Toward Smart and Sustainable Energy Systems Enabled by Artificial Intelligence, Optimization, and Resilience

Topic Information

Dear Colleagues,

The global energy sector is undergoing a profound transformation driven by the urgent need for decarbonization, digitalization, and sustainability. The increasing penetration of renewable energy resources, widespread adoption of electrified transportation and heating, and integration of green hydrogen and storage technologies have significantly increased the complexity of modern energy systems. This complexity creates new challenges for system stability, demand flexibility, and operational resilience, especially under the uncertainties brought about by climate change and extreme events. Moreover, the sustainable utilization and management of natural resources, such as critical materials for renewable technologies, are becoming central to ensuring a circular energy economy.

To address these challenges, artificial intelligence (AI) and advanced optimization techniques have emerged as powerful tools to enable intelligent forecasting, adaptive decision-making, and autonomous system operation. Meanwhile, new concepts of resilience-oriented planning and control are becoming essential for ensuring that energy systems can withstand, adapt to, and recover from diverse disruptions. By combining AI, optimization, and resilience-oriented strategies, researchers and practitioners can pave the way toward smart and sustainable energy systems that are efficient, reliable, and aligned with global carbon neutrality targets.

This Topic aims to bring together innovative research and real-world applications that explore how AI and optimization techniques can enhance the resilience and sustainability of energy systems, covering the electricity, heat, hydrogen, and transport sectors.

Topics of interest include, but are not limited to, the following:

• Artificial intelligence for energy forecasting, operation, and planning;
• Advanced optimization and quantum-inspired methods for multi-energy systems; Resilience-oriented operation and planning under uncertainty;
• Demand response and flexibility in electricity, heat, hydrogen, and transport; Integration of renewable and distributed energy resources;
• Cross-sectoral coupling of electricity, heat, hydrogen, and transport;
• Digitalization, IoT, and digital twins for smart energy infrastructures; Applications and case studies in smart grids, microgrids, and energy hubs;
• Sustainable management and utilization of natural and energy resources.

Dr. Zhengmao Li
Dr. Haixiang Zang
Dr. Yanli Liu
Dr. Yunyun Xie
Dr. Yingjun Wu
Dr. Om Hari Gupta
Dr. Changbin Hu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Automation automation	2.9	4.5	2020	24.8 Days	CHF 1200	Submit
Electronics electronics	2.9	7.0	2012	14.8 Days	CHF 2400	Submit
Energies energies	3.9	8.3	2008	16.7 Days	CHF 2600	Submit
Processes processes	3.4	5.7	2013	14.7 Days	CHF 2400	Submit
Resources resources	4.3	7.3	2012	20.3 Days	CHF 1800	Submit
Sustainability sustainability	4.1	8.9	2009	16.9 Days	CHF 2400	Submit

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Published Papers (10 papers)

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44 pages, 7181 KB  

Open AccessArticle

Collaborative Scheduling Optimization of Logistics–Multi-Energy Coupled Port Shore-to-Ship Power System Under Demand Response Incentives

by Yuncai Tan, Leping Sun, Xianlin Chen, Xiaogui Chen, Tingzhe Pan, Yulin Gong and Zhongwei Sun

Energies 2026, 19(18), 4419; https://doi.org/10.3390/en19184419 - 18 Sep 2026

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Abstract

Traditional ports adopt separate scheduling modes for logistics and multi-energy subsystems without deep bidirectional coupling, which leads to low demand response (DR) participation, severe mismatch between the logistics power demand and multi-energy supply, and failure to fully exploit the thermal flexibility of port [...] Read more.

Traditional ports adopt separate scheduling modes for logistics and multi-energy subsystems without deep bidirectional coupling, which leads to low demand response (DR) participation, severe mismatch between the logistics power demand and multi-energy supply, and failure to fully exploit the thermal flexibility of port facilities. Single-link logistics optimization cannot generate complete time-varying load curves, making it impossible to coordinate vessel operation efficiency with port economic operation. To tackle the above limitations, this paper proposes HTGA-AFADMM, namely a hybrid topology genetic algorithm associated asynchronous fuzzy alternating direction method of multipliers (ADMM), as an integrated two-layer collaborative solver. HTGA-AFADMM presents four progressive core innovations. First, it establishes a DR-driven coupled architecture with shore power (SPS) as the core hub, building bidirectional information interaction channels to guide proactive logistics load shifting via thermal flexibility. This breaks passive energy matching under separate scheduling. Second, it embeds a customized hybrid topology genetic solver to solve the NP-hard five-stage flexible flow shop scheduling problem, coordinating vessels, SPS, and all handling equipment to strengthen constraint adaptability and convergence performance. Third, it constructs a heterogeneous-unit oriented distributed robust framework, eliminating idle waiting via asynchronous iteration and quantifying uncertainties with fuzzy membership factors. Fourth, it realizes nested closed-loop iteration between two layers, which takes logistics power curves as coupling signals and iteratively updates consensus variables to obtain optimal scheduling schemes. The proposed HTGA-AFADMM collaborative framework effectively coordinates port logistics scheduling and multi-energy optimal dispatch. It achieves satisfactory economic performance while suppressing power-balance deviations under communication delay and load uncertainty and exhibits good convergence and adaptability for practical port cross-domain scheduling scenarios. Full article

(This article belongs to the Topic Toward Smart and Sustainable Energy Systems Enabled by Artificial Intelligence, Optimization, and Resilience)

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33 pages, 8442 KB  

Open AccessArticle

Decision-Focused Learning-Based Optimization for Renewable Imbalance Settlement and Flexible Resource Dispatch

by Hong Zhang, Zhenjiang Shi, Shiyu Liu, Rui Min, Bo Ning, Mu Li, Haochen Li, Yu Xin and Zhongfu Tan

Energies 2026, 19(17), 3972; https://doi.org/10.3390/en19173972 - 24 Aug 2026

Viewed by 234

Abstract

High renewable penetration makes imbalance settlement inseparable from the physical decisions governing reserve procurement and flexibility activation. This paper develops a decision-focused learning-based optimization framework that trains renewable-deviation and flexible-resource deliverability representations through downstream dispatch, reliability, and settlement consequences. The mathematical contribution is [...] Read more.

High renewable penetration makes imbalance settlement inseparable from the physical decisions governing reserve procurement and flexibility activation. This paper develops a decision-focused learning-based optimization framework that trains renewable-deviation and flexible-resource deliverability representations through downstream dispatch, reliability, and settlement consequences. The mathematical contribution is a settlement-aware learning objective that couples learned uncertainty, resource-time credible-capacity certification, network-constrained multi-stage dispatch, and counterfactual marginal-contribution allocation while retaining an exact revenue-adequacy identity. The 33-node Zhangjiakou-type regional case uses 15 min intervals and comprises five resource classes: independent storage, data-center flexibility, industrial adjustable load, commercial demand response, and electric-vehicle aggregation. Relative to a fixed-ratio reserve rule, the proposed method lowers the regional balancing cost from 950 to 618 thousand USD (34.9%), achieves 97.8% renewable accommodation, limits the shortage probability to 0.7%, and attains a settlement-fairness index of 0.92. The framework solves a 500-asset instance in 118 s. External validation uses 4027 half-hour observations from the 2025 Elexon/BMRS market, including measured wind and solar output, day-ahead forecasts, load, imbalance prices, and procured-reserve prices. On the 1487-interval December test set, the proposed model reduces the replay cost from 2953.3 to 2598.2 thousand GBP (12.0%), decreases the shortage-interval frequency from 4.64% to 1.28%, and reaches 99.74% renewable accommodation. Comparisons with forecast-then-optimize, Wasserstein distributionally robust optimization, off-policy reinforcement learning, and graph-based behavioral cloning establish that the improvement comes from jointly learning which uncertainty matters for dispatch and which flexible capacity is deliverable. Full article

(This article belongs to the Topic Toward Smart and Sustainable Energy Systems Enabled by Artificial Intelligence, Optimization, and Resilience)

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33 pages, 2732 KB  

Open AccessArticle

AC-Screened Robust Restoration of Weather-Stressed PV–Storage–EV Distribution Networks via Graph Learning and Multi-Agent Control

by Jicheng Wei, Sipei Sun, Liang Zhang, Yu Wang, Liang Feng and Xueshen Zhao

Energies 2026, 19(17), 3943; https://doi.org/10.3390/en19173943 - 22 Aug 2026

Viewed by 275

Abstract

Extreme weather couples spatially correlated component damage with photovoltaic (PV) derating, changing electric-vehicle (EV) demand, repair delay, and time-varying network topology. This paper develops a coordinated restoration architecture for multi-area feeders containing PV, battery energy storage, and charging stations. Its weather-facing layer constructs [...] Read more.

Extreme weather couples spatially correlated component damage with photovoltaic (PV) derating, changing electric-vehicle (EV) demand, repair delay, and time-varying network topology. This paper develops a coordinated restoration architecture for multi-area feeders containing PV, battery energy storage, and charging stations. Its weather-facing layer constructs joint outage-risk, renewable-error, charging-demand, and voltage-vulnerability descriptors. Those descriptors parameterize a two-stage mixed-integer second-order-cone program with a finite-support optimal-transport ambiguity set that remains well defined for discontinuous mixed-integer recourse. Regional actor–critic agents propose five-minute corrections around the hourly robust schedule; constrained projection, non-linear AC power-flow screening, emergency fallback, and margin-tightened re-optimization retain the authority to accept or reject each proposal. The evaluation uses public 33-node and 123-node feeders together with synthetic 240-node and 850-node stress networks. A pre-fit manifest allocates 240 records to training, 80 to validation, and 320 to final testing, while aggregate operational outcomes cover 50 random streams. Within this controlled benchmark, accepted schedules restore 93.6% of critical-load energy (SD 2.1 percentage points), serve 96.7% of total demand (SD 1.8 percentage points), retain 82–86% of EV service across hazard classes, and reduce the modeled 24 h objective by 25.8% relative to deterministic dispatch. The full pipeline records two to four candidate-stage voltage-limit events by hazard, and 4.9% of candidates undergo tightened re-optimization before accepted schedules reach zero reported AC voltage-limit violations. Between-method comparisons are descriptive and unpaired; the larger synthetic cases are structural stress tests rather than feeder-transfer tests. Full article

(This article belongs to the Topic Toward Smart and Sustainable Energy Systems Enabled by Artificial Intelligence, Optimization, and Resilience)

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35 pages, 766 KB  

Open AccessArticle

Safety-Constrained Deep Reinforcement Learning for Source–Load–Storage Coordinated Operation of Green Low-Carbon Data Centers

by Zheng Shi, Min Xu, Ziyu Fu, Jiaojiao Deng, Yingying Hu, Yonghao Zhang, Yao Wang and Liwei Ju

Energies 2026, 19(15), 3492; https://doi.org/10.3390/en19153492 - 24 Jul 2026

Viewed by 568

Abstract

Green low-carbon data centers operate as coupled cyber-energy systems whose dispatch must coordinate renewable generation, grid exchange, battery storage, cooling load, flexible computing workload, carbon-intensity signals, and reliability constraints. This study develops and evaluates a safety-constrained deep reinforcement learning framework for source–load–storage coordinated [...] Read more.

Green low-carbon data centers operate as coupled cyber-energy systems whose dispatch must coordinate renewable generation, grid exchange, battery storage, cooling load, flexible computing workload, carbon-intensity signals, and reliability constraints. This study develops and evaluates a safety-constrained deep reinforcement learning framework for source–load–storage coordinated operation of a grid-connected green data center. The operating problem is formulated as a constrained Markov decision process with state variables describing the IT load, deferrable workload backlog, renewable availability, electricity price, marginal carbon intensity, battery state of charge, server-room temperature, reserve margin, and calendar context. The action space covers grid import and export, renewable utilization, storage charge and discharge, workload shifting, and cooling control. The learning architecture combines a constrained actor–critic policy, adaptive Lagrangian safety critics, and a control barrier function (CBF)-based action shield that projects unsafe actions onto an explicitly defined operating set before plant execution. The shield is specified as a low-dimensional quadratic projection over state-dependent SOC, thermal, reserve, SLA, and grid-interface constraints, while cumulative risks are priced through Lagrangian safety budgets during policy training. The evaluation uses a controlled and auditable benchmark simulation with normalized public-data-compatible profiles, declared scenarios, random seeds, neural-network settings, and mechanism-matched baselines; it is not a telemetry-based verification or hardware certification of a deployed data center. Within this declared benchmark, the proposed safe DRL controller produces a simulated 13.1% emission reduction relative to the Rule-based controller, 95.8% renewable utilization, a normalized annual cost of 0.91, and fewer boundary contacts than the tested unconstrained, Lagrangian-only, and shield-only PPO variants. These percentages are simulator outputs relative to the stated benchmark and must not be interpreted as measured field savings. The results show how separating reward learning, cumulative safety pricing, and one-step engineering projection changes low-carbon dispatch within the specified model. Full article

(This article belongs to the Topic Toward Smart and Sustainable Energy Systems Enabled by Artificial Intelligence, Optimization, and Resilience)

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23 pages, 2493 KB  

Open AccessArticle

Physics-Informed Distributionally Robust Multi-Agent Reinforcement Learning for Coordinated New-Type Power System Operation

by Fei Liu, Outing Zhang, Jun Yin, Baomin Fang, Ruiming Fan, Zehua Xue and Zhongfu Tan

Energies 2026, 19(14), 3382; https://doi.org/10.3390/en19143382 - 17 Jul 2026

Viewed by 466

Abstract

High renewable penetration and large-scale green hydrogen production are accelerating the formation of the new-type power system (NTPS), in which electrical dispatch, electrolysis, hydrogen storage, fuel-cell reconversion, and flexible demand must be coordinated under nonlinear network physics and uncertain renewable, load, and hydrogen-demand [...] Read more.

High renewable penetration and large-scale green hydrogen production are accelerating the formation of the new-type power system (NTPS), in which electrical dispatch, electrolysis, hydrogen storage, fuel-cell reconversion, and flexible demand must be coordinated under nonlinear network physics and uncertain renewable, load, and hydrogen-demand trajectories. This study develops a physics-informed distributionally robust multi-agent reinforcement learning (PI-DRO-MARL) framework for coordinated NTPS operation with integrated electricity–hydrogen coupling. The operational objective is to minimize worst-case expected operating cost, including generation and grid-exchange cost, electrolysis and hydrogen-delivery cost, storage degradation, renewable curtailment, and load- or hydrogen-shedding penalties, while satisfying AC power-flow balance, voltage limits, line-loading limits, ramping limits, battery state-of-charge constraints, hydrogen-storage dynamics, and electrolysis/fuel-cell conversion constraints. The framework embeds physics-informed residuals and projection operators into a centralized-training decentralized-execution architecture; represents renewable, electrical-load, hydrogen-demand, and price uncertainty through statistically calibrated Wasserstein ambiguity sets; and trains agents with robust value estimation and feasibility-aware action correction. Validation is conducted on a modified IEEE 33-bus distribution network coupled with a 12-node hydrogen system, with additional scalability checks on modified IEEE 69-bus and IEEE 123-node reference systems. Across ten random seeds, the primary case shows an operating cost of USD 8850 with a 95% confidence interval of USD 8770–8940, a mean constraint-violation rate of 0.37%, and a shifted-scenario cost increase of 12.6%, outperforming deterministic optimization, stochastic programming, standard reinforcement learning (RL), proximal policy optimization (PPO), soft actor–critic (SAC), multi-agent deep deterministic policy gradient (MADDPG), constrained RL, safe RL, and robust RL baselines. Ablation, Wasserstein-radius, time-step, and stress-test analyses further show that distributional robustness, physics-informed projection, and multi-agent coordination provide distinct and complementary benefits. The results support PI-DRO-MARL as a simulation-validated architecture for real-time, uncertainty-aware NTPS dispatch, while field deployment still requires digital-twin calibration, hardware-in-the-loop testing, and site-specific operational validation. Full article

(This article belongs to the Topic Toward Smart and Sustainable Energy Systems Enabled by Artificial Intelligence, Optimization, and Resilience)

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25 pages, 2878 KB  

Open AccessArticle

Modeling Institutional Adaptation Under Large Language Model-Generated Strategic Behavior: A Synthetic Simulation with a Power-Grid Governance Interpretation

by Yun Huang, Guozhou Ke, Yuetao Du, Kangheng Feng and Yi Su

Energies 2026, 19(14), 3230; https://doi.org/10.3390/en19143230 - 8 Jul 2026

Viewed by 426

Abstract

Institutional governance has traditionally been analyzed under the assumption that the space of potential violations is finite, enumerable, and progressively constrainable through rule refinement and calibrated enforcement. The rapid integration of large language models into strategic and documentary decision-making challenges this premise by [...] Read more.

Institutional governance has traditionally been analyzed under the assumption that the space of potential violations is finite, enumerable, and progressively constrainable through rule refinement and calibrated enforcement. The rapid integration of large language models into strategic and documentary decision-making challenges this premise by transforming feasible deviation spaces from bounded sets into generative manifolds. This paper develops a formal simulation framework for examining institutional stability under algorithmically amplified strategic exploration. Regulatory rules are modeled as a constraint manifold characterized by effective dimensionality, while generative systems expand the behavioral strategy space through semantic recombination under detection and sanction constraints. Stability is defined through a minimum deterrence margin evaluated across the generatively reachable domain rather than only through historical violation catalogs. The study uses a 2014–2023 regulatory and violation corpus to initialize and calibrate the simulation and to conduct a limited historical hold-out check; the 250,000 LLM-generated scenarios are treated as synthetic stress-test proposals rather than observed violations. The computational specification reports the generator checkpoint, embedding model, decoding parameters, prompt templates, random seeds, filtering rules, and label partitions used in the simulation. The model introduces a dimensional dominance principle: systemic vulnerability may emerge in the simulation when the effective dimensionality of generative strategic search expands faster than the independent constraint dimensionality of the rule system. Under the reported baseline setting, the synthetic simulations show a pipeline-specific dimensional crossover, convergence limits in rule-consistency classification, and a nonlinear detection–sanction response surface. These outputs are interpreted as diagnostics of the stated computational pipeline, not as universal empirical laws about real institutions. The power-grid component is delimited accordingly: the paper does not simulate physical grid operation, power flow, dispatch, or relay-protection dynamics; it interprets the model at the documentary governance layer of power-grid enterprises, including procurement, construction supervision, maintenance records, dispatch-related documentation, customer-service reporting, and internal audit. The framework therefore provides a reproducible and cautiously delimited basis for analyzing text-mediated institutional resilience in the age of generative intelligence. Full article

(This article belongs to the Topic Toward Smart and Sustainable Energy Systems Enabled by Artificial Intelligence, Optimization, and Resilience)

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35 pages, 2972 KB  

Open AccessArticle

Multi-Agent Deep Reinforcement Learning for Dynamic Cost Overrun Mitigation in Smart Grid Construction Projects

by Yongjie Li, Xin Niu, Peng Li, Hua Liu, Ruoxi Dong, Nan Li and Zhongfu Tan

Energies 2026, 19(13), 3147; https://doi.org/10.3390/en19133147 - 2 Jul 2026

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Abstract

This study develops a cooperative multi-agent deep reinforcement learning (MARL) framework for simulation-based cost-overrun mitigation in smart grid construction projects under dynamic engineering uncertainty. Modern smart grid construction involves digital substations, renewable-energy-connected facilities, flexible transmission assets, intelligent monitoring systems, and geographically distributed contractors; [...] Read more.

This study develops a cooperative multi-agent deep reinforcement learning (MARL) framework for simulation-based cost-overrun mitigation in smart grid construction projects under dynamic engineering uncertainty. Modern smart grid construction involves digital substations, renewable-energy-connected facilities, flexible transmission assets, intelligent monitoring systems, and geographically distributed contractors; therefore, cost escalation is driven by sequential interactions among procurement, schedule execution, equipment deployment, supervision, weather, logistics, and price volatility. The proposed framework models procurement management, construction scheduling, equipment allocation, and supervision-control units as decentralized agents embedded in a calibrated construction simulation environment. The environment is parameterized from 42 smart grid construction projects in Henan Province, China and generates disturbance scenarios involving weather efficiency loss, transportation delay, market-price volatility, labor shortage, and supply-chain interruption. A hybrid DQN–PPO mechanism represents mixed decision structures: value-based DQN modules handle discrete managerial choices such as task acceleration, supplier switching, and procurement timing, whereas PPO modules adjust continuous resource-allocation and recovery-intensity decisions. A hierarchical reward function combines local departmental objectives with project-level penalties for cost overrun, schedule delay, idle resources, recovery expenditure, safety risk, and environmental impact. The experimental protocol uses 30 paired random seeds, nonparametric bootstrap confidence intervals, Holm-adjusted Wilcoxon signed-rank tests, and comparison with deterministic optimization, rolling-horizon MPC, stochastic/robust optimization, single-agent DRL, MAPPO, MADDPG/MATD3, QMIX, and HAPPO baselines. The proposed framework achieves a mean cost-overrun rate of 6.83% and a mean schedule deviation of 16.82 days, reducing cost overrun by 18.7% and schedule deviation by 21.4% relative to rule-based construction management under the reported disturbance settings. The calibrated simulation evidence establishes a statistically evaluated decision-support framework for coordinated construction cost control and provides an artifact-level reproducibility pathway through configuration files, random-seed lists, anonymized synthetic benchmarks, and aggregated logs. Full article

(This article belongs to the Topic Toward Smart and Sustainable Energy Systems Enabled by Artificial Intelligence, Optimization, and Resilience)

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29 pages, 2719 KB  

Open AccessArticle

Risk-Averse Coordinated Operation of Rural Multi-Energy Microgrids Considering Voltage Quality Control

by Jiangdong Liu, Jun Han, Jiajing Liu, Wenshu Ding, Liang Feng and Yuqing Qu

Energies 2026, 19(13), 3107; https://doi.org/10.3390/en19133107 - 30 Jun 2026

Viewed by 321

Abstract

Rural distribution networks increasingly face voltage quality challenges due to high penetration of distributed renewable energy, heterogeneous rural load behavior, and long radial feeder structures with limited voltage regulation capability. Photovoltaic generation variability and agricultural load fluctuations can lead to voltage rise, reverse [...] Read more.

Rural distribution networks increasingly face voltage quality challenges due to high penetration of distributed renewable energy, heterogeneous rural load behavior, and long radial feeder structures with limited voltage regulation capability. Photovoltaic generation variability and agricultural load fluctuations can lead to voltage rise, reverse power flow, and branch congestion, particularly in weak rural grids. Conventional deterministic voltage control approaches relying on tap changers and capacitor banks often struggle to maintain stable voltage profiles under stochastic operating conditions. This paper proposes a risk-aware coordinated operation framework for rural multi-energy microgrids that integrates stochastic scenario modeling, voltage state perception, and adaptive optimization-based control. Renewable generation uncertainty and rural load variability are represented through correlated scenario generation and Wasserstein-distance-based scenario reduction, where 100 raw joint photovoltaic-load trajectories are reduced to 20 representative scenarios after convergence and distributional-fidelity tests. A stochastic optimization model is developed to coordinate photovoltaic inverters, battery energy storage systems, demand-side flexibility, and reactive compensation devices while satisfying network power-flow, voltage-security, storage, and communication-delay-aware implementation constraints. To mitigate extreme voltage deviation events, the framework incorporates a Conditional Value-at-Risk formulation that penalizes tail-risk voltage violations and maintains voltages within a preferred operating band of $0.97$–$1.03$ p.u. Case studies on a modified IEEE 33-bus rural distribution system with $2.00$ MW of photovoltaic capacity and $2.50$ MWh of battery storage demonstrate consistent performance improvements across deterministic, risk-neutral stochastic, chance-constrained, and robust baselines. The proposed strategy reduces peak branch loading from $0.95$ in the deterministic benchmark to $0.72$, while the 95th percentile voltage deviation risk decreases from 0.0071 p.u.² to 0.0020 p.u.². Sensitivity, scenario-convergence, scalability, and seasonal representative-day analyses further confirm that the CVaR layer suppresses rare but severe voltage excursions without imposing excessive curtailment or computational burden. Full article

(This article belongs to the Topic Toward Smart and Sustainable Energy Systems Enabled by Artificial Intelligence, Optimization, and Resilience)

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26 pages, 2715 KB  

Open AccessArticle

Robust Representation of Solar Photovoltaic Variability via Wasserstein Distributional Modeling

by Andi Liu, Mengqi Liu, Tairan Li, Liang Feng and Chuanliang Xiao

Energies 2026, 19(11), 2665; https://doi.org/10.3390/en19112665 - 31 May 2026

Viewed by 526

Abstract

The increasing penetration of solar photovoltaic (PV) systems in modern distribution networks introduces significant variability, uncertainty, and spatiotemporal heterogeneity that challenge conventional data-driven modeling approaches. Existing methods predominantly rely on deterministic representations or simplified statistical summaries, which fail to capture the complex distributional [...] Read more.

The increasing penetration of solar photovoltaic (PV) systems in modern distribution networks introduces significant variability, uncertainty, and spatiotemporal heterogeneity that challenge conventional data-driven modeling approaches. Existing methods predominantly rely on deterministic representations or simplified statistical summaries, which fail to capture the complex distributional structure of PV generation and its interaction with energy storage and environmental factors. To address this limitation, this paper proposes a distributionally robust data representation framework that models PV outputs as ambiguity sets of probability distributions rather than single trajectories. Leveraging Wasserstein metrics, the framework constructs data-driven uncertainty sets that explicitly encode temporal variability, cross-resource correlations, and distributional perturbations arising from weather dynamics and measurement noise. A unified modeling architecture is developed to integrate multi-source data, including PV generation, storage state-of-charge, and meteorological variables, and to extract robust statistical descriptors through worst-case expectation formulations. In addition, a generation mechanism scenario is designed to produce representative and extreme trajectories from the ambiguity sets, enabling enhanced coverage of rare but critical operating conditions such as rapid irradiance fluctuations. Wasserstein ambiguity sets are not treated as a new theory in this work; they are used as a representation layer for PV, ESS, meteorological, and load trajectories before downstream analysis. Extensive case studies on a modified IEEE 123-bus distribution system demonstrate that the proposed approach improves out-of-sample performance, reduces scenario-level standard deviation relative to deterministic representation in repeated-run evaluation, and maintains more stable error behavior under controlled distribution shifts. Furthermore, the framework achieves up to 40–50% reduction in scenario requirements while preserving high approximation quality, indicating strong computational efficiency. The validation includes confidence intervals, variance and standard deviation definitions, ablation results, sensitivity checks, and repeatability details for the modified IEEE 123-bus test system. Full article

(This article belongs to the Topic Toward Smart and Sustainable Energy Systems Enabled by Artificial Intelligence, Optimization, and Resilience)

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39 pages, 9552 KB  

Open AccessArticle

Stochastic Optimal Scheduling of a Multi-Energy Complementary Base Considering Multi-Resource Reserve and Thermal Power Unit Doped with Ammonia-Concentrated Solar Power Coordination

by Yunyun Yun, Kaidi Li, Xiaomin Liu, Shuaibing Li, Kai Hou, Zeyu Liu and Junmin Zhu

Energies 2026, 19(10), 2384; https://doi.org/10.3390/en19102384 - 15 May 2026

Viewed by 549

Abstract

Aiming to mitigate renewable energy curtailment and curb the carbon emissions of traditional thermal power units (TPUs), this paper proposes a stochastic optimal scheduling of a multi-energy complementary base considering multi-resource reserve and TPU doped with ammonia-concentrated solar power coordination. Firstly, the proton [...] Read more.

Aiming to mitigate renewable energy curtailment and curb the carbon emissions of traditional thermal power units (TPUs), this paper proposes a stochastic optimal scheduling of a multi-energy complementary base considering multi-resource reserve and TPU doped with ammonia-concentrated solar power coordination. Firstly, the proton exchange membrane (PEM) electrolyzer (EL) and coal-to-hydrogen (C2H) technology are combined to produce hydrogen, and a mixed-hydrogen-source ammonia production model is constructed. The low-carbon characteristics of ammonia gas are used for thermal power mixed ammonia combustion. Secondly, to alleviate the operational burden on TPUs, a collaborative operating framework integrating a concentrating solar power (CSP) plant, an electric heater (EH), and an ammonia-coal co-fired power unit (ACCPU) is introduced. Furthermore, its low-carbon mechanisms during both peak and off-peak load intervals are thoroughly investigated. Thirdly, the ‘electricity–hydrogen–ammonia’ conversion link inside the deep excavation base and the reserve potential of the CSP plant are constructed, and a variety of flexible resource collaborative reserve models are constructed. Building upon this foundation, to account for the diverse uncertainties associated with load demand, wind, and PV generation, a fuzzy chance-constrained programming method is formulated. Seeking to enhance economic efficiency, the framework focuses on lowering the aggregate operational expenditures. Ultimately, the example results demonstrate that the presented approach effectively expands the accommodation capacity for renewable energy, lowers the base’s carbon emission, and alleviates the operational strain on TPUs. Full article

(This article belongs to the Topic Toward Smart and Sustainable Energy Systems Enabled by Artificial Intelligence, Optimization, and Resilience)

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