Pub Date : 2026-02-04DOI: 10.1016/j.epsr.2026.112811
Gederson Alvaro da Cruz , Sérgio Haffner , Mariana Resener
We propose a mixed-integer linear programming (MILP) model for assessing the reliability of active distribution networks under both normal and contingency conditions. The objective function combines system operation costs with reliability costs. The model integrates self-healing mechanisms, including topology adjustments, system restoration via microgrids, and tie lines. Additionally, a linear formulation is developed to represent reward-penalty schemes and assess reliability costs. To demonstrate the applicability of the proposed model, tests are conducted on a 12-node system and a 136-node system, the latter based on real data. The results illustrate that the model provides a reliable diagnosis of the network and supports operators in decision-making to enhance network performance under various operational scenarios. A sensitivity analysis considering different failure rates, distributed generation capacities, and reward–penalty scheme coefficients is also presented, providing insights into their impact on system reliability and operational performance.
{"title":"MILP model for reliability assessment of active distribution networks with microgrids","authors":"Gederson Alvaro da Cruz , Sérgio Haffner , Mariana Resener","doi":"10.1016/j.epsr.2026.112811","DOIUrl":"10.1016/j.epsr.2026.112811","url":null,"abstract":"<div><div>We propose a mixed-integer linear programming (MILP) model for assessing the reliability of active distribution networks under both normal and contingency conditions. The objective function combines system operation costs with reliability costs. The model integrates self-healing mechanisms, including topology adjustments, system restoration via microgrids, and tie lines. Additionally, a linear formulation is developed to represent reward-penalty schemes and assess reliability costs. To demonstrate the applicability of the proposed model, tests are conducted on a 12-node system and a 136-node system, the latter based on real data. The results illustrate that the model provides a reliable diagnosis of the network and supports operators in decision-making to enhance network performance under various operational scenarios. A sensitivity analysis considering different failure rates, distributed generation capacities, and reward–penalty scheme coefficients is also presented, providing insights into their impact on system reliability and operational performance.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112811"},"PeriodicalIF":4.2,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-04DOI: 10.1016/j.epsr.2026.112816
Ganesh Kumar Budumuru , Papia Ray , Manish Kumar Babu
Distributed generation sources such as wind and photovoltaic are being developed effectively to meet the current energy demands. If the Power Quality Disturbances (PQDs) are correctly identified and properly classified by both producers and consumers, these issues can be resolved. One of the most important aspects of Power Quality (PQ) problem handling is the automatic classification of PQDs. To achieve intelligent PQD classification, we present a particular kind of recurrent neural network (RNN) called Long Short-Term Memory (LSTM) network is made specially to learn long-term dependencies and solve sequence prediction tasks. This research implements the powerful Histogram of Oriented Gradient (HOG) technique for PQD feature extraction. To implement the proposed method, twenty different PQDs are generated in the MATLAB/SIMULINK environment. The HOG technique is then applied to extract key features such as amplitude, phase, energy, variance, entropy, and mean deviation. The HOG data is subsequently fed to LSTM model for classification of PQDs. The proposed model (HOG-LSTM) gives 99.69 % classification testing accuracy with less training time, and it is compared with other classification models. This model is validated with real time signals which are generated with laboratory hardware setup. The main advantages of the suggested method include quick detection, less computational time, and good classification efficiency.
{"title":"A novel cascade model for power quality detection and classification based on histogram of oriented gradient and long short-term memory network","authors":"Ganesh Kumar Budumuru , Papia Ray , Manish Kumar Babu","doi":"10.1016/j.epsr.2026.112816","DOIUrl":"10.1016/j.epsr.2026.112816","url":null,"abstract":"<div><div>Distributed generation sources such as wind and photovoltaic are being developed effectively to meet the current energy demands. If the Power Quality Disturbances (PQDs) are correctly identified and properly classified by both producers and consumers, these issues can be resolved. One of the most important aspects of Power Quality (PQ) problem handling is the automatic classification of PQDs. To achieve intelligent PQD classification, we present a particular kind of recurrent neural network (RNN) called Long Short-Term Memory (LSTM) network is made specially to learn long-term dependencies and solve sequence prediction tasks. This research implements the powerful Histogram of Oriented Gradient (HOG) technique for PQD feature extraction. To implement the proposed method, twenty different PQDs are generated in the MATLAB/SIMULINK environment. The HOG technique is then applied to extract key features such as amplitude, phase, energy, variance, entropy, and mean deviation. The HOG data is subsequently fed to LSTM model for classification of PQDs. The proposed model (HOG-LSTM) gives 99.69 % classification testing accuracy with less training time, and it is compared with other classification models. This model is validated with real time signals which are generated with laboratory hardware setup. The main advantages of the suggested method include quick detection, less computational time, and good classification efficiency.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112816"},"PeriodicalIF":4.2,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-04DOI: 10.1016/j.epsr.2026.112806
Luís H.T. Bandória , Ricardo Torquato , Madson C. Almeida , Renato M. Monaro
Differentiating the operational condition of distribution transformers is essential for their effective management. Traditional approaches based on overloading or equivalent aging capture only limited aspects of operation and offer restricted support for management decisions. This paper proposes a performance index that prioritizes distribution transformers by quantifying key aspects of equipment operation through comprehensive feature engineering applied to data commonly available to utilities. Using information such as rated capacity, active power demand, monthly energy, and consumer connections, 13 features were derived and incorporated into a two-step learning framework based on dimensionality reduction that produces a single valued and interpretable performance index. Results show that the index effectively separates units according to their utilization patterns and provides a practical decision support tool for targeted management interventions.
{"title":"Towards a performance index for distribution transformer prioritization and management","authors":"Luís H.T. Bandória , Ricardo Torquato , Madson C. Almeida , Renato M. Monaro","doi":"10.1016/j.epsr.2026.112806","DOIUrl":"10.1016/j.epsr.2026.112806","url":null,"abstract":"<div><div>Differentiating the operational condition of distribution transformers is essential for their effective management. Traditional approaches based on overloading or equivalent aging capture only limited aspects of operation and offer restricted support for management decisions. This paper proposes a performance index that prioritizes distribution transformers by quantifying key aspects of equipment operation through comprehensive feature engineering applied to data commonly available to utilities. Using information such as rated capacity, active power demand, monthly energy, and consumer connections, 13 features were derived and incorporated into a two-step learning framework based on dimensionality reduction that produces a single valued and interpretable performance index. Results show that the index effectively separates units according to their utilization patterns and provides a practical decision support tool for targeted management interventions.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112806"},"PeriodicalIF":4.2,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
On‑line dissolved gas analysis (DGA) of transformer oil is essential for diagnosing incipient insulation faults and ensuring the operational reliability of power transformers. However, long‑term operation leads to sensor drift and reduced sensitivity, which eventually result in inadequate calibration and degraded diagnostic accuracy. To address these issues, this study proposes a comprehensive field‑deployable calibration platform based on precisely prepared standard oil samples. An integrated preparation‑calibration platform is developed, incorporating controlled single‑component gas injection, constant‑temperature and constant‑pressure oil‑gas equilibrium, automated multi‑level concentration switching, and pipeline self‑cleaning.
The proposed platform enables accurate preparation of dissolved‑gas reference samples with high linearity (R² ≥ 0.99). A complete on-site calibration workflow is established and validated on a 220 kV hydropower transformer. Based on comparative calibration using the prepared standard oil samples, results show that the tested commercial on‑line DGA device exhibits substantial deviations, indicating systematic accuracy limitations under field operating conditions. Statistical analysis including standard deviation, coefficient of variation, and error‑distribution plots further confirms poor repeatability of the device. A qualitative post‑calibration improvement trend is also provided to illustrate the expected correction behavior. The proposed methodology provides a full‑chain evaluation framework and a practical, standardized solution for field calibration of on‑line DGA systems, forming a methodological basis for large‑scale deployment of condition-based maintenance (CBM) strategies in smart‑grid applications.
{"title":"A field‑deployable calibration platform for on‑line dissolved gas monitoring systems in power transformer oil","authors":"Yue Ma, Xiaofeng Ma, Ronghui Wang, Jianhua Li, Kai Guan, Xiaofeng Chen","doi":"10.1016/j.epsr.2026.112782","DOIUrl":"10.1016/j.epsr.2026.112782","url":null,"abstract":"<div><div>On‑line dissolved gas analysis (DGA) of transformer oil is essential for diagnosing incipient insulation faults and ensuring the operational reliability of power transformers. However, long‑term operation leads to sensor drift and reduced sensitivity, which eventually result in inadequate calibration and degraded diagnostic accuracy. To address these issues, this study proposes a comprehensive field‑deployable calibration platform based on precisely prepared standard oil samples. An integrated preparation‑calibration platform is developed, incorporating controlled single‑component gas injection, constant‑temperature and constant‑pressure oil‑gas equilibrium, automated multi‑level concentration switching, and pipeline self‑cleaning.</div><div>The proposed platform enables accurate preparation of dissolved‑gas reference samples with high linearity (R² ≥ 0.99). A complete on-site calibration workflow is established and validated on a 220 kV hydropower transformer. Based on comparative calibration using the prepared standard oil samples, results show that the tested commercial on‑line DGA device exhibits substantial deviations, indicating systematic accuracy limitations under field operating conditions. Statistical analysis including standard deviation, coefficient of variation, and error‑distribution plots further confirms poor repeatability of the device. A qualitative post‑calibration improvement trend is also provided to illustrate the expected correction behavior. The proposed methodology provides a full‑chain evaluation framework and a practical, standardized solution for field calibration of on‑line DGA systems, forming a methodological basis for large‑scale deployment of condition-based maintenance (CBM) strategies in smart‑grid applications.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112782"},"PeriodicalIF":4.2,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-03DOI: 10.1016/j.epsr.2026.112809
Charalambos A. Charalambous , Andreas Dimitriou , Nikolaos Kokkinos , Nikolaos Kioupis , Theagenis Manolis
Lightning strikes pose significant challenges to buried pipelines, especially with modern coating layers, as recent incidents illustrate. While factors like buried depth and distance from lightning sources influence the impact on pipelines, precise predictions are complex due to interacting variables. Understanding the mechanisms behind lightning-induced failures remains limited. Increased driving voltage and available current during lightning strikes elevate the risk of pipeline damage, challenging assumptions about coating defense. Recent incidents on the Hellenic Gas Transmission System, involving severe pipeline damage from lightning strikes, highlight the urgent need for improved risk assessment and mitigation. This work explores the mechanisms of lightning-induced damage, the role of corrosion and coating defects, and effective protection strategies. Emphasis is placed on comprehensive cathodic protection upgrades, continuous monitoring, and key mitigation technologies such as earthing and spark gap devices.
{"title":"Lightning strikes and buried gas pipelines: Mechanisms, risk assessment, mitigation, and case insights from the hellenic gas transmission system","authors":"Charalambos A. Charalambous , Andreas Dimitriou , Nikolaos Kokkinos , Nikolaos Kioupis , Theagenis Manolis","doi":"10.1016/j.epsr.2026.112809","DOIUrl":"10.1016/j.epsr.2026.112809","url":null,"abstract":"<div><div>Lightning strikes pose significant challenges to buried pipelines, especially with modern coating layers, as recent incidents illustrate. While factors like buried depth and distance from lightning sources influence the impact on pipelines, precise predictions are complex due to interacting variables. Understanding the mechanisms behind lightning-induced failures remains limited. Increased driving voltage and available current during lightning strikes elevate the risk of pipeline damage, challenging assumptions about coating defense. Recent incidents on the Hellenic Gas Transmission System, involving severe pipeline damage from lightning strikes, highlight the urgent need for improved risk assessment and mitigation. This work explores the mechanisms of lightning-induced damage, the role of corrosion and coating defects, and effective protection strategies. Emphasis is placed on comprehensive cathodic protection upgrades, continuous monitoring, and key mitigation technologies such as earthing and spark gap devices.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112809"},"PeriodicalIF":4.2,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-02DOI: 10.1016/j.epsr.2026.112783
N. Çetin Acar , S. Börekci
In this study, a novel maximum power point tracking (MPPT) technique is proposed for photovoltaic (PV) systems using an interleaved boost converter operated in critical conduction mode. Unlike conventional methods, the maximum power point (MPP) is detected by analyzing the instantaneous inductor current and PV voltage within a single switching period, enabling precise determination of MPP in the order of microseconds. The interleaved boost topology significantly reduces input current ripple, thereby minimizing power oscillations around the MPP and improving overall system efficiency. The developed algorithm continuously samples inductor current and PV voltage, calculates instantaneous power, and updates the switching frequency accordingly under varying irradiance and temperature conditions. Experimental validation with a 40 W PV panel demonstrates that the proposed method achieves an accuracy of up to 99.5% when the PV system operates above 65% of nominal power. Compared to the conventional variable resistive method, the proposed MPPT technique provides faster response, higher accuracy, and lower power fluctuation, offering an efficient solution for next-generation PV energy conversion systems.
{"title":"A direct MPPT method using inductor-based I–V measurement in a critical conduction mode (CrCM) interleaved boost converter","authors":"N. Çetin Acar , S. Börekci","doi":"10.1016/j.epsr.2026.112783","DOIUrl":"10.1016/j.epsr.2026.112783","url":null,"abstract":"<div><div>In this study, a novel maximum power point tracking (MPPT) technique is proposed for photovoltaic (PV) systems using an interleaved boost converter operated in critical conduction mode. Unlike conventional methods, the maximum power point (MPP) is detected by analyzing the instantaneous inductor current and PV voltage within a single switching period, enabling precise determination of MPP in the order of microseconds. The interleaved boost topology significantly reduces input current ripple, thereby minimizing power oscillations around the MPP and improving overall system efficiency. The developed algorithm continuously samples inductor current and PV voltage, calculates instantaneous power, and updates the switching frequency accordingly under varying irradiance and temperature conditions. Experimental validation with a 40 W PV panel demonstrates that the proposed method achieves an accuracy of up to 99.5% when the PV system operates above 65% of nominal power. Compared to the conventional variable resistive method, the proposed MPPT technique provides faster response, higher accuracy, and lower power fluctuation, offering an efficient solution for next-generation PV energy conversion systems.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112783"},"PeriodicalIF":4.2,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-02DOI: 10.1016/j.epsr.2026.112789
Chen Wang , Ying Wang , Yulong Jin , Tao Zheng , Enrico Zio , Kaifeng Zhang
Integrated energy systems (IESs) inherently involve multiple energy carriers and operational objectives, making the exploration of the Pareto frontier a central task in their optimal operation. However, existing multi-objective optimization methods for IESs usually require the double-layer iterative computation to obtain Pareto optimal solutions, where the upper layer determines the weights of the objectives and the lower layer performs optimization computations for the determined weights, resulting in a considerable computational burden. To address this challenge, this paper proposes a novel balance-supervised reinforcement learning framework that accelerates the Pareto optimization of IESs by integrating dynamic weights and agent learning. First, a novel balance supervisor agent is proposed to directly endow weights with learnable uncertainty via the Bayesian-based balance algorithm. Then, the balance-supervised framework can transform the double-layer computations into single-layer computations and enable the objectives to adapt to a theoretically infinite range of weight combinations, significantly reducing the computational burden and expanding the exploration of Pareto frontier. In addition, the Bayesian uncertainty layer is proposed to enhance the model’s adaptability to uncertainty. Case study shows that, compared to the state of the art methods, the proposed framework can effectively achieve optimal performance of the IESs in multi-objective scenarios.
{"title":"Accelerating pareto optimization of integrated energy systems using balance-supervised reinforcement learning","authors":"Chen Wang , Ying Wang , Yulong Jin , Tao Zheng , Enrico Zio , Kaifeng Zhang","doi":"10.1016/j.epsr.2026.112789","DOIUrl":"10.1016/j.epsr.2026.112789","url":null,"abstract":"<div><div>Integrated energy systems (IESs) inherently involve multiple energy carriers and operational objectives, making the exploration of the Pareto frontier a central task in their optimal operation. However, existing multi-objective optimization methods for IESs usually require the double-layer iterative computation to obtain Pareto optimal solutions, where the upper layer determines the weights of the objectives and the lower layer performs optimization computations for the determined weights, resulting in a considerable computational burden. To address this challenge, this paper proposes a novel balance-supervised reinforcement learning framework that accelerates the Pareto optimization of IESs by integrating dynamic weights and agent learning. First, a novel balance supervisor agent is proposed to directly endow weights with learnable uncertainty via the Bayesian-based balance algorithm. Then, the balance-supervised framework can transform the double-layer computations into single-layer computations and enable the objectives to adapt to a theoretically infinite range of weight combinations, significantly reducing the computational burden and expanding the exploration of Pareto frontier. In addition, the Bayesian uncertainty layer is proposed to enhance the model’s adaptability to uncertainty. Case study shows that, compared to the state of the art methods, the proposed framework can effectively achieve optimal performance of the IESs in multi-objective scenarios.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112789"},"PeriodicalIF":4.2,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.epsr.2026.112791
Biao Feng, Li Zhang, Qi Han
Virtual synchronous generator (VSG) control has become one of the core control strategies for grid-connected converters by providing virtual inertia and damping that can effectively improve system stability. However, in low resistance-to-reactance ratio (R/X) grids, their power loops are prone to synchronous frequency resonance (SFR). This paper establishes a small-signal frequency-domain model of the VSG power loops to reveal the mechanism of SFR, and uses dynamic relative gain array (DRGA) to quantify the exacerbating effect of resonance on power coupling in low R/X systems, elucidating the influence of R/X on resonance peak values and stability margins. Furthermore, an adaptive dynamic virtual resistor (ADVR) method based on online impedance identification (OII) is proposed: this method suppresses resonance through dynamic virtual resistors combining OII, and adaptively adjusts the virtual resistors to accelerate resonance decay while avoiding exacerbating power coupling, it effectively addresses the issue of resonance suppression failure caused by changes in line impedance parameters. This article presents an electromagnetic transient simulation model developed in Matlab/Simulink, validating theoretical analysis and evaluating the effectiveness of the proposed method for enhanced accuracy.
{"title":"Adaptive dynamic virtual resistor method for suppressing synchronous frequency resonance","authors":"Biao Feng, Li Zhang, Qi Han","doi":"10.1016/j.epsr.2026.112791","DOIUrl":"10.1016/j.epsr.2026.112791","url":null,"abstract":"<div><div>Virtual synchronous generator (VSG) control has become one of the core control strategies for grid-connected converters by providing virtual inertia and damping that can effectively improve system stability. However, in low resistance-to-reactance ratio (<em>R</em>/<em>X</em>) grids, their power loops are prone to synchronous frequency resonance (SFR). This paper establishes a small-signal frequency-domain model of the VSG power loops to reveal the mechanism of SFR, and uses dynamic relative gain array (DRGA) to quantify the exacerbating effect of resonance on power coupling in low <em>R/X</em> systems, elucidating the influence of <em>R</em>/<em>X</em> on resonance peak values and stability margins. Furthermore, an adaptive dynamic virtual resistor (ADVR) method based on online impedance identification (OII) is proposed: this method suppresses resonance through dynamic virtual resistors combining OII, and adaptively adjusts the virtual resistors to accelerate resonance decay while avoiding exacerbating power coupling, it effectively addresses the issue of resonance suppression failure caused by changes in line impedance parameters. This article presents an electromagnetic transient simulation model developed in Matlab/Simulink, validating theoretical analysis and evaluating the effectiveness of the proposed method for enhanced accuracy.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112791"},"PeriodicalIF":4.2,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.epsr.2026.112786
Syed Mahboob Ul Hassan
Integrating high levels of solar photovoltaic (PV) generation into combined heat and power (CHP) microgrids presents scheduling challenges due to forecast uncertainty and thermal coupling between electric and heating demands. Traditional point-forecast scheduling is unreliable under forecast errors and inconsistent with joint electric-heating behavior, while scenario-based stochastic methods are computationally expensive for day-ahead operations. This study proposes a dependence-aware deterministic unit commitment and economic dispatch (UC/ED) framework that addresses uncertainty in PV output and coupled electric-heating demands using quantile regression forecasting. The method produces day-ahead quantile forecasts, then uses a rolling historical window to estimate empirical joint quantile-occurrence distributions for electric and heating loads and marginal distributions for PV. These distributions construct hourly probability-weighted day-ahead profiles that serve as deterministic inputs to a single mixed-integer UC/ED optimization. Five scheduling strategies are compared across different rolling window lengths (7-, 12-, 17-, and 30-day) versus median-only dispatch. The 30-day window achieves optimal performance with operating costs of $251.12, representing a 16.07% reduction from median-only scheduling ($299.22). Savings derive primarily from reduced CHP fuel consumption and improved battery energy storage system efficiency.
{"title":"Dependence-aware day-ahead unit commitment and economic dispatch for a CHP-centered microgrid","authors":"Syed Mahboob Ul Hassan","doi":"10.1016/j.epsr.2026.112786","DOIUrl":"10.1016/j.epsr.2026.112786","url":null,"abstract":"<div><div>Integrating high levels of solar photovoltaic (PV) generation into combined heat and power (CHP) microgrids presents scheduling challenges due to forecast uncertainty and thermal coupling between electric and heating demands. Traditional point-forecast scheduling is unreliable under forecast errors and inconsistent with joint electric-heating behavior, while scenario-based stochastic methods are computationally expensive for day-ahead operations. This study proposes a dependence-aware deterministic unit commitment and economic dispatch (UC/ED) framework that addresses uncertainty in PV output and coupled electric-heating demands using quantile regression forecasting. The method produces day-ahead quantile forecasts, then uses a rolling historical window to estimate empirical joint quantile-occurrence distributions for electric and heating loads and marginal distributions for PV. These distributions construct hourly probability-weighted day-ahead profiles that serve as deterministic inputs to a single mixed-integer UC/ED optimization. Five scheduling strategies are compared across different rolling window lengths (7-, 12-, 17-, and 30-day) versus median-only dispatch. The 30-day window achieves optimal performance with operating costs of $251.12, representing a 16.07% reduction from median-only scheduling ($299.22). Savings derive primarily from reduced CHP fuel consumption and improved battery energy storage system efficiency.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112786"},"PeriodicalIF":4.2,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-30DOI: 10.1016/j.epsr.2026.112779
Kin Cheong Sou , Gabriel Malmer , Lovisa Thorin , Olof Samuelsson
Network reconfiguration can significantly increase the hosting capacity (HC) for distributed generation (DG) in radially operated systems, thereby reducing the need for costly infrastructure upgrades. However, when the objective is DG maximization, jointly optimizing topology and power dispatch remains computationally challenging. Existing approaches often rely on relaxations or approximations, yet we provide counterexamples showing that interior point methods, linearized DistFlow and second-order cone relaxations all yield erroneous results. To overcome this, we propose a solution framework based on the exact DistFlow equations, formulated as a bilinear program and solved using spatial branch-and-bound (SBB). Numerical studies on standard benchmarks and a 533-bus real-world system demonstrate that our proposed method reliably performs reconfiguration and dispatch within time frames compatible with real-time operation.
{"title":"Power distribution network reconfiguration for distributed generation maximization","authors":"Kin Cheong Sou , Gabriel Malmer , Lovisa Thorin , Olof Samuelsson","doi":"10.1016/j.epsr.2026.112779","DOIUrl":"10.1016/j.epsr.2026.112779","url":null,"abstract":"<div><div>Network reconfiguration can significantly increase the hosting capacity (HC) for distributed generation (DG) in radially operated systems, thereby reducing the need for costly infrastructure upgrades. However, when the objective is DG maximization, jointly optimizing topology and power dispatch remains computationally challenging. Existing approaches often rely on relaxations or approximations, yet we provide counterexamples showing that interior point methods, linearized DistFlow and second-order cone relaxations all yield erroneous results. To overcome this, we propose a solution framework based on the exact DistFlow equations, formulated as a bilinear program and solved using spatial branch-and-bound (SBB). Numerical studies on standard benchmarks and a 533-bus real-world system demonstrate that our proposed method reliably performs reconfiguration and dispatch within time frames compatible with real-time operation.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112779"},"PeriodicalIF":4.2,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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