristics and operational advantages. Our findings reveal that while the EC configuration offers enhanced power accuracy and stability, it requires a larger number of units. In contrast, the DR configuration, characterized by fewer but larger units, shows a trade-off with reduced power stability. This paper contributes significantly to the field by presenting a first-of-its-kind comparative analysis of these configurations, offering insights into their practical applications and scalability. The proposed strategies and findings lay a foundation for future research and development in gravity energy storage systems, marking a step forward in pursuing sustainable and reliable energy solutions. [Display omitted] • Two typical unit capacity configuration strategies for M-GES power plants are proposed. • The unit scheduling method of the M-GES power plant is proposed. • The coordination control method is provided for each proposed configuration strategy. • Equal capacity configuration has better power accuracy but requires more units. • Double rate configuration needs fewer but larger units and less power stability. [ABSTRACT FROM AUTHOR]

Energy storage materials have been receiving attention during the past two decades. Supercapacitors, in specific, have emerged as promising energy storage devices, especially for flexible electronics. The development of supercapacitor materials is crucial to advance their performance and multifunctionality. Supercapacitors have been shown to possess higher energy densities than conventional capacitors and higher power densities than batteries. Advancements in electrochemical supercapacitor cells are heavily sought after. This review showed that the progress made in supercapacitors' materials led to the development of novel electrode materials, heat-resistant separators, flexible supercapacitors, and highly conductive electrolytic solutions. Therefore, supercapacitors' electrochemical performance, power density, charge storage density, specific capacitance, and charge/discharge rates were eventually enhanced. Moreover, research is being conducted to operate supercapacitors in soft electronics by improving their solid-state flexibility. This paper also provided a comprehensive overview of the recent developments in high-temperature capacitive energy storage, the various applications of supercapacitor cells in the industry, supercapacitors design, as well as the challenges involved. This work concluded that the biggest challenge of producing supercapacitors is manufacturing them at a low cost to make them economically viable energy storage options. [Display omitted] • A review of the recent developments of materials design for supercapacitors is presented. • Machine learning and 3D printing appear to be promising for flexible supercapacitors. • Novel electrolyte materials include ionic liquids and anhydrous gel electrolytes. • The challenges include the safety, the cost, and the durability of electrolyte materials. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive evaluation of the efficiency and cost implications of a modular cogeneration plant integrated with a Battery Energy Storage System (BESS) and an Organic Rankine Cycle (ORC) plant. The objective is to assess the performance of the system when waste heat recovery through the ORC cycle is added to the exhaust line and compare it with the original system. Techno-economic parameters were analyzed for two different configurations: one with the maximum BESS size and another with the maximum Primary Energy Savings (PES). The results demonstrate that the incorporation of the ORC plant yields several notable outcomes. While there is a slight increase in the Simple Payback Period (SPB) and Net Present Value (NPV) in both configurations, the magnitudes of these increases are relatively low. In contrast, the introduction of the ORC cycle leads to substantial reductions in carbon dioxide (CO2) emissions and significant improvements in Primary Energy Savings (PES). For the Max PES configuration, a remarkable 25% reduction in CO2 emissions and a substantial 22% improvement in PES were observed. To further investigate the system's response to varying economic conditions, a sensitivity analysis was performed. The analysis considered the effects of changes in the cost of battery, Combined Heat and Power (CHP) unit, ORC components, electricity, and fuel on the system's Simple Payback Period (SPB) and Net Present Value (NPV). The results revealed that the sensitivity of the Max PES configuration to economic parameters was higher compared to the Max BESS system. These findings highlight the considerable advantages of integrating an ORC plant into a cogeneration system with a BESS. The significant reduction in CO2 emissions and the noteworthy improvement in Primary Energy Savings (PES) outweigh the relatively minor negative impact on the Simple Payback Period (SPB) and Net Present Value (NPV). This integrated configuration demonstrates both environmental benefits and energy efficiency improvements. The results provide valuable insights for the design, optimization, and decision-making processes involved in the implementation of cogeneration systems with energy storage, promoting sustainable and cost-effective solutions. • Integration of an ORC plant into a modular cogeneration system with a BESS. • Techno-economic analysis for two CHP configurations: max BESS size and max PES. • ORC cycle introduction significantly cut CO2 emissions while SPB and NPV increases. • Max PES configuration exhibited 25% CO2 reduction and a 22% PES improvement. • Sensitivity analysis revealed system response to component and energy cost changes. [ABSTRACT FROM AUTHOR]

Energy hubs (EHs) have substantially paved the way for the coordinated operation of various energy carriers, converters, and storage. However, the establishment of optimal planning and operation of the EH include several challenges, e.g., the stochastic nature of non-dispatchable generation assets, obtaining a satisfactory performance from the storage assets with reasonable energy and power density costs, and long-term load growth. Accordingly, this paper proposes a two-stage stochastic optimal resource allocation and operation approach for multiple smart energy hubs-based microgrids. The first stage optimizes the location of each EH and different assets' capacities. On the other hand, the second stage optimizes the charging/discharging rates of each asset and parking lot power. These microgrids combine renewable energy sources. Moreover, for research gap and unlike existing research, the cooperative operation of hybrid storage systems (i.e., solar-powered compressed air energy storage, hydrogen storage systems, battery energy storage systems, thermal energy storage systems) along with plug-in electric vehicles (PEV) has been investigated. Furthermore, annual load growth and price-based demand response (PBDR) are employed in this study. The simulation results demonstrate that the controlled charging/discharging of the PEVs reduces the total annualized cost by 5.49%, while the operation and emission costs are reduced by 21.3% and 19.86%, respectively. Furthermore, employing PBDR has led to a reduction of 10.1% in capital and operational costs. • Developing a stochastic two-stage long-term planning and operation of multiple smart energy hubs-based microgrid. • Analyzing the performance of various EESs , considering the costs of capital energy capacities and power rates. • Integrating multiple EV parking lots considering the system performance under G2V and V2G modes. • Evaluating the influence of the PBDR on the planning framework , taking into account the end-user's discomfort cost. • Considering the annual electrical and thermal load growth in the proposed model. [ABSTRACT FROM AUTHOR]

—Batteries are acknowledged as an important carrier for next-generation energy and future electrification. Capacity degradation is an inevitable process that will eventually lead to a decline in battery performance. This paper presents a battery degradation trajectory prognostics approach under storage conditions based on the data-driven method with necessary knowledge inspiration. This data-driven approach is based on Support Vector Regression (SVR), incorporating mechanisms and empirical knowledge of battery calendar aging as the kernel. The developed data-driven approach is evaluated using two real but different capacity degradation datasets: Dataset A has two storage temperatures (25 °C and 45 °C) and storage SoCs (50% and 90%), while Dataset B has the same storage temperatures but four different storage SoCs (20%, 50%, 70% and 95%). The illustrative results show that the developed SVR-based data-driven approach with the battery knowledge-motivated kernel can make accurate future capacity prognostics under different storage conditions. Due to the superiority of its flexibility and knowledge-data-motivation, the developed approach could be extended and help engineers monitor or capture the future capacity degradation trajectory of other types of batteries when their relevant data become available, which in turn will benefit the life management of battery-based energy storage applications. • Knowledge-motivated data-driven method for battery degradation is achieved. • Mechanism knowledge and empirical knowledge are completely coupled. • A long-term capacity degradation trajectory prediction method is proposed. • The methodologies are completely validated based on experiments. [ABSTRACT FROM AUTHOR]

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. • Developing a model for assessing the efficiency and effectiveness of hydrogen storage and bio-waste units farm. • Considering an integrated energy system to evaluate the economic and technical objectives of the DSO. • The utilization of an integrated hydrogen storage and RESs system as a means to enhance the network flexibility. • Simultaneous modeling of economic, operation, reliability, and flexibility objectives of the DSO. [ABSTRACT FROM AUTHOR]

Fossil fuel power plants continue to contribute significantly to carbon emissions, necessitating a transition towards cleaner energy sources. Despite the growing presence of renewables within the power systems, the incorporation of carbon capture technologies into the traditional thermal power plants holds great potential in emissions reduction. In this paper, the integration of renewable energy sources (RES) and coal-fired power generation units outfitted with carbon capture schemes is addressed. Multiple demand response (DR) programs and hydropower plants are strategically utilized to increase the power system flexibility. To effectively plan the day-ahead (DA) operation of the power system, a presumed market-clearing framework is adopted and modelled as a risk-constrained two-objective stochastic mixed-integer linear programming problem. The proposed framework helps to tackle the uncertainties related to RES and demand variations by employing a hidden Markovian process (HMP) technique. To simultaneously minimize the system's operational costs and CO 2 emissions, an enhanced version of the augmented ɛ -constraint method is employed. To prove its value, the proposed framework is devoted to the 24-bus IEEE reliability test system (IEEE-RTS). The system features substantial penetration of RES (exceeding 87% of peak load) and standard DR options capacities (less than 25% of peak load). The results show a 24% reduction in load peaks, an over 63% decrease in emissions, and a 17% reduction in the overall operation costs. • Proposing a comprehensive linearized mathematical framework accelerating the energy transition. • Coordinating the responsive power systems with carbon capture and hybrid ESS (BESS, PHS, HES). • Enhancing an effective augmented ɛ -constraint technique to simultaneously optimize cost and CO 2. • Presenting robust decision-making with two-stage SP, hidden Markovian process, and CVaR strategy. • Integrated approach: 87% of RES, cost & CO 2 reductions, fewer load peaks, enhanced flexibility. [ABSTRACT FROM AUTHOR]

This paper studies the cooperative control problem of flywheel energy storage matrix systems (FESMS). The aim of the cooperative control is to achieve two objectives: the output power of the flywheel energy storage systems (FESSs) should meet the reference power requirement, and the state of FESSs must meet the relative state-of-energy (SOE) variation rate constraints. When the above objectives are reached, the FESSs are able to track reference power command while ensuring that all flywheels are fully charged or discharged at the same time. To achieve these two objectives, a distributed power allocation strategy is proposed. A distributed estimator is designed to estimate the global state information required for each FESS in the allocation strategy. In addition, a fixed-time estimator based on a dynamic average consensus algorithm is designed, which is capable of quickly estimating the global average information. The convergence of the fixed-time estimator under the boundedness assumption of the time derivative of input signal is analyzed by the Lyapunov stability theory. The simulation results show that the FESSs interact with neighbors through local information such that the evolution of the SOE among the FESSs satisfies the constraints and the total power tracks the total reference power. Furthermore, compared with the existing methods, the proposed dynamic average consensus estimators achieve an accurate tracking of the global state information in a fixed time, regardless of the initial conditions. • Existing control strategies of flywheels mainly address energy balancing problem. • An allocation strategy is proposed to address the cooperative problem of flywheels. • The proposed strategy avoids the mutual charge and discharge among flywheels. • A fixed-time estimator is proposed to improve the performance of the allocation strategy. [ABSTRACT FROM AUTHOR]

On-site sonar measurements reveal a high proportion of salt caverns with irregular contours, which poses a potential risk. Therefore, repairing these irregularities is crucial for the safe and economic operation of gas storage salt caverns. This paper proposes criteria for assessing the rationality of repairing irregular salt caverns, which can be used to judge the rationality of repair work before it is carried out. A procedure is proposed for determining the technical parameters by conducting an in-depth investigation of the correlation between these parameters and the factors influencing them. Methods are also proposed to quantitatively determine the range of certain parameters that affect technical parameters. By using the technical parameters determined by these methods, the repair efficiency can be significantly improved and the best repair effect can be obtained. The research findings have broad applicability and offer valuable guidance for future repair techniques of irregular salt caverns. • Putting forward methods to judge the rationality of repair. • Making a thorough study of the technical parameters of irregular salt caverns repair. • Analyzing the relationship between the technical parameters and influencing factors. • Putting forward the determination procedure of the technical parameters. • Giving the determination methods of the technical parameters and the range of them. [ABSTRACT FROM AUTHOR]

The volatility and intermittency of the renewable energy and the varied air pressure in air reservoir cause the CAES system to operate under complex off-design conditions. However, little attention is paid to the impact of complex variable operating conditions on system design in previous study, that is the reason why the actual system efficiency is relatively low in the whole process. In order to solve above issues, this paper proposes a new coupled design methodology (CDM) concerning complex off-design operation of CAES system. The compression process, expansion process, and the entire system is optimized with the CDM and the proposed probability-load (PL) factor. Through the CDM process, the average mass flow ratio of the compression process is decreasing while the actual mean back pressure increase under the specific power load curve, with the exergy efficiency increasing by 0.63 %. The average mass flow ratio of the expansion process is increasing, and the efficiency increases by 1.32 %. Meanwhile, in the compression process (or expansion process) design, no matter how the design parameters change, with the increase of input power (or output power) in a certain range, the efficiency increases firstly and then decreases. Eventually, the energy storage efficiency of entire system increases by 2.36 % with the proposed CDM. • The CDM concerning complex off-design operation for CAES systems is proposed. • New working-condition analysis framework with PL factor is established. • System optimal design is conducted for TS-CAES systems with the CDM. • The system efficiency can be improved by 2.36 % after CDM optimization. [ABSTRACT FROM AUTHOR]

Rapid and accurate battery fault diagnosis and distinction is of great importance in electrical vehicles and electrochemical energy storage system. However, misdiagnosis and missed diagnosis happened occasionally. In this paper, a statistical analysis-based multi-fault diagnosis method is proposed to detect and localize short circuit faults, electrical connection faults and voltage sensor faults in LFP battery packs. This method uses non-redundant interleaved voltage measurement topology to detect battery voltages, where every voltage sensor measures the sum of two neighboring batteries and one connection resistor between them. The statistical analysis method sets detection thresholds based on the battery operating data, and captures fault characteristics by analyzing abnormal changes in battery voltage unrelated to current. Theoretical analysis and tests verified that this method can diagnose these three kinds of faults. Sensor faults of excessive error and data sticking can also be distinguished. • The proposed method can diagnose short circuit faults, electrical connection faults and sensor faults. • The non-redundant interleaved voltage measurement topology does not require additional voltage sensors, which saves costs. • The introduction of time window avoids blocking the fault diagnosis process when a fault occurs. • Sensor faults of excessive error and data sticking can be distinguished. [ABSTRACT FROM AUTHOR]

A new method of inter-stage double heat exchange is proposed, which combines compressed air energy storage with traditional coal-fired power unit. It can not only reduce the heat storage investment of compressed air energy storage system, but also broaden the peak regulation margin of coal-fired power unit, and promote the stability of power grid under high proportion of new energy access. In this paper, the characteristics of the coupling system under different working conditions of coal-fired power unit are studied, and the techno-economic calculation and analysis are carried out. The results show that the round-trip efficiency of the compressed air energy storage system coupled with the coal-fired power unit can reach more than 70% under different working conditions, and the return on investment and payback period are optimistic. Among them, the efficiency and economy of the system are the best when the coal-fired power unit is operating at 50%THA. At this time, the rate of return on investment can reach 18.44%, and the payback period is about 5.14 years. • A new method of inter-stage double heat exchange was proposed for coupling. • The thermodynamic model of the CAES system coupled with CFP unit was established. • The coupling performance and economic performance of the system were analyzed. • The round-trip efficiency of each working condition could reach more than 70%. • The payback period of each working condition was within 6 years. [ABSTRACT FROM AUTHOR]

Recently, the decarbonization of the power industry has gained increasing attention as global warming problem has become more acute. However, the existing system planning models targeting to tackle emission reduction rarely consider to improve the flexibility of power system. In this paper, a co-planning model considering the expansion of carbon capture and storage (CCS) units and energy storage systems (ESSs) is proposed, in which the carbon emission target is embedded in the objective function via setting different carbon emission tax. Here, the system flexibility is improved by Source-Load-Storage (SLS) response, which is achieved via CCS units, ESSs and flexible load. In addition, a subsidy method for cost recovery based on Vickrey-Clark-Groves (VCG) mechanism is proposed to stimulate market members participating in SLS response. Numerical results from a modified IEEE 118-bus power system illustrate the impact on system cost and emission results owing to specific carbon emission tax, investment budget and SLS response. Finally, we display the contribution rate and subsidy of different participants in a typical day. • We propose an expansion co-planning model of ESSs and CCS units. • Source-load-storage response is considered at operation stage. • A methodology based on VCG mechanism is designed for cost recovery. • Our model provides a transition solution towards low-carbon power systems. [ABSTRACT FROM AUTHOR]

The emergence of smart grids has fostered new participants in the electricity market with innovative business models. Among these new market agents, aggregator systems play a crucial role and require modern economic methods to support informed investment decision-making. This paper presents a novel approach to assess investments in aggregator systems using compound real options. The proposed methodology incorporates sequential options, involving the deferral of the initial investment in the aggregator system followed by contingent expansions in energy storage. Uncertainties related to investment costs of the storage and aggregator systems are modeled by a stochastic process and integrated into the valuation framework. A comprehensive sensitivity analysis of key variables is depicted in threshold maps delimiting decision regions. Results of the case study demonstrate that under current and a wide range of projected conditions, aggregator systems with storage are not financially feasible. However, variations in some assumptions indicate that integrating other business models within the aggregator system can render the project profitable. These findings highlight the importance of designing effective incentive and regulatory mechanisms for such businesses during the early stages of technology adoption. • Aggregator and storage systems are new business models that need to be addressed. • New stochastic processes are proposed to forecast the aggregator and storage costs. • A compound real options method is proposed as an investment assessment framework. • Decision regions aid scenario visualization for effective decisions. • Findings highlight the importance of designing effective incentive mechanisms. [ABSTRACT FROM AUTHOR]

Hybrid energy storage offers a new way to increase the renewable energy proportion (REP) of near-zero energy community energy system (NZECES). However, the optimization operation method of NZECES combining hybrid energy storage is not yet mature. Therefore, this paper proposes integrating electricity storage, heat storage and hydrogen storage into the NZECES, and conducting multi-time scale (MTS) operation optimization research. Firstly, a NZECES with electricity-heat-hydrogen storage is constructed. Secondly, a MTS optimization operation model of day-ahead scheduling, intra-day rolling, and real-time adjustment is established. Among them, the hourly scheduling scheme targeting the lowest daily operating cost is obtained through the optimization of the day-ahead stage. In the intra-day stage, the scheduling scheme with a time interval of 15 min is obtained by the rolling optimization. The operation scheme of electrical-related equipment with the minimum adjustment cost is obtained in the real-time stage. Finally, a NZEC with different building types is used as the energy supply object. The results show that compared to the traditional day-ahead scheduling scheme, the REP is increased by 3.6 % and the operation cost of the energy system is reduced by 5.3 % by using the MTS operation optimization method. • A near-zero energy community energy system with hybrid energy storage is constructed. • A multi-time scale operation scheme of day-ahead, intra-day, real-time is proposed. • The scheme can improve the renewable energy proportion and operation economy. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective framework for designing a hybrid photovoltaic energy and battery storage system (PV/Battery) with the aim of minimizing the cost of electricity, loss of energy expectation, and loss of load expectation. Focusing on meeting the energy demand of a commercial complex in Al-Jubail industrial city, Saudi Arabia, utilizing real-world data, the proposed framework employs the multi-objective improved manta ray foraging optimization algorithm in conjunction with fuzzy decision-making. To enhance algorithm performance and prevent premature convergence, a Learning-based Hunting Movement Strategy is incorporated. Results highlight the superior performance of Case 3, achieving an optimal balance between cost and reliability. The obtained values for cost of electricity, loss of energy expectation, and loss of load expectation are 0.2255 $/kWh, 170.67 kWh/yr, and 14 h/yr, respectively. This study emphasizes the strengthened capability of the algorithm, augmented through the Learning-based Hunting Movement Strategy, establishing its superiority over established multi-objective methods with a higher percentage of dominant solutions. • Multi-objective design of an HPV/Battery system with data from Al-Jubail industrial city, Saudi Arabia. • Multi-objective optimization framework considering the objectives of COE, LOEE, and LOLE. • Fuzzy decision-making to determine the interactive solution from the Pareto solutions. • Improved manta ray foraging optimization based on a learning-based hunting movement strategy. • Comparison of the MOIMRFO with well-known multi-objective algorithms in design problem. [ABSTRACT FROM AUTHOR]

To facilitate the integration of greater amounts of renewable energy into the power grid, it is crucial to enhance the peak shaving capabilities of conventional thermal power units. This paper proposes a novel system that combines compressed steam energy storage with the Rankine cycle of a thermal power plant (referred to as the coupling system), and focuses on modeling a 200 MW thermal power unit. The performance evaluation and parameter selection of the coupling system are conducted through comprehensive energy analysis, exergy analysis, and economic analysis. The results indicate that the energy storage cycle in the coupled system can prevent boilers and turbines in the Rankine cycle from operating at extremely low loads during deep peak shaving. Specifically, when the boiler and steam turbine operate at 66.45 % of their design load, the external power of the coupling system can be reduced to 30 % of the rated power. This results in a reduction of coal consumption by 63.42 g/W * h compared to direct peak shaving of the original thermal power unit, and an improvement in exergy efficiency by 2.99 %. In this study, a 1300 m3 energy storage circulating water storage tank capacity is used as an example, and it is found that the 200 MW unit can achieve continuous deep peak regulation operation for 8.58 h. The study also reveals that a 1 % increase in the isentropic efficiency of energy storage cycle compressors can lead to a reduction in coal consumption by 2.4 g/kW*h. Moreover, economic analysis suggests that maintaining a compressor efficiency of 86 %, utilizing 7 compression stages, and prioritizing the effectiveness of the heat exchanger are optimal for maximizing the system's revenue. These factors collectively result in a shorter payback period for the coupling system and an overall increase in total income. • A new thermal power plant compressed steam energy storage and Rankine cycle coupling system is proposed. • Compared with other energy storage systems, this system occupies a small area. • The economic analysis of the system was carried out to determine the most suitable component performance and quantity for the system. • Compared the advantages and disadvantages of different energy storage systems in thermal power peak regulation. [ABSTRACT FROM AUTHOR]

Digitization has dramatically facilitated the development of internet data centers (IDCs), which have become necessary demand response resources for distribution networks. To improve equipment utilization, integrated coordination of IDCs, photovoltaics, energy storage systems, and flexible substations is essential. This also ensures power supply reliability and the operational economy of IDC park. This paper proposes a distributionally robust planning method for an IDC park considering operational economy and reliability. First, a coordinated planning model of an IDC park is proposed with multiple resources, in which the workload dispatch of the IDC is incorporated under fault conditions. The reliability index of the IDC park is designed to quantify its reliability cost. Then, considering the fluctuation of distributed generators and workload, a distributionally robust planning model is established. A planning scheme for the IDC park under the worst scenario probability distribution is obtained. Furthermore, the influence of uncertainties on the planning scheme is analyzed. Finally, a practical data center park is used for case studies. The established planning scheme effectively balances the operational economy, reliability, and planning conservatism. • Coordinated planning for data center parks was conducted with multiple resources. • Workload dispatch of data center is considered under fault conditions. • Reliability indices were designed to quantify reliability costs of IDC park. • A robust planning model was established for data center parks with uncertainties. • Planning scheme achieved a balance between economy, reliability, and robustness. [ABSTRACT FROM AUTHOR]

Pumped thermal energy storage (PTES) technology offers numerous advantages as a novel form of physical energy storage. However, there needs to be a more dynamic analysis of PTES systems. This paper proposes a dynamic simulation model of the PTES system using a multi-physics domain modeling method to investigate the dynamic response of key system parameters during the system's start-up and variable operating conditions. Additionally, the impact of equipment parameters on system characteristics was analyzed. Furthermore, a comparative analysis was conducted to assess the variation in system characteristics when considering the different working medium properties. The results show that the system cavity volume should be selected as 5 m3, and the initial pressure of the system during the charge/discharge period should be selected as 2.1 MPa and 3.8 MPa, respectively. The system runs to the design operating conditions during the start-up process in about 1400 s. In the process of variable operation conditions, the power load in the charge/discharge period can be increased or reduced from the rated value by gas filling or venting, and the system characteristics change accordingly. The simulation model and research findings will offer theoretical insights for optimizing the design of the PTES system. • A dynamic simulation physical model of the PTES system was proposed. • The dynamic changes in system characteristics were analyzed. • The TES tank's thermal inertia and heat management of the PTES system were analyzed. • The effects of main equipment parameters on system characteristics were analyzed. • System characteristics were compared when using different gas properties. [ABSTRACT FROM AUTHOR]

—With the development of energy storage technology and sharing economy, the shared energy storage in integrated energy system provides potential benefit to reduce system operation costs and carbon emissions. This paper presents a bi-level carbon-oriented planning method of shared energy storage station for multiple integrated energy systems. Firstly, the energy-carbon relationship of the multiple integrated energy systems is established, and the node carbon intensity models of power grid, integrated energy system and shared energy storage station are established. Secondly, a bi-level planning model of shared energy storage station is developed. The upper layer model solves the optimal capacity planning problem of shared energy storage station to minimize average emission reduction cost in a long time scale. The lower layer model solves the optimal operation problem of multiple integrated energy systems with the goal of minimizing the operation cost in a short time scale. Furthermore, considering the uncertainty of wind and photovoltaic power in integrated energy system, the conditional value at risk method is adopted as the risk measurement index in the lower layer operation cost. Finally, the case study verifies the advantages of the proposed method in economy and environmental friendliness through the comparative analysis of three different energy storage planning cases. Simulation results show that, compared with the energy storage planned separately for each integrated energy system, it is more environmental friendly and economical to provide energy storage services for each integrated energy system through shared energy storage station, the carbon emission reduction rate has increased by 166.53 %, and the system operation cost decreases by 33.48 %. • Node carbon intensity model is established for integrated energy system. • Carbon emission reduction model of integrated energy system is developed. • Carbon-oriented planning model of shared energy storage is established. [ABSTRACT FROM AUTHOR]

Improving the peaking capacity of coal-fired units is imperative to ensure the stability of the power grid, thus facilitating the grid integration and popularization of large-scale renewable energy. To address this issue, this paper introduces a new concept that combines molten salt energy storage with coal-fired power plants. The proposed design consists of extracting a portion of steam from the turbine side and adjusting the extracted steam mass flow rate by adjusting the valve opening to improve the dynamic characteristics of a coal-fired power plant in terms of both increasing the peaking depth and peaking speed. A coal-fired boiler with integrated thermal energy storage was dynamically modeled using Dymola and its accuracy was verified. The results show that the highest equivalent round-trip efficiency is achieved by extracting from the main steam and discharging into the feedwater during charging process and from reheater1 and discharging into the feedwater during discharging process; The peaking potential during charging and discharging is 12.83 % P e and 6.86 % P e respectively, with maximum peaking rates of 9.27 % P e /min and 5.11 % P e /min respectively, and the optimal peaking method is also proposed according to the different peaking situations. The total cost of equipment and materials to retrofit the conventional coal-fired units was 19,948,193 USD and the levelized cost of delivery was 151.29 USD/MWh. • Coal-fired power plant systems incorporating molten salt energy storage. • Dynamic characteristics and economic analysis of coal-fired power plants. • A peaking potential of 12.83 % P e during charging process and 6.86 % P e during discharging process. • A theoretical maximum peaking rate of 9.27 % P e /min during charging process and 5.11 % P e /min during discharging process. • LCOD is determined to be 151.29 USD/MWh. [ABSTRACT FROM AUTHOR]

The accelerating increase in the power capacity of intermittent renewable energy sources has a significant impact on the operation of power systems. First, it causes a growth in electricity price volatility in energy markets. Second, it requires balancing the system to meet the demand also in windless hours at night. Li-ion batteries, as a promising energy storage technology, are considered a response to the abovementioned challenges. However, their use must generate profits to encourage potential investors. With this in mind, the objective of this paper is to examine the economic profitability of Li-ion batteries based on price arbitrage in the European day-ahead market. For this purpose, 7 years of data from 22 European markets are analysed. These data are multiplied to maintain the volatility of electricity prices within the entire lifetime of the energy storage system. The results provide for each country (i) sets of hours of electricity purchase and sale that corresponds to hours of storage charging and discharging, (ii) a number of charging/discharging cycles, (iii) potential profits of transactions in day-ahead markets, and (iv) net present values of investments for current and projected costs. The study contributes to the existing literature by pointing out the most competitive markets for Li-ion batteries, depending on economic results, market size and renewable generation in power systems. The results of NPV are below zero in each case analysed when the current capital cost is considered. However, a reduction of capital costs increases the profitability of the system, indicating Finnish and Romanian markets as the most competitive for Li-ion batteries. • Volatility of wholesale electricity prices is analysed. • The profitability of Li-ion batteries in European day-ahead markets is compared. • With the current costs of the system, NPV is below zero in all countries. • Price arbitrage is profitable when a forecast reduction in capital costs is assumed. • Finish and Romanian markets are the most competitive. [ABSTRACT FROM AUTHOR]

Abstract Utilization of renewable energy sources (RESs) has been increased due to economic-environmental aspects. However, uncertain nature of wind, solar power, market clearing price (MCP), and load complicates the energy management (EM) process of microgrids. This paper studies the EM problem of a grid-connected microgrid from the generating side's perspective. Firstly, the mathematical formulation of microgrid components including wind turbine (WT), photovoltaic (PV), micro turbine (MT), fuel cell (FC) and energy storage system (ESS) has been presented. An improved incentive-based demand response program (DRP) is applied to provide generation-consumption balance by modifying the load pattern. Considering the intra-day market in the formulation of EM is the first contribution of this paper. Furthermore, a new hybrid copula-scenario based uncertainty modeling technique has been presented in this paper. Formulating operational cost and environmental pollution as the objective functions, the proposed EM problem will be solved by multi-objective group search optimization (MOGSO) algorithm. Simulation results demonstrate the good performance of the proposed method in solving microgrid EM problem. Highlights • A new hybrid uncertainty modeling technique is proposed which combines copula function and scenario generation method. • The proposed method has generated conditional PDF for real and forecasted values using copula. • An equivalent weighted scenario is introduced. • Application of an improved incentive-based DRP in EMS of microgrid has been studied. • Microgrid EMS problem is solved in both intra-day and day-ahead markets. [ABSTRACT FROM AUTHOR]

Abstract The load demand modeling of Plug-in Electric Vehicles (PEVs) has been taken more attention in today's power system studies. Big-data should be handled for accurate modeling of PEVs load demand. Therefore, the utilization of data mining tools will be helpful for PEVs data analytics and clustering. In this paper, a Factor Analysis (FA) based method is introduced for the PEVs data mining. The load profiles of PEVs that are extracted by Monte Carlo Simulation (MCS) are clustered in some groups optimally using FA method. The clustered data is applied on Energy Storage Systems (ESSs) and Distributed Generation (DGs) planning procedure, separately. The simulation results show the power demand of PEVs effect on both ESSs and DGs planning, however, the temporal feature of PEVs profiles affects only on ESS planning, but not considerably on DG planning. This temporal feature, here called Storage to Storage (S2S) effect, reflects the nature of PEVs and ESS long-term memory which is discussed in this paper. The simulation results show that the optimal ESSs capacity is reduced if the PEVs data are clustered especially in high PEVs penetration. However, the optimal capacities of DGs is the same with and without PEVs data clustering scenarios. Highlights • Factor analysis method is applied for PEVs data mining and uncertainty handling. • A new feature, namely Storage to Storage, is introduced for the first time in distribution components planning. • The clustered data is applied on both ESSs and DGs planning. • The impact of long memory feature of energy storage and PEVs in component planning is investigated. [ABSTRACT FROM AUTHOR]

Abstract With the increase of renewable energy permeability and the development of distributed grid, energy storage plays an increasingly important role in the power system. A lot of studies have shown that energy storage can already be economically feasible. However, most previous studies concentrated on the value of energy storage in the free electricity market. In China, the power grid monopolizes the process of electricity transmission, distribution and retail, and the feed-in tariff and retail prices of electricity are regulated by government. It is difficult to analyze the application value of energy storage for China's electricity due to the lacking of data. The major contribution of this paper is to evaluate the application value according to the data of a provincial power grid. The results support the argument that energy storage can generate positive returns. The optimal storage capacity and operational strategy are also discussed in this paper. Highlights • Estimating the application value of energy storage in China's power grid. • The efficiency of the power grid can be improved by using EES. • The EES can bring remarkable value in China. • The optimal storage capacity and operational strategy are determined. [ABSTRACT FROM AUTHOR]

Abstract Energy storage technologies will become an important grid integration part of the renewable energy systems (RES) in near future. Using energy storage with RES is the best way of utilizing renewable power and reducing the conventional fossil fuel consumption. Sudden variation of load demand requires energy storage with high power density ability. This paper includes a hybrid energy storage system (HESS) that contains both high energy and power density storage battery bank and ultra-capacitor unit respectively to respond the above-mentioned necessities. The proposed power system arrangement and the dynamic energy management algorithm can vigorously supply the dynamic load demand supported by the components of the hybrid energy storage system, photovoltaic power and grid connection. Control of the unit by an energy management algorithm, depending on the dynamic changes in the system is provided. Experimental test results are presented in this paper to demonstrate the feasibility of the proposed energy management structure. The validity of the proposed dynamic energy management algorithm with efficient converter is experimentally tested in laboratory based prototype HESS in smart grid. Highlights • Dynamic energy management algorithm is developed for a hybrid energy storage system. • The hybrid energy storage system consisting of battery bank and ultra-capacitor unit is investigated. • Integration of 3-phase 4-wire inverter structure to smart grid is experimentally tested. • The hybrid energy storage device has high power density, fast response, and high efficiency is proposed. • The smart energy management control strategy has been verified on a laboratory test set-up. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a new framework for optimizing the operation of Industrial MicroGrids (IMG). The proposed framework consists of three levels. At the first level, a Profit Based Security Constrained Unit Commitment (PB-SCUC) is solved in order to minimize the total expected cost of IMG via maximizing the IMG revenue by transacting in the day-ahead power market and optimizing the scheduling of the units. In this paper, the tendency of IMG for participating in the day-ahead power market is modelled as a quadric function. At the second level, a Security Constrained Unit Commitment is solved at the upper grid for minimizing the upper grid operation and guaranteeing its security. At this level, the accepted IMG bids in the day-ahead power market would be determined. Finally, at the third level, the IMG operator must settle its units on the basis of its accepted bids. Therefore, a rescheduling problem is solved in the third level. Notably, Renewable Energy Sources (RESs), Combined Heat and Power (CHP) units, thermal and electrical storage systems are considered in the IMG. As the RESs and day-ahead market price have stochastic behaviours, their uncertainty is taken into account by implementing stochastic programming. Further, different cases for grid-connected and island modes of IMG are discussed, and the advantages of utilizing RES and storage systems are given. The simulation results are provided based on the IEEE 18-bus test system for IMG and IEEE 30-bus test system for the upper grid. Highlights • A new tri-level framework for optimal operation of an industrial microgrid is presented. • Optimal day-ahead scheduling of Combined Heat and Power is addressed. • Different operational modes of industrial microgrids are discussed. • The impacts of considering the upper grid configuration are demonstrated. [ABSTRACT FROM AUTHOR]

This paper describes an approach to compute the maximum value of energy storage systems (ESS) in grid applications under uncertain energy prices. The value obtained is based on an optimal operation (consisting of charge/discharge sequences) of the ESS. In other words, it is the maximum value that may be obtained when the ESS charge/discharge sequence is adapted to the expected operational conditions. To obtain that optimal value, this paper describes a dynamic program approach, with the particularity that the switching decisions are optimized considering an uncertain price evolution and a dynamic calculation of the aging cost. A practical implementation of this approach is proposed, in which the problem is conveniently sliced into matrices corresponding to single decisions. It is shown that such an arrangement, combined with shift and re-indexing operators, provides a fast solution to the optimization problem consisting of a huge number of decision evaluations. The algorithm is then applied to a number of European electricity markets, with a particular focus on arbitrage. The particularities of the algorithm solutions are analyzed, and it is shown that not considering the imperfect foresight and the aging impacts leads to considerable errors in valuing an ESS. [ABSTRACT FROM AUTHOR]

The paper presents a solution to the problem of large fluctuations of power demand from a fossil-fueled steam turbine cycle. The fluctuations are due to the presence of the renewable energy sources in the power grid. Steams turbines have a minimal load that limits their flexibility, meaning the range of the possible response to the fluctuation in the grid. The solution presented here aims to lower this limit to increase the flexibility. This is achieved by combining a steam turbine cycle with a molten salt storage system. The combined systems allow to store the excessive power and feed the grid with the power output well below the minimal load. Under these conditions a steam turbine becomes much more flexible and is a better fit for the renewables. The paper describes the modifications of existing power generating units required to integrate them with the storage system. The numerical modelling presented in the paper shows the power generation efficiency of the modified cycles for different loads. It also presents the number of hours that the discharging process takes, when the steam for the turbine is generated from the stored energy only. • Renewable energy sources cause steam turbines to operate with highly variable load. • Steam turbines load has a limiting minimal value that affects its flexibility. • A method is proposed to increase the flexibility of a steam turbine. • A turbine may cooperate in a closed-loop with a molten salt storage system. [ABSTRACT FROM AUTHOR]