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The shared energy storage system is recognized as a promising business model for the coordinated operation of integrated energy systems (IES) to improve the utilization of energy storage and the consumption of renewable energy. As the hydrogen energy gradually receives more attention, this paper constructs the structure of a hybrid hydrogen energy storage system shared by an IES alliance in a dynamic pricing mode. A bi-level optimization model for the shared hybrid hydrogen energy storage system (SHHESS) is proposed to optimize the capacity configuration decisions and the pricing strategy jointly. The upper level determines the capacity and dynamic price of SHHESS with maximum profits and the lower level obtains the optimal operation of the IES alliance minimizing the total operation costs. The proposed model is solved by an improved PSO-GA algorithm and CPLEX solver. Finally, numerical tests are conducted in different scenarios. The results show that the hybrid energy storage system improves the daily profits of SHHESS by 70.3% and 5.44%, and reduces the renewable energy curtailment by 80.93% and 48.92% respectively compared to the battery-only and hydrogen-only systems. Additionally, the daily operation cost of the IES alliance is 35.2% lower than the individual operation scenario. The proposed model confirms the feasibility and superiority of the hybrid hydrogen energy storage system in a sharing business mode. • Design an interactive structure of a shared hybrid hydrogen energy storage system. • Propose a bi-level planning optimization framework for shared hybrid hydrogen energy storage. • The dynamic price of energy storage sharing service is optimized. • Determine the optimal operation strategy of the integrated energy system alliance. • Propose a hybrid method combining an improved PSO-GA and CPLEX optimizer. [ABSTRACT FROM AUTHOR]

The development of large energy systems based on renewable energy sources is accompanied by an increase in the input capacities of large grid storages. Taking into account the vector for decarbonization and maneuverability, the use of hydrogen as a fuel in the cycle of a diabatic compressed air energy storage (CAES) is a promising scientific and technical direction. This paper analyzes the key performance indicators of a compressed air energy storage in the presence and absence of thermal energy recovery within the cycle. In addition, an assessment was made of the prospects for the use of a methane-hydrogen mixture in gas turbines. At the end of the paper, a calculation of the process of plasma pyrolysis of methane is given as one of the possible methods for the production of hydrogen for use in energy purposes. [ABSTRACT FROM AUTHOR]

In the context of renewable energy systems, microgrids (MG) are a solution to enhance the reliability of power systems. In the last few years, there has been a growing use of energy storage systems (ESSs), such as hydrogen and battery storage systems, because of their environmentally-friendly nature as power converter devices. However, their short lifespan represents a major challenge to their commercialization on a large scale. To address this issue, the control strategy proposed in this paper includes cost functions that consider the degradation of both hydrogen devices and batteries. Moreover, the proposed controller uses scenarios to reflect the stochastic nature of renewable energy resources (RESs) and load demand. The objective of this paper is to integrate a stochastic model predictive control (SMPC) strategy for an economical/environmental MG coupled with hydrogen and battery ESSs, which interacts with the main grid and external consumers. The system's participation in the electricity market is also managed. Numerical analyses are conducted using RESs profiles, and spot prices of solar panels and wind farms in Abu Dhabi, UAE, to demonstrate the effectiveness of the proposed controller in the presence of uncertainties. Based on the results, the developed control has been proven to effectively manage the integrated system by meeting overall constraints and energy demands, while also reducing the operational cost of hydrogen devices and extending battery lifetime. • Implementation of an SMPC strategy based on a scenario-based approach. • Reduction of operation and maintenance costs and integration of uncertainties. • Minimization of the overall operational costs and maximization the profits. • Reduction of complexity and computational time. • Implementation of a unique control architecture to manage a microgrid in different modes. [ABSTRACT FROM AUTHOR]

Regional integrated energy systems (RIES) can economically and efficiently use regional renewable energy resources, of which energy storage is an important means to solve the uncertainty of renewable energy output, but traditional electrochemical energy storage is only single electrical energy storage, and the energy efficiency level is low. Hydrogen energy storage has the advantages of large energy storage capacity, long storage time, clean and pollution-free, and can realize the synergistic and efficient utilization of electricity and thermal power. Based on this, this paper proposes a synergistic planning method for an integrated energy system with hydrogen storage taking into account the coupled use of electric-thermal energy, which effectively reduces the system carbon emission and improves the comprehensive energy efficiency level. Firstly, this paper constructs an electric-thermal coupling model of the hydrogen energy storage unit and proposes an optimization strategy for the integrated energy system containing hydrogen storage taking into account the utilization of electricity and thermal power. Secondly, a planning optimization model with the lowest economy and carbon emission and the highest comprehensive energy efficiency was constructed. Third, the CSPO-GE optimization algorithm is proposed for solving the problem, which significantly improves the solution efficiency. Finally, a planning optimization simulation of RIES for a residential community W in northern China verifies the effectiveness of the model and method proposed in this paper. The comparative analysis of the three schemes shows that compared with the integrated energy system with conventional electrochemical energy storage and heat storage tank as the main form of energy storage and the integrated energy system with only hydrogen storage, the integrated energy system with hydrogen storage and heat storage tank can reduce carbon emissions by 43.8% and 7.61%, respectively, and improve the integrated energy efficiency level by 337.14% and 14.44%. • An electrothermal coupled production strategy based on dynamic equilibrium feedback regulation is proposed. • A hydrogen-containing RIES planning and optimization model considering economy-environment-efficiency is constructed. • A multi-objective chaotic particle swarm algorithm (CPSO-GE) based on greedy and elite retention strategies is proposed • The impact of cost variation of hydrogen storage devices on the economics of integrated energy systems is considered. [ABSTRACT FROM AUTHOR]

The All-electric ship (AES) concept has revolutionized the maritime sector. Indeed, AES is a promising solution for modern vessels where decreasing emission pollution, suppressing fossil fuel consumption, and making economical operation are its brilliant merits. Fuel cell (FC) technology based on clean hydrogen energy sources can play a significant role in meeting these requirements in the AES platform. Because of the slow transient response of the FC, an energy storage system (ESS) is required to improve AES performance in different operation scenarios. Therefore, optimal sizing of the FC and the ESS and proper design of energy management strategy is a challenging problem. This paper proposes a systematic approach for the optimal design of hybrid FC-ESS of AES power systems. The proposed method is based on an optimization technique to determine the optimal sizing of the FCs and the ESS. Capital investment, fuel consumption, personnel electrical safety, and occupied volume of the AES power system are the main objectives. The FC size, the battery size, the scheduled output power of the FC, and the battery are the decision variables. Genetic Algorithm (GA) is used to solve the optimization problem. To validate the numerical results, the optimization problem is also solved by Mayfly Algorithm (MA) separately. To assess the feasibility of the proposed strategy, the optimal design of hybrid FC-ESS for a vessel is implemented based on the proposed method. The simulation results validate the proposed approach's sufficiency to investigate and optimize the economic, safety, and dimensional aspects of designing the FC ship's IPS. Numerical results are reported for each design aspect. Considering the minor differences of the numerical results obtained by GA and MA, the optimum points are validated by the two techniques. Using the obtained results, various perceptions can be derived from the FC ship's IPS design. For example, the IPS cost in the cases with optimum volume, weight, and safety, has 2.3%, 2.6%, and 7.3% penalties, respectively compared to the case with the minimum cost. • Fuel cell-based electrification of a fishing ship based on practical information. • Systematic approach for optimal power system design of hydrogen-powered ship. • Proposing safety objective function for integrated power system of fuel cell ship. • Fuel cell hybrid IPS optimization based on volume, mass, cost and safety. • Estimation of propulsion power demand based on ship's resistance and speed profile. [ABSTRACT FROM AUTHOR]

This paper presents a framework for the efficient design and evaluation of a standalone hybrid renewable energy system (HRES) to meet the energy requirements of a rural community in the north-eastern region of Nigeria. The proposed microgrid system incorporates solar photovoltaic, wind turbines, biomass gasifier, fuel cell, and Battery storage. The sizing of each component is determined through the utilization of real local meteorological data and the load demand over a year, employing the Levy flight-salp swarm algorithms (LF-SSA). The optimization objective is to minimize the annualized system cost (ASC) of the HRES, while also considering the reliability constraint of Loss of power supply probability (LPSP). The comparative outcomes demonstrate that the LF-SSA surpasses other examined algorithms, namely the salp swarm, genetic algorithm, and HOMER software by achieving substantial cost savings of $29033, $50796, $191771 respectively in relation to the proposed HRES. The result further shows that, the LF-SSA provides the lowest LCOE of $0.933162/kWh, in contrast to SSA at $0.947737/kWh, GA at $0.958660/kWh, and HOMER at $1.075351/kWh. Additionally, the results indicate that the implemented Energy Management System (EMS) has successfully facilitated the establishment of an environmentally friendly and cost-effective energy system. • An efficient energy storage system is essential for managing intermittent energy supply. • The fuel cell can generate DC power by combining hydrogen with oxygen. • The amount of power generated by a PV panel depends on temperature and solar radiation intensity. • Deterministic rule-based controllers operate based on pre-defined rules. • Energy Management System coordinate the flow of power between different components. [ABSTRACT FROM AUTHOR]

Optimal energy management of microgrids enables efficient integration of renewable energies by considering all system flexibilities. For systems with significant seasonal imbalance between energy production and demand, it may be necessary to integrate seasonal storage in order to achieve fully decarbonized operation. This paper develops a novel model predictive control strategy for a renewable microgrid with seasonal hydrogen storage. The strategy relies on data-based prediction of the energy production and consumption of an industrial power plant and finds optimized energy flows using a digital twin optimizer. To enable seasonal operation, incentives for long-term energy shifts are provided by assigning a cost value to the storage charge and adding it to the optimization target function. A hybrid control scheme based on rule-based heuristics compensates for imperfect predictions. With only 6% oversizing compared to the optimal system layout, the strategy manages to deliver enough energy to meet all demand while achieving balanced hydrogen production and consumption throughout the year. • PV with seasonal H 2 storage enables fully sustainable operation of industrial microgrids. • Model predictive control overcomes perfect foresight limitation in energy management. • H 2 production for long-term storage is incentivized by adding an extra cost term. • Hybrid predictive control enables a low cost system with nearly perfect yearly operation. [Display omitted] [ABSTRACT FROM AUTHOR]

Due to the environmental and economic advantages of combined heat and power (CHP) units, their use in power grids has expanded. The entry of CHP into power systems increases the complexity of the economic power flow problem. This complexity is due to the introduction of multiple constraints into problem. A mere electricity supply is not optimal in today's networks, and energies such as heat, power and gas must be planned and managed simultaneously as an energy hub. Therefore, in this paper, an intelligent multi-energy microgrid (MG) consisting of power generation units, CHP units and gas units is modeled for day-ahead energy management (DAEM). The economic distribution problem focuses on the amount of power generation, heat and gas of the units in the system. In contrast, the total generation cost of the system is minimized, and all the equality and inequality constraints of the problem are observed. The proposed microgrid includes various energy-dependent equipment such as CHP units, gas boilers, electricity-to-gas units, power and heat storage units and electric heat pumps. Also, price-based load management was included to reduce costs due to the transfer of information between the consumer and the generator in the context of smartization. Since the above problem is difficult to solve due to various constraints and decision parameters, a newly developed optimization method based on water flows was proposed. The simple movement of water flows on the ground is efficient and optimal and always follows the shortest and fastest path to reach the deepest point. In the proposed algorithm, simple movements of water in routing, a change of direction and even the creation of rapids and vortices were simulated as various mathematical operators. Finally, the proposed model and method were examined in different scenarios. The numerical outcomes demonstrated that, the proposed modeling framework is superior to hub-based multi-carrier microgrid models in terms of power system security. The sensitivity of operational expenses to changes in initial values of energy storage systems (ESS) and thermal storage system (TSS) is proved that the cost of operation reduces as the baseline values of ESS and TSS are reduced to 0.2% of the maximum capacity. Because DAEM performance is less flexible when the primary values are reduced by 0.2% of the maximum value, the system running expenses increase marginally. [Display omitted] • Introducing a novel developed algorithm based on water flows. • DAEM in the microgrid in the form of an energy hub. • Application of internal grids & energy flow of energy carriers in multi-energy MGs. [ABSTRACT FROM AUTHOR]

The world is experiencing unprecedented development in the clean energy sector in residential and industrial applications. This paper provides a case study assessing different scenarios of greenizing the electrical energy demand in El-Mostakbal city in Egypt. Three scenarios are studied with consideration of a photovoltaic (PV) system integrated with the grid-connected city with different integrated system configurations. The scenarios for the grid-connected city are scenario-I: only PV, scenario-II: PV with batteries for electricity storage along with grid electricity, and scenario-III: PV with hydrogen production, storage, and utilization for covering the electric demand along with grid electricity, these scenarios are assessed technoeconomically, and the results show an optimized case where the electricity demand of the city can be met with 64.3% produced from solar energy, at $71.7 M of the net present cost. • Energy demand is evaluated using different proposed green scenarios that are assessed techno-economically. • Electricity demand of the city can be met with 64.3% produced from solar energy. • The cost of energy generation for scenario-I is found to be $0.0512 per kWh. [ABSTRACT FROM AUTHOR]