[Paper Review] Non Cooperative Game Theoretic Approach for Residential Energy Management in Smart Grid

Ilyes Naidji, Moncef Ben Smida+2 more|arXiv (Cornell University)|Aug 23, 2019

Smart Grid Energy Management18 references4 citations

TL;DR

This paper proposes a non-cooperative game-theoretic approach for residential energy management in smart grids, using dynamic pricing to guide consumers in scheduling appliances to minimize both daily energy costs and discomfort levels. The Nash equilibrium is computed via NSGA-II, achieving a 37% cost reduction with only a 20% discomfort increase compared to the reference scenario, outperforming cost-only strategies in balancing efficiency and comfort.

ABSTRACT

Demand side management (DSM) is one of the main functionalities of the smart grid as it allows the consumer to adjust its energy consumption for an efficient energy management. Most of the existing DSM techniques aim at minimizing the energy cost while not considering the comfort of consumers. Therefore, maintaining a trade-off between these two conflicting objectives is still a challenging task. This paper proposes a novel DSM approach for residential consumers based on a non-cooperative game theoretic approach, where each player is encouraged to reshape its electricity consumption pattern through the dynamic pricing policy applied by the smart grid operator. The players are guided to select the best strategy that consists of scheduling their electric appliances in order to minimize the daily energy cost and their discomfort level. The Nash Equilibrium of the energy management game is achieved using Non-Sorting Genetic Algorithm NSGA-II. Simulation results show the effectiveness of the distributed non cooperative game approach for the residential energy management problem where an appreciable energy cost reduction is reached while maintaining the discomfort in an acceptable level.

Motivation & Objective

• To address the trade-off between minimizing residential energy costs and maintaining consumer comfort in demand response (DR) strategies.
• To develop a distributed, non-cooperative game model where consumers independently optimize appliance scheduling based on dynamic pricing.
• To incorporate discomfort level as a quantifiable objective alongside energy cost in energy management.
• To achieve a fair and equilibrium-based solution using game theory that reflects real consumer behavior.
• To evaluate the approach through multi-objective optimization using NSGA-II, balancing conflicting goals effectively.

Proposed method

• Formulates a non-cooperative game where each residential consumer is a player optimizing their own appliance scheduling.
• Models consumer discomfort as the deviation between preferred and scheduled appliance operation times.
• Uses dynamic pricing as an incentive mechanism to shift consumption to off-peak hours.
• Applies NSGA-II to solve the multi-objective optimization problem, seeking Nash equilibrium for cost and discomfort.
• Defines decision variables as start and end times of appliances within time windows, with constraints on power and duration.
• Evaluates performance across three scenarios: reference (welfare maximization), cost-only, and cost-discomfort balanced.

Experimental results

Research questions

• RQ1 How can residential energy management be optimized to reduce daily energy costs while maintaining consumer comfort?
• RQ2 What is the impact of dynamic pricing on consumer appliance scheduling behavior in a non-cooperative setting?
• RQ3 Can a game-theoretic approach achieve a balanced equilibrium between cost minimization and discomfort reduction?
• RQ4 How does the proposed multi-objective approach compare to cost-only or comfort-only strategies in real-world scenarios?
• RQ5 To what extent can NSGA-II effectively compute the Nash equilibrium in a residential energy management game with conflicting objectives?

Key findings

• The proposed cost-discomfort scenario reduced daily energy cost by approximately 37% compared to the reference scenario.
• The discomfort level increased by 20% compared to the reference scenario, but was reduced by 10% compared to the cost-only scenario.
• The cost-only scenario achieved the lowest cost (40% reduction from reference) but caused a 30% discomfort increase.
• The multiobjective approach successfully balanced cost and comfort, achieving near-optimal performance in both metrics.
• Prosumers (p-players) with local generation maintained higher comfort by scheduling appliances during preferred times.
• The NSGA-II-based solution effectively computed the Nash equilibrium, demonstrating the feasibility of distributed, non-cooperative energy management.