arXiv subjects

Sebastian Zwickl-Bernhard

Publications and source records attributed to Sebastian Zwickl-Bernhard.

4 recordsLinked to original sources

arXiv2026

Market Power and Distributed Solar Integration in Microgrids under Limited Regulation

Decentralized electricity systems increasingly emerge where centralized grids fail to provide reliable supply. In such settings, privately operated neighborhood microgrids, often based on diesel generators, exhibit significant market power, limited regulatory oversight, and high environmental externalities. In parallel, households increasingly deploy off-grid solar photovoltaic (PV) systems to gain control over electricity supply. However, these systems suffer from curtailed excess generation during peak solar hours and unreliable access at other times. While prior studies have optimized microgrids in low-reliability grid contexts from a techno-economic perspective, they largely neglect the market power exerted by monopolistic private generators. This paper addresses this gap by developing a bi-level game-theoretic model that enables household-generated electricity to be fed into the microgrid while explicitly accounting for the market power of a neighborhood diesel generator company (DGC). The regulator sets price and feed-in-tariff caps to maximize household economic surplus (HES), while the DGC acts as a profit-maximizing agent controlling access and supply. The model is illustrated using high-resolution empirical data from Lebanon. Results show that: (i) price and feed-in-tariff caps substantially increase HES and consistently induce significant household PV feed-in to the microgrid; (ii) higher DGC budgets or greater PV-owner penetration lead to pronounced gains in HES; and (iii) the renewable energy share reaches 60% under base conditions and approaches 100% at sufficiently high budgets or PV-owner penetration levels, compared to 0% under the status quo.

eess.SY↗

arXiv2026

Household coping mechanisms under grid failure: Evidence from a high electrification context in Lebanon

Despite near-universal electrification in many countries, electricity supply shortages continue to shape household energy use. This paper examines how households adapt to chronic grid failure in high-electrification, high-dependence contexts, using Lebanon as a case study. Drawing on original survey data from 1,000 households, we analyze both supply-side coping mechanisms such as diesel generators and solar photovoltaic (PV)-battery systems, and demand-side adaptations, including load shifting and demand suppression. The results reveal a landscape of household responses, where socioeconomic status plays a central role in determining access to backup solutions and the extent of met demand. While diesel generators remain widespread, a transition toward PV-battery systems is observed, especially among financially capable households. However, decentralized self-generation is associated with inefficiencies, including substantial levels of curtailed solar generation. On the demand side, households exhibit reductions in electricity use, leading to distinct consumption profiles depending on the type of backup system employed. These findings highlight the importance of distinguishing between met and unmet demand when assessing energy needs under unreliable supply. The paper contributes to the literature by providing a quantitative characterization of the interaction between self-generation and demand adaptation in a supply-constrained high-electrification context. It also offers empirical demand profiles that incorporate suppressed consumption, addressing a key gap in electricity system planning. From a policy perspective, the results underscore the need to account for unmet demand, address inequities in access to coping technologies, and reduce inefficiencies in decentralized systems.

econ.GN↗

arXiv2026

Conversational Demand Response: Bidirectional Aggregator-Prosumer Coordination through Agentic AI

Residential demand response depends on sustained prosumer participation, yet existing coordination is either fully automated, or limited to one-way dispatch signals and price alerts that offer little possibility for informed decision-making. This paper introduces Conversational Demand Response (CDR), a coordination mechanism where aggregators and prosumers interact through bidirectional natural language, enabled through agentic AI. A two-tier multi-agent architecture is developed in which an aggregator agent dispatches flexibility requests and a prosumer Home Energy Management System (HEMS) assesses deliverability and cost-benefit by calling an optimization-based tool. CDR also enables prosumer-initiated upstream communication, where changes in preferences can reach the aggregator directly. Proof-of-concept evaluation shows that interactions complete in under 12 seconds. The architecture illustrates how agentic AI can bridge the aggregator-prosumer coordination gap, providing the scalability of automated DR while preserving the transparency, explainability, and user agency necessary for sustained prosumer participation. All system components, including agent prompts, orchestration logic, and simulation interfaces, are released as open source to enable reproducibility and further development.

cs.AI↗

arXiv2025

Agentic AI Home Energy Management System: A Large Language Model Framework for Residential Load Scheduling

The electricity sector transition requires substantial increases in residential demand response capacity, yet Home Energy Management Systems (HEMS) adoption remains limited by user interaction barriers requiring translation of everyday preferences into technical parameters. While large language models have been applied to energy systems as code generators and parameter extractors, no existing implementation deploys LLMs as autonomous coordinators managing the complete workflow from natural language input to multi-appliance scheduling. This paper presents an agentic AI HEMS where LLMs autonomously coordinate multi-appliance scheduling from natural language requests to device control, achieving optimal scheduling without example demonstrations. A hierarchical architecture combining one orchestrator with three specialist agents uses the ReAct pattern for iterative reasoning, enabling dynamic coordination without hardcoded workflows while integrating Google Calendar for context-aware deadline extraction. Evaluation across three open-source models using real Austrian day-ahead electricity prices reveals substantial capability differences. Llama-3.3-70B successfully coordinates all appliances across all scenarios to match cost-optimal benchmarks computed via mixed-integer linear programming, while other models achieve perfect single-appliance performance but struggle to coordinate all appliances simultaneously. Progressive prompt engineering experiments demonstrate that analytical query handling without explicit guidance remains unreliable despite models' general reasoning capabilities. We open-source the complete system including orchestration logic, agent prompts, tools, and web interfaces to enable reproducibility, extension, and future research.

cs.AI↗