The increasing penetration of distributed energy resources (DERs), together with the growing participation of end users, is reshaping the operation of modern distribution networks. In this context, demand response (DR) can provide valuable flexibility to improve system operation, but the large number of small-scale residential loads creates challenges related to scalability, data exchange, privacy, and coordination. Distributed Energy Resource Management Systems (DERMS) offer a promising framework for managing DER integration. However, residential DR is often only implicitly represented and lacks explicit aggregation, disaggregation, and coordination mechanisms. This paper proposes a hierarchical DERMS framework that explicitly integrates residential DR through an aggregation and disaggregation architecture. The proposed approach represents large populations of residential DR participants using compact aggregated flexibility models, thereby reducing the upstream optimization dimension and limiting the exchange of individual customer-level data. A Utility-DERMS scheduling model is combined with a machine-learning-guided Sequential Adjustment Method (SAM) to restore voltage feasibility. The framework is validated on the IEEE 123-node test system under stressed loading, multi-period DR scheduling, and uncertainty-based scenarios. Simulation results demonstrate the effectiveness of the proposed DERMS framework in reducing upstream DR representation and data exchange while achieving voltage-feasible operation, fewer SAM iterations, and effective coordination of DR load shifting and rebound behavior. Benchmarking against centralized OPF and non-aggregated implementations further confirms that the proposed framework provides a practical balance between scalability, privacy-conscious coordination, computational tractability, and voltage-secure operation.
The day-ahead electricity market is crucial for planning the operation of a power grid's assets. However, this market currently faces the challenge of strategic behaviour by residential agents, a feature enabled by the deployment of transactive energy systems at the residential level. In an iterative negotiation approach, strategic agents may exploit weaknesses in the mechanism by reporting false information to maximise financial gains, thereby compromising the program's long-term sustainability. This paper proposes a distributed privacy-preserving mechanism that prevents strategic residential agents from reporting false consumption plans in a day-ahead electricity market. The mechanism comprises three phases: (i) a negotiation phase, modelled as a convex bilevel stochastic optimisation problem, where residential agents and a coordinator iteratively agree on electricity prices, penalties for deviations, and individualised consumption plan; (ii) an execution phase, where residential agents optimise their real-time consumptions based on disclosed uncertainties; and (iii) a settlement phase, where the coordinator charges the customers with an electricity bill by applying the formal payment rule reported before the negotiation starts. Theoretical analysis demonstrates that truthful reporting constitutes a dominant strategy at every iteration, yielding a strategy-proof mechanism. Simulation results validate the mechanism's effectiveness, showing a 19.1% reduction in peak energy consumption and a decrease in average electricity prices from 8.05 cent to 4.03 cent/kWh, thereby satisfying participants' individual rationality. While the mechanism satisfies incentive compatibility, ex-ante IR, and potentially ex-ante efficiency, it exhibits a budget imbalance. This fact suggests that long-term financial viability requires external subsidies or complementary market strategies.
Unlined dam spillways have been subjected to severe rock mass scour/erosion over the years, and this erosion has been considered a critical safety concern in the design of these hydraulic structures. Development of a reliable assessment method necessitates the proper understanding of the mechanisms involved in the scouring process and the associated scour parameters. This study investigates the effect of rock block geometry and its orientation on block stability using physical model testing. An analytical model is developed to evaluate the block stability using the results of model tests under varied geomechanical parameters, especially, joint orientation and block protrusion. The effect of block geometry like aspect ratio and exposure length on the instability is further examined through a comprehensive parametric analysis. The stability analysis presents the possibility of block removal by overturning in blocks oriented at − 45° to the flow direction with factor of safety less than 1 under different pressure combinations and by uplift in blocks oriented at + 45° and 0° under specific protrusion configurations with factor of safety less than 1 under certain pressure combinations. The proposed approach presents a novel experimental–analytical framework linking quantifiable hydraulic and geomechanical parameters to predict probable scour mechanisms. The findings of this study offer practical insights to better evaluate the erosion risks and enhance dam safety assessments.
This paper proposes a data-driven framework for monitoring and diagnosing erosive cavitation in hydraulic turbines using supervisory control and data acquisition data only, with the objective of replacing costly and sparsely deployed condition monitoring systems. A deep-learning-based indirect measurement approach is developed to estimate the instantaneous cavitation rate from operational variables and is validated on multi-year real industrial data from two Francis turbine units, while ensuring model transferability across machines. Beyond point prediction, the framework explicitly addresses uncertainty quantification to support reliable decision-making, and several methods, including heteroscedastic regression, Bayesian neural networks, split conformal prediction, and conformalized deep ensembles, are benchmarked using calibration metrics and evaluated under distribution shifts. Diagnostics is performed by aggregating instantaneous predictions into an uncertainty-bounded cumulative degradation trajectory, enabling an uncertainty-aware assessment of cavitation progression and supporting reliability- and safety-informed maintenance decisions. Results demonstrate that operational data can effectively replace condition monitoring systems for cavitation monitoring and that conformalized deep ensembles provide the most reliable uncertainty estimates under realistic industrial conditions.
On a global scale, human activities have significantly fragmented forest landscapes, resulting in over 20% of the remaining forests being located within 100 m of an edge. Forests adjacent to disturbances experience edge effects that can affect aboveground carbon storage through changes in forest structure. Our main objective was to assess aboveground carbon stocks and their drivers at forest edges across a large bioclimatic gradient of upland sites in the temperate and boreal forests of Eastern Canada, using powerline rights-of-way as a case study. We quantified carbon stocks in living and dead trees of all sizes and measured tree growth at stand and tree levels in forests adjacent to powerline rights-of-way. Compared with reference forests (> 50 m from right-of-way), aboveground carbon stocks at forest edges (< 20 m) were up to 60 - 75% higher in boreal spruce forests, 30% higher in temperate forests, but only 2% higher in boreal fir forests. Higher carbon stocks were linked to increased stand density, and thus a higher stand basal area, rather than larger tree diameters. Edge effects on tree characteristics (diameter, total height, crown length and area, and basal area increment) showed no clear pattern and depended on the characteristics of the forest. No edge effect was found in a stand with a recently established right-of-way (less than three years), suggesting that the magnitude of the edge effect varies over time. This study will improve the assessment of the carbon footprint of fragmented forest landscapes.
