[Paper Review] Loss Allocation in Joint Transmission and Distribution Peer-to-Peer Markets

Fabio Moret, Andrea Tosatto+2 more|arXiv (Cornell University)|Jan 15, 2020

Smart Grid Energy Management24 references46 citations

TL;DR

The paper develops a convex, market-clearing framework for decentralized P2P electricity markets that includes transmission and distribution system operators as active market participants and introduces loss allocation policies to study their impact on prices and fairness.

ABSTRACT

Large deployment of distribute energy resources and the increasing awareness of end-users towards their energy procurement are challenging current practices of electricity markets. A change of paradigm, from a top-down hierarchical approach to a more decentralized framework, has been recently researched, with market structures relying on multi-bilateral trades among market participants. In order to guarantee feasibility in power system operation, it is crucial to rethink the interaction with system operators and the way operational costs are shared in such decentralized markets. We propose here to include system operators, both at transmission and distribution level, as active actors of the market, accounting for power grid constraints and line losses. Moreover, to avoid market outcomes that discriminate agents for their geographical location, we analyze loss allocation policies and their impact on market outcomes and prices.

Motivation & Objective

• Incorporate transmission and distribution system operators as active market participants in decentralized electricity markets.
• Account for grid constraints and line losses within a unified market-clearing framework.
• Introduce and evaluate loss allocation policies that affect market outcomes and fairness.
• Enable end-to-end market equilibria that reflect power grid constraints and losses (endogenous P2P market).
• Provide a basis for fairness evaluation of payments under different loss allocation schemes.

Proposed method

• Formulate a unified market-clearing optimization that combines prosumers, TSOs, DSOs, and a market operator into a single convex problem.
• Use a linear/convex approximation of AC power flows (PTDF-based) to represent line flows and losses.
• Introduce a loss allocation matrix A to distribute line losses among trades and agents.
• Propose two loss allocation policies: socialization (A^soc) and individual allocation (A^ind), along with a combined policy A = χ A^soc + (1−χ) A^ind.
• Show that the market equilibrium corresponds to a Nash equilibrium solved via the equivalent global optimization (5).
• Discuss potential decomposition via ADMM for decentralized clearing.

Experimental results

Research questions

• RQ1 How do different loss allocation policies influence energy trade prices and participant payments in a joint TSO-DSO decentralized market?
• RQ2 What is the impact of including power losses as market products and of explicit TSO/DSO interactions on market feasibility and fairness?
• RQ3 Can the decentralized market be formulated as a convex optimization that yields a Nash (equilibrium) outcome?
• RQ4 How does the combination of socialized and individual loss allocations affect fairness and efficiency?

Key findings

• Loss allocation policies directly affect market outcomes and trade prices, including the perceived price π_i for each agent.
• Energy and loss prices are linked through grid constraints, so loss allocation coefficients influence energy prices as well.
• Socialization of losses distributes costs evenly among participants, reducing geographical discrimination but not signaling local impact of trades.
• Individual loss allocation assigns losses to the specific trades that generate them, promoting locality but potentially disadvantaging distant participants.
• A linear combination of policies (A = χ A^soc + (1−χ) A^ind) offers a tunable trade-off between fairness and local accountability.
• The framework enables fairness assessment and endogenous market equilibria by accounting for TSO/DSO constraints within a P2P market setting.