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Active distribution networks increasingly rely on stochastic optimization to manage uncertainties arising from renewable generation, flexible demand, distributed storage, electric vehicles, and local energy markets. These uncertainties affect voltage quality, congestion, operational cost, reliability, and the value of flexibility. This systematic review categorizes and evaluates uncertainty-aware stochastic optimization techniques for demand response, DER coordination, and flexibility provision in active distribution networks. It focuses on methodological transparency, thematic synthesis, and identification of research gaps. A PRISMA-guided systematic review of 31 peer-reviewed journal articles published from 2017 to 2023 was conducted. A structured search, screening, eligibility assessment, and data extraction process was used to organize the evidence. The review maps dominant stochastic frameworks, including two-stage stochastic programming, chance-constrained optimization, robust-stochastic formulations, and distributionally robust optimization. Five synthesis tables are used across the full manuscript to summarize study selection, article characteristics, method taxonomies, application mappings, and research gaps. Stochastic optimization supports secure and efficient operation of active distribution grids, but important gaps remain in scalability, real-world validation, interoperability, and regulatory integration. The review outlines an agenda for more adaptive, transparent, and implementation-ready optimization methods.

Residential prosumers with rooftop photovoltaic systems and behind-the-meter batteries are changing the operating conditions of low-voltage and medium-voltage distribution feeders. When these resources respond independently to retail tariffs, their aggregate behavior can intensify reverse power flows, transformer loading, and voltage excursions. This paper proposes a bilevel optimization model that coordinates residential demand response, rooftop photovoltaic generation, and battery storage under distribution-feeder congestion constraints. The objective is to align prosumer economic decisions with distribution system operator requirements for congestion relief and voltage compliance. The upper-level problem represents the distribution system operator and minimizes feeder congestion, voltage deviations, and coordination costs. The lower-level problem represents residential prosumers that minimize their individual energy costs through load shifting, photovoltaic self-consumption, and battery dispatch. The proposed formulation produces coordinated schedules that reduce peak feeder loading and voltage violations relative to uncoordinated operation. Battery arbitrage and flexible demand are redirected from purely price-driven operation toward feeder-aware congestion mitigation. The bilevel coordination framework provides an operational method for managing residential distributed energy resources without replacing prosumer autonomy. It enables distribution system operators to procure flexibility while maintaining economically rational prosumer behavior.

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.

The value of distribution-level flexibility is increasingly important as electricity systems become more decentralized, variable, and prosumer-oriented. Prosumers, aggregators, and network constraints jointly shape whether flexible capacity becomes technically useful and economically valuable. Existing research lacks a unified theory that explains how flexibility value emerges from the interplay of prosumer decision-making, DER aggregation, and local congestion management. As a result, valuation is often treated as either a market-pricing problem, a network-operations problem, or a behavioural-participation problem rather than as a co-determined process. This article builds a theory model linking prosumer demand response, DER aggregation, and local congestion management to the valuation of grid flexibility. The model explains how distributed flexibility becomes valuable when behavioural willingness, aggregation capability, and network scarcity align. The model is developed conceptually, drawing on microeconomics, mechanism design, contract theory, and grid operation principles. It is intended to generate causal explanations and testable propositions rather than empirical estimates or optimization results. The model identifies causal pathways from prosumer behaviour and aggregator strategies to flexibility value, moderated by congestion intensity. Seven propositions articulate testable relationships, while the full model distinguishes flexibility creation, coordination, scarcity revelation, and value allocation. The theory provides a foundation for future empirical testing and can inform market design and regulatory decisions for flexibility services. It clarifies why flexibility value cannot be attributed to DER assets alone but must be understood as an institutional and network-contingent outcome.

Reinforcement learning can autonomously dispatch demand response actions under changing prices, loads, and grid constraints. However, operator acceptance requires explanations that clarify why specific customers or aggregations are selected. This article presents an explainable reinforcement learning framework for demand response dispatch using customer flexibility profiles, decomposed rewards, and operator review. The objective is to preserve dispatch performance while improving transparency and controllability. The framework combines a profile-aware RL dispatcher, a decomposed reward model, an explanation generator, and a structured operator review protocol. Evaluation is performed through simulation on realistic demand response scenarios with household and flexible-load representations. The proposed explainable RL framework is designed to achieve dispatch performance comparable to black-box RL while providing interpretable action rationales. Operator review is incorporated to approve, reject, or modify dispatch recommendations before execution. The framework bridges autonomous RL control and trustworthy grid-edge operation. It provides an algorithmic blueprint for human-centered demand response dispatch.

Demand response can provide valuable grid flexibility, but its effectiveness depends on respecting consumer comfort, aligning with prosumer preferences, and supporting decarbonization goals through low-carbon signals. Integrating these aspects within a human-in-the-loop optimization framework remains an operational challenge for aggregators managing heterogeneous flexible loads. This article proposes an optimization model that dispatches flexible residential and building-cluster loads while incorporating thermal comfort constraints, prosumer willingness-to-accept, and carbon intensity signals. The framework allows a human operator to review optimized schedules, adjust objective weights, and override selected dispatch decisions before execution. The model is formulated as a mixed-integer linear program with a weighted-sum objective capturing energy cost, carbon emissions, comfort deviations, and preference adherence. A realistic residential-cluster case study with weather-driven thermal dynamics, appliance flexibility, time-varying tariffs, and carbon intensity data validates the model. The proposed dispatch reduces feeder peak demand by 18.6% and carbon emissions by 11.9% relative to a non-carbon-aware baseline while maintaining the comfort violation index below 0.035 degree-hours per occupied interval. Operator overrides occur in 6.8% of dispatch decisions and primarily adjust HVAC curtailment during extreme outdoor-temperature periods and high-price events. The proposed framework offers a practical decision-support tool for demand response aggregators and grid operators. It balances competing operational objectives while retaining human oversight for exceptional comfort, reliability, and participation-risk conditions.

Smart-meter data often contain missing intervals that hamper analytics, billing, and demand response verification. Privacy regulations and consumer confidentiality expectations further restrict access to raw grid-edge data, even though accurate reconstruction remains essential for operational decision-making. Existing imputation methods may restore missing values but often ignore the physical constraints of electricity demand. At the same time, methods that rely on real customer records can create privacy risks when individual consumption patterns are exposed or reconstructed too precisely. This article proposes a conceptual physics-guided generative framework for reconstructing missing smart-meter data using synthetic load profiles. The framework is designed to avoid dependence on real consumer records while supporting demand response verification. The proposed framework could combine generative modelling with physical and operational constraints such as non-negative demand, ramp-rate limits, and daily periodic load patterns. It would use synthetic load profiles to represent realistic demand behaviour while preserving privacy. Benchmark approaches are discussed qualitatively rather than experimentally. The expected behaviour of the framework is described in terms of reconstruction fidelity, privacy preservation, physical consistency, and demand response verification usefulness. Four original tables summarise missing-data impacts, privacy risks, qualitative benchmark comparisons, and proposed evaluation metrics. The proposed framework offers a privacy-preserving and physics-aware pathway for smart-meter data reconstruction. It is intended as a conceptual foundation for future validation using synthetic, public, or utility-approved datasets.