Impact of Social Welfare Metrics on Energy Allocation in Multi-Objective Optimization

Abstract

Resource allocation problems are at the core of the smart grid where energy supply and demand must match. Multi-objective optimization can be applied in such cases to find the optimal allocation of energy resources among consumers considering energy domain factors such as variable and intermittent production, market prices, or demand response events. In this regard, this paper considers consumer energy demand and system-wide energy constraints to be individual objectives and optimization variables to be the allocation of energy over time to each of the consumers. This paper considers a case in which multi-objective optimization is used to generate Pareto sets of solutions containing possible allocations for multiple energy intensive consumers constituted by commercial greenhouse growers. We consider the problem of selecting a final solution from these Pareto sets, one of maximizing the social welfare between objectives. Social welfare is a set of metrics often applied to multi-agent systems to evaluate the overall system performance. We introduce and apply social welfare ordering using different social welfare metrics to select solutions from these sets to investigate the impact of the type of social welfare metric on the optimization outcome. The results of our experiments indicate how different social welfare metrics affect the optimization outcome and how that translates to general resource allocation strategies.

A multi-objective optimization platform for artificial lighting system in commercial greenhouses

“…Although some proposed concepts in this paper have been explained and utilized in previous works (Umair et al 2019;Clausen et al 2020), such as posterior normalization, social welfare metrics, and relative importance graph, the previous works only focused on a specific implementation point of one of the two versions of the MOO platform DynaLight. In this paper, the entire implementation process of the two versions of DynaLight is elaborated, including the GA evolution process and the final optimal light plan selection.…”

“…Once the relative importance relations between every objective are established, a relative importance graph can be generated accordingly (Umair et al 2019;Clausen et al 2020). A RIG is a directed graph, and there are two key elements in it, which can be described as RIG = < V, E>.…”

Demand Response in Smart Grids

“…This paper provides a summary of the Energies Special Issue, covering the published articles [1][2][3][4][5][6][7][8][9][10], which address several topics related to demand response and smart grids. Table 1 identifies the most relevant topics in each publication.…”

“…The Special Issue "Demand Response in Smart Grids" includes 10 papers [1][2][3][4][5][6][7][8][9][10]. Demand Response (DR) and Smart Grids (SG) are two rather broad key topics in the operation of power and energy systems.…”

Agent based coordination protocol for system of cyber-physical systems

“…In order to ensure social welfare among the TCs within each CA, which is considered as a suitable metric for assessing system performance (Chevaleyre et al 2005), the Mediator employs cardinal preference aggregation mechanism based on Social Welfare Orderings (SWOs). There exists several SWO methods to measure social welfare among TCs, namely utilitarian, egalitarian, elitist, Nash product, median rank dictator, leximin ordering, approximated fairness, fairness analysis, quantitative fairness and entropy explained in (Umair et al 2019;Clausen et al 2020). Before applying the selection criterion based on SWO, the cost of each TC t is normalized for each contract C in the Pareto set P f .…”