Search Results (164)

Industrial-park integrated electricity–heat–hydrogen energy systems (IEHESs) face a challenging rolling dispatch problem because strong multi-energy coupling, intertemporal storage dynamics, and forecast uncertainty make it difficult to achieve economy, low-carbon operation, and hard-constraint feasibility simultaneously. To address this issue, this paper proposes a graph spatiotemporal world-model-driven rolling model predictive control (MPC) framework, termed GraphWorldModel_MPC, for low-carbon economic dispatch of industrial-park IEHESs. First, a unified graph-based representation is constructed to characterize the topology-aware coupling relationships among the electricity, heat, and hydrogen subsystems. Second, a graph spatiotemporal world model is developed to learn multi-step state transitions, while constraint-aligned physics-consistency terms are incorporated to align the predicted trajectories with multi-energy balance, storage-boundary evolution, and ramping semantics. In addition, the learned dynamics are embedded into a hard-constrained economic MPC framework, and a quantile-based safety-tightening mechanism is adopted to mitigate residual prediction uncertainty and enhance closed-loop feasibility. Case studies on an industrial-park IEHES show that the proposed method achieves an average 24-step normalized root mean square error (NRMSE) of 4.28% and reduces the monthly total operating cost by 6.07%, 3.83%, and 10.79% compared with conventional economic MPC (EMPC), distributionally robust adaptive MPC (DRAMPC), and GRU-MPC, respectively. It also reduces equivalent carbon emissions by 6.89%, 4.52%, and 9.50% relative to these benchmarks, while maintaining zero dispatch violations in the tested monthly horizon. Full article

With the increasing penetration of wind power, enhancing the renewable energy accommodation rate and reducing the carbon footprint of the IES, this study proposes a comprehensive evaluation method to assess the impact of a novel dynamic Green Certificate Trading (GCT) and Green Hydrogen Certificate Trading (GHCT) joint mechanism. First, considering the integration of the IES into the carbon trading market, a coupled dynamic GCT-GHCT framework is established. This framework links dynamic green electricity certificate revenues with green hydrogen certificate revenues, leveraging cross-subsidization to incentivize renewable energy consumption. Subsequently, an optimal operation model for the IES is formulated with the objective of minimizing comprehensive costs, which encompass energy procurement, green certificates, carbon trading, and wind curtailment penalties. A piecewise linearization approach is applied to transform the optimization model into a Mixed-Integer Linear Programming problem for efficient solving. Furthermore, based on the dispatch results, a multidimensional evaluation index system is constructed, extracting key indicators from economic, technical, and environmental perspectives. To ensure the rationality of the evaluation, a dynamic reward–penalty asymmetric cloud matter-element (ACME) comprehensive evaluation method based on game theory combinatorial weighting is introduced to calculate the index weights and the final comprehensive evaluation value. Finally, multi-scenario simulations are conducted to verify the superiority of the integrated GCT-GHCT trading framework. The results reveal that the proposed approach not only maximizes renewable energy integration but also provides a robust decision-making tool for the low-carbon transition of multi-energy systems. Full article

With the ongoing low-carbon transition of energy systems and the increasing penetration of distributed energy resources, the coordinated operation of heterogeneous prosumers has become essential for improving the economic and environmental performance of integrated energy systems. However, existing studies have not sufficiently addressed the joint coordination of electricity sharing, carbon emission trading, green certificate trading, and demand-side flexibility. To address this gap, this paper proposes a hybrid game-based optimal operation model for a multi-energy prosumer alliance coordinated by an Electricity Balance Service Provider (EBSP). The model is developed under coupled carbon emission trading (CET) and green certificate trading (GCT) markets. A piecewise linear dynamic pricing mechanism and a mutual recognition rule are introduced to describe the interaction between CET and GCT. Meanwhile, a price-based demand response model considering reducible and shiftable loads is incorporated to exploit load-side flexibility. On this basis, a Stackelberg-cooperative hybrid game is formulated to coordinate electricity pricing, integrated dispatch, electricity sharing, and benefit allocation between the EBSP and the prosumer alliance. The proposed model is solved using particle swarm optimization and the alternating direction method of multipliers. Case studies show that, compared with the corresponding benchmark scenarios, the proposed method reduces the alliance operating cost by 7.19%, the carbon trading cost by 41.35%, and total carbon emissions by 3.66%. It also decreases the peak-to-valley load difference ratio by 3.78 percentage points. These results demonstrate the effectiveness of the proposed method in improving economic performance, promoting low-carbon operation, and enhancing the peak-shaving and valley-filling capability of the prosumer alliance. Full article

Sustainable power-system operation requires carbon-reduction strategies that are emission-effective, physically deliverable, economically feasible, and compatible with user-side decarbonization claims. As Scope 2 carbon accounting increasingly emphasizes temporal, spatial, and physical consistency, dispatch models need to link user-level carbon claims with network-constrained power delivery. This paper proposes a User-Centric Carbon Cost-Constrained Low-Carbon Dispatch (CCC-LCD) framework that integrates carbon emission flow (CEF), nodal carbon intensity (NCI), network-constrained optimal dispatch, and endogenous demand response. A PTDF-based DC-OPF model represents active-power deliverability, while dual virtual flow variables determine carbon-flow directions endogenously. The model minimizes the target user’s physically traced Scope 2 emissions under a cost-tolerance budget and flexible-load constraints. Case studies on a modified IEEE 14-bus system show that nodal decarbonization is topology-dependent: high-load and high-NCI nodes obtain larger reductions from source-side generation substitution, whereas renewable-adjacent nodes exhibit limited marginal gains. The CEF-DR strategy outperforms single-mechanism cases, indicating the value of coordinating physical carbon-flow constraints with flexible demand. From a sustainability perspective, the proposed framework supports verifiable low-carbon electricity consumption, improves the economic feasibility of user-side decarbonization, and provides a practical dispatch tool for sustainable energy transition and corporate Scope 2 emission reduction. Full article

To achieve low-carbon and cost-effective operation of multi-park electricity–heat–hydrogen integrated energy systems (EHHSs), this paper proposes a low-carbon dispatch framework based on federated safe reinforcement learning. First, a multi-park EHHS dispatch model is established by considering heterogeneous park characteristics, electricity–heat–hydrogen coupling, stepped carbon trading, and peer-to-peer (P2P) energy trading. Then, to address the coupled challenges of privacy preservation, operational coupling, and safety constraints, the dispatch problem is formulated as a constrained Markov decision process (CMDP). On this basis, a federated safe proximal policy optimization algorithm (FedSafePPO) is developed by integrating PPO, Lagrangian-based safety constraint handling, and federated parameter aggregation. The proposed method enables each park to learn a local dispatch policy from private data while sharing global knowledge without exchanging raw operational data. In addition, an actor–dual-critic architecture is adopted to jointly evaluate economic returns and constraint costs, thereby improving convergence stability and dispatch feasibility. Case studies involving three heterogeneous parks—industrial, commercial, and residential—demonstrate that the proposed method effectively reduces total operating costs and carbon emissions while satisfying system constraints. Compared with PPO, FedPPO, and SafePPO, the proposed FedSafePPO achieves superior low-carbon economic performance, greater training stability, and better adaptability to heterogeneous operating conditions. The results verify the effectiveness and engineering applicability of the proposed method for the low-carbon dispatch of multi-park EHHSs. Full article

Traditional fixed or linear carbon prices often fail to reflect the nonlinear incentives of real carbon markets. To address this, we propose a multi-objective optimal dispatch framework for integrated energy systems (IESs) incorporating a tiered carbon trading mechanism. The system—comprising photovoltaics, wind power, a gas turbine, energy storage (ESS), power-to-gas (P2G), and grid interaction—aims to minimize operating and carbon trading costs while maximizing renewable utilization. This is solved using an improved multi-objective particle swarm optimization (IMOPSO) algorithm. Simulations across five configurations reveal that tiered pricing nonlinearly penalizes high emissions, reshaping the Pareto front toward low-carbon outcomes. Consequently, the ESS evolves from a simple economic arbitrageur into a proactive “carbon buffer”, absorbing midday photovoltaic surpluses and substituting gas turbine output during evening peaks. Compared to a grid-only baseline, the optimized multi-energy configuration (gas turbine + ESS + P2G) reduced operating costs by 13.1% and carbon emissions by 9.9%, while increasing renewable utilization by 8.5%. Ultimately, this study demonstrates that a well-designed nonlinear carbon pricing mechanism is decisive for guiding the IES to achieve coordinated economic and low-carbon operation. Full article

Optimized dispatch of microgrids is crucial for improving the economic performance and long-term sustainability of modern low-carbon power systems. In particular, accurate economic dispatch modeling for battery energy storage systems (BESSs) is essential for properly evaluating the operational benefits and lifetime costs of microgrids. However, when both battery cycle aging and calendar aging are considered, the resulting scheduling model becomes highly nonlinear, high-dimensional, non-convex, and multimodal, which poses substantial challenges to conventional optimization methods. To alleviate the above problem, a symmetry-guided multi-strategy differential hybrid slime mold algorithm (MDHSMA) is introduced for the day-ahead economic dispatch of microgrids under a refined battery degradation framework. First, a chaotic bimodal mirrored Latin hypercube sampling strategy is designed to exploit symmetry during population initialization, thereby enhancing diversity and improving structured coverage of the search space. Second, a history-driven adaptive differential evolution mechanism is integrated to balance global exploration and local exploitation more effectively during the iterative search process. Third, a state-aware stagnation handling framework is incorporated to maintain population vitality and further improve convergence accuracy and robustness. MDHSMA is evaluated against 12 state-of-the-art optimizers on the CEC2017 and CEC2022 benchmark suites and two representative engineering optimization problems to verify its overall performance. In addition, it is applied to a microgrid case study with refined BESS degradation modeling. The results show that MDHSMA achieves the lowest comprehensive operating cost by effectively coordinating electricity arbitrage and battery life consumption. Moreover, it guides the energy storage system toward shallow charge–-discharge patterns, thereby mitigating accelerated degradation caused by excessive cycling. These results confirm the effectiveness and practical value of the proposed method for sustainable microgrid dispatch in complex nonconvex optimization scenarios. Full article

With the rapid penetration of renewable energy, grid-connected microgrids have become a cornerstone of low-carbon power systems, while also posing major challenges for coordinated scheduling under coupled economic and environmental goals. The resulting dispatch problem is highly nonlinear and high-dimensional, featuring tight operational constraints and conflicting cost–emission trade-offs that often undermine the efficiency and reliability of conventional optimization methods, thereby limiting overall economic productivity. This paper presents an adaptive economic–environmental dispatch framework for grid-connected microgrids formulated as a multi-objective optimization problem that simultaneously minimizes operating cost and environmental protection cost. To navigate the rugged and constrained search landscape, we develop an enhanced metaheuristic termed the Lévy–Triangular Walk Dung Beetle Optimizer (LTWDBO). The LTWDBO integrates (i) chaotic population initialization to improve diversity and feasibility coverage, (ii) a geometry-inspired triangular walk operator to strengthen local exploitation, and (iii) an adaptive Lévy-flight strategy to boost global exploration, achieving a robust exploration–exploitation balance over the entire optimization process, representing a process innovation in metaheuristic-driven dispatch optimization. The proposed method is validated on a representative grid-connected microgrid comprising photovoltaic generation, wind turbines, micro gas turbines, and battery energy storage. Comparative experiments against representative baselines (DBO, WOA, TDBO, and NSGA-II) demonstrate that the LTWDBO achieves consistently better solution quality. Our LTWDBO attains the lowest optimal objective value of 255,718.34 Yuan, compared with 357,702.68 Yuan (DBO), 347,369.28 Yuan (TDBO), and 3,854,359.36 Yuan (WOA). The LTWDBO also yields the best average objective value of 673,842.24 Yuan, an improvement of over 1,001,813.10 Yuan (DBO). Full article

This paper proposes a low-carbon optimization dispatch method for hydrogen-based multi-agent regional integrated energy system (RIES) that incorporate CET-GCT. The approach aims to coordinate the interests of all parties within the regional integrated energy system while reducing overall system carbon emissions. First, based on Stackelberg game theory, the interactions between the energy operator and agents on the supply side and demand side are fully characterized, establishing a “one main, two subordinate” multi-agent game model. Second, the model incorporates refined power-to-gas (P2G) technology to enhance system flexibility. Subsequently, a combined carbon trading-green certificate trading mechanism is introduced to effectively constrain the carbon emission behaviors of all stakeholders. Finally, an improved Ivy algorithm is integrated with the CPLEX solver to solve the proposed model. Simulation results demonstrate that while each entity maximizes its own benefits by adjusting its strategy, the system’s overall carbon emissions decrease by 5.12% and total revenue increased by 11.49%, yielding significant low-carbon economic benefits. This validates the effectiveness of the proposed model and methodology. Full article

Remote off-grid microgrids are often locked into diesel-backed operation because renewable variability creates multi-day and seasonal energy gaps that short-duration batteries cannot economically bridge. This work examines how renewable hydrogen can complement batteries and dispatchable biomass to push an existing hybrid microgrid toward near-autonomous, low-carbon operation, while remaining robust under future electrification demands. The analysis is based on real operational load insights from a remote off-grid system, combined with techno-economic optimization in HOMER Pro. The examined architecture includes PV panels, battery energy storage, a biomass CHP unit, and a diesel generator as backup; the hydrogen pathway additionally incorporates an electrolysis, storage and a PEMFC. Three scenarios are considered: a baseline PV/BAT configuration, an intermediate PV/BAT/BIO configuration that strengthens dispatchable renewable supply and short-term flexibility, and a PV/BAT/BIO/H₂ configuration targeting an increase in renewable energy penetration (REP). Results show that hydrogen integration shifts the system from curtailment-limited, diesel-supported operation to storage-enabled operation: surplus renewable production that would otherwise be curtailed is converted into hydrogen and later dispatched during prolonged deficits, enabling deep diesel displacement without compromising reliability. Hydrogen-enabled configurations achieve 90–99% REP, reduced diesel consumption, and lower CO₂ emissions, primarily by converting curtailed surplus into storable hydrogen. A rule-based EMS highlights technology complementarity across timescales, with batteries providing diurnal balancing and hydrogen covering longer deficits, which also reduces battery cycling stress. Overall, the study clarifies key design trade-offs, especially the need for coordinated PV expansion and storage sizing, and illustrates how a multi-storage portfolio can support high renewable penetration in such systems. Full article

This paper proposes a safety-constrained Transformer–PPO framework for low-carbon economic dispatch with settable incentive-based demand response (DR) in wind–PV integrated electro–thermal–hydrogen industrial-park energy systems. Hydrogen is modeled as exogenous hydrogen-domain demand and is satisfied through electrolyzer production and hydrogen inventory dynamics. A causal Transformer captures long-horizon multi-energy coupling and intertemporal constraints and is trained with PPO under uncertainty. A dual-layer safety mechanism combines dual-variable (Lagrange multiplier) updates for statistical constraints with an execution-layer quadratic-programming action projection to enforce hard physical constraints, including operating limits, ramping, battery SOC, hydrogen inventory bounds, and energy balance. Baseline–verification–settlement rules and budget-ledger states are embedded to ensure verifiable response quantities and settlement outcomes that are traceable and independently recompilable. Case studies on a real industrial-park scenario in Inner Mongolia show reduced peak-hour maximum grid purchase demand and constraint violations, together with lower total cost, carbon cost, and curtailment penalties versus MILP, PPO-MLP, and Transformer–PPO without safety mechanisms. Full article

Planning capacity for wind–photovoltaic (PV)–thermal–storage systems with high renewable penetration requires models that address investment costs, operational feasibility, and strict carbon limits under uncertainty. This paper presents a two-stage robust optimization model for integrated wind–PV–thermal–storage capacity expansion that guarantees carbon compliance under worst-case renewable realizations. Unlike conventional approaches that relax carbon constraints through price penalties, we enforce the annual carbon emission cap as a hard operational constraint, ensuring candidate portfolios remain feasible even under adverse renewable conditions. To reflect practical storage design, a fixed energy-to-power (E/P) ratio couples storage energy capacity with power converter ratings, preventing unrealistic storage expansions. Renewable uncertainty is captured through a Bertsimas–Sim budgeted polyhedral set defined over representative days, balancing robustness with computational tractability. A tailored decomposition framework integrates economic dispatch and carbon-compliance verification within an outer column-and-constraint generation (C&CG) loop, simultaneously certifying worst-case operating cost and minimum achievable emissions. By exploiting strong duality, we generate two families of valid inequalities iteratively: economic cuts from the Economic subproblem (Economic-SP) and carbon-feasibility cuts from the Carbon subproblem (Carbon-SP). This dual-certification approach ensures capacity plans remain both economically optimal and carbon-compliant across all uncertainty realizations. Case studies on a realistic wind–PV–thermal–storage system demonstrate that the method produces carbon-compliant, robust capacity plans with manageable computational effort, converging in 10–15 iterations. The model explicitly captures operational coupling among renewables, thermal generation, and storage, providing a decision-support tool for low-carbon power systems under deep decarbonization targets. Full article

Global data center electricity demand is projected to double to 945 TWh by 2030, yet no optimization framework jointly sizes renewable generation, battery storage, hydrogen export infrastructure, and flexible computing loads within a single industrial hub. This paper develops a two-layer techno-economic workflow for an integrated renewable–hydrogen–data center hub in Yanbu Industrial City, Saudi Arabia. HOMER Pro provides baseline capacity sizing and dispatch across four scenarios; a Pyomo-based mixed-integer linear program, calibrated to within 2% of the baseline, then extends the system to include a 60 MW data center (30 MW critical, 30 MW flexible), multi-sink hydrogen allocation (domestic, ammonia, methanol), and low-grade waste heat recovery. Battery storage emerges as the dominant cost–carbon lever: its removal raises the levelized cost of electricity (LCOE) from 0.052 to 0.181 USD/kWh (+250%) and increases CO₂ emissions from 1.83 to 2763 kt/yr, a factor of 1510. The Integrated Hub reduces annualized costs by 8.2% (36.9 M USD/yr) and emissions by 28% relative to a separate-build counterfactual, driven by shared PV–battery infrastructure and hydrogen export revenues of 58.5 M USD/yr. Export demand raises the electrolyzer capacity factor from 8.65% to 24.3%, cutting the levelized cost of hydrogen from 10.5 to 6.8 USD/kg. Waste heat recovery reduces the levelized cost of heat by 17%, and co-location lowers the levelized cost of compute by 23% (from 0.055 to 0.042 USD/GPU/hr). These results provide quantitative design principles for industrial hub planners considering data center co-location in high-solar regions with hydrogen export ambitions. Full article

Italy’s abundant solar resources and its strategic Mediterranean location offer strong opportunities to accelerate the transition to a low-carbon energy system. This study presents a comparative techno-economic assessment of concentrating solar power (CSP) plants with 8 h of thermal energy storage (TES) and a 1 MW photovoltaic (PV) plant to evaluate their roles in exploiting Italy’s solar potential. The analysis covers four representative locations (Montalto, Val Basento, Ferrara, and Priolo) and examines solar availability, seasonal performance, capacity factor, electricity generation, land use, and levelized cost of electricity (LCOE). Both technologies show marked seasonal variability, with lower winter performance and summer peaks. Southern sites outperform the northern ones, with Priolo achieving the highest generation and Ferrara the lowest. CSP benefits from dispatchable operation enabled by TES, providing nearly constant rated output and summer capacity factors up to 78%, with annual production exceeding 4 GWh at the best site. In contrast, PV operates non-dispatchably, with capacity factors below 31% and annual generation between 1.47 and 1.72 GWh. The North–South performance gradient is stronger for CSP due to its dependence on direct normal irradiance. PV technology offers higher land use efficiency, producing over twice the energy per unit area compared to CSP technology, while CSP technology requires larger areas but ensures greater operational flexibility. Economically, PV technology achieves a lower LCOE, whereas CSP technology entails higher costs but adds value through dispatchability and improved grid integration. Overall, combining CSP and PV systems can enhance grid stability, reduce emissions, and strengthen Italy’s energy security, highlighting the importance of coordinated planning and investment in complementary solar technologies for decarbonization and for regions with similar climatic conditions. Full article

As renewable energy penetration accelerates, battery energy storage systems have become essential for enhancing flexibility, reliability, and economic efficiency in power system operations. For the daily operations of grids, the unit commitment (UC) problem plays a central role in determining the optimized scheduling of generation resources, but current formulations rarely incorporate battery degradation dynamics. The accurate representation of battery aging is crucial, as degradation costs may influence dispatch. This review provides a synthesis of existing approaches for integrating battery degradation into UC formulations. We survey and compare major classes of degradation models and then examine how these models have been embedded into UC frameworks, highlighting trade-offs between modeling accuracy and tractability. This paper concludes with identified research gaps and recommendations for future UC formulations that more faithfully capture battery degradation while maintaining computational efficiency. This review aims to serve as a foundation for researchers and system operators seeking to incorporate realistic battery aging mechanisms into operational decision-making for the evolving low-carbon grid. Full article