This paper investigates the problem of proportionally fair double-sided energy auction involving buying and selling agents. The grid is assumed to be operating under islanded mode. A distributed auction algorithm that can be implemented by an aggregator, as well as a possible approach by which the agents may approximate price anticipation is considered. Equilibrium conditions arising due to price anticipation is analyzed. A modified auction to mitigate the resulting loss in efficiency due to such behavior is suggested. This modified auction allows the aggregate social welfare of the agents to be arbitrarily close to that attainable with price taking agents. Next, equilibrium conditions when the aggregator collects a surcharge price per unit of energy traded is examined. A bi-objective optimization problem is identified that takes into account both the agents' social welfare as well as the aggregator's revenue from the surcharge. The results of extensive simulations, which corroborate the theoretical analysis, are reported.

In this paper, a general-purpose double auction mechanism that is specifically relevant for energy transactions among competing buyers and sellers, i.e., agents, in a microgrid has been investigated. This auction algorithm is implemented by means of a microgrid controller (MC), and is individually rational, weakly budget balanced, and efficient (social welfare maximizing). Furthermore, the mechanism does not violate any physical grid constraints and does not require agents' private information. These characteristics of the auction are accomplished by a suitable surrogate objective function which implicitly maximizes the social welfare while the agents place bids in a selfish manner and can sell or procure energy. The sellers of energy are modeled as prosumers that are equipped with photovoltaic panels as well as advanced smart meters and are willing to participate in selling their excess energy due to saturation in their energy consumption utility curves. Buyers of energy are assumed to be conventional consumers with smart meter capabilities to communicate with the MC and place bids to get optimal allocations. Buyers aim to maximize their energy usage utility plus their payment amount whereas sellers maximize their own energy consumption utility plus the reimbursement that they receive for selling their excess generation. Furthermore, the mechanism allows the possibility of sellers' coalition where a fair redistribution of the energy supply is possible in a competitive market with less demand and more supply.

Distribution locational marginal pricing (DLMP) can adversely affect users in a grid-constrained transactive distribution system market (DSM) that are at larger distances from the substation, thereby requiring longer paths to connect to it. When the grid operates closer to its physical limits in terms of line capacities and voltage deviations, these users are more likely to cause grid violations than others in the vicinity of the substation. Conversely, increased energy consumption by users near the substation can choke off supply to those at the grid’s extremities. This research describes a novel mechanism to charge users in a more equitable manner, by regularizing the distribution system operator (DSO)’s social welfare objective function with the Jain’s index of fairness. The overall problem entails the maximization of the regularized objective within a set of linear constraints that ensure that the grid’s physical limits are not violated. Dual decomposition is applied to the constrained optimization problem. The dual variables and unit costs are incrementally updated by the DSO using the augmented Lagrangian method (ALM). Simulation results confirm the effectiveness of the proposed approach.