Energy Management and Peer-to-peer Trading in Future Smart Grids: A Distributed Game-Theoretic Approach

Abstract

We consider the economic dispatch problem for a day-ahead, peer-to-peer (P2P) electricity market of prosumers (i.e., energy consumers who can also produce electricity) in a distribution network. In our model, each prosumer has the capability of producing power through its dispatchable or nondispatchable generation units and/or has a storage energy unit. Furthermore, we consider a hybrid main grid & P2P market in which each prosumer can trade power both with the main grid and with (some of) the other prosumers. First, we cast the economic dispatch problem as a noncooperative game with coupling constraints. Then, we design a fully-scalable algorithm to steer the system to a generalized Nash equilibrium (GNE). Finally, we show through numerical studies that the proposed methodology has the potential to ensure safe and efficient operation of the power grid.

Operationally-Safe Peer-to-Peer Energy Trading in Distribution Grids: A Game-Theoretic Market-Clearing Mechanism

“…(ii) Algorithm 1 is semi-decentralized, i.e., the prosumers rely on a reliable central coordinator (i.e., the DNO) that gathers local variables in aggregative form and then broadcasts signals, such as dual variables, to all prosumers, see Figure 2. Such communication architecture is particularly efficient to design fast and scalable equilibrium seeking algorithms in games [16]; (iii) The local primal update of each prosumer (lines: [8][9][10][11] involves the solution of a quadratic programming problem 4 , for which very efficient solvers are available, e.g. [32]; (iv) The primal update of the DNO (lines: [26][27][28][29]…”

“…However, we formulate the market clearing as a GNE problem, in which the players (i.e., prosumers and network operators) have coupling objective functions and constraints (Section II). Our market formulation extends our preliminary work [11] by including nonlinear network operational constraints and system operators in the model, which considerably complicate the analysis.…”

Data-Driven Behaviour Estimation in Parametric Games

“…swarms of birds (Molloy et al, 2018), automatic control, e.g. mobility systems (Censi et al, 2019), internet of things (Chi et al, 2021), and power systems (Belgioioso et al, 2020), and machine learning (Heaton et al, 2021).…”

Projected gradient-tracking in multi-cluster games and its application to power management

“…However, such privacy restrictions are even more essential in noncooperative scenarios where local information needs to be kept from competitors. In the context of Smart Grids, cooperative optimization has been intensively employed in the context of economic dispatch [4], [5], [6] and optimal power flow problems [8], while energy management and energy market situations are often modelled by non-cooperative game theory [9], [10]. However, less research has been dedicated to scenarios, in which both cooperative and non-cooperative situations arise.…”