Search Results (3,223)

This study presents a technical assessment of solar energy systems for integrated agricultural use and rural electrification. A model village comprising 30 households was considered, and high-resolution hourly load profiles were developed to characterize consumption dynamics, including peak demand and sectoral distribution across residential, agricultural, public, healthcare, and commercial users. A 60 kW photovoltaic (PV) system was designed in conjunction with an independent solar thermal installation for hot water supply. The system configuration was established through component sizing and numerical modeling, incorporating heat transfer mechanisms and operational constraints. Time-dependent simulations performed in MATLAB (R2022b) evaluated PV power output, battery storage cycling, and thermal system performance over a 24-h horizon. A comparative analysis of standalone PV, hybrid PV/T, and decoupled PV–thermal configurations was conducted based on performance and operational criteria. The results indicate that separated electrical and thermal subsystems achieve improved cost-effectiveness, enhanced reliability, and reduced maintenance requirements. The proposed approach demonstrates the technical viability of solar-based energy systems for rural applications, supporting energy autonomy, reduced fossil fuel dependence, and sustainable agricultural development. Full article

As modern electrical grids increasingly incorporate renewable generation—specifically from wind and solar–thermal installations—they face heightened volatility and operational complexities, which severely complicate load frequency regulation. While fractional-order proportional-integral-derivative (FOPID) controllers are commonly employed for this purpose, their conventional formulations rely on fixed fractional parameters that cannot adapt to fluctuating network conditions. To address this limitation, the present study develops an adaptive FOPID (AFOPID) control architecture capable of real-time adjustment of fractional orders, thereby enhancing regulatory effectiveness. The Coot Optimization Algorithm (COA) is utilized to optimally determine the operational parameters of all controllers under investigation. The proposed strategy is validated on a simulated hybrid power system comprising wind generation, solar–thermal units, and physical nonlinearities including governor dead band and generation rate constraints. A comparative analysis is conducted across four distinct operating scenarios, benchmarking the COA-tuned AFOPID against conventional PI, PID, and standard FOPID controllers. Quantitative results demonstrate that the proposed COA-AFOPID configuration achieves superior performance, with improvements in settling time up to 46.06% and reductions in ITAE index up to 89.89% compared to traditional methods. These findings confirm the enhanced stability and robustness of the proposed approach for frequency regulation in sustainable energy networks. Full article

With the gradual improvement of residential electricity reliability, the lower design strength of the distribution network makes it more prone to large-scale power outages in resisting natural disasters. Among them, the cold load start-up effect will significantly prolong the recovery time and affect reliability indicators. Based on this, this article proposes a reliability evaluation method for urban distribution networks based on a lightning search algorithm, which is used for optimal recovery planning and reliability calculation of urban power systems with highly concentrated load under constant temperature control. Firstly, a delay index model is used to establish a time-sharing power demand calculation model for cold load start-up events, and an optimal recovery model with the goal of minimizing recovery time and its corresponding constraints are proposed. Then, the cold load start-up event is incorporated into the Monte Carlo simulation platform for reliability assessment, and the lightning search algorithm is used to develop the optimal recovery plan. The recovery time and sequence are determined based on the duration of the power outage and the electricity demand at the time of recovery. Finally, the test distribution system was used to verify that the optimal recovery plan considering cold load start events does not violate the constraint conditions, and the stability and convergence robustness of the lightning search algorithm are stronger than the current mainstream algorithms. It can effectively improve the reliability of the distribution grid when considering cold load start events. Full article

This study presents the prototype manufacturing and experimental validation of a 1 kW, 750 rpm three-phase outer-rotor permanent magnet-assisted synchronous reluctance motor (PMASynRM) designed for in-wheel electric vehicle applications. The work is based on a previously reported electromagnetic design and finite element method (FEM)-based optimization framework and focuses on the physical implementation and experimental evaluation of the proposed motor. The prototype was manufactured using M470-50A grade electrical steel laminations and arc-shaped N35H NdFeB permanent magnets embedded within a three-barrier transversally laminated anisotropic rotor structure. A custom-built experimental test bench consisting of the PMASynRM prototype, a PMSM generator with a controllable resistive load bank, a torque transducer, and a precision power analyzer was developed to evaluate motor performance under controlled operating conditions. Experimental investigations were carried out under four steady-state load conditions—no-load, 13 Nm, 20 Nm, and 26 Nm—as well as during dynamic stepwise load transitions representative of in-wheel drive operation. The measured results show good agreement with FEM predictions, with a maximum efficiency of 90.55% at nominal load and efficiency values remaining above 87% under overload conditions up to 26 Nm. Minor differences between simulation and experimental results are mainly associated with mechanical friction, bearing losses, and manufacturing tolerances that are not fully captured in the numerical model. The study provides experimental validation of an outer-rotor PMASynRM prototype under multi-load steady-state and dynamic operating conditions for in-wheel electric vehicle applications. Full article

The development of electric bus superstructures requires an integrated engineering approach combining structural design, numerical simulation, experimental validation and durability assessment. This need is particularly important for electric buses, where heavy roof-mounted battery systems and auxiliary components influence structural load paths, fatigue durability and rollover crashworthiness. This paper presents a measurement-supported workflow for the development of a virtual electric bus superstructure model, including finite element analysis, multibody dynamics simulations, operational load assessment, fatigue-oriented evaluation and rollover crashworthiness analysis. The finite element model is used to assess static load cases, modal properties and structural response under selected design conditions. A multibody vehicle model with nonlinear suspension characteristics is applied to simulate representative operating scenarios and to support the definition of dynamic load cases. Operational measurement data from previous work are used as a basis for realistic load characterization. Experimental torsional stiffness and modal tests are used to validate the numerical model. The main contribution of the study is the integration of these numerical, experimental and operational-data-based activities into a consistent early-stage verification process. The proposed workflow supports early identification of critical structural regions, assessment of design modifications and reduction in prototype-based design iterations. Full article

Decarbonising the maritime sector demands a transition away from conventional fossil fuel combustion toward zero-carbon alternatives, yet the technical and operational implications of integrating ammonia-based power systems into existing vessel architectures remain insufficiently characterised. This study presents a dynamic simulation framework for the component sizing and performance evaluation of ammonia-based marine power systems, applied to a case study vessel across six power system configurations: a conventional MGO diesel generator baseline, an ammonia dual-fuel generator benchmark, and four hybrid configurations integrating solid oxide fuel cells at different power coverage scopes. The methodology combines an operationally based component sizing model with a time-domain dynamic simulation that captures load-dependent SOFC performance, stack degradation, transient battery buffering, heat recovery interactions, and energy management under realistic voyage conditions, a combination not previously applied to ammonia-SOFC marine power system assessment. Results demonstrate that dynamic simulation is essential for reliable sizing of transient-sensitive components, yielding battery capacities of 1500 kWh and 2900 kWh for auxiliary-only and auxiliary-plus-manoeuvring SOFC coverage scopes respectively. The ADFG–SOFC-B configuration achieves the strongest performance across all indicators: a 26.7% reduction in total annual energy consumption, a net electrical efficiency of 50.7%, and a well-to-wake GHG emission reduction of 85.6% relative to the diesel baseline. All ammonia dual-fuel configurations maintain IMO Net-Zero Framework compliance through 2039 or beyond, with SOFC-integrated configurations avoiding Tier 2 penalties through 2045. These findings establish that a full transition to green ammonia as the primary fuel, rather than SOFC integration alone, is the prerequisite for achieving both deep decarbonisation and long-term regulatory viability in maritime power systems. Full article

Economic dispatch in grid-connected microgrids is challenged by the variability of renewable generation, the uncertainty of demand, and the need to simultaneously satisfy technical and economic constraints under different operating conditions. This study proposes an integrated predictive economic dispatch strategy for power grids with interconnected microgrids, structured as a unified optimization framework. The approach integrates nodal electrical modeling, Optimal Power Flow (OPF)-based optimization, multi-scenario analysis, and post-optimization feasibility verification based on performance indicators within a single decision-support structure. The methodology is applied to a modified 14-node power grid interconnected with a microgrid, where simulations are conducted under three representative load scenarios (100%, 70%, and 40%) and two operational configurations (hybrid and renewable-only), enabling a comprehensive assessment of system behavior. Results show that the hybrid configuration consistently outperforms the renewable-only case, achieving loss reductions of up to 7.3 MW, increases in spinning reserve exceeding 50 MW, and a transition from net power import to export of approximately 50 MW under high demand. Additionally, the microgrid plays an active operational role, dynamically switching between import and export modes based on load levels and the generation mix. The proposed framework enables identification of operationally efficient and technically feasible configurations by incorporating bidirectional power exchange, electrical constraints, and reserve requirements. The main contribution lies in integrating technical, operational, and interaction variables within a single deterministic Optimal Power Flow (OPF)-based assessment scheme to support decision-making in interconnected microgrid-based power grids. Full article

Hydro-dominant distribution grids with high penetrations of distributed photovoltaic (PV) generation exhibit a clear operational asymmetry. PV output changes rapidly at the minute scale, whereas hydropower regulation is constrained by reservoir water balance, turbine ramping capability, and hydraulic safety limits. During high-inflow periods, mandatory hydropower generation further reduces the downward regulation margin and restricts midday PV accommodation. To address this issue, this paper develops an asymmetry-aware adaptive bi-level planning framework for photovoltaic hosting capacity (PVHC) assessment. A db4 discrete wavelet transform is used to decompose PV output into low-frequency energy trends and high-frequency fluctuation components. The upper layer performs hourly economic dispatch while maintaining reservoir water balance, and the lower layer conducts minute-level constrained tracking under ramping and vibration-zone avoidance constraints. A bisection-type capacity-search procedure is then used to identify the PVHC boundary by jointly checking curtailment, ramping, frequency proxy, voltage, line-loading, point-of-common-coupling exchange, and vibration-zone residence constraints. Case studies based on a 15 min PV dataset from a 30 MW station, hydropower operation records, and a modified 15-node feeder in Southwest China show that hydrological asymmetry materially affects PV accommodation. The obtained PVHC ranges from 53.17 MW under the most restrictive high-proxy condition to 65.33 MW under low-proxy operation. Compared with the no-coordination case, representative-month PVHC increases from 49.80 MW to 65.33 MW, while the simulated residence time within the predefined vibration-prone zone decreases from 447 min to 0 min. These results indicate that PVHC evaluation in hydro-dominant feeders should jointly consider electrical constraints, hydrological asymmetry, and hydraulic safety limits. Full article

With increasing residential electricity demand, hybrid energy systems capable of simultaneously improving affordability, reliability, and environmental performance have become increasingly important. This paper develops an integrated techno-economic and environmental assessment framework for grid-connected residential energy systems under unreliable grid conditions and applies it to a real-world residential case study in Fayoum, Egypt. In the proposed framework, the utility grid is treated as the primary electricity source, while PV, diesel generation, and battery storage are evaluated as backup/support options. Six grid-connected hybrid configurations, namely Grid/Diesel, Grid/PV/Diesel, Grid/PV/Diesel/Battery, Grid/Diesel/Battery, Grid/PV/Battery, and Grid/Battery, were evaluated under identical load, solar resource, and economic conditions to identify the minimum net present cost (NPC)configuration capable of satisfying a specified service level, expressed in terms of the maximum allowable unmet load ratio. The optimization problem was formulated as a single-objective model that minimizes NPC, subject to technical constraints and a service level constraint represented by a zero unmet load requirement in this study. Additional indicators, including levelized cost of energy (LCOE), renewable fraction, CO₂ emissions, and electricity purchased from the grid, were used for comparative performance evaluation. The candidate systems were simulated and optimized under frequent grid outage conditions using HOMER Pro. The results identify the Grid/PV/Battery configuration as the preferred base case backup/support configuration among the evaluated alternatives, achieving the lowest NPC of USD 8949, the lowest LCOE of USD 0.135/kWh, the highest renewable fraction of 55.1%, and the lowest annual CO₂ emissions of 2333 kg/yr, while satisfying the zero unmet load requirement. Compared with the base Grid/Diesel system, the optimal configuration reduces annual operating cost from USD 1204/yr to USD 648.19/yr and lowers emissions by approximately 50%, despite requiring a higher initial capital investment. Sensitivity analysis shows that the preferred solution remains robust across most of the examined financing parameter space. The PV derating factor analysis further indicates that the Grid/PV/Battery configuration remains optimal at higher PV derating levels of 70–80%, whereas the preferred solution shifts toward Grid/Diesel at lower derating levels of 50–60%. Overall, the results demonstrate that combining service-level-constrained NPC minimization with comparative techno-economic and environmental evaluation provides a robust basis for identifying suitable backup-supported grid-connected residential energy solutions under unreliable grid conditions. Full article

Traditional air conditioning consumes substantial electricity, exacerbates the urban heat island effect, and creates a maladaptive feedback loop, necessitating a shift toward passive-first net-zero pathways. This study takes a typical six-story residential building in Nanjing’s hot summer and cold winter climate zone as a case study. Using EnergyPlus hourly simulations, three progressive passive strategy packages are designed to quantify the impact of building envelope heat exchange on cooling loads, grid stress, and heat resilience. Package A includes external shading and natural ventilation. Package B adds reflective coating and a green roof. Package C further adds night ventilation precooling and high-performance windows. The results show that Package C achieves a 62.5% reduction in peak cooling load and a 63.0% reduction in seasonal cooling load. Daytime peak inward heat gain decreases from 68 W/m² to 22 W/m², while nighttime outward heat dissipation increases from 12 W/m² to 38 W/m². Under an extreme heat day of 41.2 °C with no active cooling, indoor peak temperature drops from 36.8 °C to 29.4 °C, and heat risk hours decrease by 73.6%. Peak-hour power demand is reduced by 70.4%, with a systemic leverage factor of 1.08. Innovations include achieving over 60% load reduction using only mature passive strategies, introducing the systemic leverage factor to quantify urban heat island mitigation benefits, and establishing a vulnerability-to-resilience transformation framework. The passive-first pathway validates building envelope as the first line of defense for net-zero futures. However, the findings are based on a typical six-story residential building in Nanjing and require validation through field measurements or broader application across different climate zones and building typologies before generalization. Full article

Port operators must now reduce emissions without weakening the reliability of cargo-handling and logistics services. Two load groups are especially important in this setting: vessels connected to shore-side facilities during berthing and heavy-duty vehicles working inside the terminal area. Their energy-use patterns shape both dispatch stability and the carbon intensity of the port energy system. This paper therefore proposes an integrated port energy management model that jointly schedules wind power, photovoltaic generation, hydrogen production and storage, shore power, conventional purchases, berthed-vessel demand, and low-carbon heavy-duty transport demand. The model combines price-based demand response with a tiered carbon-trading penalty so that flexible electricity consumption and emission costs are reflected in the dispatch decision. Numerical simulations show that the joint use of demand response and the carbon-penalty mechanism lowers total economic dispatch cost by about $11.05\%$ and reduces carbon emissions by $24.52\%$. The results indicate that coordinated renewable-energy and logistics-aware scheduling can improve the economic and environmental performance of port operations. Full article

With the advancement of energy market reform, integrated energy systems (IESs) have achieved rapid development. Considering insufficient research on an electricity–heat coupled master–slave game and the local optimum defect of traditional algorithms, this paper proposes a Stackelberg game optimization strategy for IES considering integrated demand response (IDR), with microgrid operator (MGO) as the leader and load aggregator (LA) as the follower. Firstly, an IDR model containing rigid, shiftable electric loads and reducible thermal loads is established, and a bi-level game model is built: the upper MGO optimizes electricity and heat pricing to maximize profit, while the lower LA adjusts flexible loads for maximum consumer surplus. Secondly, an improved snake optimizer (ISO) is constructed via Hammersley sequence initialization, Lévy flight and random perturbation and combined with quadratic programming (QP) to form the ISO-QP hybrid solving method. Benchmark function and CEC2017 tests verify the superior convergence and stability of ISO against multiple classical intelligent algorithms. Case simulation obtains the Stackelberg equilibrium result, and repeated experiments and parameter sensitivity analysis verify model robustness. Results show that the proposed method smooths load fluctuations via price guidance and synchronously improves MGO revenue and LA consumer surplus on the premise of guaranteed user satisfaction. Full article

Urban industrial parks have high electricity demand, and rooftop photovoltaic (PV)-battery systems can help reduce grid dependence and carbon emissions. However, battery degradation affects battery replacement timing and long-term economic performance, which should be considered in capacity sizing. This study proposes a degradation-aware techno-economic sizing method for rooftop PV-battery systems in urban industrial parks. GIS-based rooftop assessment, EnergyPlus load modeling, TRNSYS system simulation, battery SOH tracking, and NPV evaluation were integrated into one framework. A case study was conducted for an urban industrial park in Shanghai, China. The usable rooftop area was estimated as 113,208 m², corresponding to a PV capacity of approximately 18,765 kWp. The annual PV generation was 24.7 GWh, accounting for 24.7% of the park’s annual electricity demand. Battery capacities from 5000 to 40,000 kWh were evaluated. The results show that increasing battery capacity improves load shifting and reduces direct grid supply, but the marginal benefit gradually decreases. The maximum NPV is obtained at 30,000 kWh, with an NPV of 128.36 million CNY, a simple payback period of 4.6 years, and a discounted payback period of 6.0 years. The rooftop PV system achieves a 25-year CO₂ emission reduction of approximately 335,967 tCO₂ after considering PV degradation. Sensitivity analyses show that BES cost, tariff spread, and discount rate are key factors affecting the recommended capacity. Full article

This study addresses the technical, environmental, economic, and systemic role of multi-apartment residential buildings as hydrogen consumption nodes within urban energy systems. A representative five-story building comprising 30 apartments and 2400–2800 m² of heated floor area, located in a cold European climate, was modelled with an annual heat demand of approximately 185,000 kWh. Four heating configurations were assessed: a conventional natural gas/biomethane boiler (baseline), a hydrogen boiler, a hydrogen-fuel-cell combined heat and power (CHP) system, and a hybrid heat-pump–hydrogen solution. Dynamic simulations indicate that all hydrogen-based systems can fully satisfy space heating and domestic hot water demand without modifications to the internal hydronic distribution network. The fuel cell CHP achieved an overall efficiency of 93%. It generated approximately 54,000 kWh/year of on-site electricity, while the hybrid configuration reached a seasonal efficiency of 108% and the highest primary energy reduction (46%). Operational CO₂ emissions decreased from 37,800 kg/year (gas baseline) to 1900 kg/year (green hydrogen boiler), 1200 kg/year (fuel cell CHP), and 900 kg/year (hybrid system), corresponding to reductions of up to 98%. Peak-load analysis demonstrated improved operational stability in CHP and hybrid systems, characterised by reduced cycling frequency and enhanced thermal resilience through hydrogen storage integration. Capital expenditure (CAPEX) ranged from 41,000 EUR (gas baseline) to 101,000 EUR (fuel cell CHP), reflecting additional storage, safety, and control requirements. Over a 20-year lifecycle (5% discount rate), the hybrid system achieved the lowest levelized cost of heat (0.076 EUR/kWh), followed by fuel cell CHP (0.081 EUR/kWh), compared to 0.087 EUR/kWh for gas. Payback periods ranged between 9 and 13 years, depending on configuration and hydrogen pricing assumptions. Sensitivity analysis identified a break-even hydrogen price of approximately 0.085 EUR/kWh, while carbon pricing above 100 EUR/t CO₂ significantly improves economic competitiveness. District-scale aggregation modelling suggests that hydrogen-equipped multi-apartment buildings can reduce grid electricity imports by 30–40% through on-site generation and seasonal storage. The findings confirm that multi-apartment buildings offer structural and economic advantages for early hydrogen deployment compared to dispersed housing typologies. By combining high demand density, centralised infrastructure, and compatibility with sector-coupling strategies, such buildings can function as distributed energy hubs within decarbonized urban systems. Full article

This data descriptor presents CINES-REC-CITY, an open synthetic dataset providing high-resolution load profiles for energy community research. The dataset represents a typical German urban district with 70 apartments across eight multi-family buildings, including diverse socioeconomic characteristics. Three main components are provided at 15 min resolution for a full year: non-controllable residential electricity consumption for all apartments, charging profiles for 17 battery electric vehicles with trip information, and heat pump operation data for both variable-speed and hysteresis-controlled ground-source systems. All profiles were generated using validated bottom-up stochastic simulation models accounting for realistic user behavior, mobility patterns, and thermal building physics. The modular structure allows for selective combination of components, enabling investigation of different technology penetration scenarios. The dataset serves as a reference benchmark for reproducible research, allowing for direct comparison of optimization approaches, business models, and control strategies using identical underlying consumption patterns. It is suitable for techno-economic analysis, algorithm development for flexible load control, and grid impact assessment. All data is provided in CSV format with weather data for consistent extensions. Full article