Game-theoretic approach to demand-side energy management for a smart neighbourhood in Sydney incorporating renewable resources

Highlights: • Optimization based load and DER scheduling model is presented based on Nash game theory. • An improved game theoretic DSM framework has been developed to provide cost saving and PAR reduction. • Novel Real time tariff model based on historical and predicted wholesale prices is developed. Recent developments in smart grid technologies have enabled interactions between energy suppliers and consumers, leading to profits made by both parties via Demand-side management (DSM). Demand-side management enables consumers to control their energy profile to reap economic benefits. It also helps energy providers to reduce the peak average ratio (PAR) by leveraging the flexibility of distributed energy resources (DERs) and renewable-energy resources (RESs) to supplement grid power, thereby avoiding the use of expensive peak-power plants. This paper presents an improved game-theoretic DSM framework for a neighbourhood area to provide cost savings for the consumer and reduce the PAR for the neighbourhood. The proposed DSM framework utilizes the flexibility of DERs and RESs to allow energy sharing among neighbours to reduce the demand peaks. A novel real-time price (RTP) retail tariff model has been established based on historical and predicted wholesale prices. A Nash-game-theory-based optimization model is developed for scheduling the building loads and DERs. The optimization model minimizes the energy cost to the consumer while maintaining an optimal comfort level for the consumer and satisfying consumption constraints to reduce peak demand. The proposed DSM framework and optimization model is verified via case studies with real building consumption data for a neighbourhood in Sydney, Australia. Game-theoretical analysis ensures that users do not make profits if they deviate from their assigned consumption pattern. The performance of various algorithms is evaluated and their effects on the peak average ratio (PAR) and energy costs are discussed. The effectiveness of the proposed game-theoretic optimization model is validated and compared with traditional non-game-theoretic models. The results of the proposed algorithm show reductions in the peak average ratio of the community and the cost incurred by the consumers. The PARs of the game-theoretic approach during summer and winter are 1.76 and 1.81 respectively. The cost reduction of the game-theoretic model is 9.17% during summer and 9.68% during winter compared to the non-cooperative approach. The numerical results represent the efficacy of the proposed DSM model in reducing the PAR of the community and the energy cost to the consumer.