Distribution Loss Reduction by Household Consumption Coordination in Smart Grids

Abstract

In this work, we address the problem of optimizing the electrical consumption patterns for a community of closely located households, with a large degree of flexible consumption, and further some degree of local electricity production from solar panels. We describe optimization methods for coordinating consumption of electrical energy within the community, with the purpose of reducing grid loading and active power losses. For this we present a simplified model of the electrical grid, including system losses and capacity constraints. Coordination is performed in a distributed fashion, where each consumer optimizes his or her own consumption pattern, taking into account both private objectives, specific to each individual consumer, as well as objectives common to all consumers. In our work, the common objective is to minimize active losses in the grid, and ensure that grid capacity limits are obeyed. These objectives are enforced by coordinating consumers through a nonlinear penalty on power consumption. We present simulation test-cases, illustrating that significant reduction of active losses, can be obtained by such coordination. The distributed optimization algorithm employs the alternating directions method of multipliers.Index Terms-Alternating direction method of multipliers (ADMM), consumer behavior, distributed networks, energy management, smart grid.

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“…One central control element collecting information from remote sites and deciding set-points for remote actuation; examples are [31]- [37]. T2: (D)istributed.…”

Electric energy management in residential areas through coordination of multiple smart homes

“…End-users are charged based on the ratio of their individual consumption over the aggregated consumption. As a result, the aggregated electricity cost of the residential area is reduced from $44.77 to $37.90 for the simu- Multi-agent systems [94], [95], [96], [97] Game theory [98], [99], [100] Optimization techniques [85], [101], [102], [103], [104], [105] lation duration, and the PAR is decreased from 2.1 to 1.8.…”

“…In the literature, problems can take into account grid conditions (e.g., congestion) in the optimization. Formulations can also consider stochastic problems (e.g., with uncertainty on consumption [85] and renewable generation [101]), multiple objectives [102,103], or other objectives such as maintaining voltage stability [104] or decreasing active power losses [105].…”

“…In [105], the optimization problem aims to minimize the users electricity bill, and considers the cost of active power losses, as well as the need to avoid line overload. Investigations focus on the optimal coordination of residential resources, in a distributed fashion, and consider private objectives as well as objectives common to all consumers (here, minimizing losses).…”

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