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Electric power systems are rapidly evolving into deeply digital, cyber-physical infrastructures in which large fleets of distributed energy resources must be coordinated as system-level flexibility across multiple spatial and temporal scales. Despite growing distributed energy resource deployment, existing grid and market architectures lack scalable, interoperable mechanisms to reliably translate device-level flexibility into grid-aware services, creating risks to reliability, affordability, and resilience at high penetration. We propose that scalable and reliable coordination of distributed energy resource-based flexibility in future power systems is fundamentally an architectural problem that can be addressed through laminar cyber-physical design using minimal, standardized interoperability interfaces that link device autonomy with system-level objectives. To assess this claim, we present and discuss a layered cyber-physical systems architecture and explicate its implementation through standards-based interfaces, Flexibility Functions, hierarchical control, and case studies spanning U.S. and Danish regulatory, market, and operational contexts. Empirical evidence from New York’s Grid of the Future proceedings, Danish Smart Energy Operating System pilots, and operational aggregator deployments demonstrates that such architecture enables predictable, grid-aware flexibility while preserving device autonomy, interoperability, reliability, and quality of service. These results support a cross-Atlantic research agenda centered on joint testbeds, harmonized interoperability mechanisms, and coordinated policy experiments to accelerate the deployment of resilient, scalable, and flexible clean energy systems. 

Rapid deployment of distributed energy resources (DERs), such as solar photovoltaics (PV), poses a risk to the distribution grid under high penetration. Therefore, studying hosting capacity (HC) limits considering grid physics and demand variability is crucial. This paper introduces an improved framework for determining the HC of radial distribution networks by enhancing an existing convex inner approximation (CIA) approach. The proposed method achieves a more accurate and larger inner approximation, resulting in better HC limits. We also consider time-varying demand and the design of objective functions to ensure equitable access to grid resources. A case study with solar PV integration is conducted using a modified IEEE-37 radial network to examine the impact of increased PV capacity, demonstrating that with no more than 5 % annual solar PV energy curtailed, it is possible to increase solar PV hosting capacity by at least 50 % with no negative grid impacts and a net positive economic impact when accounting for the cost of carbon. Results show that fair allocation methods can lead to higher net profits and reduced PV curtailment and CO2. 

To optimize the dispatch of batteries, a model is required that can predict the state of energy (SOE) trajectory for a chosen open-loop power schedule to ensure admissibility (i.e., that schedule can be realized). However, battery dispatch optimization is inherently challenging when batteries cannot simultaneously charge and discharge, which begets a non-convex complementarity constraint. In this letter, we develop a novel composition of energy storage elements that can charge or discharge independently and provide a sufficient linear energy storage model of the composite battery. This permits convex optimization of the composite battery dispatch while ensuring the admissibility of the resulting (aggregated) power schedule and its disaggregation to the individual elements. 

This paper uses convex inner approximations (CIA) of the AC power flow to tackle the optimization problem of quantifying a 3-phase distribution feeder’s capacity to host distributed energy resources (DERs). This is often connoted hosting capacity (HC), but herein we consider separative bounds for each node on positive and negative DER injections, which ensures that injections within these nodal limits satisfy feeder voltage and current limits and across nodes sum up to the feeder HC. The methodology decomposes a 3-phase feeder into separate phases and applies CIA-based techniques to each phase. An analysis is developed to determine the technical condition under which this per-phase approach can still satisfy network constraints. New approaches are then presented that modify the per-phase optimization problems to overcome conservativeness inherent to CIA methods and increase overall HC, including selectively modifying the per-phase impedances and iteratively relaxing per-phase voltage bounds. Discussion is included on trade-offs and feasibility. To validate the methodology, simulation-based analysis is conducted with the IEEE 37-node test feeder and a real 534-node unbalanced radial distribution feeder. 

Scenario reduction (SR) aims to identify a small yet representative scenario set to depict the underlying uncertainty, which is critical to scenario-based stochastic optimization (SBSO) of power systems. Existing SR techniques commonly aim to achieve statistical approximation to the original scenario set. However, SR and SBSO are commonly considered as two distinct and decoupled processes, which cannot guarantee a superior approximation of the original optimality. Instead, this paper incorporates the SBSO problem structure into the SR process and introduces a novel problem-driven scenario reduction (PDSR) framework. Specifically, we project the original scenario set in distribution space onto the mutual decision applicability between scenarios in problem space. Subsequently, the SR process, embedded by a distinctive problem-driven distance metric, is rendered as a mixed-integer linear programming formulation to obtain the representative scenario set while minimizing the optimality gap. Furthermore, ex-ante and ex-post problem-driven evaluation indices are proposed to evaluate the SR performance. Numerical experiments on two two-stage stochastic economic dispatch problems validate the effectiveness of PDSR, and demonstrate that PDSR significantly outperforms existing SR methods by identifying salient (e.g., worst-case) scenarios, and achieving an optimality gap of less than 0.1% within acceptable computation time.