Sharing Coefficient-Based Price Signals for Demand Response in Renewable Energy Communities

Jul 16, 2026

Renewable energy communities can raise local photovoltaic (PV) utilization, yet feeder-level surplus may still create reverse power flow in low-voltage networks. Existing sharing coefficient methods mainly support ex-post surplus allocation and billing, lacking guidance to shift demand toward feeder-hours where shared PV reduces exports. This work introduces a sharing-coefficient-based demand response framework that transforms allocation results into day-ahead household price signals, accounting for feeder location via iterative optimization and convex load reshaping.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

Abstract

Introduction

Sharing Coefficient-Based Price Signals for Demand Response in Renewable Energy Communities Alireza Shooshtari, Member, IEEE, Antonio Pepiciello, Member, IEEE, and Jos Luis Dom´ınguez-Garc´ıa, Senior Member, IEEE arXiv :2607 .09357v1 [ ee ss . SY] 10 Jul 2026 Abstract—Renewable energy communities can increase local photovoltaic (PV) use, but feeder-level surplus can still cause reverse power flow in low-voltage networks. Existing sharing coefficient methods are mainly used ex-post for surplus allocation and billing, so they do not directly guide demand toward hours and feeders where shared PV can reduce export. This paper proposes a sharing coefficient-based demand response framework that converts dynamic proportional allocation outcomes into household specific day-ahead price signals. The feeder-aware design first shares surplus within each feeder, while the feederagnostic design shares surplus through a single community pool. The energy community manager iteratively computes the allocation from submitted demand and PV forecasts, decomposes purchased energy into same-feeder, inter-feeder, and grid-import components, and coordinates household load reshaping through a convex optimization model. Using measured profiles from 15 households and AC power flow analysis, the framework reduces feeder reverse energy by 45.0% and 44.6% on selected high reverse energy days, and by 69.0% and 66.3% over the annual window, for the feeder-aware and feeder-agnostic cases, respectively. These results show that sharing coefficients can be used not only for ex-post billing, but also as operational price signals for demand response, with feeder-aware allocation providing an additional network benefit by accounting for household location in the low-voltage network. Index Terms—Demand response, energy communities, sharing coefficients I. INTRODUCTION THE transition toward low-carbon power systems is ac celerating the decentralization of electricity generation, mainly driven by the increasing deployment of distributed energy resources such as residential photovoltaic (PV) systems. As a result, end users are evolving from passive consumers to active prosumers, and distribution networks are becoming active platforms for integrating local renewable energy. In this context, energy communities have emerged as a framework to involve citizens and local actors in the energy transition. They enable participants to jointly organize local energy activities and share the resulting economic benefits [1],[2] . In Europe, this concept has been supported by the Clean Energy for all Europeans package [3] and formalized through Directive (EU) 2018/2001 and Directive (EU) 2019/944 [4],[5] . In residential PV-based communities, surplus generation can be shared among members through collective selfconsumption schemes, improving local renewable energy utilization and reducing electricity imports and costs [6], [7] . Alireza Shooshtari, Antonio Pepiciello, and Jos Luis Dom´ınguez-Garc´ıaare with the Catalonia Institute for Energy Research (IREC), Barcelona, Spain (e-mail: [ashooshtari@irec.cat](ashooshtari@irec.cat); [apepiciello@irec.cat](apepiciello@irec.cat); [jldominguez@irec.cat](jldominguez@irec.cat)). The authors declare no conflicts of interest. Energy sharing is typically implemented through sharing coefficients, which define how surplus generation is allocated among participants. These coefficients can be fixed, proportional, priority-based, or dynamic. While they provide a structured mechanism for allocation and settlement, they do not directly influence consumption behavior, and therefore have limited impact on aligning demand with periods of local renewable generation [8] . Demand response addresses this limitation by adjusting flexible loads in response to price signals [9], [10] . However, the effectiveness of such signals also depends on the distribution network, since the value of shared energy is influenced not only by its quantity b

FAQs

Why do renewable energy communities still face reverse power flow issues?

Even when PV surplus is shared within the community, surplus at the feeder level can cause reverse power flow in low-voltage networks.

What gap exists in conventional sharing coefficient approaches?

Conventional methods mainly allocate surplus and settle bills ex-post, so they do not directly steer demand toward the times and feeders where shared PV would reduce exports.

How does the proposed framework enable demand response using sharing coefficients?

It converts dynamic proportional allocation outcomes into household-specific day-ahead price signals, decomposes purchased energy into same-feeder, inter-feeder, and grid-import components, and coordinates load reshaping via convex optimization.

Jul 16, 2026

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