Search Results (23)

Against the backdrop of China’s dual carbon goals, cross-regional low-carbon power transmission from large-scale clean energy bases is a pivotal direction for energy transition. Formulating their power delivery curves requires precise alignment with the load demand characteristics of receiving provinces and the coordinated operation of hydropower, wind power, photovoltaic (PV) power, and pumped-storage hydropower (PSH). To address the limitations of existing methods, such as the lack of linearized modeling for core operational constraints, low solution efficiency and inadequate integration of multi-energy coupling constraints, this paper proposes a tailored linearized optimization modeling approach. By adopting auxiliary variables, binary variables and the Big M method, core constraints including PSH pumping power supply, stepwise power delivery and multi-energy coordinated operation are linearized. A monthly rolling linear optimization model is constructed with triple objectives: minimizing the renewable curtailment rate and the absolute error between delivery and load curves, and maximizing delivered electricity volume. Multi-objective coordinated optimization is realized via the linear weighted summation method, and the model is solved with the Gurobi solver. Case validation on an integrated hydro–wind–solar clean energy base in Southwest China and its corresponding receiving provincial power grid shows that the proposed method effectively improves the curve matching degree, controls the wind–PV curtailment rate within around 12% (engineering tolerance), and strictly meets engineering safety constraints such as PSH operation and HVDC transmission requirements. Comprehensive optimization of the three objectives is achieved when the weight coefficients for curtailment rate, load matching error and delivered electricity volume are set to 0.3–0.8, 0.1–0.2 and 0.1–0.6, respectively. This method resolves the problems of traditional nonlinear models being disconnected from engineering practice and low solution efficiency, providing a reliable technical reference for the refined dispatching of cross-regional power transmission and scientific formulation of power delivery curves for clean energy bases. Full article

The large-scale integration of distributed photovoltaics (DPV) and their inherent uncertainties have significantly increased the operational risks of distribution networks. Moreover, frequent outages caused by extreme events further impose substantial losses on these networks, highlighting the urgent need to enhance their disaster resilience and load-supply capabilities. To address these challenges, this paper proposes an energy storage allocation method that simultaneously considers economic performance and comprehensive vulnerability. First, a vulnerability assessment framework for distribution networks is established from both pre-disaster and post-disaster perspectives. In the pre-disaster stage, an improved electrical betweenness index, voltage deviation index, and network-balance index are employed to identify weak lines and nodes. In the post-disaster stage, based on the identified weak components, two types of scenarios, namely random line failures and worst-case failures, are constructed to emulate extreme events, and an enhanced network supply efficiency index is developed to quantitatively evaluate the network’s recovery capability. Subsequently, a multi-objective optimal allocation model for energy storage is formulated with economic cost and comprehensive vulnerability as objective functions, and an Enhanced Beluga Whale Optimization algorithm is adopted to obtain the optimal siting and sizing of energy storage systems. Case studies on an improved IEEE 33-bus distribution system show that, compared with the no-ESS scheme, the proposed plan yields about a 66.4% reduction in network loss cost, around 22% improvement in average voltage deviation, and a roughly 10% reduction in the comprehensive vulnerability index under normal operation. Under random and targeted line outage scenarios, the proposed scheme also achieves the highest area under curve and average network effectiveness indices and the lowest performance volatility among the benchmark strategies. These results demonstrate that, for the tested IEEE 33-bus system, the vulnerability-driven ESS planning framework can markedly enhance both economic efficiency and resilience to extreme events. Full article

Prosumer energy storage behavior alongside national rooftop photovoltaics (RTPV) penetration metrics is essential for decarbonization pathways in buildings. A research gap persists in quantitatively assessing storage strategies under varying regulatory frameworks that integrate both technical and financial dimensions while accounting for behavioral heterogeneity and policy feedback. This study introduces a novel degradation-aware, feedback-preserving framework that optimizes behind-the-meter storage design and operation, enabling realistic modeling of prosumer responses on large-scale RTPV adoption scenarios. Long Short-Term Memory (LSTM) and Compound Annual Growth (CAGR) models applied for the RTPV penetration rates projections in European urban contexts. The increasing rates in the Netherlands, Spain, and Italy respond to second-order regression behavior, with the former to emit signals of saturation and the latter to perform mixed anelastic and reverse elastic curves of elasticities. Accordingly, Germany, France, the United Kingdom (UK), and Greece remain in an inelastic area by 2030. The building RTPV energy storage arbitrage formulation is treated as a linear programming (LP) problem using a convex and piecewise linear cost function, a Model Predictive Control (MPC), Auto Regressive Moving Average (ARMA) and Auto Regressive Integrated Moving Average (ARIMA) statistical forecasts and rolling horizon in order to address the uncertainty of the load and the ratio κ of the sold to purchased electricity price. Weekly arbitrage gains may drop by up to 9.1% due to stochasticity, with maximized gains achieved at battery capacities between 1C and 2C. The weekly gain per cycle performs elastic, anelastic, and reverse behavior of the prosumer across the range of κ values responding to different regulatory mechanisms of pricing. The variability of economic incentives suggests the necessity of flexible energy management strategies. Full article

With the global commercialization of hydrogen fuel cell vehicles, the number of hydrogen refueling stations is steadily increasing. On-site hydrogen production stations are expected to play a key role in future power systems by absorbing renewable energy and supplying electricity during peak grid loads, aiding in peak shaving and load leveling. However, renewable energy sources like photovoltaic (PV) systems have highly fluctuating power generation curves, making it difficult to provide stable energy for hydrogen production. Traditional stations mainly use alkaline electrolyzers (AE), which are sensitive to power fluctuations, leading to operational instability. To address this, this paper proposes using capacitors and energy storage batteries to mitigate PV fluctuations and introduces a combined AE and Proton Exchange Membrane (PEM) electrolyzer hydrogen production method. Study cases demonstrate that capacitors and energy storage batteries reduce the variance of PV power output by approximately 0.02. Building on this, the hybrid approach leverages the low cost of AE and the rapid response of PEM electrolyzers to better adapt to PV fluctuations and maximize PV absorption. The model is mathematically formulated and the station’s equipment planning and operational strategy are optimized using CPLEX. The results show that, compared to pure AE and PEM hydrogen production, the combined AE and PEM hydrogen production method reduces the total annual cost of the hydrogen refueling station by 4.3% and 5.9%, respectively. Full article

With the widespread deployment of photovoltaic (PV) power stations, timely identification and rectification of module defects are crucial for extending service life and preserving efficiency. PV arrays, subjected to severe outside circumstances, are prone to defects exacerbated by dust accumulation, potentially leading to complex compound faults. The resemblance between individual and compound faults sometimes leads to misclassification. To address this challenge, this paper presents a novel hybrid deep learning model, ResGRU, which integrates a residual network (ResNet) with bidirectional gated recurrent units (BiGRU) to improve fault diagnostic accuracy. Additionally, a Squeeze-and-Excitation (SE) module is incorporated to enhance relevant features while suppressing irrelevant ones, hence improving performance. To further optimize inter-class separability and intra-class compactness, a center loss function is employed as an auxiliary loss to enhance the model’s discriminative capacity. This proposed method facilitates the automated extraction of fault features from I-V curves and accurate diagnosis of individual faults, partial shading scenarios, and compound faults under varying levels of dust accumulation, hence aiding in the formulation of efficient cleaning schedules. Experimental findings indicate that the suggested model achieves 99.94% accuracy on pristine data and 98.21% accuracy on noisy data, markedly surpassing established techniques such as artificial neural networks (ANN), ResNet, random forests (RF), multi-scale SE-ResNet, and other ResNet-based approaches. Thus, the model offers a reliable solution for accurate PV array fault diagnosis. Full article

To effectively represent photovoltaic (PV) modules while considering their dependency on changing environmental conditions, three novel mathematical and empirical formulations are proposed in this study to model PV curves with minimum effort and short timing. The three approaches rely on distinct mathematical techniques and definitions to formulate PV curves using function representations. We develop our models through fractional derivatives and stochastic white noise. The first empirical model is proposed using a fractional regression tool driven by the Liouville-Caputo fractional derivative and then implemented by the Mittag-Leffler function representation. Further, the fractional-order stochastic ordinary differential equation (ODE) tool is employed to generate two effective generic models. In this work, multiple commercial PV modules are modeled using the proposed fractional and stochastic formulations. Using the experimental data of the studied PV panels at different climatic conditions, we evaluate the proposed models’ accuracy using two effective statistical indices: the root mean squares error (RMSE) and the determination coefficient (R²). Finally, the proposed approaches are compared to several integer-order models in the literature where the proposed models’ precisely follow the real PV curves with a higher R² and lower RMSE values at different irradiance levels lower than 800 w/m², and module temperature levels higher than 50 °C. Full article

This research addresses the problem regarding the efficient operation of photovoltaic (PV) plants in monopolar direct current (DC) distribution networks from a perspective of convex optimization. PV plant operation is formulated as a nonlinear programming (NLP) problem while considering two single-objective functions: the minimization of the expected daily energy losses and the reduction in the expected CO₂ emissions at the terminals of conventional generation systems. The NLP model that represents the energy management system (EMS) design is transformed into a convex optimization problem via the second-order cone equivalent of the product between two positive variables. The main contribution of this research is that it considers the uncertain nature of solar generation and expected demand curves through robust convex optimization. Numerical results in the monopolar DC version of the IEEE 33-bus grid demonstrate the effectiveness and robustness of the proposed second-order cone programming model in defining an EMS for a monopolar DC distribution network. A comparative analysis with four different combinatorial optimizers is carried out, i.e., multiverse optimization (MVO), the salp swarm algorithm (SSA), the particle swarm optimizer (PSO), and the crow search algorithm (CSA). All this is achieved while including an iterative convex method (ICM). This analysis shows that the proposed robust model can find the global optimum for two single-objective functions. The daily energy losses are reduced by 44.0082% with respect to the benchmark case, while the CO₂ emissions (kg) are reduced by 27.3771%. As for the inclusion of uncertainties, during daily operation, the energy losses increase by 22.8157%, 0.2023%, and 23.7893% with respect to the benchmark case when considering demand uncertainty, PV generation uncertainty, and both. Similarly, CO₂ emissions increase by 11.1854%, 0.9102%, and 12.1198% with regard to the benchmark case. All simulations were carried out using the Mosek solver in the Yalmip tool of the MATLAB software. Full article

Partial shading conditions lead to power mismatches among photovoltaic (PV) panels, resulting in the generation of multiple peak power points on the P-V curve. At this point, conventional MPPT algorithms fail to operate effectively. This research work mainly focuses on the exploration of performance optimization and harnessing more power during the partial shading environment of solar PV systems with a single-objective non-linear optimization problem subjected to different operations formulated and solved using recent metaheuristic algorithms such as Cat Swarm Optimization (CSO), Grey Wolf Optimization (GWO) and the proposed Chimp Optimization algorithm (ChOA). This research work is implemented on a test system with the help of MATLAB/SIMULINK, and the obtained results are discussed. From the overall results, the metaheuristic methods used by the trackers based on their analysis showed convergence towards the global Maximum Power Point (MPP). Additionally, the proposed ChOA technique shows improved performance over other existing algorithms. Full article

This research proposes an efficient energy management system for standalone and grid-connected direct current (DC) distribution networks that consider photovoltaic (PV) generation sources. A complete nonlinear programming model is formulated to represent the efficient PV dispatch problem while taking three different objective functions into account. The first objective function corresponds to the minimization of the operational costs with respect to the energy purchasing costs at terminals of the substation, including the maintenance costs of the PV sources. The second objective function is the reduction of the expected daily energy losses regarding all resistive effects of the distribution lines. The third objective function concerns the minimization of the total emissions of CO${}_2$ into the atmosphere by the substation bus or its equivalent (diesel generator). These objective functions are minimized using a single-objective optimization approach through the application of the Salp Swarm Algorithm (SSA), which is combined with a matrix hourly power flow formulation that works by using a leader–follower operation scheme. Two test feeders composed of 27 and 33 nodes set for standalone and grid-connected operation are used in the numerical validations. The standalone grid corresponds to an adaptation of the generation and demand curves for the municipality of Capurganá, and the grid-connected system is adapted to the operating conditions in the metropolitan area of Medellín, i.e., a rural area and a major city in Colombia. A numerical comparison with three additional combinatorial optimizers (i.e., particle swarm optimization (PSO), the multiverse optimizer (MVO), and the crow search algorithm (CSA)) demonstrates the effectiveness and robustness of the proposed leader–follower optimization approach to the optimal management of PV generation sources in DC grids while considering different objective function indices. Full article

Temperature-control loads, such as residential air conditioners (ACs) and electric water heaters (EWHs), have become important demand response resources in the power system. However, due to the impact of various factors on users’ response behavior, it has been difficult for power grid operators to accurately evaluate the response potential under complex factor relationships to derive optimal incentive strategy. Therefore, it cannot achieve a win-win economic benefit between the grid and users. In this paper, a method combining Analytic Network Process (ANP) and Fuzzy logical inference is proposed to predict the user’s willingness firstly by taking residential AC load as an example. The weight of each factor affecting users’ willingness is analyzed, and main factors are selected as inputs of fuzzy logic inference to derive the willingness of the resident to actively regulate the AC. Then, this method is applied in evaluating the response potential of certain residential area in Beijing according to the survey. By further considering users’ house size and the sacrificed comfort temperature under the incentive strategy, the power potential curve of the AC load under different incentives is obtained by using the first-order equivalent thermal parameter (ETP) model and the regulation willingness. Finally, with the objective of maximizing the consumption of the photovoltaic (PV) power, the optimal operation is achieved through the coordinated regulation of residential ACs and EWHs based on the potential curve, and the corresponding optimal incentive strategy for the flexible temperature-control loads is obtained. Simulation results show that the optimal incentive strategy proposed not only increases the PV consumption ratio to 98.35% with an increase of 24.71%, but also maximizes the economic benefits of both sides of the power grid and users. This method of deriving incentive strategy can be used as a reference for grid companies to formulate the incentive strategy to realize optimal operation, such as the maximum new energy consumption. Full article

Photovoltaic (PV) arrays are gaining popularity for electricity generation due to their simple and green energy production. However, the power transfer efficiency of PV varies depending on the load’s electrical properties, the PV panels’ temperature, and the insolation conditions. Maximum Power Point Tracking (MPPT) is a method formulated as an optimization problem that adjusts the PV output voltage to deliver maximum power to the load based on these criteria (maximum power in the P-V curve). MPPT is a convex optimization problem when the Sun’s rays completely cover the PV surface (full insolation). Several power points are formed in the Power vs. Voltage (P-V) curve, rendering MPPT as a non-convex problem during incomplete insolation (partial shadowing) on the PV surface due to barriers such as passing clouds or trees in the path of the Sun and the PV’s surface. Unfortunately, mathematical programming techniques, such as gradient ascent and momentum, are not good optimization candidate algorithms because they cannot distinguish between the local and global maximum of a function (the case of non-convex problems). On the other hand, metaheuristic algorithms have better search space exploration capability, making it easier to discern the P-V curve’s local and global power peaks. However, due to their pseudorandom search space exploration (random with some intuition), there is plenty of room for improving their performance. In this work, we elaborate on the Advanced Limited Search Strategy (ALSS), a technique we proposed in one of our previous works on MPPT. We prove its universal usefulness by applying it to other MPPT algorithms to enhance their performance. The ALSS first finds the direction where it is most probable to discover the MPP using the finite difference between two candidate duty cycles and then computes a duty cycle between two bounds designated by the previous direction. After that, the resulting duty cycle is further updated according to the metaheuristic update equation. Therefore, the single solution update is another advantage of ALSS that further improves the computational cost of the MPPT algorithms. Full article

The Korean government has declared the goal of net-zero-carbon emissions with a focus on renewable energy expansion. However, a high proportion of baseload generators and an increasing proportion of variable renewable energy (VRE) may cause problems in the power system operation owing to the low cycling capability of baseload generators and variability of VRE. To maintain system reliability, the government is planning to construct pumped-storage hydropower (PSH) plants, which can provide flexibility to the system. This study evaluated the operating cost savings obtained by different types of PSH: the adjustable-speed PSH (AS-PSH) and fixed-speed PSH (FS-PSH), based on the duck-curve phenomenon and the increase in spinning reserve requirement. In this study, the reserve-constrained unit commitment was formulated using a mixed-integer-programming considering the operational characteristics of AS-PSH and conventional generators. To consider the duck-shaped net-load environment, the projected VRE output data were calculated through physical models of wind turbines and photovoltaic modules. The operating costs for the non-PSH, FS-PSH, and AS-PSH construction scenarios were KRW 43,129.38, 40,038.44, and 34,030.46, respectively. The main factor that derived this difference was determined to be the primary reserve of AS-PSH’s pumping mode. Full article

The focus of this research is to design a ground-mounted photovoltaic system at optimal tilt angle and interrow space to meet high demand of electrical energy. The Department of Electrical Engineering and Technology, GC University Faisalabad has been considered to perform the simulation test. This study is conducted using Meteonorm software for solar resource assessment. Furthermore, HelioScope software is used for modeling of a ground-mounted photovoltaic system, study of PV system’s performance in terms of annual generation, system losses and performance ratio and analysis of photovoltaic module’s performance, current-voltage and power-voltage curves for different irradiance levels. From SLD, it is seen that 11 strings are connected to each inverter and inverters output power are combined by using 20.0 A circuit interconnects. The performance of photovoltaic systems is impacted by tilt angle and interrow spacing. From simulation results of all cases, it is concluded that the PV system installed at 15° tilt angle with 4 feet interrow spacing are more efficient than the other installed PV systems, because total collector irradiance is maximum (1725.0 kWh/m²) as compared to other tilt angles. At 15° tilt angle, the annual production of photovoltaic system is 2.265 GWh and performance ratio of PV system is 82.0%. It is envisioned that this work will provide the guidance to energy system designers, planners and investors to formulate strategies for the installation of photovoltaic energy systems in Pakistan and all over the world. Full article

The identification of actual photovoltaic (PV) model parameters under real operating condition is a crucial step for PV engineering. An accurate and trusted model depends mainly on the accuracy of the model parameters. In this paper, an accurate and enhanced methodology is intended for PV module parameters extraction in outdoor conditions. The proposed methodology combines numerical methods and analytical formulations of the one diode model to derive the five unknown parameters in any operating condition of irradiance and temperature. First, the measured I-V curves at a random weather condition are translated to standard test conditions (i.e., G = 1000 W/m², T = 25 °C), using translation equations. The second step consists of using an optimization algorithm namely the moth flame algorithm (MFO) to find out the five parameters at standard test conditions. Analytical formulations, at a random irradiance and temperature, are then used to express the unknown parameters at any irradiance and temperature. The proposed approach is validated under outdoor conditions against measured I-V curves at different irradiances and temperatures. The validation has also been performed under dynamic operation where the measured maximum power point coordinates (MPP) are compared to the predicted maximum power points. The obtained results from the adopted hybrid methodology confirm the accuracy of the parameter extraction procedure. Full article

This paper discusses the simulation framework developed for an in-campus pilot micro-grid at MCAST, Malta, to enhance its efficiency and reliability. One year of real-time metered data were used to arrive at the load curves, categorize the loads as essential and non-essential ones, and decide the micro-grid domain within MCAST. The potential scenarios were modeled to observe the behavior of the present status of the micro-grid, with an increased photovoltaic (PV) generation capacity, by using an optimum battery storage system with a diesel generator of suitable capacity and finally integrating electric vehicles (EVs) to discuss the potential of vehicle to grid (V2G) operation modes. The existing building management system (BMS) of MCAST was interfaced within the micro-grid to introduce the geographic information system (GIS) and Building Information Modeling (BIM) for developing an intelligent 3D model of the micro-grid. The results of the simulation framework for various potential case scenarios were obtained in a MATLAB/Simulink environment to assess the performance of the micro-grid. Previously formulated key performance indices (KPIs) that describe the financial aspects of micro-grid operation and ecological benefits of the investigated micro-grid were evaluated. A sensitivity analysis of these KPIs shows encouraging results with the potential of cost-competitiveness. Full article