Cooperative game theory approach for multi-objective home energy management with renewable energy integration

This study proposes a mathematical model of an intelligent multi-objective home energy management (HEM) scheme with the integration of small-scale renewable energy sources. The main aim of the proposed model is to handle the residential load demand in a smart way to minimise both the consumer's energy bill and the system peak demand simultaneously. To generate the best compromise solution of the proposed multi-objective problem, a cooperative game theory approach is used in this study on the basis of super-criterion and a Pareto optimal solution concept. In the cooperative game process, each HEM objective is assigned as a player and every player tries to maximise their own payoff. Bargaining model in the form of super criterion is considered in this game approach. Finally, all players can get win–win nature with collective negotiations. Generally, HEM method deals with various controllable devices having distinct operating characteristics. Because of this, the proposed HEM problem is modelled as a mixed-integer problem. Consequently, a mixed-integer non-linear programming is applied in this game process to maximise the super-criterion. To show the effectiveness of the proposed model, different case studies and various scenarios are carried out

Energy cost reduction in residential nanogrid under constraints of renewable energy, customer demand fitness and binary battery operations

Intermittence of renewable energy is a challenge to demand side management in distributed grid technologies. Time-of-use tariffs are often applied to implement traditional strategies such as peak shaving and valley filling that are more functional to conventional grids. Time-of-use tariffs are however not suitable to matching customer demands as the periodic charges are fixed and cannot be match with stochastic renewable power generations. This paper proposes a time-of-use fitness in a grid connected photovoltaic/wind/battery nanogrid for energy cost reduction and maintained customer comforts. The proposed method considers three configurations of the nanogrid optimized using nested integer linear programming. Fitness functions are applied to either critical or flexible demands based on real-time residential consumptions, renewable generation and main grid imported power. Demand criticalities and customer fitness are used in preserving customer comforts. The method achieves 1.72–5.75% and 15.63–21.88% reduction in energy consumption costs against $120.30 and$145.14 flat and conventional time-of-use rates respectively in the nanogrid configurations. Use of battery in binary states of operation, as demand or supply further reduces consumption costs by 13.38–43.40% and 28.20–53.09% against the benchmarks. It is envisaged that better performance of the method can be achieved by multiplying operational scenarios of the battery

Optimal PI-Controller-Based Hybrid Energy Storage System in DC Microgrid

Power availability from renewable energy sources (RES) is unpredictable, and must be managed effectively for better utilization. The role that a hybrid energy storage system (HESS) plays is vital in this context. Renewable energy sources along with hybrid energy storage systems can provide better power management in a DC microgrid environment. In this paper, the optimal PI-controller-based hybrid energy storage system for a DC microgrid is proposed for the effective utilization of renewable power. In this model, the proposed optimal PI controller is developed using the particle swarm optimization (PSO) approach. A 72 W DC microgrid system is considered in order to validate the effectiveness of the proposed optimal PI controller. The proposed model is implemented using the MATLAB/SIMULINK platform. To show the effectiveness of the proposed model, the results are validated with a conventional PI-controller-based hybrid energy storage system