Search Results (317)

Hybrid renewable energy systems are regarded as one of the most promising solutions for the autonomous power supply of remote and weakly electrified sites, where diesel generation remains a costly and carbon-intensive energy source. This study presents the optimization of an off-grid PV/BESS/DGU microgrid for three representative regions of Kazakhstan—North, Central/East, and South/South-West—under different environmental scenarios. The aim of the study was to determine the optimal installed photovoltaic capacity, battery storage capacity, diesel generator rated power, and annual load coverage balance using the Generalized Reduced Gradient (GRG) method. The optimization was carried out using two objective functions: the conventional levelized cost of electricity, $LCOE$, and the environmentally adjusted cost of electricity, ${LCOE}_{\mathrm{e}\mathrm{n}\mathrm{v}}$, which includes the monetized cost of emissions associated with diesel generator operation. The model was formulated as a constrained nonlinear programming problem incorporating hourly energy balance, battery state-of-charge constraints, diesel generator operating constraints, and carbon price scenarios of 0, 25, 50, and 100 USD/tCO₂. The results show that an increase in the carbon price systematically shifts the optimum toward a higher share of photovoltaic generation and reduced diesel generator use in all regions. The strongest response is observed in the South/South-West region, followed by Central/East, whereas the North exhibits the lowest sensitivity due to the more pronounced seasonality of solar generation. Under the considered scenarios, the optimal PV capacity increases by approximately 24–28%, while the share of diesel generation in annual load coverage decreases by approximately 28% in the North, 44% in Central/East, and 61% in the South/South-West. At the same time, the rated diesel generator capacity remains unchanged in most scenarios, indicating the persistence of its backup function. The results confirm that the PV/BESS/DGU configuration constitutes a technically and economically justified baseline architecture for autonomous power supply under Kazakhstan’s conditions, while the inclusion of environmental costs supports the cost-effective displacement of diesel generation. The GRG method proved to be suitable for the transparent and efficient optimization of hybrid microgrid parameters. Full article

The synergistic utilization of post-mining spaces and geothermal energy through underground seasonal thermal energy storage (USTES) provides a promising pathway for sustainable heating and the low-carbon redevelopment of mining regions. To advance the thermal management and reveal the thermo-hydraulic evolution patterns within these repurposed environments, this study proposes an integrated approach that utilizes post-mining roadways as heat storage reservoirs, within the scope of a single idealized case study. A comprehensive USTES heating system model was established to systematically evaluate operational characteristics and environmental impacts under diverse conditions assuming homogeneous rock properties and idealized thermal boundaries. Results demonstrate that the surrounding ground temperature and the low thermal conductivity of the rock mass contribute to limiting heat dissipation and maintaining stable seasonal storage performance. For a roadway with a 20,000 m³ water storage capacity and an optimal 3900 m² solar collector area, the system successfully satisfies the thermal demand of 30,000 m² of building area. The configuration achieves 1239 MWh of cumulative heat storage over a 245-day cycle, maintaining a direct heating-to-heat-pump-upgraded heating ratio of 1.02. Furthermore, the implementation of variable-frequency thermal management strategies demonstrates remarkable economic and environmental superiority, yielding a 35.8% cost reduction compared to coal-fired heating, an overall energy saving rate of 77.5% relative to electric heating systems and a 13.5% decrease in CO₂ emissions relative to gas-fired systems. This research provides fundamental design parameters for the synergistic exploitation of mineral and geothermal resources, advancing the development of green heating and the sustainable utilization of post-mining spaces. Full article

This paper presents the design, implementation, and experimental validation of a portable photovoltaic charging station with IoT-based monitoring for autonomous low-power applications. The system integrates a 120 W photovoltaic module, LiFePO₄ battery storage, MPPT regulation, DC/AC conversion, and an ESP32-S3-based acquisition unit connected to a cloud platform for real-time telemetry. Electrical and environmental variables were recorded to evaluate energy balance, conversion losses, State of Charge evolution, and load compatibility under different seasonal operating conditions. Field tests showed that under high-irradiance summer conditions, the prototype supplied multiple laptop loads for approximately 4.5 h with stable operation. In contrast, winter trials revealed a structural energy deficit equivalent to 120% of the initial 24 Ah storage capacity, mainly due to reduced irradiance and cumulative conversion losses ranging from 15% to 25%. Based on the experimental data and deterministic energy-balance modelling, an optimized configuration using a 100 Ah LiFePO₄ battery bank and MPPT regulation was assessed through deterministic energy-balance modelling, thus reducing the required State of Charge to 28.8% under the analyzed operating profile. The results demonstrate the feasibility of a low-cost, IoT-enabled photovoltaic platform for renewable energy harvesting, autonomous power supply, and real-time performance assessment. Full article

The increasing penetration of photovoltaic (PV) systems in agricultural irrigation poses significant challenges in terms of energy self-sufficiency and operational cost, particularly when installed capacity is insufficient to cover pumping demand. This comparative study evaluates the energy and economic performance of three storage-based configurations applied to a real PV-powered irrigation system, using a PV capacity of 112 kWp as a common baseline. The methodology combines hourly energy balance modelling with linear programming optimization, implemented under both a grid energy minimization objective and a net cost minimization objective, within a model predictive control framework. Three scenarios are compared against a passive reference case: battery storage integration (Scenario 1), reservoir-based hydraulic storage (Scenario 2), and a combined electrical and hydraulic storage configuration (Scenario 3). Results show that system performance is strongly conditioned by the chosen objective function. When self-sufficiency is prioritized, Scenario 3 achieves the greatest reduction in grid imports by combining intraday electrical flexibility with demand rescheduling. When net cost minimization is the primary criterion, Scenario 2 proves most competitive, exploiting pumping flexibility and surplus compensation revenues. These findings highlight that storage technology selection in PV irrigation systems should be driven by the primary operational objective rather than by a single performance indicator. Full article

This study presents a novel iterative dual-loop methodology for the technical sizing of DC-coupled PV-BESS systems. The method was implemented for a commercial broiler farm characterized by a highly variable electricity demand profile (annual consumption: 7.6 MWh; coefficient of variation: 53%). The methodology introduces two original energy utilization indicators—the photovoltaic-to-converter matching factor (W_{PV_S}) and the photovoltaic-to-BESS matching factor (W_{PV_B})—enabling purely technical optimization independent of economic conditions. Minimization of the radius of curvature of the WPVB characteristic curve is applied as a rigorous mathematical criterion for determining the optimal BESS capacity. Simulation results indicate that the optimal configuration consists of a 9.7 kWp photovoltaic system, a 7 kW DC converter, and a 15 kWh battery storage system. The integration of an optimally sized energy storage system increased the self-consumption coverage ratio from 38% to 59% and improved the photovoltaic energy utilization factor from 35% to 54%. Additional economic analysis demonstrates that the PV-only subsystem achieves a simple payback period ranging from 8 to 18 years, depending on the selected pricing scenario. Consequently, the technically optimal configuration identified using the proposed methodology represents a practically feasible investment for broiler production facilities operating under Polish net-billing conditions. The proposed methodology provides a reproducible, economically independent framework for the design of DC-coupled PV-BESS systems in agricultural prosumer facilities, addressing a critical gap in the optimization literature and offering practical sizing guidelines applicable to similarly high-variability load profiles. Full article

Addressing the challenge of power supply stability caused by the intermittent nature of photovoltaic power generation in off-grid microgrids, this study uses a commercial park in Wuhan as a case study and optimizes the capacity configuration of a photovoltaic–storage–hydrogen fuel cell hybrid microgrid system based on HOMER Pro software. First, a topology of the off-grid microgrid is constructed, comprising photovoltaic (PV), lithium-ion batteries, hydrogen fuel cells, and a diesel generator as backup. The power output characteristics, efficiency curves, and life-cycle cost models of each component are accurately established. On this basis, two typical operation strategies, namely Load Following (LF) and Cycle Charging (CC), are proposed and compared. The influence of different strategies on the optimal capacity configuration and operational economics is systematically analyzed, and the Cycle Charging strategy is identified as the optimal operation strategy for this scenario. Subsequently, a multi-scenario capacity optimization design is further conducted based on the optimal operation strategy. The minimization of net present cost (NPC) is taken as the primary objective, while multiple evaluation indicators such as renewable fraction (RF), levelized cost of electricity (LCOE), energy storage cycle life degradation, and system redundancy rate are comprehensively considered. The results show that, while ensuring 100% power supply reliability, the proposed model reduces the net present cost (NPC) by approximately 14.4% compared with the conventional PV-storage scheme. The renewable fraction (RF) reaches 95.8%, while the reliance on lithium-ion battery capacity is significantly reduced (battery capacity configuration decreased by 24.3%). This effectively extends the energy storage lifespan and enhances the overall economic and environmental benefits. The results provide a theoretical basis and technical reference for the planning and design of off-grid microgrids with high penetration of renewable energy. Full article

The intermittent nature of renewable energy systems and the mismatch between power generation and load demand necessitate the integration of efficient energy storage systems (ESSs). Among large-scale energy storage technologies, pumped hydro-energy storage systems (PHESs) are widely recognized as one of the most cost-effective and longest-lifetime storage solutions under favorable geographical conditions. This study proposes and optimizes a hybrid renewable energy system (HRES) for the Wadi Baish region in Saudi Arabia as a real case study, where the significant elevation difference between the nearby mountains and the existing lake provides favorable conditions for PHES implementation. A nested optimization framework is developed to determine the optimal sizing and operation of the HRES components. The external optimization loop employs the non-dominated sorting genetic algorithm II (NSGA-II) to optimize system sizing, while the internal optimization loop uses mixed-integer linear programming (MILP) to optimally dispatch the PHES, battery energy storage system (BESS), and hydrogen energy storage system (HESS). In addition, demand-side management (DSM) is coordinated with the MILP dispatch strategy to improve system performance and reliability. The results show that the optimized system can supply a 10 MW average load with a renewable energy penetration of 98.7%. The proposed configuration achieves a total lifecycle cost of USD 231.37 million and avoids approximately 898.58 kt of CO₂ emissions over the project lifetime. PHES operates as the primary bulk energy storage technology due to its high storage capacity and low degradation characteristics. Furthermore, the degradation-aware model predicts battery replacement every 12 years and HESS replacement every 5 years. Compared with rule-based control, the MILP-based dispatch strategy reduces grid dependency by 87%. The coordinated DSM and MILP operation also reduces the levelized cost of energy to USD 0.066/kWh while improving overall system reliability. These findings demonstrate the importance of coordinated energy management and accurate degradation modeling in the optimal design and operation of renewable-based HRES configurations. Full article

Under the goals of carbon peaking and carbon neutrality and the development of zero-carbon parks, the continuous expansion of distributed photovoltaic (PV) installations has made grid-export constraints increasingly prominent. To investigate their influence on energy storage configuration and system operation, this paper incorporates the grid-export ratio constraint into the planning and scheduling process of a park-level PV-storage-charging system. A bilevel optimization model is established, in which the upper level minimizes the annual total cost (ATC), while the lower level minimizes the annual operating cost (AOC), considering time-of-use electricity prices, PV curtailment penalty, power shortage penalty, and battery degradation cost. The model is solved by a genetic algorithm (GA) and CPLEX. The results show that, for the studied industrial park, the 20% grid-export ratio is an important case-specific turning point under the given PV capacity, load level, electricity price, storage cost, and grid-connection conditions. Compared with the scheme without energy storage, the scheme with energy storage achieves lower PV curtailment and better economic performance. Sensitivity analyses further show that the PV curtailment penalty coefficient, energy storage investment cost, and PV installed capacity affect the optimal storage configuration and system economics. This study can provide a reference for energy storage planning and operation optimization of park-level PV-storage-charging systems under grid-export constraints. Full article

The distributed energy system emerges as a promising and valuable technology. However, various factors are hindering the development of the algorithm, including the diversity of units and their respective output constraints. Additionally, the multiple-layer algorithm still faces difficulties searching for the best coordination between integer and continuous variables. To address these challenges, this paper proposes a simultaneous optimization method to aid the design of distributed energy systems. Considering the influence of the outputs of the different units, the proposed method introduces a dual-cycle structure that separates the storage energy units and other units according to the output mode. Additionally, the proposed method yields the hourly outputs of the different units and their respective rated capacities simultaneously. At the same time, an originally designed random walk algorithm with compulsive evolution is integrated into the proposed method. Moreover, a time-series optimization method is applied for the storage energy device to enhance the computational efficiency. To validate the proposed method, the configuration of the distributed energy system (DES) and the hourly output of the different units in three scenarios are analyzed in detail. Quantitative results show that the proposed RWCE-DC reduces the average daily total cost compared to a standard differential evolution algorithm with penalty functions (from 413,628 to 241,716 CNY/day in Scenario II). Across three grid-interaction scenarios, RWCE-DC yields daily costs of 243,271 CNY/day (Scenario I), 209,716 CNY/day (Scenario II), and 178,896 CNY/day (Scenario III) while automatically removing redundant units (e.g., gas boiler in Scenario I) and strictly respecting storage state-of-charge constraints without penalty functions. However, the analysis has several limitations. First, the economic model uses a simplified annualized cost approach without taxes, subsidies, inflation, or discount rate variations. Second, only one geographic location with specific solar and load profiles is considered. Third, the current algorithm focuses on single-objective cost minimization and does not yet incorporate multi-objective trade-offs. These factors should be considered when interpreting the absolute cost values and when applying the method to other regions or policy contexts. These results confirm that the proposed dual-cycle method provides an efficient and numerically validated optimization approach for DES synthesis. Full article

Aqueous zinc-ion batteries are promising for large-scale energy storage due to their intrinsic safety, low cost, and environmental friendliness. However, their practical application is severely impeded by water-induced parasitic reactions and uncontrollable dendrite growth at the anode interface. Furthermore, the freezing of aqueous electrolytes at subzero temperature restricts their all-weather viability. Herein, we report a hydrogel electrolyte with interfacial regulation capabilities. By optimizing interfacial ion transport, the hydrogel electrolyte guides uniform Zn²⁺ deposition, effectively mitigating parasitic reactions and dendrite growth while enabling exceptional low-temperature tolerance. Consequently, the symmetric Zn//Zn cell using the hydrogel electrolyte delivers ultra-high cycling stability for 4000 h at 0.5 mA cm⁻² under −30 °C. When assembled into full cells, the Zn//NH₄V₄O₁₀ configuration operates stably for 4000 cycles at 5 A g⁻¹, exhibiting outstanding capacity retention. Furthermore, the assembled flexible pouch cell maintains 86% of initial capacity after 900 cycles at 3 A g⁻¹. Notably, the pouch cells demonstrate reliable operation and structural integrity under severe conditions, such as ice baths, bending, and piercing. This work provides an effective strategy for durable, wide-temperature, and intrinsically safe flexible aqueous energy storage systems. Full article

This study presents a comprehensive exergy-based thermodynamic analysis of a standalone liquid air energy storage (LAES) system integrated with internal thermal storage and an Organic Rankine Cycle (ORC) for sustainable large-scale energy storage applications. Unlike conventional studies, this work focuses on providing a scalable design framework by quantifying storage fluid requirements on a per-unit-mass-flow and per-MWh-capacity basis, enabling the results to be generalized for various power outputs and storage capacities. The proposed system configurations with two- and three-stage compression were compared in terms of liquid yield, round-trip efficiency (RTE), exergy efficiency, and storage fluid requirements. Results indicate that the optimal operating pressures are 190 bar for charging and 130 bar for discharging. At 200 bar charging pressure, the liquid yield increases from 0.51 (at 60 bar) to 0.86, while the maximum RTE reaches 62% in the base case and 68% with ORC integration. Incorporating ORC enhances the RTE by approximately 6–7% compared with conventional configurations through improved low-grade waste heat recovery and energy utilization. The two-stage compression configuration with ORC demonstrates the best thermodynamic performance, providing higher exergy efficiency, greater net power output, and lower thermal storage requirements. Furthermore, the reduction in thermal storage fluid demand contributes to improved resource utilization and lower infrastructure requirements for large-scale deployment. Additional sensitivity analyses indicate that thermal losses significantly reduce system performance, whereas ambient temperature fluctuations within ±15 K have only a minor influence on round-trip efficiency and liquid yield due to compensating effects between charging and discharging processes. The findings of this study provide scalable design insights for LAES systems and demonstrate the potential of ORC-assisted LAES technology to support renewable energy integration, sustainable grid flexibility, and low-carbon energy infrastructure development. Full article

The emergence of citizen energy communities under the European Clean Energy Package is creating new opportunities for neighboring households to collectively reduce electricity costs through local energy sharing. This paper presents a distributed multi-agent energy management system for a two-household residential energy community in which each household is equipped with photovoltaic generation and a battery energy storage system operating under realistic hourly-varying electricity prices. Each household is managed by an independent Deep Q-Learning agent that learns a cost-optimal charging and discharging policy using only local observations. In parallel, a coordination agent, implemented on the SPADE platform with XMPP-based messaging, oversees real-time peer-to-peer energy transfers between households, enabling energy exchange whenever one household has surplus generation and another faces a deficit. The two households are deliberately configured with complementary profiles: one has higher PV generation capacity while the other has higher energy consumption. This setup creates natural opportunities for local energy sharing between them. Performance is assessed through a three-level evaluation framework: (i) individual household economics (cost reduction, battery management, grid exchanges), (ii) coordination efficiency (transfer frequency, direction, and volume), and (iii) aggregate community performance, which isolates the added value of peer-to-peer sharing beyond what each household achieves through individual BESS optimization. Numerical experiments using GEFCom2014 solar generation data, synthetic residential load profiles calibrated following documented consumption patterns, and day-ahead price signals representative of the Spanish electricity market demonstrate that both Deep Q-Learning agents independently learn effective charge/discharge strategies aligned with price signals and PV availability. They also show that the coordination layer further reduces community grid dependence by routing surplus energy locally rather than exchanging it with the main grid at less favorable rates. The results confirm that a well-engineered integration of decentralized reinforcement learning with a lightweight coordination protocol can deliver measurable economic benefits in realistic residential energy communities without requiring centralized training, shared data, or complex multi-agent reinforcement learning architectures. Full article

With the large-scale deployment of distributed photovoltaics (PVs) on the user side, integrated PV-storage systems have become a critical means to reduce electricity costs and enhance energy flexibility. However, the volatility of PV output and the dynamic nature of time-of-use (TOU) pricing render the economic viability of such systems highly dependent on the coordinated optimization of capacity configuration and operational strategies. To address this, a bi-level optimization model is developed. The upper level maximizes the equivalent annual economic benefit by determining the installed capacities of PV and storage, explicitly incorporating power-sensitive operation and maintenance costs. The lower level, formulated as a mixed-integer programming problem, minimizes the daily net electricity cost by optimizing charging/discharging schedules and grid interaction. The model is solved through an iterative hierarchical approach combining the chaotic sparrow search algorithm (CSSA) and the CPLEX solver. A case study using actual data from an industrial park demonstrates that, compared with scenarios without PV-storage and with PV only, the joint PV-storage configuration reduces total electricity costs by 17.3% and 4.5%, respectively. Furthermore, the asymmetric impacts of PV forecast errors on operational economics are quantitatively analyzed: when PV output is underestimated, the failure to pre-reserve accommodation capacity leads to an increase in electricity procurement costs of RMB 1927.84 compared with the ideal scenario. To mitigate this, a risk-aware fault-tolerant scheduling strategy is proposed, which reserves a 5% accommodation margin through conservative biasing, reducing the additional cost caused by forecast errors by 20.14% and significantly enhancing the system’s economic robustness under forecast uncertainty. Full article

In response to the dual pressures of energy consumption and environmental pollution faced by the global shipping industry, this paper proposes an optimization method for the energy storage capacity of a ship’s hybrid energy system based on a two-layer optimization model, aiming to enhance the energy utilization efficiency and operational stability of the system. A DNN-IPSO optimization framework integrating deep neural networks (DNN) and the improved particle swarm optimization algorithm (IPSO) was constructed, and combined with robust control strategies, it optimized the energy storage capacity configuration problem under complex dynamic conditions. The results show that the proposed method exhibits superior performance in terms of energy utilization efficiency, system dynamic response, and stability. The energy utilization efficiency of the system has been increased to 91.3%, the bus voltage fluctuation has been reduced to 3.98%, the load tracking error has been decreased to 17.6 kW, and the average convergence iteration times have been reduced to 71 times. The 17.6 kW load tracking error accounts for only 1.76% of the rated propulsion power of the 1 MW-level experimental platform, which is approximately 38% lower than that of the GA-PSO method. The experimental results on the real ship show that after using the DNN-IPSO optimization, the unit voyage energy consumption has been reduced to 41.7 kWh/km, the propulsion power stability coefficient has been increased to 0.956, the system transient recovery time has been shortened to 3.2 s, and the power reserve margin has been increased to 18.4%. The proposed method can effectively enhance the energy management capability, dynamic response performance, and operational stability of the ship’s hybrid energy system in the actual operating environment, providing reliable technical support for the engineering application of the integrated energy system of ships. Full article

The development of a new-type power system requires addressing the long-timescale imbalance between electricity supply and demand caused by the high penetration of wind and solar energy, which places higher demands on the secure and stable operation of power systems. Conventional single-type energy storage cannot simultaneously satisfy short-term power regulation and medium- to long-term energy balancing requirements. Therefore, coordinated optimal allocation of multi-type energy storage is necessary. This study investigates the optimal capacity allocation of short- and long-duration energy storage in high-renewable-penetration power grids to improve renewable energy accommodation, enhance system flexibility, and optimize life-cycle cost. A mathematical model of a Multi-Type Energy Storage Coupled System (MTESCS) considering both power and energy balance is first established, together with a life-cycle economic model. Then, a source-load time-series reduction method based on Ward’s method is adopted to preserve the original temporal trends while reducing optimization complexity, and an optimal capacity allocation model is developed with the objective of minimizing system life-cycle cost. Finally, different storage configuration scenarios are constructed for comparative analyses under various renewable energy penetration levels. Results show that the proposed MTESCS can effectively improve renewable energy accommodation and economic performance, providing useful support for system design and engineering applications. Full article