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This paper proposes a multi-objective framework for sustainable scheduling of hybrid hydrogen-power systems. In the proposed model, the microgrid system incorporated renewable energy systems, battery energy storage systems, non-renewable resources, power-to-hydrogen, hydrogen-to-power, demand response programs, and hydrogen storage systems to present a highly flexible structure in the energy systems. The purpose of the proposed model is to provide a complete operation mechanism for the daily scheduling of microgrids considering environmental, economic, and sustainable problems. To this end, a stochastic three-objective optimization model is proposed to consider the intermittent behavior of renewable generation, loads, and market prices and simultaneously tries to minimize the carbon emission, total cost, and peak loads. The peak load reduction is important because it increases the reliability, renewable energy integration, power quality, and reduced cost of reserve capacity. Therefore, a min-max approach is presented to reduce the peak load in the proposed model. Also, the hydrogen-to-power and power-to-hydrogen resources are integrated as flexible resources to increase the efficiency of microgrids during operation scheduling. The proposed model is evaluated on a standard case study and the numerical results show the multi-objective model reduces the carbon emission by 28.05 kg and increases the load factor by 6.26%, respectively. • Description of a multi-objective problem that integrates electricity and hydrogen. • Proposing an enviro-economic model to reduce carbon emission and operating cost. • The proposed model significantly uniforms the load profile by min-max approach. • Hydrogen storage tank and DR programs are considered to increase the flexibility. [ABSTRACT FROM AUTHOR]

Renewable energy production through electrolysis of water to obtain "green hydrogen" has gained interest as a clean energy storage option. However, integrating variable renewable energy resources into the grid poses challenges to maintaining energy balance and stability. In this paper, a hybrid energy storage system combining short-term battery energy storage system and long-term hydrogen-based energy storage system is proposed for an isolated DC microgrid with a structure similar to a hydrogen refueling station. Passivity-Based Control (IDA-PBC) is utilized for power converters regulation, ensuring global stability based on Lyapunov theory while accounting for the nonlinear characteristics of the physical system. The control strategy is designed to ensure a reliable supply of electrical energy to the loads, preserve the quality of electrical variables in the network, and secure the safe operation of system components. Additionally, to safeguard devices from overvoltage, a Sliding Mode Reference Conditioning loop (SMRC) is proposed. It intervenes only when there is a potential risk of exceeding predefined boundaries. Simulations results under demanding conditions demonstrate the effectiveness of incorporating hydrogen energy storage systems with IDA-PBC and SMRC for this class of problems. • An isolated MG with H2 storage, typical structure of a refueling station, was modeled. • The IDA-PBC ensures the stability of the nonlinear and complex MG. • SMRC ensures overvoltage protection in the bus. • The control confirms that H2 storage integrated with RES contributes to MG stability. [ABSTRACT FROM AUTHOR]

In this paper, certain issues envisaged in the framework of development of design methodology for intelligent autonomous distributed hybrid power complexes (ADHPC) with green hydrogen and energy storage, functioning in grid mode and in the mode of interaction with the global (national) grid (GN) have been solved. Depending on their energy deficit or surplus, relative to the global grid, ADHPCs can operate as a load or as an energy source, respectively, as follows: based on the analysis of the energy balance management process in the ADHPC, a reasonable choice of the structure of the system of accumulation and distribution of power flows (SADCF) was made from the point of view of increasing the reliability of its functioning and ensuring the physical feasibility of the energy balance management process in this structure, i.e. keeping the actual power consumption of the consumers close to the required rated power at each given time t. This is achieved by including a condenser connected to the SADCF system on its assembly and distribution bus and a storage system BS with double-level ((BS1, BS2), double-circuit ((BS1(1), BS1(2)), (BS2(1), BS2(2))) structure, whereby: BS1of the level 1 - to manage the capacity balance in the SADCF under normal ADHPC regime and the variation of green hydrogen and consumption capacities within their confidence intervals assessed at the design stage; BS2 of the level 2 - to coordinate ADHPC and GN modes of operation and to control, together with BS1 of the level 1, GN, diesel generator (DG), the power balance in the SADCF when the ADHPC fails and when RES and consumption power are outside their confidence intervals; alternating charge/discharge operation of the parallel circuits will extend the life of the BS system; – a comprehensive definition of optimal ADHPC system situational energy balance management task is formulated. [ABSTRACT FROM AUTHOR]

With the extensive utilization of fossil fuels, environmental concerns have been growing. Hydrogen-powered vehicles (HPVs) as well as hydrogen refueling stations (HRSs) are expected to proliferate to alleviate the pollution. In order to promote the economic valibity of HRSs, this paper proposes an optimal scheduling model for distributed HRSs to maximize HRSs' revenue during a day. HRSs serves both the mobility sector with fuel individually and the power system with reserve demand response collaboratively. The elaborated operating of electrolysers and relaxed control of hydrogen storage are involved to enhance practicability and flexibility of the scheduling. Considering that this model has multiple variables, intractable high-dimension nonlinear constraints, a transfer multi-agent reinforcement learning algorithm is developed. The algorithm consists of two parts: the one is the multi-agent optimization method by transforming multi-decision optimization into a multi-agent optimization; the other is to transfer the empirical knowledge of the source task to reinforcement learning according to the similarity between the source task and the target task, making it cut down the blindness of exploration and accelerate convergence. Numerical studies reveal that participating in the reserve services market collaboratively increases overall revenues of HRSs by up to 32.90%, and the computation time is sharply shortened by approximately 34 times compared to the basic reinforcement learning. • A joint operation model of distributed HRSs is proposed to lift revenue by 32.90%. • Relaxed control of hydrogen storage is utilized to minimize service deviation. • An evolutionary transfer reinforcement learning shortens solving time by 34 times. [ABSTRACT FROM AUTHOR]

In renewable-based microgrids, intermittency caused by some energy sources highlights the important role of energy storage systems. Nowadays, hydrogen and thermal energy storage are expected to play key roles in grid-scale applications, for facilitating effective penetration of clean energy sources. This paper, through a multilevel control framework for the energy management of a renewable and reversible solid oxide based microgrid, develops medium level controls accounting for the dynamic behaviour of storage systems. The storage systems considered are a hydrogen storage tank and a thermal energy storage based on phase change material technology. In particular, the proposed algorithm is helpful in the microgrid design phase as well as for clearly assessing high-level energy management strategies, since dynamic and transient behaviours of tanks during both charging and discharging phases are validated. In this way, top-level energy management strategies can be further refined and optimized in addition to a techno-economic design being pursued, primarily through shrinking the initial sizes of storages and thereby achieving significantly lower initial capital costs. For the analyzed microgrid, its feasibility is demonstrated by reducing by as much as 40% the size of the hydrogen tank and by up to 20% the energy capacity of the thermal storage. Finally, the proposed medium level control in a multilevel algorithm framework for a hydrogen-based microgrid is also seen to be fruitful for avoiding waste energy, which can be beneficial against the background of distributed energy systems acting in virtual power plants. • An rSOC-microgrid relying on renewables and innovative storages (i.e., HST and PCM-TES). • Multilevel control strategy checking storages resiliency in terms of energy satisfaction. • HST and TES sub-models considering transient phases for charging and discharging. • Scenario analyses aimed at optimizing both energy and economic aspects of energy storages. [ABSTRACT FROM AUTHOR]

This paper presents a novel energy management strategy (EMS) to control a wind-hydrogen microgrid which includes a wind turbine paired with a hydrogen-based energy storage system (HESS), i.e., hydrogen production, storage and re-electrification facilities, and a local load. This complies with the mini-grid use case as per the IEA-HIA Task 24 Final Report, where three different use cases and configurations of wind farms paired with HESS are proposed in order to promote the integration of wind energy into the grid. Hydrogen production surpluses by wind generation are stored and used to provide a demand-side management solution for energy supply to the local and contractual loads, both in the grid-islanded and connected modes, with corresponding different control objectives. The EMS is based on a hierarchical model predictive control (MPC) in which long-term and short-term operations are addressed. The long-term operations are managed by a high-level MPC, in which power production by wind generation and load demand forecasts are considered in combination with day-ahead market participation. Accordingly, the hydrogen production and re-electrification are scheduled so as to jointly track the load demand, maximize the revenue through electricity market participation and minimize the HESS operating costs. Instead, the management of the short-term operations is entrusted to a low-level MPC, which compensates for any deviations of the actual conditions from the forecasts and refines the power production so as to address the real-time market participation and the short time-scale equipment dynamics and constraints. Both levels also take into account operation requirements and devices' operating ranges through appropriate constraints. The mathematical modeling relies on the mixed-logic dynamic (MLD) framework so that the various logic states and corresponding continuous dynamics of the HESS are considered. This results in a mixed-integer linear program which is solved numerically. The effectiveness of the controller is analyzed by simulations which are carried out using wind forecasts and spot prices of a wind farm in center-south of Italy. • Design of control algorithms for islanded and grid-connected microgrids. • Development of hierarchical MPCs to target long-term and short-term operations. • Tracking the load demand and maximizing the revenue via electricity markets. • Minimization of the HESS operational costs. [ABSTRACT FROM AUTHOR]

After a natural disaster, electrical grids might go into a self-healing mode to restore service and reconnect any customers that lost power. In the self-heal system, the fast operation and restoring the critical loads (CLs) during a fault by available resources can increase the flexibility and resilience of the network. In addition to renewable energy sources and electric vehicles (EVs) storage devices, the hydrogen storage system and the process of converting hydrogen into electric energy by micro-gas turbine (MGT) have been modeled in order to assess the impact of this storage system in electrical loads restoration in critical conditions. Self-healing functionality is enhanced by using alternative and novel energy sources. In such a situation, how to interact with the stochastic nature of network components will be challenging. This paper formulates the optimal service restoration (SR) problem using chance-constrained Adaptive Distributionally Robust Optimization (ADRO) to address the ambiguities introduced by the uncertain parameters. A tri-level robust optimization model is developed such that the optimal switching in the network until clearing the fault is determined at the upper level, and the maximization of the SR based on the load demand values is determined at the lower level. The proposed method is demonstrated by simulation results on the modified Civanlar test system. In an equal operating condition and considering β = 3, it can be seen that the initial hydrogen State of the Tank (SoT) by 50 % can improve the load restoration by 7.5 % compared to the uncharged hydrogen tank. By increasing the initial SoT to the nominal value (10 Kg), the load restoration can increase to 13.2 %. [Display omitted] • Customized hydrogen into electric energy conversion model is developed in the energy management field. • Each uncertain parameter is considered ambiguous in the manner that follows an unknown but bounded distribution. • EVs, PHEVs, flexible loads, and the coordinated operation of the hydrogen-to-power system are all taken into account simultaneously. • A tri-level MILP model is formulated to determine the service restoration schedule. • CVaR is employed to effectively deal with the uncertainty of the parameters. [ABSTRACT FROM AUTHOR]

In this paper, a two-layer hierarchical control strategy for an isolated DC microgrid with a hybrid energy storage system is considered. The DC microgrid studied is composed of a short-term battery energy storage system, long-term hydrogen-based energy storage system and renewable energy resources. The proposed control strategy, combines concepts of Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) as a primary level and Model Predictive Control (MPC) as a supervisory level. The IDA-PBC layer guarantees a correct power balance and ensures global stability according to Lyapunov theory, considering the non-linear behavior of the physical system. On the other hand, the MPC layer calculates the reference signals for the primary level, ensuring null stationary error in the desired variables. As known, it is important to keep the electrolyzer, the fuel cell and the batteries in a safe operating mode to increase their useful life and guarantee the quality of the hydrogen produced. In this sense, the benefits of MPC are exploited by considering system constraints in the controller design. The main objective of the two-layer control strategy is to ensure the supply of electrical energy to the loads and quality in the electrical variables of the network. Representative simulation results under demanding conditions verify the effectiveness of combining IDA-PBC and MPC for this class of problems. • An isolated MG with H2 storage, typical structure of a refueling station, was modeled. • The hierarchical control strategy successfully combines IDA-PBC and MPC techniques. • The IDA-PBC ensures the stability of the nonlinear and complex MG. • Supervisor MPC ensures zero stationary error and safe operating mode of the MG. • The control confirms that H2 storage integrated with RES contributes to MG stability. [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]