Organizing prosumers into electricity trading communities: Costs to attain electricity transfer limitations and self‐sufficiency goals

Abstract

Summary Among household electricity end users, there is growing interest in local renewable electricity generation and energy independence. Community‐based and neighborhood energy projects, where consumers and prosumers of electricity trade their energy locally in a peer‐to‐peer system, have started to emerge in different parts of the world. This study investigates and compares the costs incurred by individual households and households organized in electricity trading communities in seeking to attain greater independence from the centralized electricity system. This independence is investigated with respect to: (i) the potential to reduce the electricity transfer capacity to and from the centralized system and (ii) the potential to increase self‐sufficiency. An optimization model is designed to analyze the investment and operation of residential photovoltaic battery systems. The model is then applied to different cases in a region of southern Sweden for year 2030. Utilizing measured electricity demand data for Swedish households, we show that with a reduced electricity transfer capacity to the centralized system, already a community of five residential prosumers can supply the household demand at lower cost than can prosumers acting individually. Grouping of residential prosumers in an electricity trading community confers greater benefits under conditions with a reduced electricity transfer capacity than when the goal is to become electricity self‐sufficient. It is important to consider the local utilization of photovoltaic‐generated electricity and its effect on the net trading pattern (to and from the centralized system) when discussing the impact on the electricity system of a high percentage of prosumers.

Small-Scale Communities Are Sufficient for Cost- and Data-Efficient Peer-to-Peer Energy Sharing

“…We have based our study on residential households, some of which equipped with solar panels and battery systems. As shown in previous studies [15,16], the most determinant parameter to incentivize prosumers to more collaboration and more self-consumption of electricity is the difference in price between electricity bought from and sold to the grid (itself determined by electricity spot market prices, transfer fees, tax rates, etc). Households Demand: Consumption profiles for the 100 Northern European households of the study originate from [37] and were pre-processed as in [15].…”

“…Electricity Prices: The basis for the hourly variable electricity price (wholesale electricity price) used in this study corresponds a projection for year 2030 of a scenario representing a European system pathway with a high share of renewable power generation, shorter lifetimes for nuclear power plants, and no carbon capture and storage. Energy tax, grid fees, VAT, small reimbursement for selling to the grid and investment costs have been set as described in Section 3.2 based on current assumptions for residential PV battery systems for the year 2030 (for more details see [15,16] and references therein). Solar Production: The solar profile is based on the geographical location of the households as described in [36].…”

“…Since communication faults are not considered here, each community is equivalent to a single prosumer with aggregated PV and battery capacities. This simplification allowed us to study in depth the many parameters of the P2P network (size of the communities, energy production levels, matching of the participants and prediction models) while keeping natural and reasonable assumptions about the underlying cost model [15,16].…”

“…P2P energy sharing has been in the focus of studies within microgrids [34,40], in the form of real-time trading processes among peers [41,51], which induced high maintenance cost (due to the very fine-grained time aspect) and was very disruptive to the current power grid. Later instances of P2P energy trading assume longer time scale, with households forming sharing communities, with a predefined and pre-agreed cost-sharing mechanism [2,15] among the participating households. Virtual currency and blockchain-based approaches have been proposed as concrete payment schemes in such P2P trading markets [4,24,28].…”

“…Contributions: We present a detailed study to check practical feasibility, efficiency and network costs of P2P energy sharing in concrete scenarios, based on large-scale real measured data. The starting point of the study is to extend previous energy trading community analysis [2,15,38,43] towards realistic P2P instances with the following distinctions from previous state-of-the-art models: • Forecast Range: we explore different prediction power while replacing perfect foresight by limited prediction: at each given time, only an estimated fraction of the future produced data is available. This turns the original optimization problem, where a single optimal decision is computed in a batch-fashion, into an online decision-making problem where continuous actions are taken, which influence the future outcomes.…”