Search

Active distribution networks face growing renewable curtailment as distributed photovoltaic and wind generation increasingly encounter voltage, congestion, and flexibility constraints. Battery storage scheduling under uncertainty has become a central operational strategy for absorbing otherwise curtailed energy and improving distribution-level renewable integration. Despite substantial growth in optimization-based storage studies, the literature remains fragmented across stochastic programming, chance-constrained optimization, robust optimization, and distributionally robust formulations. No critical review has systematically compared these paradigms for storage scheduling and curtailment reduction specifically in active distribution networks. This critical review assesses and contrasts stochastic and robust optimization models for battery storage scheduling aimed at reducing renewable curtailment in active distribution networks. It evaluates how these approaches represent uncertainty, network constraints, operational objectives, and implementation readiness. A structured critical review of 27 peer-reviewed journal articles published between 2017 and 2024 was conducted. The analysis focuses on mathematical formulations, uncertainty modeling, storage representation, curtailment objectives, network constraints, and operational applicability. Stochastic models offer strong expected-performance reasoning but often depend on scenario quality and can become computationally demanding. Robust models provide stronger protection against adverse uncertainty realizations but may become conservative when uncertainty sets are poorly calibrated, while gaps remain in real-time scheduling, multi-service operation, degradation modeling, and field validation. The choice between stochastic and robust optimization depends on operator risk preference, data availability, computational resources, and distribution network complexity. Hybrid, data-driven, and implementation-oriented methods offer promising future pathways for reducing renewable curtailment through battery storage scheduling.

Renewable-rich microgrids face operational complexity from variable generation, storage dynamics, flexible loads, and the need for human oversight. A layered architecture can manage this complexity by separating functions while preserving coordination. Existing microgrid control architectures often entangle forecasting, scheduling, demand response, and supervisory control. This can make energy management systems brittle, difficult to maintain, and insufficiently accommodating of operator override. This article proposes a layered architecture that cleanly separates forecasting, battery scheduling, demand response, and operator override functions. The aim is to support modular design, resilient operation, and transparent human supervisory control. The architecture is developed conceptually using established ideas from microgrid energy management, hierarchical control, renewable integration, storage scheduling, demand response, and human-in-the-loop supervision. No experiments are conducted, and no quantitative results are presented. The architecture comprises four layers with defined functions, inputs, outputs, decision horizons, and interfaces. A validation plan is reserved for the second part of the article, while this part establishes the architecture logic and the forecasting layer. The proposed architecture offers a blueprint for building modular, resilient, and human-centred microgrid energy management systems. It is intended as a conceptual reference model for future implementation, testing, and refinement.

Day-ahead distribution-grid operation requires anticipating customer demand, renewable generation, network loading, and available demand-side flexibility. Smart-meter data provide a practical basis for identifying recurring consumption patterns and embedding those patterns into operational scheduling. This article presents an original forecasting-optimization decision-support system for day-ahead distribution-grid operation. The system integrates smart-meter clustering, renewable prediction, and flexible load scheduling to support distribution system operators. The proposed system processes smart-meter profiles into consumer clusters, predicts day-ahead photovoltaic and wind generation from weather-driven features, and schedules flexible loads through constrained optimization. The decision-support workflow is designed to produce feeder-level schedules, expected voltage conditions, congestion indicators, and operator-facing recommendations. The clustering module identifies consumer groups with different peak timing, ramping behavior, and flexibility availability. The integrated DSS is structured to reduce peak loading, improve voltage profiles, and lower operational cost compared with a baseline day-ahead plan without forecast-informed scheduling. The proposed DSS offers a practical framework for data-driven distribution-grid operation. It supports renewable integration by translating forecasts and customer flexibility into actionable schedules for day-ahead planning.

Depot-based electric vehicle fleets are becoming concentrated electricity loads at distribution level. Real-time charging data and flexibility scheduling can reduce transformer stress while preserving fleet service readiness. This article proposes an integrated framework that coordinates EV fleet charging with transformer constraints using real-time charging analytics. The framework links vehicle-level telemetry, depot-level flexibility estimation, transformer thermal modeling, and operational scheduling. The framework collects charging power, state-of-charge, arrival time, departure time, charger status, and depot base-load data in near real time. These data streams are processed through forecasting, flexibility identification, and transformer thermal modules before charging setpoints are issued to the depot charging system. In the synthesized evaluation, the framework reduces peak transformer loading by 18.7%, decreases daily transformer loss-of-life by 31.4%, and maintains fleet readiness above 98.6%. The analytics design and evaluation structure are summarized through two original tables. The proposed framework provides a practical operating layer for depot managers and distribution utilities. It supports safer fleet electrification by converting charging data into transformer-aware scheduling decisions.

A virtual power plant (VPP) serves as a promising solution by aggregating distributed energy resources (DERs) and fulfilling the prosumer’s requirements for power availability, quality, and reliability. This study presents the optimized scheduling of an interconnected VPP within a multi-area framework, with tie-line connections and various renewable resources. The scheduling process begins with a day-ahead (hourly) interval, then advances to an hour-ahead interval (intra-hour and real-time), specifically using 15-minute and 5-minute time steps for the proposed VPP in the multi-area setting. The problem features two primary objective functions: net profit (to be maximized) and emissions (to be minimized). Given the involvement of renewables in the energy portfolio and the inherent complexity of the problem, the suggested multi-area VPP is evaluated using an advanced nature-inspired metaheuristic algorithm. Additionally, the formulation is extended to a multi-objective framework, with various scheduling approaches implemented to simultaneously lower emissions and enhance overall profit margins by boosting cumulative system profits. Moreover, an extensive numerical assessment across different optimization horizons demonstrates fast convergence and low computational time in achieving the optimal solution. 

This research proposes a novel, low-power routing protocol tailored for IoT networks that merges intelligent data-aggregation clustering with optimized path selection to overcome critical data-handling challenges in smart urban environments. The protocol relies on a layered architecture that includes Data Fusion Heads (DFH), intermediate relay DFHs, and the marine predators algorithm—an effective nature-inspired optimizer equipped with a purpose-built fitness function. This function simultaneously refines multiple criteria: the geographic clustering density of Sensor Nodes (SNs) inside each data fusion group (DFG), the total hop distance toward the sink, intra-group node proximity, residual battery level (RE), Average Scale of Building Occlusions (ASBO), and the rotation interval of the Primary DFH (PDFH). One of the protocol’s core advancements is the systematic application of in-network data fusion, which drastically reduces duplicate packet forwarding and markedly improves the accuracy and usefulness of collected information across all DFGs. By aggregating readings from multiple sensors, the volume of outbound traffic is greatly reduced, yielding substantial improvements in energy efficiency. Two complementary forwarding strategies are supported: single-hop forwarding, where already fused payloads travel directly to the sink, and multi-hop forwarding, in which an appropriate PDF relay is dynamically elected using a weighted relay selection metric that incorporates remaining energy (RE), sink distance, and ASBO. By integrating robust fault-tolerance procedures, intelligent redundancy elimination, and efficient fusion mechanisms, the protocol achieves markedly greater dependability and sustains strong operation under adverse or unpredictable network conditions. Detailed simulation experiments, along with side-by-side benchmarking against contemporary methods, confirm the protocol’s clear advantage across essential performance indicators—namely, network longevity, total energy expenditure, data throughput, and mean end-to-end latency. Compared with leading recent approaches such as the Particle Swarm Optimization-based energy-efficient clustering protocol (PSO-EEC), linearly decreasing inertia weight PSO (LDIWPSO), Optimized Fuzzy Clustering Algorithm (OFCA), and Novel PSO-based Protocol (NPSOP), the presented solution delivers outstanding gains. Concretely, network lifetime is prolonged by 299% versus PSO-EEC, 264% versus LDIWPSO, 306% versus OFCA, and 249% versus NPSOP. Energy usage drops by 254% relative to PSO-EEC, 264% relative to LDIWPSO, 247% relative to OFCA, and 253% relative to NPSOP. Throughput rises by 67% over PSO-EEC, 59% over LDIWPSO, 53% over OFCA, and 50% over NPSOP. By skillfully combining data aggregation with carefully tuned routing decisions, this protocol establishes a new benchmark for power-efficient organization and communication within IoT-based data fusion groups, delivering a dependable and adaptable framework suitable for diverse smart-city and IoT scenarios.