Search Results (485)

Distributed economic dispatch in AC distribution systems relies heavily on communication networks and is therefore vulnerable to denial-of-service (DoS) attacks. To address this issue, this paper proposes a resilient event-triggered distributed economic dispatch control strategy. Two typical DoS attack scenarios, namely communication-link blocking and node isolation, are first modeled, and an event-triggered distributed economic dispatch controller is then developed to maintain incremental cost consensus and system power balance while reducing communication overhead. Based on Lyapunov stability theory and a linear matrix inequality approach, sufficient conditions for the asymptotic stability of the closed-loop system are derived, tolerable bounds on the frequency and duration of DoS attacks are established, and the absence of Zeno behavior is proved. Simulations on the IEEE 33-bus AC distribution system show that, under load disturbances, dispatch-command variations, and DoS attacks, the proposed strategy can maintain stable system operation, restore dispatch performance after attacks, and reduce communication overhead by 91.86% compared with a fixed-step periodic updating baseline. These results demonstrate the effectiveness and resilience of the proposed method for distributed economic dispatch in AC distribution systems under DoS attacks. Full article

With the high penetration of distributed photovoltaic and storage systems, active distribution grids are prone to experiencing “active power surplus and reactive power shortage” during the evening peak, leading to voltage sags at the network end. Although electric vehicle (EV) grid-connected inverters possess four-quadrant reactive power regulation capabilities without causing the additional chemical cyclic aging of the battery cells, existing dispatch systems often treat them as unconditional response resources, overlooking users’ actual willingness to cede control and the associated strategic interactions. To address this, this paper proposes a “grid-load” coordinated reactive power optimization strategy that accounts for EV users’ willingness to respond: a Logit model incorporating price incentives, initial energy consumption, and parking duration is constructed based on discrete choice theory. By combining a truncated normal distribution with the Monte Carlo method to eliminate micro-sampling errors, a model of the expected reactive power capacity of charging stations under dynamic incentives is established; considering the physical constraints of SVCs and EVs, a scalarized single-objective optimization model is constructed with grid loss-equivalent costs, ancillary service costs, and voltage deviation as objectives, and solved using an improved particle swarm optimization algorithm with linearly decreasing weights. Simulations on a modified 33-node IEEE system incorporating storage indicate that this strategy can assign optimal compensation prices to each node based on the spatial value of reactive power. Compared to traditional single-voltage regulation and fixed subsidies, it not only stabilizes the grid-wide voltage within a safe range but also avoids overcompensation, achieving global optimization of both power quality and economic efficiency. Full article

The transition toward modern power systems with high renewable penetration has significantly increased the demand for system flexibility. However, existing reserve capacity assessment methods often overestimate the actual deliverable flexibility by neglecting network topology constraints. This paper proposes a topology-constrained flexibility assessment framework based on the Regional Security-Constrained Economic Dispatch (R-SCED) model to quantify the true deliverable reserve of adjustable resources in electricity spot markets. Unlike conventional approaches, the proposed framework explicitly captures the spatial distribution of load increments and their interactions with transmission constraints. Through multi-scenario analysis, we reveal a critical “capacity restriction effect”, where network bottlenecks drastically reduce the theoretically available reserve. Case studies on an 88-node multi-area system show that the actual upward flexibility is reduced from a theoretical level of 67,000 MW to a constrained range of 5200–19,000 MW. The results demonstrate that flexibility in modern power systems is fundamentally limited by network topology rather than generation capacity, highlighting the necessity of topology-aware reserve assessment for real-time market operation. This work provides important insights for improving dispatch strategies and enhancing system flexibility under high renewable penetration. 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

Cross-border e-commerce fulfillment depends on coordinated inland container movements across factories, inland container depots (ICDs), and port gateways, yet many container trucking operations still follow synchronous one-truck-one-order execution. This study models the fulfillment network as a platform-enabled socio-technical transportation system in which the ICD acts as a digital–physical coordination node for spatiotemporal decoupling. A drop–buffer–pick task architecture is developed to represent direct execution, relay execution, and delayed dispatch, and a mixed-integer linear programming (MILP) model optimizes task assignment and tractor sequencing under loading-time, port cutoff, inventory, and working-time constraints. In the certified-optimal 10-order instance, gross positive cost decreases from CNY 27,540 to CNY 19,915 (−27.7%); after applying the same post hoc coordination-credit accounting rule, net total fulfillment cost decreases to CNY 18,734 (−32.0%). The 10 orders are served with five tractors under the tested platform configuration, compared with 10 tractors under the restricted benchmark. To address sustainability explicitly, the analysis also reports distance-based emissions and energy-use proxies; the proposed schedule lowers cost and fleet deployment but increases total mileage, showing that economic efficiency and emissions performance do not automatically move together. The evidence is a deterministic baseline for later stochastic, mixed import/export, and collaborative-platform extensions. Full article

The increasing integration of renewable energy resources into modern microgrids requires reliable scheduling methods capable of managing uncertainty, seasonal variability, operating cost, and environmental impact. This study proposes a stochastic day-ahead scheduling approach for a representative grid-connected multi-energy microgrid comprising photovoltaic generation, wind generation, a microturbine, a fuel cell, an energy storage system, and utility-grid exchange. The proposed model was implemented and simulated in a MATLAB (2024b) environment. The Birds of Prey-Based Optimization algorithm is applied to determine the optimal 24 h dispatch schedule by minimizing a weighted objective function that combines operating and emission costs. Uncertainties in solar irradiance, wind speed, electrical load, ambient temperature, and electricity prices are modeled using probabilistic distributions and Monte Carlo simulations. To improve computational efficiency, 1000 generated scenarios are reduced to 10 representative scenarios using Fast Forward Selection based on Kantorovich distance. Seasonal case studies for winter, spring, summer, and autumn are used to evaluate the proposed method. Compared with five metaheuristic algorithms, the proposed approach achieves the lowest fitness value in all seasons, with reductions of 15.2%, 26.5%, 6.8%, and 23.9%, respectively. The results confirm improved economic and environmental microgrid operation under seasonal renewable uncertainty. 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

The provision of power and grid services requires the co-ordination between Day-Ahead Market (DAM) and Ancillary Service Market (ASM) to attain reserve services and technically feasible operating conditions for market players and for the network. In this context, this work proposes a multi-stage approach to evaluate the dispatched power to balance the forecast updates of renewable energy sources and load from DAM to ASM, taking into account network and Unit Commitment (UC) constraints. The DAM is solved considering a zonal market framework and neglecting the UC constraints. Then, a mechanism to adjust the ASM bids is developed, defining time-varying costs for each regulation. Finally, the ASM is modelled as a network-constrained UC and economic redispatch (NCUCER) optimization problem, aiming at minimizing the overall cost, in order to procure secondary reserve requirement and to adjust the DAM schedules, taking into account network and UC constraints and balancing forecast updates. DC load flow sensitivity factors are exploited to evaluate the influence of redispatch actions and forecast updates on the observed power flow. This procedure is applied to NREL 118-Bus Test System assessing its performances throughout a yearly time horizon. Full article

Scenario generation and reduction based on a single variable (e.g., photovoltaic power or load forecasting) is a mainstream approach in current power system planning. However, such methods often overlook the temporal correlation between source and load, which can compromise the credibility of the generated scenarios and lead to suboptimal planning outcomes. To address this issue, this paper proposes an integrated model for microgrid energy storage planning and operation that explicitly considers the joint distribution of source–load scenarios. First, a comprehensive similarity metric is developed by combining dynamic time warping (DTW) distance, slope distance, and source–load correlation distance. An improved K-medoids clustering algorithm is then employed to cluster the joint source–load time series, generating a set of typical scenarios that effectively preserve the coupling characteristics between photovoltaic generation and load demand. Subsequently, a bi-level optimization model is formulated, with energy storage capacity as the primary decision variable. The upper-level planning problem aims to maximize the return on investment (ROI) under energy storage investment constraints, determining the optimal capacity configuration. The lower-level operational problem maximizes the daily net revenue by optimizing the charging and discharging strategies of the energy storage system. Through iterative interaction between the two levels, the model achieves optimal coordination between investment decisions and economic dispatch. Case studies on a campus microgrid demonstrate that the proposed joint scenario generation method effectively captures the temporal correlation between source and load, enhancing both the credibility of the scenarios and the economic rationality of the integrated planning and operation framework. Full article

To address the voltage over-limit issue in rural distribution networks caused by high-penetration distributed photovoltaic (DPV) integration, as well as the frequent voltage disturbances resulting from frequent load switching and variable operating conditions in agricultural grid systems, this paper proposes a dual-layer optimized configuration and adaptive voltage control method for grid-forming energy storage systems. First, an outer-layer siting and sizing model is established with constraints including voltage stability, deviation, network losses, and economic factors. The configuration scheme is solved using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Then, an inner-layer rolling optimized dispatch model is constructed with Model Predictive Control (MPC) as the core, and an adaptive reactive power–voltage control method based on Virtual Synchronous Generator (VSG) control is proposed to enhance transient voltage support under disturbance conditions. Simulation analysis based on an actual 10 kV rural distribution line verifies that the proposed method can effectively mitigate overvoltage issues and alleviate reverse power flow during typical daily operation, while significantly improving node voltage recovery speed and reactive power support capability under voltage disturbances. Full article

Remote communities in Canada face high electricity costs, energy insecurity, and significant greenhouse gas emissions due to heavy dependence on diesel generation. This study proposes and evaluates an AC-coupled hybrid distribution microgrid for remote off-grid communities, using Black Tickle, Newfoundland and Labrador as a representative case study. The system integrates two 200 kW wind turbines, a 200 kW diesel backup generator, a 16 MWh lithium-ion battery storage system, and a bidirectional converter, modeled and optimized in HOMER Pro 3.18.3 using local meteorological data, community load profiles, and a cycle-charging dispatch strategy. The optimized configuration achieves 86.7% wind penetration and 100% supply reliability with zero unmet load, yielding a total net present cost of USD 13.6 million and a levelized cost of energy of 0.999 USD/kWh over a 25-year horizon. Battery storage accounts for 73.5% of annualized costs, representing the primary economic challenge for wider deployment. Sensitivity analyses show that diesel price fluctuations exert approximately 4.1 times greater influence on system economics than equivalent carbon pricing changes, while the optimal configuration remains robust across all tested policy scenarios. These findings demonstrate that AC-coupled wind–diesel–battery microgrids offer a viable pathway for reducing fossil fuel dependence and supporting clean energy transition in remote, harsh-climate communities. Full article

This paper proposes a bi-level optimal dispatch model for a wind–solar–thermal-storage hybrid power system that considers the optimal curtailment rate. The upper-level model minimizes net-load fluctuations and curtailment penalties by coordinating renewable curtailment and energy storage scheduling under multiple uncertainty scenarios. The lower-level model minimizes the total operating cost by optimizing thermal unit commitment and dispatch while accounting for deep peak-regulation costs, spinning reserve costs, environmental taxes, and the environmental benefits of renewables. A piecewise nonlinear cost model is introduced to characterize the increasing wear-and-tear and oil-support costs of thermal units operating under deep peak regulation. Simulation results obtained on a modified IEEE 30-bus system demonstrate that, compared with benchmark models, the proposed approach significantly smooths the net-load curve, reduces the peak-to-valley difference, and lowers the total system operating cost. The results further indicate that moderate active curtailment, when coordinated with energy storage, can be more economical than rigid full renewable accommodation. Consequently, active curtailment should be regarded not merely as a loss of renewable energy utilization but as a flexible and economically rational resource for enhancing system security, flexibility, and overall dispatch performance. Full article

This article assesses the potential for intra-day redistribution of the electrical load of water intake systems under different electricity tariff models, using water supply systems in Belarus and China as case studies. It demonstrates how tariff policy influences the electrical load profile of a water intake system and quantitatively evaluates the economic effect of optimizing the operating modes of pumping equipment. The analysis is based on daily profiles of electric power and water supply. For the Belarusian water supply system, data for 2019 were considered, corresponding to the baseline operating mode without targeted load management, and data for 2023 were considered after the transition to dispatch-based control of well activation with account taken of tariff constraints (without automation tools). For the Chinese water intake system, hourly data for 2025 were used. The load redistribution potential was assessed on the basis of lagged correlation between power and water supply profiles. In addition, the F-index was applied as an aggregated diagnostic indicator intended for the comparative assessment of potential load transferability across technological stages, taking into account their share in total energy consumption. For the Chinese case, it was shown that the maximum correlation between water supply and electricity consumption across all technological stages is achieved near zero lag, which indicates a high adaptation of system operating modes to current demand; at the same time, the R values were 0.19 for reservoir intake, 0.86 for water treatment, and 0.51 for the pumping station. In the Belarusian case, for the first-lift stage, the maximum correlation is shifted by −6 h relative to zero lag, indicating a less rigid linkage of pump operation to current demand and a more inertial response of the system. A comparison of 2019 and 2023 for the Belarusian facility showed that targeted regulation of well activation and load redistribution across tariff zones reduced the total electricity cost by 1.58%, confirming the potential for further optimization of electricity consumption regimes. Full article

Extreme disasters often induce multiple simultaneous faults in distribution networks, posing significant risks to power supply reliability. Although network reconfiguration and intentional islanding are critical strategies for enhancing system resilience, existing studies typically address them separately and fail to adequately account for the uncertainties associated with renewable energy generation and load demand. To address these limitations, this paper presents a collaborative optimization model for resilient distribution network restoration. A multi-time-step dynamic restoration framework is developed to coordinate network reconfiguration, emergency repair scheduling, distributed generation dispatch, and load shedding. This framework enables unified decision-making for island formation and topology reconfiguration, and incorporates an island integration mechanism to broaden the feasible solution space. To manage source–load uncertainties, chance-constrained programming is introduced, transforming probabilistic security constraints into deterministic equivalents using risk indicator variables, thereby striking a balance between operational security and economic efficiency. In addition, the model optimizes repair sequences under multi-fault conditions to enhance resource utilization. Simulations on a modified IEEE 33-node system validate the effectiveness of the proposed approach in reducing load curtailment, accelerating restoration, and achieving a favorable trade-off between operational risk and economic performance. Full article

As power-electronic-interfaced renewable generation displaces synchronous machines, modern power systems face coupled day-ahead challenges: net-load variability demands peak shaving, while declining inertia necessitates explicit frequency-regulation scheduling. In sequential security-constrained unit commitment (SCUC) and Security-Constrained Economic Dispatch (SCED), the reserve procured in SCUC may lose deliverability after redispatch because the same storage bandwidth is reassigned to energy service. This paper proposes a two-stage day-ahead framework that addresses both challenges for low-inertia systems with high inverter-based resource (IBR) penetration. Stage I embeds Rate-of-Change of Frequency (RoCoF), frequency nadir, and quasi-steady-state (QSS) constraints in SCUC, with a piecewise-linear outer approximation for the non-convex nadir limit. Stage II strictly inherits the SCUC commitment and reserve reservation, and it applies bandwidth deduction to prevent peak-shaving redispatch from consuming committed frequency reserve. A technology-aware partition further assigns fast-response Lithium Iron Phosphate (LFP) batteries to sub-second frequency support and long-duration Vanadium Redox Flow Batteries (VRFBs) to energy shifting. Evaluated under the adopted reduced-order frequency-response framework and disturbance representation, tests on a modified IEEE 39-bus system under an extreme-wind scenario demonstrate that explicit frequency constraints eliminate all post-contingency violations, the inheritance mechanism closes a 23.85 MW reserve gap after redispatch, and heterogeneous storage partitioning preserves essentially the same disturbance sensitivity while increasing the peak-shaving ratio to 45.85%, lowering the day-ahead cost to CNY $10.483\times {10}^6$ and reducing the average system price to 209.33 CNY/MWh. Full article