Search Results (315)

The rapid electrification of transportation and the proliferation of rooftop solar photovoltaics (PVs) in urban environments are reshaping the operational dynamics of power distribution networks. However, the inherent uncertainty in electric vehicle (EV) behavior—including arrival times, charging preferences, and state-of-charge—as well as spatially and temporally variable solar generation, presents a profound challenge to existing scheduling frameworks. This paper proposes a novel data-driven distributionally robust optimization (DDRO) framework for solar-powered EV charging coordination under spatiotemporal uncertainty. Leveraging empirical datasets of EV usage and solar irradiance from a smart city deployment, the framework constructs Wasserstein ambiguity sets around historical distributions, enabling worst-case-aware decision-making without requiring the assumption of probability laws. The problem is formulated as a two-stage optimization model. The first stage determines day-ahead charging schedules, solar utilization levels, and grid allocations across an urban-scale distribution feeder. The second stage models real-time recourse actions—such as dynamic curtailment or demand reshaping—after uncertainties are realized. Physical grid constraints are modeled using convexified LinDistFlow equations, while EV behavior is segmented into user classes with individualized uncertainty structures. The model is evaluated on a modified IEEE 123-bus feeder with 52 EV-PV nodes, using 15 min resolution over a 24 h horizon and 12 months of real-world data. Comparative results demonstrate that the proposed DDRO method reduces total operational costs by up to 15%, eliminates voltage violations entirely, and improves EV service satisfaction by more than 30% relative to deterministic and stochastic baselines. This work makes three primary contributions: it introduces a robust, tractable optimization architecture that captures spatiotemporal uncertainty using empirical Wasserstein sets; it integrates behavioral and physical modeling within a unified dispatch framework for urban energy-mobility systems; and it demonstrates the value of robust coordination in simultaneously improving grid resilience, renewable utilization, and EV user satisfaction. The results offer practical insights for city-scale planners seeking to enable the reliable and efficient electrification of mobility infrastructure under uncertainty. Full article

As part of the carbon peak and neutrality drive, an influx of renewable energy into the grid is imminent. However, the unpredictability of renewables like wind and solar can lead to significant curtailment if the power system relies solely on traditional generators. This paper presents a demand response mechanism to enhance renewable energy uptake by defining an optimal load curve for each node, considering the generator’s dynamic impact, system operations, and renewable energy projections. Once the ideal load curve is published, consumers, influenced by incentives, voluntarily align their consumption, steering the actual load to resemble the proposed curve. This strategy not only guides flexible generation resources to better utilize renewables but also minimizes the communication and control expenses associated with large-scale customer demand response. Additionally, a new evaluation metric for user response is proposed to ensure equitable incentive distribution. The model has been shown to lower both consumer power costs and system generation expenses, achieving a 22% reduction in renewable energy wastage. Full article

The integration of high-penetration renewable energy sources (RESs) into transmission networks introduces profound uncertainty that challenges traditional infrastructure planning approaches. Existing transmission expansion planning (TEP) models either rely on static scenario sets or over-conservative worst-case assumptions, failing to capture the operational stress triggered by rare but structurally impactful renewable behaviors. This paper proposes a novel bi-level optimization framework for transmission planning under adversarial uncertainty, coupling a distributionally robust upper-level investment model with a lower-level operational response embedded with physics and market constraints. The uncertainty space was not exogenously fixed, but instead dynamically generated through a physics-informed spatiotemporal generative adversarial network (PI-ST-GAN), which synthesizes high-risk renewable and load scenarios designed to maximally challenge the system’s resilience. The generator was co-trained using a composite stress index—combining expected energy not served, loss-of-load probability, and marginal congestion cost—ensuring that each scenario reflects both physical plausibility and operational extremity. The resulting bi-level model was reformulated using strong duality, and it was decomposed into a tractable mixed-integer structure with embedded adversarial learning loops. The proposed framework was validated on a modified IEEE 118-bus system with high wind and solar penetration. Results demonstrate that the GAN-enhanced planner consistently outperforms deterministic and stochastic baselines, reducing renewable curtailment by up to 48.7% and load shedding by 62.4% under worst-case realization. Moreover, the stress investment frontier exhibits clear convexity, enabling planners to identify cost-efficient resilience strategies. Spatial congestion maps and scenario risk-density plots further illustrate the ability of adversarial learning to reveal latent structural bottlenecks not captured by conventional methods. This work offers a new methodological paradigm, in which optimization and generative AI co-evolve to produce robust, data-aware, and stress-responsive transmission infrastructure designs. Full article

The growing integration of renewable energy sources (RESs) into modern power systems calls for enhanced flexibility and control mechanisms. Conventional demand response (DR) strategies, such as price-based and incentive-driven methods, often encounter challenges that limit their effectiveness. This paper proposes a novel DR approach grounded in Customer Directrix Load (CDL) and formulated through Stackelberg game theory. A bilevel optimization framework is established, with air conditioning (AC) systems and electric vehicles (EVs) serving as the main DR participants. The problem is addressed using a genetic algorithm. Simulation studies on a modified IEEE 33-bus distribution system reveal that the proposed strategy significantly improves RES accommodation, reduces power curtailment, and yields mutual benefits for both system operators and end users. The findings highlight the potential of the CDL-based DR mechanism in enhancing operational efficiency and encouraging proactive consumer involvement. Full article

Despite the growing application of storage for curtailment mitigation, its cost-effectiveness remains uncertain. This study evaluates the Levelized Cost of Storage, which also represents an implicit threshold revenue, for Lithium-ion Battery Energy Storage Systems deployed for photovoltaic curtailment mitigation. Specifically, the LCOS is assessed—using a mathematical simulation model—for various curtailment scenarios defined by maximum levels (10–40%), hourly profiles (upper limit and proportional), and growth rates (2, 5, and 10 years) at three storage system capacities (0.33, 0.50, 0.67 h) and two European locations (Cagliari and Berlin). The results indicate that the LCOS of batteries deployed for curtailment mitigation is, on average, comparable to that of systems used for bulk energy storage applications (155–320 EUR/MWh) in Cagliari (180–410 EUR/MWh). In contrast, in Berlin, the lower and more variable photovoltaic generation results in significantly higher LCOS values (200–750 EUR/MWh). For both locations, the lowest LCOS values (180 EUR/MWh for Cagliari and 200 EUR/MWh for Berlin), obtained for very high curtailment levels (40%), are significantly above average electricity prices (108 EUR/MWh for Cagliari and 78 EUR/MWh for Berlin), suggesting that BESSs for curtailment mitigation are competitive in the day-ahead market only if their electricity is sold at a significantly higher price. This is particularly true for lower curtailment levels. Indeed, for a curtailment level of 10% reached in 5 years, the LCOS for a 0.5 h BESS capacity is approximately 255 EUR/MWh in Cagliari and 460 EUR/MWh in Berlin. The study further highlights that the curtailment scenario significantly affects the Levelized Cost of Storage, with the upper limit hourly profile being more conservative. Full article

This study presents an optimization framework for a utility-scale hybrid power plant (HPP) that integrates wind power plants (WPPs), solar power plants (SPPs), and battery energy storage systems (BESS) using historical and probabilistic weather modeling, regulatory incentives, and multi-objective trade-offs. By employing multi-objective particle swarm optimization (MOPSO), the study simultaneously optimizes three key objectives: economic performance (maximizing net present value, NPV), system reliability (minimizing loss of power supply probability, LPSP), and operational efficiency (reducing curtailment). The optimized HPP (283 MW wind, 20 MW solar, and 500 MWh BESS) yields an NPV of $165.2 million, a levelized cost of energy (LCOE) of $0.065/kWh, an internal rate of return (IRR) of 10.24%, and a 9.24-year payback, demonstrating financial viability. Operational efficiency is maintained with <4% curtailment and 8.26% LPSP. Key findings show that grid imports improve reliability (LPSP drops to 1.89%) but reduce economic returns; higher wind speeds (11.6 m/s) allow 27% smaller designs with 54.6% capacity factors; and tax credits (30%) are crucial for viability at low PPA rates (≤$0.07/kWh). Validation via Multi-Objective Genetic Algorithm (MOGA) confirms robustness. The study improves hybrid power plant design by combining weather predictions, policy changes, and optimizing three goals, providing a flexible renewable energy option for reducing carbon emissions. Full article

Effective energy management in microgrids is essential for integrating renewable energy sources and maintaining operational stability. Machine learning (ML) techniques offer significant potential for optimizing microgrid performance. This study provides a comprehensive comparative performance evaluation of four ML-based control strategies: deep Q-networks (DQNs), proximal policy optimization (PPO), Q-learning, and advantage actor–critic (A2C). These strategies were rigorously tested using simulation data from a representative islanded microgrid model, with metrics evaluated across diverse seasonal conditions (autumn, spring, summer, winter). Key performance indicators included overall episodic reward, unmet load, excess generation, energy storage system (ESS) state-of-charge (SoC) imbalance, ESS utilization, and computational runtime. Results from the simulation indicate that the DQN-based agent consistently achieved superior performance across all evaluated seasons, effectively balancing economic rewards, reliability, and battery health while maintaining competitive computational runtimes. Specifically, DQN delivered near-optimal rewards by significantly reducing unmet load, minimizing excess renewable energy curtailment, and virtually eliminating ESS SoC imbalance, thereby prolonging battery life. Although the tabular Q-learning method showed the lowest computational latency, it was constrained by limited adaptability in more complex scenarios. PPO and A2C, while offering robust performance, incurred higher computational costs without additional performance advantages over DQN. This evaluation clearly demonstrates the capability and adaptability of the DQN approach for intelligent and autonomous microgrid management, providing valuable insights into the relative advantages and limitations of various ML strategies in complex energy management scenarios. Full article

In distribution networks, energy storage serves as a crucial means to mitigate power fluctuations from renewable energy sources. However, due to its high cost, energy storage remains a resource whose large-scale adoption in power systems faces significant challenges. In recent years, the emergence of shared energy storage business models has provided new opportunities for the efficient operation of multi-distribution networks. Nevertheless, distribution network operators and shared energy storage operators belong to different stakeholders, and traditional centralized scheduling strategies suffer from issues such as privacy leakage and overly conservative decision-making. To address these challenges, this paper proposes a Nash bargaining game-based optimal energy management and trading strategy for multi-distribution networks with shared energy storage. First, we establish optimal scheduling models for active distribution networks (ADNs) and shared energy storage operators, respectively, and then develop a cooperative scheduling model aimed at maximizing collaborative benefits. The interactive variables—power exchange and electricity prices between distribution networks and shared energy storage operators—are iteratively solved using the Alternating Direction Method of Multipliers (ADMM). Finally, case studies based on modified IEEE-33 test systems validate the effectiveness and feasibility of the proposed method. The results demonstrate that the presented approach significantly outperforms conventional centralized optimization and distributed robust techniques, achieving a maximum improvement of 3.6% in renewable energy utilization efficiency and an 11.2% reduction in operational expenses. While maintaining computational performance on par with centralized methods, it effectively addresses data privacy concerns. Furthermore, the proposed strategy enables a substantial decrease in load curtailment, with reductions reaching as high as 63.7%. Full article

Recent advancements in photovoltaic (PV) and battery technologies, combined with improvements in power electronic converters, have accelerated the adoption of rooftop PV systems and electric vehicles (EVs) in distribution networks, while these technologies offer economic and environmental benefits and support the transition to sustainable energy systems, they also introduce operational challenges, including voltage fluctuations, increased system losses, and voltage regulation issues under high penetration levels. Traditional Voltage and Var Control (VVC) strategies, which rely on substation on-load tap changers, voltage regulators, and shunt capacitors, are insufficient to fully manage these challenges. This study proposes a novel Voltage, Var, and Watt Control (VVWC) framework that coordinates the operation of PV and EV resources, conventional devices, and demand responsive loads. A mixed-integer nonlinear multi-objective optimization model is developed, applying a Chebyshev goal programming approach to balance objectives that include minimizing PV curtailment, reducing system losses, flattening voltage profile, and minimizing demand not met. Unserved demand has, in particular, been modeled while incorporating the concepts of distributional and recognition energy justice. The proposed method is validated using a modified version of the IEEE 123-bus test distribution system. The results indicate that the proposed framework allows for high levels of PV and EV integration in the grid, while ensuring that EV demand is met and PV curtailment is negligible. This demonstrates an equitable access to energy, while maximizing renewable energy usage. Full article

The inherent intermittency and uncertainty of renewable energy generation pose significant challenges to the safe and stable operation of power grids, particularly when power demand does not match renewable energy supply, leading to issues such as wind and solar power curtailment. To effectively promote the consumption of renewable energy while leveraging electric vehicles (EVs) in virtual power plants (VPPs) as distributed energy storage resources, this paper proposes an ordered scheduling strategy for EVs in campus areas oriented towards renewable energy consumption. Firstly, to address the uncertainty of renewable energy output, this paper uses Conditional Generative Adversarial Network (CGAN) technology to generate a series of typical scenarios. Subsequently, a mathematical model for EV aggregation is established, treating the numerous dispersed EVs within the campus as a collectively controllable resource, laying the foundation for their ordered scheduling. Then, to maximize renewable energy consumption and optimize EV charging scheduling, an improved Particle Swarm Optimization (PSO) algorithm is adopted to solve the problem. Finally, case studies using a real-world testing system demonstrate the feasibility and effectiveness of the proposed method. By introducing a dynamic inertia weight adjustment mechanism and a multi-population cooperative search strategy, the algorithm’s convergence speed and global search capability in solving high-dimensional non-convex optimization problems are significantly improved. Compared with conventional algorithms, the computational efficiency can be increased by up to 54.7%, and economic benefits can be enhanced by 8.6%. Full article

This study proposes an improved multi-objective black-winged kite algorithm (MOBKA-QL) integrating Q-learning with adaptive mutation strategies for optimizing multi-objective scheduling in integrated energy systems (IES). The algorithm dynamically selects mutation strategies through Q-learning to enhance solution diversity and accelerate convergence. First, an optimal scheduling model is established, incorporating a carbon capture system (CCS), power-to-gas (P2G), solar thermal, wind power, and energy storage to minimize economic costs and carbon emissions while maximizing energy efficiency. Second, the heat-to-power ratio of the cogeneration system is dynamically adjusted according to load demand, enabling flexible control of combined heat and power (CHP) output. The integration of CCS+P2G further reduces carbon emissions and wind curtailment, with the produced methane utilized in boilers and cogeneration systems. Hydrogen fuel cells (HFCs) are employed to mitigate cascading energy losses. Using forecasted load and renewable energy data from a specific region, dispatch experiments demonstrate that the proposed system reduces economic costs and CO₂ emissions by 14.63% and 13.9%, respectively, while improving energy efficiency by 28.84%. Additionally, the adjustable heat-to-power ratio of CHP yields synergistic economic, energy, and environmental benefits. Full article

This paper presents a real-time remote-control platform for small wind turbines (SWTs) equipped with a permanent magnet synchronous generator (PMSG). The proposed system integrates a DC–DC boost converter controlled by an Arduino^® microcontroller, a Raspberry Pi^® hosting a WebSocket server, and a desktop application developed using MATLAB^® App Designer (version R2024b). The platform enables seamless remote monitoring and control by allowing upper layers to select the turbine’s operating mode—either Maximum Power Point Tracking (MPPT) or Power Curtailment—based on real-time wind speed data transmitted via the WebSocket protocol. The communication architecture follows the IEC 61400-25 standard for wind power system communication, ensuring reliable and standardized data exchange. Experimental results demonstrate high accuracy in controlling the turbine’s operating points. The platform offers a user-friendly interface for real-time decision-making while ensuring robust and efficient system performance. This study highlights the potential of combining open-source hardware and software technologies to optimize SWT operations and improve their integration into distributed renewable energy systems. The proposed solution addresses the growing demand for cost-effective, flexible, and remote-control technologies in small-scale renewable energy applications. Full article

Accurate electricity wholesale price (EWP) forecasting is crucial for advancing sustainability in the energy sector, as it supports more efficient utilization and integration of renewable energy by informing when and how it should be consumed, dispatched, curtailed, or stored. However, high fluctuations in EWP, often resulting from demand–supply imbalances typically caused by sudden surges in electricity usage and the intermittency of renewable energy generation, and unforeseen external events, pose a challenge for accurate forecasting. Incorporating local temporal information (LTI) in time series, such as hourly price changes, is essential for accurate EWP forecasting, as it helps detect rapid market shifts. However, existing methods remain limited in capturing LTI, either relying on point-wise input sequences or, for fixed-length, non-overlapping segmentation methods, failing to effectively model dependencies within and across segments. This paper proposes the Local-Temporal Convolutional Transformer (LT-Conformer) model for day-ahead EWP forecasting, which addresses the challenge of capturing fine-grained LTI using Local-Temporal 1D Convolution and incorporates two attention modules to capture global temporal dependencies (e.g., daily price trends) and cross-feature dependencies (e.g., solar output influencing price). An initial evaluation in the Australian market demonstrates that LT-Conformer outperforms existing state-of-the-art methods and exhibits adaptability in forecasting EWP under volatile market conditions. Full article

This paper focuses on the provincial integrated energy system in northern China, which is characterized by the large-scale integration of renewable energy, thorough coupling of electricity and heat, and interactive operation of sources, loads, and storages. When conducting time-series production simulation with the daily rolling optimization dispatching method, the embedded daily optimal dispatching model fails to effectively charge and discharge electric and thermal energy storages across days to accommodate the curtailed electricity from renewable energy. Thus, a new embedded daily optimal dispatching model is proposed. The new model adopts a strategy of converting the stored energy of electric and thermal energy storages at the end of the dispatching day into equivalent coal consumption, respectively, and deducting it from the objective function of the optimal dispatching model. Through theoretical analysis, the reasonable range of the conversion coefficient is determined, enabling the model to use electric and thermal energy storages to store the curtailed electricity during surplus power generation in a dispatching day and accommodate it in subsequent days. A case study based on a provincial electricity–heat integrated energy system in northern China shows that the curtailment of renewable energy with the suggested strategy is much less than that with the traditional strategy, verifying the effectiveness of the proposed model. Full article

This paper presents a methodology for the co-expansion planning of transmission lines and energy storage systems, considering unit commitment constraints and uncertainties in load demand and wind generation. The problem is formulated as a mixed-integer nonlinear program and solved using a decomposition-based approach that combines a genetic algorithm with mixed-integer linear programming. Uncertainties are modeled through representative day scenarios obtained via clustering. The methodology is validated on a modified IEEE 24-bus system. The results show that co-planning reduces total expansion costs by 14.69%, annual operating costs by 26.19%, and wind curtailment by 91.99% compared to transmission only expansion. These improvements are due to the flexibility introduced by energy storage systems, which enables more efficient thermal dispatch, reduces fuel consumption, and minimizes renewable energy curtailment. Full article