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Electric-Steam Integrated Energy Systems (ES-IES) have garnered considerable attention in industrial applications due to their high energy utilization efficiency and energy density. Nonetheless, the limited thermal storage capacity of the steam system impacts the stability of ES-IES, posing a challenge for its implementation in scenarios with a high proportion of renewable energy sources. To address this limitation, this study demonstrates how the integration of a Steam Accumulator (SA) can enhance the stability of ES-IES through a dynamic simulation-based approach. Firstly, a novel method for simulating the steam system is introduced. This method utilizes a resolution-adaptive strategy based on the central implicit difference format to balance simulation accuracy and speed. Furthermore, a simulation framework for ES-IES is delineated, detailing the solution order of subsystems and system coupling interface parameter interaction method. Meanwhile, a comprehensive model and solution for the SA are proposed, aligning well with the aforementioned ES-IES simulation methods in terms of accuracy and complexity. Finally, case study demonstrated that equipping SA in ES-IES can effectively reduce its fault-recovery time by 57.9%, highlighting the efficacy of SA in promoting system stability and security. • A steam accumulator dynamic model and solution. • A novel steam system dynamic simulation approach. • An ES-IES simulation framework. • Fault-state ES-IES operation stability improvement. [ABSTRACT FROM AUTHOR]

Intelligent design and operation optimization allow energy systems to take advantage of the flexibility that multi-generation provides. This study proposes a basic solar-driven system integrated with thermal energy storage for round-the-clock energy harvesting. A modified configuration is then designed incorporating innovative multi-heat recovery approaches to increase the capacity and product diversity of the basic system. The modified system is able to cover vital urban utilities such as electricity, fresh water, cooling, and hydrogen throughout the day. To overcome the time-consuming procedure of dynamic techno-economic simulation as well as the limitation of commercial engineering equation solvers for tri-objective optimization, a deep learning approach is developed to reduce the computational complexity and improve the analysis accuracy. In this regard, the trained neural networks play an intermediary role in coupling the developed code with the MATLAB optimization toolbox. A comparison between the modified and conventional configurations indicates that implementing the multi-heat recovery approach results in a 29% increase in power generation while only increasing the overall system cost by 1.97%. From an economic perspective, the Sankey diagram depicts that the storage unit with a cost rate of 12.25 $/h accounts for 6.77% of the plant's cost rate, which enables the system to operate continuously. According to the sensitivity analysis and contour plots, the number of collectors significantly affects the total cost rate and fresh water production capacity while it has no tangible effect on the exergy efficiency. • A novel multigeneration system with thermal energy storage is designed. • A modified configuration with heat recovery is introduced to boost the capacity. • The comparative study indicates a 17.6% improvement in the power generation. • A dynamic simulation code is developed based on meteorological data. • A deep learning techno-economic optimization is applied to the system. [ABSTRACT FROM AUTHOR]

• An innovative slim decentralised plug-and-play tank is proposed for DHW production. • Model calibration is performed by experimental data in charge/discharge process. • Retrofit solution cuts required annual energy in half of which 82% is RES quota. • Optimised charging scheme reduces the total annual electrical energy by 5.2 %. • A degree increment in draw-off temperature increases annual CO 2 emission by 103 kg. The present study deals with a multi-objective analysis of an innovative decentralised system to produce and store domestic hot water (DHW), emphasising on the combined effects of the technological aspect, control strategy and user's behaviour. The proposed system, by relying on thermal energy storage, decouples energy production and demand while shaves peaks in the energy demand and, at the same time, provides more autonomy to users through local storages. To identify subtle interactions in components of DHW system, dynamic simulations are carried out by establishing a coupled TRNSYS-MATLAB code, calibrated and validated by experimental measurements. The energy analysis implies that the proposed system cuts the required annual electrical energy in half, of which up to 82% of needed primary energy is supplied from renewable sources, compared to previous electrical-decentralised system. The optimisation of the results through applying control strategies indicates that adopting a three-time charging scheme is advantageous in terms of providing a more stable temperature profile as well as a higher hot water temperature. Compared to an available-by-demand operation, this scheme reduces the required total annual electricity by 5.2 % and enhances total thermal loss from components up to 4.0%. Furthermore, a sensitivity analysis on the results emphasises the striking role of the user behaviour in electrical energy consumption either via draw-off temperature or adjusting the pre-defined temperature for activation of the built-in auxiliary heater. [ABSTRACT FROM AUTHOR]

As renewable energies are incorporated in larger shares in the electricity grid and district heating and cooling networks, the integration of storage solutions becomes more important. Thermal Energy Storage is an effective way to store heat and utilize the synergies between different energy carriers. Stratified storage tanks are a promising technology because of their low cost, simplicity and reliability. However, the modeling of the thermocline region in a stratified tank remains a challenge. There is a need to develop a fast and accurate 1D model for simulations and optimizations of TES. In this paper, a new discretization scheme is applied to the vertical axis of the storage tank. Orthogonal Collocation accurately represents the temperature profiles inside the storage tank with less points than the traditional multinode model, therefore running faster. Oscillations appear in the temperature profiles computed with orthogonal collocation if the thermocline represented is too steep and a low number of discretization points is used. But if a realistic thermocline is used as initial condition, the model performs well. Thus, it is appropriate to represent the real behavior of a storage tank, where uniform temperature conditions are avoided. Orthogonal Collocation on Finite Elements runs even faster and represents a good perspective for optimization studies. The model developed in this paper is validated with real data from a solar thermal plant with storage. A continuous and smooth model is also developed for natural convection inside the storage tank. The limitations of the model are discussed and perspectives on the modeling of natural convection for optimization models are given. • Orthogonal collocation is suggested for the 1D storage tank model discretization. • The proposed model runs faster than the multinode model for the same accuracy. • Orthogonal collocation on finite elements is suggested for optimization studies. • The storage tank model is validated with real plant data. • A continuous and smooth model for natural convection is developed. [ABSTRACT FROM AUTHOR]

The capability to dynamically plan, predict, and control indoor conditioning allows to adapt to individual preferences of inhabitants and enables demand side management. While former mainly improves thermal comfort of inhabitants so does latter unlock ecological and financial opportunities mostly for energy utilities. Commonly, dynamic indoor conditioning is based on piece-wise constant indoor temperature constraints. This paper's contribution is the presentation of additional constraints: in particular ones expressed relative to the nominal behavior of a hydronic heating system. This allows to simultaneously harness the relevant process variables in particular during the pre-loading and post-loading phases of a load reduction. The findings are based on data sets acquired on 10 inhabited, residential buildings in Stockholm over a whole year. One of the findings is that building models need to be adaptable if predictive control is applied in practice. This adaptability is assured by a novel concept, i.e. a so called model manager on which the control is relying for the selection of the most accurate model. Centralized optimal control of buildings connected to a district heating network is challenging in practice due to a high computational load. In order to reduce it, the herein presented method elaborates plans only every hour instead of at every control step for optimal control. Since these plans cannot be optimal due to the lack of regular update a hitherto unknown cascaded control logic has been developed that corrects planning errors and other disturbances. Capabilities are demonstrated and compared to conventional controllers in dynamic simulations of a multi-zoned building. The herein presented method is to our knowledge the first to provide all flexibility desired by energy utilities and inhabitants alike through harnessing the consequences of transitions. [ABSTRACT FROM AUTHOR]

As a result of the increased penetration of intermittent renewable energy sources, Combined Heat and Power (CHP) units are being looked upon as one of the sources that might provide for the ever growing need for electrical flexibility. However, CHP units are often considered as must-run units on the grid for their main purpose is generally to cover the heat demand of an adjoining District Heating Network (DHN). This paper demonstrates how a CHP–DHN system may be used as a frequency reserve without excessively compromising the lifetime of the CHP, using either a specific storage tank or the DHN's thermal inertia in order to compensate for the resulting heat imbalance. In the latter case, it is shown that a buffer tank, although smaller than a specific Thermal Energy Storage (TES), is required due to restrictions for acceptable DHN temperature gradients. In both approaches, the size of the tank has been mapped out considering frequency reserve's duration and capacity. The results show that a simplistic static model of the DHN is sufficient for the design of a specific TES, while a detailed dynamic simulation is required when the DHN is used as storage, to prevent overestimating the flexibility of the CHP–DHN system. This research could be used to assess the potential for improving CHP–DHN systems flexibility, using them as frequency reserves, and to design the required storage or buffer tanks. • A control strategy to use a CHP-DHN system as a frequency reserve is proposed. • Dedicated storage tanks and/or DHN thermal inertia are used. • The DHN can be used as heat storage, but a buffer tank is required. • A detailed dynamic DHN model is needed to determine the DHN flexibility. • The flexibility of the CHP–DHN system was determined for 370 MW CHP. [ABSTRACT FROM AUTHOR]

[Display omitted] • Thermochemical heat storage using Ca(OH) 2 /CaO in a fluidized bed reactor was modeled. • Variable renewable energy fluctuation can be absorbed by using steam as fluidizing gas. • The energy efficiencies for nitrogen were higher than that for steam. • The simulated data agreed with the experimental data, albeit not perfectly. A simplified dynamic model of a Ca(OH) 2 /CaO–containing fluidized bed reactor was developed by combining a continuously stirred tank reactor model in the solid phase with a series of continuously stirred tank reactors in the gas phase for mass transport. The heat supplied to the thermochemical heat storage system was allowed to fluctuate to evaluate the absorption of variable renewable energy fluctuation. In addition, the performance of the fluidized bed was assessed using nitrogen or steam as the fluidizing gas. For nitrogen, the fluctuation of bed temperature increased with the increasing time step of heat change. The bed temperature was affected by the magnitude of the fluctuation of the supplied heat more strongly for nitrogen than for steam, mainly because the rate of dehydration under these conditions was more strongly dependent on temperature than in the case of steam. The thermochemical heat storage efficiency (calculated by considering reaction heat) and energy storage efficiency (calculated by considering reaction heat and sensible heat) equaled 14.1% and 34.1% for steam and 29.9% and 62.7% for nitrogen, respectively. The differences between the efficiencies for steam and nitrogen were ascribed to the latent heat of H 2 O. Sensitivity analysis showed that both efficiencies increased with increasing heat supply because of the concomitant decrease in the time required to heat the system to the reaction temperature. During this time, thermochemical heat storage did not occur, which resulted in lower efficiency. Therefore, the fluctuation from variable renewable energy can be absorbed by using thermochemical heat storage. [ABSTRACT FROM AUTHOR]

This work presents a dynamic analysis of an anaerobic digestion plant, in which concentrating photovoltaic/thermal collectors are used to match a part of both heating and power demand of the process. The system is supplied by the organic fraction of municipal solid waste. The system also includes a thermal storage tank and an auxiliary heating system. An up-grade section is also included, to produce biomethane, suitable for injection into the natural gas pipeline network. For such hybrid solar-biomass system, a comprehensive simulation model was developed in MATLAB®, calculating the time-dependent production of biomethane as a function of the operating temperature within the digester. The model, based on differential equations and thermal balances, accounts for both thermal and biological phenomena occurring within the process, taking into consideration the geometrical and structural characteristics of the system. The consistent Anaerobic Digestion Model 1 is used to model the biological process, evaluating the biogas production as a function of a series of operating variables: the digester operating temperature, mass flowrate and temperature of the hot water entering the digester, ambient temperature, mass flowrate and composition of the organic waste in input. The model also calculates the electric consumption of the upgrading process, used to convert the biogas into biomethane. Such model was integrated into the simulation platform of the overall plant, developed in TRNSYS, evaluating the energy, environmental and economic performance of the entire system. A case study is presented, showing the dynamic performance of the system under evaluation: for such case, a primary energy saving of 24% was found, with respect to a conventional digester; around 20% of the overall thermal energy demand is met by solar energy; finally, a promising payback time of about 3 years was estimated. [ABSTRACT FROM AUTHOR]