Search Results (19)

With the deepening implementation of the dual-carbon strategy, the penetration rates of distributed power sources and flexible loads in new distribution grids continue to rise, posing significant challenges to system security and stability due to output fluctuations and randomness. To enhance voltage quality and achieve low-carbon economic operation in distribution grids, this paper proposes a multi-objective optimization model for Distributed Energy Storage System allocation. The model integrates power quality, economic benefits, and net carbon emissions. To efficiently solve this high-dimensional nonlinear problem, an improved Multi-Objective Gray Wolf Optimization algorithm is proposed. It employs a chaotic map to initialize the population, enhancing global distribution uniformity. A nonlinear convergence factor is introduced to dynamically balance global exploration and local exploitation. A dynamic grouping collaboration strategy is designed, combining Lévy flight and the elite crossover strategy to enhance search capability and convergence accuracy. Simulations on an IEEE 33-node system show that the improved MOGWO-optimized energy storage scheme reduces average voltage deviation by 37.0%, total operating costs by 7.0%, and net carbon emissions by 4.1%, compared to a no-storage scenario. Compared to the standard MOGWO algorithm, the proposed method achieves further optimization across all objectives, validating its effectiveness and superiority in realizing coordinated energy storage planning that balances safety, economy, and low-carbon goals. Full article

Accurate assessment of source-load complementarity and system regulation capacity is critical for secure dispatch and planning in high-penetration renewable power systems. Addressing limitations of existing methods—which rely heavily on static metrics, struggle to capture temporal and tail dependence characteristics, and provide insufficient support for dispatch decisions—this paper proposes a multi-level integrated evaluation framework. First, from a source—load matching perspective, we develop a novel complementarity metric, integrating real-time rate of change, temporal consistency, and tail dependency. An improved adaptive noise-complete set empirical mode decomposition combined with a hybrid Copula model is employed to isolate noise and to precisely quantify dynamic dependency structures. Second, we introduce the Minkowski measure and construct a net load fluctuation domain accounting for extreme fluctuations and coupling relationships. Subsequently, combining the Analytic Hierarchy Process (AHP) with probabilistic convolution enables multi-level comparative quantification of resource capacity and fluctuation domain requirements under varying confidence levels. Simulation results demonstrate that the proposed framework not only provides a more robust assessment of source-load complementarity but also quantitatively outputs the adequacy and risk level of system regulation capacity. This delivers hierarchical, actionable decision support for dispatch planning, significantly enhancing the engineering applicability of evaluation outcomes. Full article

With the advancement of the “dual carbon” goal, the power system is accelerating its transition towards a clean and low-carbon structure, with a continuous increase in the penetration rate of renewable energy generation (REG). However, the volatility and uncertainty of REG output pose severe challenges to power grid operation. Traditional distribution networks face immense pressure in terms of scheduling flexibility and power supply reliability. Active distribution networks (ADNs), by integrating energy storage systems (ESSs), soft open points (SOPs), and demand response (DR), have become key to enhancing the system’s adaptability to high-penetration renewable energy. This work proposes a DR-aware scheduling strategy for ESS-integrated flexible distribution networks, constructing a bi-level optimization model: the upper-level introduces a price-based DR mechanism, comprehensively considering net load fluctuation, user satisfaction with electricity purchase cost, and power consumption comfort; the lower-level coordinates SOP and ESS scheduling to achieve the dual goals of grid stability and economic efficiency. The non-dominated sorting genetic algorithm III (NSGA-III) is adopted to solve the model, and case verification is conducted on the standard 33-node system. The results show that the proposed method not only improves the economic efficiency of grid operation but also effectively reduces net load fluctuation (peak–valley difference decreases from 2.020 MW to 1.377 MW, a reduction of 31.8%) and enhances voltage stability (voltage deviation drops from 0.254 p.u. to 0.082 p.u., a reduction of 67.7%). This demonstrates the effectiveness of the scheduling strategy in scenarios with renewable energy integration, providing a theoretical basis for the optimal operation of ADNs. Full article

The increasing integration of wind and photovoltaic energy into power systems brings about large fluctuations and significant challenges for power absorption. Wind–solar–hydro–storage multi-energy complementary systems, especially joint dispatching strategies, have attracted wide attention due to their ability to coordinate the advantages of different resources and enhance both flexibility and economic efficiency. This paper develops a capacity optimization model for a wind–solar–hydro–storage multi-energy complementary system. The objectives are to improve net system income, reduce wind and solar curtailment, and mitigate intraday fluctuations. We adopt the quantum particle swarm algorithm (QPSO) for outer-layer global optimization, combined with an inner-layer stepwise simulation to maximize life cycle benefits under multi-dimensional constraints. The simulation is based on the output and load data of typical wind, solar, water, and storage in Yunnan Province, and verifies the effectiveness of the proposed model. The results show that after the wind–solar–hydro–storage multi-energy complementary system is optimized, the utilization rate of new energy and the system economy are significantly improved, which has a wide range of engineering promotion value. The research results of this paper have important reference significance for the construction of new power systems and the engineering design of multi-energy complementary projects. Full article

With rising energy costs and the need for sustainable power solutions in urban South African settings, grid-tied renewable energy systems have become viable alternatives for reducing dependence on traditional grid supply. This study investigates the techno-economic feasibility of a grid-connected hybrid photovoltaic (PV) and battery storage system designed for a commercial facility located in Johannesburg, South Africa—an area characterized by a subtropical highland climate. We conducted the analysis using the HOMER Grid software and evaluated the performance of the proposed PV/battery system against the baseline grid-only configuration. Simulation results indicate that the optimal systems, comprising 337 kW of flat-plate PV and 901 kWh of lithium-ion battery storage, offers a significant reduction in electricity expenditure, lowering the annual utility cost from $39,229 to $897. The system demonstrates a simple payback period of less than two years and achieves a net present value (NPV) of approximately $449,491 over a 25-year project lifespan. In addition to delivering substantial cost savings, the proposed configuration also enhances energy resilience. Sensitivity analyses were conducted to assess the impact of variables such as inflation rate, discount rate, and load profile fluctuations on system performance and economic returns. The results affirm the suitability of hybrid grid-tied PV/battery systems for cost-effective, sustainable urban energy solutions in climates with high solar potential. Full article

Due to the development of renewable energy and the requirement of environmental friendliness, more distributed photovoltaics (DPVs) are connected to distribution networks. The optimization of stable operation and the improvement of DPV hosting capacity are urgently needed. Energy storage systems (ESSs), as a flexible resource, show great promise in DPV integration and optimal dispatching. Thus, an optimal configuration method for ESSs is proposed. Firstly, a two-layer, double-stage configuration model of ESSs is constructed. The inner layer contains two stages of network operation optimization and DPV hosting capacity improvement. The daily cost, voltage deviation and net load fluctuation rate are optimized in the first stage. The hosting capacity of the DPV is maximized in the second stage. The position and capacity of the ESS are configured in the outer layer. Secondly, a solution strategy for the proposed model is designed. Focusing on different problem-solving intelligence algorithms, solvers and analytic hierarchy process–anti-entropy weighting methods are used for effectively selecting comprised optimal solutions. Simulations verified the effectiveness and applicability of the proposed method. This method comprehensively considers the stable operation of distribution networks and the improvement of DPV hosting capacity, which provides scientific guidance for the orderly access of DPVs in distribution networks. Full article

With the increasing importance of renewable energy in the global energy transition, the microgrid has attracted wide attention as an efficient and flexible power solution. However, there are some problems in current networked microgrid systems, such as complex structure, numerous parameters, and significant fluctuations in generation capacity. Aiming at the parameter optimization problem of networked microgrids integrating multiple energy generation and energy storage forms, this paper constructs a multi-objective microgrid structure decision-making model. The model comprehensively considers operation and maintenance costs, fuel costs, power abandonment and lack-of-power punishment costs, power transaction costs, and pollution treatment costs, aiming to realize the joint optimization of economic benefits and environmental sustainability. Furthermore, an improved multi-objective particle swarm optimization (IMOPSO) algorithm is designed to solve the model. In order to verify the effectiveness of the model in the scenarios of distributed energy and energy load fluctuation, this paper uses the scenario analysis method to realize the data analysis of 2000 scenarios, and obtains four typical deterministic scenarios for simulation experiments. The experimental results show that, compared with the traditional microgrid, when the capacity configuration is determined by the number of wind driven generators, photovoltaic panels, diesel generators, and batteries being 5, 189, 2, and 107, respectively, the proposed net-connected economic dispatch optimization method based on hybrid renewable energy in this paper reduces the generation cost and environmental cost of the system by 96.76 ¥ to 428.19 ¥, and keeps the load loss rate stable between 0.34% and 4.56%. The utilization rate of renewable energy has been raised to about 95%. Full article

With the evolvement of the coal marketplace and massive growth in renewable resource power, conventional coal-fired generation is facing challenges in the operation of fluctuating loads and varying coal characteristics. The intent of this study is to predict carbon emissions from coal-fired power plants during load cycling and the operation of varying coal characteristics. The given correlation was revised by adding a new nitrogen term and using thermodynamic data from the latest JANAF tables. On the basis of the revised correlation, the quantitative impact of each element composition of coal on the carbon emission factor was worked out according to first-order Taylor series approximation. The O/C and H/C ratio of coal at the lowest carbon emission factor was evaluated in the VAN Krevelen diagram, showing that coals have the lowest carbon emission factor value of roughly 23.25 kg/GJ GCV at atomic O/C and H/C ratio values of about 0.08 and 0.98, respectively. Correlations of carbon emission with the proximate analysis of coal were established through stepwise linear regression using 247 coals for power generation. Based on the varying nature of the net heat rate with load condition expressed by the generic model derived from 11 typical units in-service, the impact of coal and load cycling on carbon emission was captured with a developed equation. Linking the above investigation to a study in a thermal power unit with a rated output of 1000 MW shows that the total variation of carbon emission due to the combined effect of coal and load cycling could be 27.44% if the unit cycles at 35% to 100% rated output with coal normally varying in the present context. Full article

Fast charge/discharge scheduling of battery storage systems is essential in microgrids to effectively balance variable renewable energy sources, meet fluctuating demand, and maintain grid stability. To achieve this, parallel processing is employed, allowing batteries to respond instantly to dynamic conditions. By managing the complexity, high data volume, and rapid decision-making requirements in real time, parallel processing ensures that the microgrid operates with stability, efficiency, and safety. With the application of deep reinforcement learning (DRL) in scheduling algorithm design, the demand for computational power has further increased significantly. To address this challenge, we propose a Ray-based parallel framework to accelerate the development of fast charge/discharge scheduling for battery storage systems in microgrids. We demonstrate how to implement a real-world scheduling problem in the framework. We focused on minimizing power losses and reducing the ramping rate of net loads by leveraging the Asynchronous Advantage Actor Critic (A3C) algorithms and the features of the Ray cluster for real-time decision making. Multiple instances of OpenDSS were executed concurrently, with each instance simulating a distinct environment and efficiently processing input data. Additionally, Numba CUDA was utilized to facilitate GPU acceleration of shared memory, significantly enhancing the performance of the computationally intensive reward function in A3C. The proposed framework enhanced scheduling performance, enabling efficient energy management in complex, dynamic microgrid environments. Full article

With the rise in the proportion of renewable energy and energy storage in modern power systems, the volatility of renewable energy and the increasing demand for loads pose a significant risk of congestion in transmission lines. Along with transmission congestion, prolonged heavy loads on transmission lines increase equipment failure rates, leading to a range of issues within the power system. This study proposes a scene clustering method for power system scheduling by leveraging the net load related with the load and renewable energy power outputs. Subsequently, a scheduling model and line load evaluation indexes were developed to analyze the line load rate of power systems with different renewable energy proportions. The simulation results indicate that the utilization rate of lines, the fluctuation rate of line load, the maximum line load, and heavy line load time increase as the installed proportion of renewable energy increases. Finally, a penalty term for heavy loads was incorporated into the objective function and methods of rescheduling and planning energy storage considering the heavy load penalty function are proposed. A case study validated the significant improvements in load management, achieving a reduction in heavy load time by approximately 30% and reducing transmission congestion by 20% under high-renewable-energy-penetration scenarios. These results illustrate the effectiveness of the heavy load cost function in enhancing system resilience and optimizing load distribution. Full article

Configuring energy storage systems (ESSs) in distribution networks is an effective way to alleviate issues induced by intermittent distributed generation such as transformer overloading and line congestion. However, flexibility has not been fully taken into account when placing ESSs. This paper proposes a novel ESS placement method for flexible interconnected distribution networks considering flexibility constraints. An ESS siting and sizing model is formulated aiming to minimize the life-cycle cost of ESSs along with the annual network loss cost, electricity purchasing cost from the upper-level power grid, photovoltaic (PV) curtailment cost, and ESS scheduling cost while fulfilling various security constraints. Flexible ramp-up/-down constraints of the system are added to improve the ability to adapt to random changes in both power supply and demand sides, while a fluctuation rate of net load constraints is also added for each bus to reduce the net load fluctuation. The nonconvex model is then converted into a second-order cone programming formulation, which can be solved in an efficient manner. The proposed method is evaluated on a modified 33-bus flexible distribution network. The simulation results show that better flexibility can be achieved with slightly increased ESS investment costs. However, a large ESS capacity is needed to reduce the net load fluctuation to low levels, especially when the PV capacity is large. Full article

Globally, the integration of electric vehicles (EVs) in the transportation sector represents a significant step towards achieving environmental decarbonization. This shift also introduces a new demand for electric power within the utility grid network. This study focuses on the design and development of a grid-connected renewable energy system tailored to meet the EV load demands in Taif, Kingdom of Saudi Arabia (KSA). The integration of renewable energy sources, specifically solar photovoltaic (SPV) and wind turbines (WT), is explored within the context of economic feasibility and system reliability. Key considerations include optimizing the system to efficiently handle the fluctuating demands of EV charging while minimizing reliance on conventional grid power. Economic analyses and reliability assessments are conducted to evaluate the feasibility and performance of the proposed renewable energy system. This article discusses the technical sizing of hybrid systems, energy reduction, and net present cost for the selected location. A rigorous sensitivity analysis is performed to determine the impact of major variables such as inflation rate, real discount rate, solar irradiation, and Lack of Power Supply Probability (LPSP) on system performance. The results demonstrate that the Pufferfish Optimization Algorithm (PFO) significantly outperforms other metaheuristic algorithms documented in the literature, as well as the HOMER software. The study found that the grid-connected renewable energy system is the best option for operating EV charging stations at the selected location. The findings underscore the potential for sustainable energy solutions in urban environments like Taif, highlighting the importance of integrating renewable energy technologies to meet growing energy demands with enhanced economic efficiency and system reliability. This initiative seeks to pave the way for a greener and more resilient energy infrastructure, aligning with global efforts towards sustainable development and clean transportation solutions. Full article

This paper investigates the aerodynamic behavior and dynamic characteristics of high-speed train pantographs under various operating conditions using advanced aerodynamic simulations and dynamic analyses. The simulations show significant fluctuations in aerodynamic loads during tunnel entry and exit, heavily influenced by train speed and pantograph position (raised/lowered). Modal simulations reveal distinct low-frequency vibrations in pantographs, significantly impacted by external aerodynamic forces. Importantly, the lowered position exposes the pantograph to upward aerodynamic forces, leading to increased bow-net contact force and off-line rate, ultimately compromising current collection stability. Both maximum contact force and off-line rate further increase with higher train speeds. To improve pantograph design, the paper proposes adjustments to the airbag’s equivalent spring stiffness and the bow head’s density. These modifications aim to mitigate contact force and enhance the stability and reliability of pantographs at high speeds. This research offers theoretical and practical insights, aiding in the design, optimization, and refinement of future pantograph systems. Full article

This study presents a novel cooling-power-desalination combined cycle for recovering shipboard diesel exhaust heat, integrating a freezing desalination sub-cycle to regulate the ship’s cooling-load fluctuations. The combined cycle employs ammonia–water as the working fluid and efficiently utilizes excess cooling capacity to pretreat reverse osmosis desalination. By adjusting the mass flow rate of the working fluid in both the air conditioning refrigeration cycle and the freezing desalination sub-cycle, the combined cycle can dynamically meet the cooling-load demand under different working conditions and navigation areas. To analyze the cycle’s performance, a mathematical model is established for energy and exergy analysis, and key parameters including net output work, comprehensive efficiency, and heat exchanger area are optimized using the MOPSO algorithm. The results indicate that the system achieves optimal performance when the generator temperature reaches 249.95 °C, the sea water temperature is 22.29 °C, and 42% ammonia–water is used as the working fluid. Additionally, an economic analysis of frozen seawater desalination as RO seawater desalination pretreatment reveals a substantial cost reduction of 22.69%, showcasing the advantageous features of this proposed cycle. The research in this paper is helpful for waste energy recovery and sustainable development. Full article

Optimizing the combustion control concepts on a pellet stove with very low heat output is time-consuming and costly. In order to shorten the required laboratory test time, a 0-D transient tool was developed within the ERA-NET project “LowEmi-MicroStove”, which simulates a 4 kW pellet stove with staged combustion and heat transfer. This approach was chosen in order to greatly simplify the description of the combustion processes and so reduce the computational complexity and simulation time. The combustion of a bed of pellets is modeled as a superposition of the combustion cycles of individual pellets, assuming no interactions between pellets. A test setup was developed and used to determine the ignition and burning cycle of individual pellets. The description of the CO emissions behavior is based upon an empirically grounded relation which is in turn based on the air/fuel ratio and the combustion chamber temperature. For the validation of the 0-D simulation results, a test rig for a 4 kW pellet stove was built. Despite its simplistic approach, good agreement was found between the simulation and 4 kW pellet stove test results for the mean values and temporal fluctuations of flue gas temperature and oxygen and carbon monoxide content during start up, stable operation and load changes. The simulation could thus be used to quantify the effect of air flow rates and distribution as well as load changes on performance and draw conclusions regarding different process control strategies. A control strategy which can operate the stove at high temperatures near the air stoichiometric limit with acceptable CO emissions has been proven to be the most promising. Additionally, the model can be used to quantify the effects of variations in other process parameters, for example the impact of fluctuations in the pellet feed. Due to its effectiveness and simplicity, this model approach can be applied for the development of control strategies for other staged, pellet combustion systems. Full article