Stochastic optimization scheduling of wind-photovoltaic-storage-hydrogen-water system in independent island zero-carbon microgrid

A stochastic optimal scheduling strategy is being proposed for a wind-photovoltaic-storage-hydrogen-water system within a zero-carbon microgrid for independent islands. This strategy is aimed at addressing the challenge of sustainable energy resource supply prevalent in traditional islands, while also considering the demand response of seawater desalination. In order to harness the clean, low-carbon, and high-efficiency characteristics of hydrogen, an electrolytic water hydrogen production system is integrated into the power system to utilize wind power generation. The desalination equipment is regarded as a controllable load within the system. It is combined with a demand response strategy to maximize the full utilization of the desalination equipment for consuming wind power on the island. Furthermore, a two-stage stochastic optimization method is being employed to handle uncertainties related to wind and photovoltaic generation. The objective is to minimize the system’s operating cost. To solve this optimization problem, the commercial solver CPLEX is being utilized. Through comparative analysis across different scenarios, simulation results indicate that the proposed strategy effectively dissipates uncertainties in wind scenarios while concurrently reducing the overall system operation cost. This approach comprehensively considers the system’s economics, thus demonstrating its potential to efficiently promote wind power consumption.


Introduction
In recent years, the development and utilization of sea islands have been emphasized by countries worldwide, resulting in the formulation of various strategies for oceanic island development [1].Amidst the context of the "dual-carbon" target and low-carbon transformation, the emergence of diverse new energy storage methods presents more viable solutions for the exploitation of island scenic resources.However, the unique geographic location and resource characteristics of islands pose significant challenges to the full harnessing of island resources, the reduction of island development costs, and the enhancement of the quality of life for island residents.Establishing an island microgrid system with new energy is imperative to ensure the safe and stable operation of the system.
Hydrogen, recognized as a form of clean energy, exhibits characteristics such as high energy content, high density, and long storage cycles, rendering it a crucial option in achieving the "double carbon" goal.The production of hydrogen from wind and photovoltaics enables the mutual conversion between electrical energy and hydrogen energy.Microgrids employing green hydrogen production technology offer an effective solution for cost-effectiveness and low-carbon emissions in remote islands [2].A novel index system is proposed that integrates economic, resilience, energy, and environmental dimensions to determine the optimal system configuration, meeting the economic, IOP Publishing doi:10.1088/1742-6596/2771/1/012036 2 sustainable, and reliable energy needs of remote island communities, and providing guidance for future island microgrid planning [3].Additionally, the performance and economics of a hybrid island microgrid system are assessed considering uncertainty, demonstrating the feasibility of such a system in diverse environments [4].Meanwhile, an isolated island multi-energy system incorporating electricity-gas coupling is proposed [5].This system utilizes game theory and particle swarm optimization to establish a flexible and reliable energy scheduling strategy aimed at reducing wind and solar power wastage.Despite extensive exploration in the literature regarding the use of green hydrogen preparation technology for efficient and low-carbon emission solutions, the involvement of hybrid electric-hydrogen energy storage in the operation and scheduling of independent island microgrids is not considered.Furthermore, limited consideration is given to the impacts of wind and photovoltaic uncertainty on the system's operation.
Meanwhile, desalination equipment proves crucial in addressing the scarcity of freshwater resources on the island.A novel energy management system was proposed, focusing on the optimal operation of an independent hybrid energy system consisting of photovoltaic panels, wind turbines, batteries, and diesel generators to tackle freshwater supply shortages in remote areas [6].The results indicated that the utilization of wind energy in a renewable energy hybrid system for seawater desalination is more cost-effective and environmentally friendly.The optimal allocation of wind-diesel storage island microgrid capacity is investigated, considering the time-varying load of desalination equipment [7].This approach mitigates the island's freshwater resource scarcity and augments both the economic and environmental benefits of the island.A multi-objective scheduling optimization model is established for an island renewable energy system that integrates electric vehicles and desalination equipment [8].The findings demonstrate that utilizing renewable energy can offer a certain level of economic viability and stability for the island's electricity, freshwater supply, and transportation.The study reviewed confirms the feasibility of integrating desalination equipment into independent island microgrids.However, it does not fully exploit the potential of desalination equipment in harnessing the resources of the island.
This paper establishes a model for a wind-photovoltaic-storage-hydrogen-water system within zero-carbon microgrids on independent islands.The actual natural conditions and living requirements of island microgrids are considered, and an operational and scheduling strategy is proposed that takes into account uncertainty and the demand response of seawater desalination.A response model for desalination equipment is established based on the topology of wind-photovoltaic-storage-hydrogenwater zero-carbon microgrids on independent islands.Additionally, a two-stage stochastic optimization method is integrated to address the uncertainties of renewable energy.Furthermore, the impact of electric-hydrogen hybrid energy storage on the operation and scheduling of independent island zero-carbon microgrids is analysed, taking into consideration system equipment operation and maintenance costs, energy abandonment costs, and demand response costs.Moreover, the potential of seawater desalination equipment to harness wind and solar energy on the island is maximized.The simulation results demonstrate that the proposed strategy effectively enhances wind power utilization and reduces system operation costs.

Systems model
The structure of the independent island zero-carbon microgrid wind-photovoltaic-storage-hydrogenwater system is depicted in Figure 1.This system comprises wind turbines, photovoltaic arrays, water electrolysis devices, hydrogen storage tanks, hydrogen fuel cells, electrical energy storage, and desalination equipment.Among these components, on the load side, in addition to general loads, some residential loads are categorized as interruptible loads, and the desalination equipment is designated as a time-shifting load.Moreover, surplus wind and solar energy is stored in the hydrogen storage tank through water electrolysis to produce hydrogen.At specific moments, this hydrogen energy is converted into electric energy via the hydrogen fuel cell, thereby breaking the temporal and spatial where 2   H EL,t P represents the hydrogen production power of the electrolyzer at time period t; EL K represents the hydrogen production efficiency of the electrolyzer; EL,t P represents the power consumption of the electrolyzer at time period t; EL,max P is the upper limit of the power consumption of the electrolyzer; EL U is a binary variable (0 or 1) denoting the operational status of the electrolyzer.E indicate the hydrogen storage at the beginning and end moments of the hydrogen tank, respectively; ǻt denotes the scheduling step, which is set at 1 hour.

Hydrogen fuel cells. Hydrogen fuel cells can convert
Furthermore, the electrolyzer and the hydrogen fuel cell are connected through a hydrogen storage tank.To prevent the simultaneous filling and discharging of hydrogen in the tank, it is necessary to impose constraints on the operating states of the electrolyzer and the hydrogen fuel cell as follows:

Demand response model.
In this paper, the incentive-based demand response strategy is explored, involving a demand response model primarily comprising two types: time-shifting loads and interruptible loads.Within these categories, the desalination equipment functions as the time-shifting load, providing auxiliary regulation to the zero-carbon microgrid system of independent islands, as illustrated in the following model: where cut t P denotes the load that can be curtailed during time period t; cut,max t P denotes the maximum load that can be curtailed during time period t.

Objective function
The goal is to minimize the overall operational cost of the system, covering system operation and maintenance expenses, energy abandonment costs, and demand response expenses.This ensures the economic feasibility of operating the independent island zero-carbon microgrid wind-photovoltaicstorage-hydrogen-water system: op, , , 1 min where F denotes the overall system operation cost; op,t C represents the equipment operation and maintenance cost during time period t; , q t C signifies the energy curtailment cost during time period t; , DR t C represents the demand response cost during time period t; T is the number of scheduling cycle periods, set as 24 hours.

Equipment operation and maintenance cost.
where EL ] , HFC ] , and ES ] denote the unit operation and maintenance cost of the electrolyzer, hydrogen fuel cell, and electric storage, respectively.

Constraints of system equipment.
The constraints for each device within the independent island zero-carbon microgrid wind-photovoltaic-storage-hydrogen-water system are primarily comprised of electrolyzer constraints, hydrogen fuel cell constraints, hydrogen storage tank constraints, electric storage constraints, and demand response constraints, as illustrated in Equation (1) to Equation (24).

3.3.
The two-stage stochastic optimization approach.Wind and solar power are profoundly affected by environmental factors such as seasons and climate, displaying short-term stochastic behaviour and long-term fluctuations.This uncertainty may potentially lead to a significant imbalance between generation power and load power, resulting in notable frequency deviation or voltage stability issues.These challenges could impact the system's safe and stable operation.Therefore, to tackle this uncertainty, a two-stage stochastic optimization method is utilized in this paper [9] .
It is assumed that the wind power error conforms to a normal distribution 2 ( , ) where the expected value of the predicted wind power is P , and the fluctuation percentage is V .Initially, using the Latin hypercube sampling method, numerous original wind power output scenarios complying with the probability distribution constraints are generated.Subsequently, these scenarios undergo refinement through the synchronized back generation reduction method based on probability distance, ultimately resulting in streamlined scenarios with corresponding probabilities.The same approach is applied to describe the uncertainty of photovoltaic power, yielding four scenarios and corresponding probabilities s P (s=1, 2, 3, 4) for wind power output.In this paper, a specific typical daily forecasted output is utilized in a 24-hour scheduling cycle, the parameters of each system device are detailed in Table 1.

Comparison of simulation results for different scenarios
To assess the efficacy of the proposed operational and scheduling strategy involving windphotovoltaic-storage-hydrogen-water for the independent island zero-carbon microgrid, this paper presents three operational scenarios for comparative analysis.Scenario პ: The participation of desalination equipment in demand response is incorporated.The system's electric energy storage power/capacity is 100 kW/300 kWh, and the hydrogen energy storage capacity/power is 50 kW/300 kWh.
Scenario ჟ: The participation of desalination equipment in demand response is disregarded.The system maintains an electric energy storage capacity/power of 100 kW/300 kWh, and the hydrogen energy storage capacity/power remains at 50 kW/300 kWh.
Scenario რ: The participation of desalination equipment in demand response is included, with the system's electric storage capacity/power at 100 kW/300 kWh, and the hydrogen storage capacity/power adjusted to 30 kW/240 kWh.
Table 2 displays the results derived from the optimization runs conducted for the three scenarios.By comparing Scenario პ with Scenario ჟ, the impact of considering the participation of desalination equipment in demand response on system operation scheduling is assessed.Table 2 illustrates that, with the integration of desalination equipment in demand response in Scenario პ, there is a decrease in its demand response cost compared to Scenario ჟ.This reduction primarily relates to the unit compensation cost of desalination equipment demand response, which is relatively lower than that of interruptible load.Additionally, the system's operation and maintenance cost in Scenario პ remains relatively stable.However, the incorporation of desalination equipment in demand response provides increased flexibility in Scenario პ, enabling greater utilization of wind power and resulting in a significant reduction in energy abandonment costs compared to Scenario ჟ.These combined factors contribute to lowering the total system operating cost in Scenario პ compared to Scenario ჟ.Furthermore, the system scheduling outcomes for Scenario პ and Scenario ჟ are presented in Figure 2 and Figure 3, respectively.Comparing Scenario პ and Scenario რ to analyse the impact of the electric-hydrogen hybrid energy storage method on system operation scheduling, the scheduling results of Scenario რ are depicted in Figure 4.The decrease in the system's hydrogen energy storage capacity significantly increases the total system operation cost.This rise can be attributed to the notable influence of reduced hydrogen energy storage capacity on the system's wind and solar energy consumption rate, subsequently leading to an increase in the system's energy abandonment cost.Consequently, this results in an overall upsurge in the total system operation cost.Therefore, the capacity of hydrogen energy storage in the electric-hydrogen hybrid energy storage system can moderately affect the system's economic efficiency.

Impact of uncertainty
The aforementioned three scenarios are executed while considering wind power uncertainty.Table 3 presents the optimization results obtained from running these scenarios with the consideration of wind power uncertainty.After comparing the outcomes presented in Table 2 and Table 3, it becomes evident that the system becomes more conservative when accounting for wind uncertainty.This leads to a slight increase in the total system operating cost across all scenarios in Table 3 compared to those in Table 2. Furthermore, an analysis of the system's energy curtailment cost in each scenario from  3 indicates that the strategy proposed in this paper continues to effectively enhance wind and solar energy utilization and reduce the system's energy disposal costs compared to the other two scenarios, even after considering the uncertainty associated.

The impact of hydrogen storage capacity on system operation scheduling
Based on the earlier comparative analysis of the scheduling outcomes between Scenario პ and Scenario რ, it is evident that changes in hydrogen storage capacity significantly affect the system's operation results.To further clarify the influence of different hydrogen storage capacities on system operation scheduling, the following comparison, based on Scenario პ, assesses the total system operation cost and the discarded energy across varying hydrogen storage capacities, as illustrated in Figure 5.
From Figure 5, it is evident that as the hydrogen storage capacity increases, both the total system operation cost and the amount of discarded energy experience significant reductions.Specifically, the energy abandonment cost of the system diminishes gradually with the incremental rise in hydrogen storage capacity until it reaches a stabilized state.This trend indicates that appropriately increasing hydrogen storage capacity is more conducive to wind energy utilization.However, upon reaching a specific threshold, the cost advantage resulting from further increases in hydrogen storage capacity becomes less evident.Moreover, the curve representing wind power abandonment in Figure 5 displays a noticeable inflection point, beyond which the impact of additional capacity increments on reducing energy abandonment diminishes.Therefore, finding the optimal hydrogen storage capacity for the system demands consideration of various factors to strike a balance between economy and operational efficiency.

Conclusion
In this paper, a stochastic optimal scheduling strategy is introduced for a zero-carbon microgrid windphotovoltaic-storage-hydrogen-water system on independent islands, considering the demand response of seawater desalination.Through comparative analysis of various scenarios, the following conclusions are drawn.
(1) An operational scheduling strategy that incorporates desalination demand response positively impacts the system's economics and operational efficiency.It leads to a reduction in the total cost of system operation while fostering the utilization of wind and solar energy.
(2) In electric-hydrogen hybrid energy storage systems, the system's economics are affected by the hydrogen energy storage capacity.Appropriately reducing the hydrogen energy storage capacity increases the system's abandonment energy rate and results in an increase in the total system operating cost.
(3) The strategy presented in this paper effectively mitigates the system's energy abandonment cost even under uncertain conditions, showcasing its robustness and superiority in uncertain environments.
(4) An appropriate increase in hydrogen storage capacity enhances the system's energy consumption capability.Nevertheless, an excessive capacity increase may not necessarily result in additional cost advantages.It is crucial to consider various factors to determine the optimal hydrogen storage capacity, achieving a balance between economic efficiency and operational effectiveness.

K
denote the charging and discharging efficiencies of electric energy storage, respectively; C,max ES P and ,max D ES P denote the maximum charging and discharging power of the electric energy storage; C ES U and D ES U are 0-1 variables indicating the charging and discharging state of the electric energy storage; max ES E and min ES E denote the maximum and minimum storage power of the electric energy storage, respectively; 1 ES E and 24 ES E denote the storage power at the beginning and end of the energy storage, respectively.

Figure 5 .
Figure 5. Hydrogen storage capacity impact on total system operating costs and curtailment energy.
Total system operation cost/¥ IOP Publishing doi:10.1088/1742-6596/2771/1/0120369 Table the energy derived from hydrogen combustion into electrical output, as illustrated in the subsequent model: The losses of hydrogen energy from hydrogen storage tanks during charging and discharging are characterized by an approximation of charging and discharging efficiency, as shown below: represent the hydrogen storage of the hydrogen tank at times t -1 and t, respectively; Prepresents the hydrogen power inputted into hydrogen fuel cells at time period t; HFC,max P denotes the maximum electric power output of hydrogen fuel cells; HFC U is a binary variable (0 or 1) representing the operational state of hydrogen fuel cells.4 2.2.3.Hydrogen tank.E denote the charging and discharging power of the electric energy storage in time period t, respectively; 2.2.4.Energy storage.The specific model is outlined as follows:P denotes the electric power output of the electric energy storage in time period t; 1 E denote the storage power of the energy storage in time periods t -1 and t, respectively; C

Table 1 .
A Parameters of system equipment.

Table 2 .
Optimization results for three scenarios.

Table 3 .
Operational optimization results for three scenarios considering scenario uncertainty.