Cost Of Degradation Research Articles

A study of home energy management considering carbon quota

The household energy management system (HEMS) has become an important system for energy conservation and emission reduction. In this study, home energy management considering carbon quota has been established. Firstly, the household photovoltaic output model, load model of various electrical appliances, battery load model, and charging and discharging of electric vehicles (EVs) model are established. Secondly, the carbon emission and carbon quota of household appliances and EVs are considered in these models. Thirdly, the energy optimization model of minimum the household user’s total comprehensive operation cost with the minimum total electricity consumption, carbon trading cost, battery degradation cost, and carbon quota income are proposed, taking into account constraints such as the comfort of users’ energy use time. Subsequently, the improved particle swarm optimization (IPSO) algorithm is used to tackle the problem. Compared to the standard particle swarm optimization (PSO), the IPSO has significantly improved the optimization effect. By comparing the optimization results in different scenarios, the effectiveness of the strategy is verified, and the influence of different carbon trading prices on optimal energy scheduling has been analyzed. The result shows that the comprehensive consideration of carbon trading cost and total electricity cost can reduce the household carbon emissions and the total electricity cost of the household user. By increasing the carbon trading price, the user’s carbon trading income and the EV carbon quota income increase, and the overall operating cost decreases; the guidance and regulation of carbon trading price can make a valuable contribution to HEMS optimization. Compared to the original situation, the household carbon emissions are reduced by 14.58 kg, a decrease of over 21.47%, while the total comprehensive operation cost are reduced by 14.12%. Carbon quota trading can guide household users to use electricity reasonably, reducing household carbon emissions and the total cost of household electricity.

Optimal dispatch of battery energy storage for multi-service provision in a collocated PV power plant considering battery ageing

This study explores how a battery energy storage system (BESS) can support photovoltaic (PV) power plant operation by simultaneously minimising the PV power plant (PVPP) clipping losses and providing grid ancillary services. For this purpose, a deterministic day-ahead control strategy is developed while considering both calendar and cycling battery ageing on the BESS multi-value stream. The battery degradation cost model has also been reformulated to integrate the proposed formulation and assess the impact of different ageing aspects. The dispatching problem is reformulated to implement a linear programming approach. It allows to optimally dispatch the power flow between the different system components and schedules the ancillary service provision. The Irish regulation for participating in the Renewable Electricity Support Scheme (RESS) and DS3 grid services markets is considered. A market participation framework has also been identified, and numerical analysis is performed while emulating a real case. The obtained outputs emphasise the value of PV-BESS in providing DS3 grid services and the potential of the multi-service provision to create an additional value from energy storage deployment. The proposed approach and control strategy can also be implemented for other BESS applications in different energy markets.

Climate-adaptive resilience in district buildings and cross-regional energy sharing in Guangzhou-Shenzhen-Hong Kong Greater Bay Area

Climate change imposes challenges on Sustainable Develop Goals (SDGs) with uncertainty in the energy supply, energy consumption, climate-adaptive design and operation, renovation and retrofit on traditional energy systems. However, academic studies focus on district energy system sizing, operation and energy analysis under typical year, without considering future climate change due to the lack of updated meteorological database. In this study, models are developed to predict meteorological parameters in Greater Bay Area. Building physical models are developed in TRNSYS 18 platform with power-to-cooling/heating conversions and thermal/electrical energy storages to quantitatively analyse the impact of climate change on cooling/heating/electric loads, rated capacity resizing and onsite renewable energy supply. Considering the differences in building intensity, types, available spatiotemporal spaces in the Greater Bay Area, spatiotemporal energy sharing networks with E-mobility in cross-boundary Guangdong-Hong Kong-Macao are developed for cross-regional renewable self-consumption and stablising local microgrid power/voltage. Results indicate that, both solar radiation and ambient temperature increase under the future climate change, while building services systems designed in the typical year fail to cover cooling demands due to future climate change. Immediate response and sufficient reaction to climate change with climate-adaptive design require gradual increase on rated capacities of chillers, together with the increase in total electricity consumptions (i.e., increased by 34.3%, 29.9%, 14.2% and 20.5% for residential buildings, hotel, office, and commercial buildings from typical year to the year 2100). Technical strategies for future climate change adaption include complementary solar-wind energy integration, E-mobility based cross-regional energy sharing, renewable-to-EV charging and EV discharging for grid support under the Vehicle-to-Grid (V2G) mode. The V2G mode is economically beneficial with overwhelming superiority in grid import cost saving than battery degradation cost. This study can contribute to future climate change prediction, climate-adaptive designs, operations and controls, and energy resilience improvement.

Plug-in electric vehicle dynamic pricing strategies for bidirectional power flow in decentralized and centralized environment

With the increasing number of plug-in electric vehicles (PEV) power demand management without increasing existing facilities is becoming challenging. To address the issue two novel pricing strategies one for centralized and other for decentralized environments considering bidirectional flow of power in a dynamic power pricing market are proposed in this paper. The two proposed strategies aim to fulfil the requirement of PEV owners and the power system operated by the Distribution System Operator (DSO). In the proposed approach load demand management with peak saving and valley filling for charging/discharging of PEV is done, while the base load connected to the system is not affected. While availing Vehicle to Grid (V2G) services battery degradation cost is considered for increasing service effectiveness and encouraging PEV owners to participate. The randomness of PEV in terms of time, SOC, battery capacity and level of charging/discharging is considered to be in accordance with the real-time scenario. The two proposed strategies are compared based on objective achievement, loading on the distribution transformer and finding a real-time suitable environment for a 24-hour duration. The numerical case study of the proposed strategies are carried out by considering Indian environment.

Water transferable, customizable highly ordered honeycomb film from polystyrene foam waste for complex surface patterning in confined space

AbstractThe disposal of plastic foam, mostly composed of polystyrene, poses significant environmental challenges due to its high popularity, slow degradation, and low cost. To address this problem, recycling polystyrene foam waste (PF) has emerged as a promising solution to reduce plastic pollution. This paper presents a novel approach to mass‐produce highly ordered, porous honeycomb‐patterned film (hc‐film) using wasted PF as the raw material. The hc‐film is produced using an improved phase separation (IPS) method that utilizes methanol as a suitable pore inducer and template droplet stabilizer. Methanol provides the hc‐film with customizable features such as pore ordering, size, and separation. The freestanding hc‐film, achieved by adopting a water‐soluble polystyrene sulfonate as a scarified layer, can be transferred and utilized as a flexible mold to pattern various solid substrates with complicated surface morphologies using the pre‐impregnated technique. This study demonstrates the potential of this cost‐effective and efficient approach for various applications, such as super/anti‐wetting surfaces, microelectronics, optical devices, sensors, and nanogenerators.

Heterogeneous environmental consciousness, carbon permit adoption, and endogenous growth: A case of China

Motivated by low participation rate in China’s pilot Emission Trading Schemes (ETS), yet there being ample empirical evidence of carbon permit activities having an impact on firms’ technological innovation choice, we develop a novel endogenous growth model with carbon accumulation where both the green behaviors of non-corporate individuals and carbon permit adoption choice of corporate owners are influenced by heterogeneous environmental consciousness. Calibrated for China based on ETS price data, we examine the impacts of both long-run policy experiments and short-run cyclicality associated with carbon permit prices. we find that long-term greater ETS trading affordability would incentivize more firms to participate and bring about a reduction in carbon ratio, though it is still largely bounded by the conventional environment-growth trade-off that it requires supplementing it with other policies. In the short run, we find that higher carbon permit trading price would be procyclical to economic expansion but at the cost of environmental degradation too. In combination, this presents a policy dilemma: while improving ETS trading affordability is a long-run policy priority in China, in the short-run, absent fundamental changes, the procyclicality between carbon permit price and economic growth would serve as counteracting forces to this.

Societal sustainable development and long‐run economic growth: How do we stand?

AbstractThe United Nations' 2030 Agenda for Sustainable Development proposes the ambitious goal of an inclusive and sustainable economy. Thus, a realistic assessment of individual countries' possibilities to implement the Sustainable Development Goals might provide helpful insights. We seek to contribute to the existing literature by proposing a working framework for assessing, at the individual country level, the impact of long‐run economic growth on societal sustainable development. The framework is based on composite models from the Structural Equation Modeling‐SEM with a Partial Least Squares Path Modeling (PLS‐PM) estimator. In contrast, societal sustainable development is viewed as emerging from its various dimensions (environmental protection, human development and public health). Based on the analyses of an extended sample of 99 countries, including data for thirty years (from 1990 to 2019), we find that economic growth matters in explaining societal sustainable development, and different countries have benefited unequally from this situation. The key finding reveals that countries aiming to follow the SDGs should recognize their significant disparities and engage in profound structural, institutional, functional and cultural transformations. Thus, a more holistic approach to societal sustainable development is currently needed to enhance government effectiveness as a transmission channel for the impact of growth. In addition, governments need to adjust their economic mechanisms to internalize the costs of environmental degradation and negative externalities for human development and public health.

A cross-scale framework for evaluating flexibility values of battery and fuel cell electric vehicles

Flexibility has become increasingly important considering the intermittency of variable renewable energy in low-carbon energy systems. Electrified transportation exhibits great potential to provide flexibility. This article analyzed and compared the flexibility values of battery electric vehicles and fuel cell electric vehicles for planning and operating interdependent electricity and hydrogen supply chains while considering battery degradation costs. A cross-scale framework involving both macro-level and micro-level models was proposed to compute the profits of flexible EV refueling/charging with battery degradation considered. Here we show that the flexibility reduction after considering battery degradation is quantified by at least 4.7% of the minimum system cost and enlarged under fast charging and low-temperature scenarios. Our findings imply that energy policies and relevant management technologies are crucial to shaping the comparative flexibility advantage of the two transportation electrification pathways. The proposed cross-scale methodology has broad implications for the assessment of emerging energy technologies with complex dynamics.

Hierarchical Energy Management of Hybrid Battery Storage Systems for PV Capacity Firming and Spot Market Trading Considering Degradation Costs

Hierarchical Energy Management of Hybrid Battery Storage Systems for PV Capacity Firming and Spot Market Trading Considering Degradation Costs

Optimal Day Ahead Active and Reactive Power Management in Residential Buildings using Particle Swarm Optimization

This paper introduces an innovative Active and Reactive Residential Building Energy Management (AR-RBEM) system, seamlessly integrated with a smart microgrid and the Electrical Power Grid (EPG) supplier. The proposed AR-RBEM computes optimal active and reactive power setpoints for each energy source over the next 24 hours based on predicted PV power generation and load consumption. The proposed strategy aims to achieve two primary objectives: minimize overall energy expenses, considering both active and reactive power consumption, and account for Energy Storage System (ESS) degradation costs. Additionally, it seeks to reduce the Peak-to-Average Ratio (PAR) and carbon emissions from the supplier’s perspective. To tackle this multi-objective problem, a metaheuristic algorithm which is Particle Swarm Optimization (PSO), is employed. To assess the effectiveness of the proposed approach, a comparative analyse is conducted, emphasizing the significance of concurrently managing active and reactive power compared to a system exclusively managing active power (A-RBEM). Both strategies are evaluated using Time of Use Tarriff (TOU) and real world data. The comparative analysis underscores the advantages of the proposed AR-RBEM, showcasing benefits in terms of cost reduction for both active and reactive power of 59,6%, a substantial 25.3% reduction in carbon emissions compared to the alternative strategy.

Recent progress in energy-saving electrocatalytic hydrogen production via regulating the anodic oxidation reaction.

Hydrogen energy with its advantages of high calorific value, renewable nature, and zero carbon emissions is considered an ideal candidate for clean energy in the future. The electrochemical decomposition of water, powered by renewable and clean energy sources, presents a sustainable and environmentally friendly approach to hydrogen production. However, the traditional electrochemical overall water-splitting reaction (OWSR) is limited by the anodic oxygen evolution reaction (OER) with sluggish kinetics. Although important advances have been made in efficient OER catalysts, the theoretical thermodynamic difficulty predetermines the inevitable large potential (1.23 V vs. RHE for the OER) and high energy consumption for the conventional water electrolysis to obtain H2. Besides, the generation of reactive oxygen species at high oxidation potentials can lead to equipment degradation and increase maintenance costs. Therefore, to address these challenges, thermodynamically favorable anodic oxidation reactions with lower oxidation potentials than the OER are used to couple with the cathodic hydrogen evolution reaction (HER) to construct new coupling hydrogen production systems. Meanwhile, a series of robust catalysts applied in these new coupled systems are exploited to improve the energy conversion efficiency of hydrogen production. Besides, the electrochemical neutralization energy (ENE) of the asymmetric electrolytes with a pH gradient can further promote the decrease in application voltage and energy consumption for hydrogen production. In this review, we aim to provide an overview of the advancements in electrochemical hydrogen production strategies with low energy consumption, including (1) the traditional electrochemical overall water splitting reaction (OWSR, HER-OER); (2) the small molecule sacrificial agent oxidation reaction (SAOR) and (3) the electrochemical oxidation synthesis reaction (EOSR) coupling with the HER (HER-SAOR, HER-EOSR), respectively; (4) regulating the pH gradient of the cathodic and anodic electrolytes. The operating principle, advantages, and the latest progress of these hydrogen production systems are analyzed in detail. In particular, the recent progress in the catalytic materials applied to these coupled systems and the corresponding catalytic mechanism are further discussed. Furthermore, we also provide a perspective on the potential challenges and future directions to foster advancements in electrocatalytic green sustainable hydrogen production.

Microgrid Optimal Energy Scheduling Considering Neural Network Based Battery Degradation

Battery energy storage system (BESS) can effectively mitigate the uncertainty of variable renewable generation. Degradation is unpreventable and hard to model and predict for batteries such as the most popular Lithium-ion battery (LiB). In this paper, we propose a data driven method to predict the battery degradation per a given scheduled battery operational profile. Particularly, a neural network based battery degradation (NNBD) model is proposed to quantify the battery degradation with inputs of major battery degradation factors. When incorporating the proposed NNBD model into microgrid day-ahead scheduling (MDS), we can establish a battery degradation based MDS (BDMDS) model that can consider the equivalent battery degradation cost precisely with the proposed cycle based battery usage processing (CBUP) method for the NNBD model. Since the proposed NNBD model is highly non-linear and non-convex, BDMDS would be very hard to solve. To address this issue, a neural network and optimization decoupled heuristic (NNODH) algorithm is proposed in this paper to effectively solve this neural network embedded optimization problem. Simulation results demonstrate that the proposed NNODH algorithm is able to obtain the optimal solution with the lowest total cost including normal operation cost and battery degradation cost.

Green Growth, Environmental Quality, Energy Consumption Nexus in OECD Countries

Sustainable economic development is desirably terrible for improving social welfare. It implies that economic development should not be at the cost of environmental degradation, but rather environmental sustainability should be maintained. This study has examined the green growth, environmental quality, and energy consumption nexus in 38 OECD countries using panel data from 1991 to 2020. We have estimated two models. The first model explores the impact of green growth on environmental quality and the second model probes the link between non-green growth and environmental degradation. For this purpose, the ARDL technique has been used to estimate the results. Green growth has not followed the environmental Kuznut Curve theory while Non-Green growth has validated the environmental Kuznut Curve theory. Furthermore, Environmentally friendly technological innovation, renewable energy consumption, environmental-related tax, and human capital turn out with negative signs while foreign direct investment and trade openness are positively related to environmental degradation. The study also suggests policies to decarbonize or minimize the emissions in the economies.

Smart and optimization-based operation scheduling strategies for maximizing battery profitability and longevity in grid-connected application

Lithium-ion battery storage has emerged as a promising solution for various energy systems. However, complex degradation behavior, relatively short lifetime, high capital, and operational costs, and electricity market volatility are critical factors that challenge its practical viability. Thus, to ensure sustained profitability of Lithium-ion batteries in real-life applications, a smart and optimal management strategy considering key influencing factors is imperative for achieving efficient battery utilization.This study proposes two day-ahead battery-behavior-aware operation scheduling strategies to maximize profitability and longevity in residential grid-connected applications with dynamic electricity pricing. Each scenario employs unique approaches to make optimal decisions for optimal battery utilization. The first scenario optimizes short-term profitability by prioritizing revenue gains under three charge/discharge rates (high, moderate, low), considering daily charge and discharge timings as decision variables. Conversely, the second scenario proposes a smart strategy capable of making intelligent decisions on a wide range of variables to simultaneously maximize revenue and minimize degradation costs, ensuring short-term and long-term profitability. Decision variables include the cycle frequency for each specific day, timings as well as durations for charging and discharging per cycle. To ensure effective long-term assessment, both scenarios accurately estimate battery performance, calendric and cyclic capacity degradations, remaining-useful-lifetime, and internal states under real operational conditions until battery reaches its end-of-life criteria. The scenarios are assessed economically using various indicators. Furthermore, the impact of battery price and size on optimization outcomes are examined.The key findings indicate that, among the first set of scenarios, the strategy with low charge/discharge rate extends the battery lifetime most efficiently, estimated at 14.8 years. However, it proved to be the least profitable, resulting in negative profit of −3€/kWh/yr. On the other hand, strategies with high and moderate charge/discharge rates resulted in positive profit of 8.3 €/kWh/year and 9.2 €/kWh/year, despite having shorter battery lifetimes, estimated at 10.1 years and 13.6 years, respectively. Furthermore, from a payback perspective, the strategy with fast charge/discharge capability led to 1.5 years shorter payback period than that of the moderate rate strategy. The findings highlight that the first set of scenarios limits the strategy’s flexibility in achieving both sustainability and profitability. In contrast, the second scenario achieves impressive profit (18 €/kWh/yr), shortest payback period (7.5 year), a commendable lifespan (12.5 years), contrasting revenue-focused scenarios, emphasizing the importance of striking optimal balance between revenue gain and degradation costs for charging/discharging actions, ensuring sustained profitability. The findings offer valuable insights for decision-makers, enabling informed strategic choices and effective solutions.

Electrification and hydrogenation on a PV-battery-hydrogen energy flexible community for carbon–neutral transformation with transient aging and collaboration operation

Considering the distinct differences in intrinsic characteristics (e.g., energy efficiency, power density, and response time), the synergy operation of combined hydrogen (H2) and battery systems within the source-grid-load-storage framework offers a promising solution to stabilize intermittent renewable energy supply, mitigate grid power fluctuations, and enhance system independence. However, the idling power of the H2 system (fuel cell and electrolyzer) poses significant limitations on H2 charging/discharging from intermittent renewable power sources, thereby affecting overall system performance. However, the current literature provides little progress on how to effectively self-consume onsite renewable energy and supply the electricity demand with high energy/power density in a fast response. To address these challenges, this study proposes and applies the H2-battery compensation operation in the hybrid H2-battery energy storage system to mitigate the adverse effects of idling power during low-carbon transformations. Additionally, transient degradation models for the battery and fuel cell (FC) are integrated into the system to dynamically quantify real-time aging, thereby avoiding performance overestimation. Moreover, the study also includes a comparative analysis of the pre-estimation on the H2 tank pressure level, and a parametrical analysis of the HV number and battery capacity. Results show that, compared to the isolated H2 energy storage system, the H2-battery synergy operation reduces the grid demand shortage coverage ratio from 91.01% to 57.8%. Furthermore, the proposed H2-battery compensation operation effectively overcomes the limitations of idling power during power charging/discharging of the H2 system, resulting in an increase of H2 demand shortage coverage ratio from 8.9% to 17.29%, and the decrease in battery degradation ratio from 1.73% to 1.65%. Afterward, a novel indicator, degradation cost per kWh energy, is proposed to quantify the techno-economic performance of the combined battery and H2 system. According to the results, the techno-economic feasibility is dependent on the proportion of battery and H2 capacities, with the FC ranging from 0.49 to 1.15 CNY/kWh and the battery ranging from 0.82 to 0.91 CNY/kWh. The research results provide valuable frontier guidelines for the design and operation of multi-energy systems, as well as innovative energy-sharing strategies, to achieve sustainable and carbon–neutral transformation.

The infrastructure cost of permafrost degradation for the Northern Hemisphere

Warming and resulting degradation of near-surface permafrost in cold regions across the globe has and will continue to lead to a series of hazards. These include land subsidence and weakening of the substrate’s bearing capacity, thus threatening infrastructure and the socioeconomics in permafrost regions. These potential hazards shorten the lifespan of infrastructure, increase the cost of infrastructure maintenance and replacement, which is of great importance to a variety of stakeholders. In Northern Hemisphere permafrost regions, more than 34% of the population and 44% of the infrastructure will be at high risk by the end of this century. Due to the degradation of permafrost, infrastructure will require an additional investment of approximately $205–572 billion to maintain the operation of engineering and service infrastructure in 2085, based on projections with a high greenhouse gas emissions scenario.

Curtailment to cashflow: Exploring BESS and hydrogen for renewable energy profitability

The sporadic nature of renewable energy sources has led to increased energy curtailment. Hence, the necessity to efficiently utilize curtailed renewable energy using Battery Energy Storage Systems (BESS) and hydrogen technology is paramount. This study explores curtailed renewable energy (wind and solar) utilization through three cases: Case 1 - BESS, Case 2 - alkaline water electrolyzer (AWE) for hydrogen energy, and Case 3 - a hybrid system (BESS and AWE). Hourly data for California in 2020 is used. Dynamic optimization tackles the mixed-integer linear programming (MILP) problem, aiming to maximize profits across scenarios. Factors include power source (wind, solar, or hybrid), energy storage (BESS, hydrogen, or hybrid), and curtailed power availability (25 % to 100 %). Results show that 100 % solar curtailment availability in Case 2 yields highest profits and rapid payback in California. Cases 1 and 3 underperform due to high BESS capital, replacement, and degradation costs. The study, catering to operators and investors, explores curtailed energy dimensions for profit. Comprehensive cost considerations, encompassing BESS degradation, operational, and variable costs, ensure precise estimates. Sensitivity analysis underscores the crucial role of technology costs, suggesting avenues for expense reduction. The study provides guidance for effective curtailed energy repurposing, considering evolving technology costs.

Soybean-Oil-Modified Petrochemical-Source Polyester Polyurethane Improves the Nutrient Release Performance of Coated Urea

Due to the difficult degradation and high cost of polyester polyols (PPs), their further development in controlled-release fertilizers (CRFs) has been limited. It was of great significance to partially substitute PPs with soybean oil polyols (SOPs) in the preparation of CRFs, which can adjust the proportion of substitution to balance the nutrient release period and membrane degradability. And it is possible to adjust the nitrogen release curve and prepare coated urea with different release days without changing the coating thickness. In this study, a two-factor five-level interaction experiment was designed with different SOP addition ratios (70%, 60%, 50%, 40%, and 30%) and mass ratios of polyols to PAPI (1:1.2; 1:1; 1.2:1; 1.4:1; and 1.6:1). In 25 groups of soybean-oil-modified polyester polyurethane-coated urea (SMPCU) treatments, with the increase in SOP, the thermal decomposition performance of the coated urea was improved, and the residual ash contents of the coating material was reduced. The coating surface was made smoother and denser with a reasonable ratio of polyols to PAPI, preventing the release of non-film-forming substances. When the proportion of soybean oil was 60% and the ratio of polyols to PAPI was 1.2:1, the N release days of the prepared SMPCU reached 137 days. Compared to soybean-oil polyol-coated urea (SOPCU) and polyester polyol-coated urea (PPCU), the nitrogen release days increased by 73.42% (79 days) and 234.15% (41 days), respectively. The ratio of polyols to polyaryl polymethylene isocyanate (PAPI) was explored, as well as the effect of polyol composition on coatings, and prepared SMPCUs with different nitrogen release days. According to the growth needs of crops and soil environmental characteristics, adjusting the proportion of coating materials, prolonging or shortening the nitrogen fertilizer release time, meets the nutritional needs of crops under different planting systems and provides conditions for farmers to plant different crops.

Rapid photocatalytic degradation of organic pollutants by Al3+ doped CuO powders under low power visible light and natural sunlight

The photocatalytic degradation of organic pollutants is a promising approach to deal with dye industry wastes released into water bodies. However rapid, inexpensive, and low power visible light photocatalytic degradation of water pollutants is challenging. This study tries to address these constraints by using inexpensive doped CuO Powders under low-power visible household LED light. Also, direct illumination of sunlight to achieve better photocatalytic degradation, reducing device and operational costs. Further, photocatalytic degradation is enhanced by doping of Al3+ in CuO. Under several optimized conditions, rapid and almost complete degradation of three different dyes, Methylene Blue (MB), Rhodamine B (RhB), and Methyl Orange (MO) were achieved within 6 min of light irradiation under household LED and within 30 min of direct irradiation of sunlight. Even after five times of repeated use of the catalyst, no significant decrease in the photocatalytic activity was observed indicating its stability and sustainability.

Layered power scheduling optimization of PV hydrogen production system considering performance attenuation of PEMEL

To analyze the additional cost caused by the performance attenuation of a proton exchange membrane electrolyzer (PEMEL) under the fluctuating input of renewable energy, this study proposes an optimization method for power scheduling in hydrogen production systems under the scenario of photovoltaic (PV) electrolysis of water. First, voltage and performance attenuation models of the PEMEL are proposed, and the degradation cost of the electrolyzer under a fluctuating input is considered. Then, the calculation of the investment and operating costs of the hydrogen production system for a typical day is based on the life cycle cost. Finally, a layered power scheduling optimization method is proposed to reasonably distribute the power of the electrolyzer and energy storage system in a hydrogen production system. In the up-layer optimization, the PV power absorbed by the hydrogen production system was optimized using MALTAB+Gurobi. In low-layer optimization, the power allocation between the PEMEL and battery energy storage system (BESS) is optimized using a non-dominated sorting genetic algorithm (NSGA-II) combined with the firefly algorithm (FA). A better optimization result, characterized by lower degradation and total costs, was obtained using the method proposed in this study. The improved algorithm can search for a better population and obtain optimization results in fewer iterations. As a calculation example, data from a PV power station in northwest China were used for optimization, and the effectiveness and rationality of the proposed optimization method were verified.