Abstract
There has been an effort for a few decades to keep energy consumption at a minimum or at least within a low-level range. This effort is more meaningful and complex by including a customer’s satisfaction variable to ensure that customers can achieve the best quality of life that could be derived from how energy is used by different devices. We use the concept of Shapley Value from cooperative game theory to solve the multi-objective optimization problem (MOO) to responsibly fulfill user’s satisfaction by maximizing satisfaction while minimizing the power consumption, with energy constrains since highly limited resources scenarios are studied. The novel method uses the concept of a quantifiable user satisfaction, along the concepts of power satisfaction (PS) and energy satisfaction (ES). The model is being validated by representing a single house (with a small PV system) that is connected to the utility grid. The main objectives are to (i) present the innovative energy satisfaction model based on responsible wellbeing, (ii) demonstrate its implementation using a Shapley-value-based algorithm, and (iii) include the impact of a solar photovoltaic (PV) system in the energy satisfaction model. The proposed technique determines in which hours the energy should be allocated to maximize the ES for each scenario, and then it is compared to cases in which devices are usually operated. Through the proposed technique, the energy consumption was reduced 75% and the ES increased 40% under the energy constraints.
Highlights
Energy is the backbone of modern society
This study provides a motivation for such a granular level of smart meter data with distinct energy uses
The satisfaction concept through the novel concepts of power satisfaction (PS) and energy satisfaction (ES) included the detrimental impact that excess consumption could have in the quality of life
Summary
Energy is the backbone of modern society. It provides the means to support everyday infrastructure, such as hospitals, schools, and homes. Yang et al [5] implemented a Nash-based game theoretic approach to optimize time-of-use (ToU) pricing strategies considering the costs of fluctuating demands to the utility company and the satisfactions costs of user. Ogunjuyigbe et al [8], on the other hand, developed a cost per unit satisfaction index Their model considered individual devices at each time of the day. This study provides a motivation for such a granular level of smart meter data with distinct energy uses. The proposed MOO consists of responsibly fulfilling user’s needs by maximizing satisfaction while minimizing the power consumption. A novel model is proposed to include customer’s satisfaction in an optimization problem to minimize the energy consumption. Energy satisfaction (ES) is proposed to capture the benefit of energy uses and to model the optimization problem. Real data from [25] was used for validation
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