Agent-Based Architectures and Algorithms for Energy Management in Smart Gribs : Application to Smart Power Generation and Residential Demand Response

TLDR: In this article, an energy management system for gas turbine power plants is designed with the objective to minimize operational costs and emissions, in the smart power generation paradigm, and a demand response system is proposed and relies on the use of the assets of residential customers to curtail and shift local loads so that the total system load remains under a given threshold.

Abstract: Due to the convergence of several profound trends in the energy sector, smart gridsare emerging as the main paradigm for the modernization of the electric grid. Smartgrids hold many promises, including the ability to integrate large shares of distributedand intermittent renewable energy sources, energy storage and electric vehicles, as wellas the promise to give consumers more control on their energy consumption. Such goalsare expected to be achieved through the use of multiple technologies, and especially ofinformation and communication technologies, supported by intelligent algorithms.These changes are transforming power grids into even more complex systems, thatrequire suitable tools to model, simulate and control their behaviors. In this dissertation,properties of multi-agent systems are used to enable a new systemic approach to energymanagement, and allow for agent-based architectures and algorithms to be defined. Thisnew approach helps tackle the complexity of a cyber-physical system such as the smart gridby enabling the simultaneous consideration of multiple aspects such as power systems, thecommunication infrastructure, energy markets, and consumer behaviors. The approach istested in two applications: a “smart” energy management system for a gas turbine powerplant, and a residential demand response system.An energy management system for gas turbine power plants is designed with the objectiveto minimize operational costs and emissions, in the smart power generation paradigm.A gas turbine model based on actual data is proposed, and used to run simulations witha simulator specifically developed for this problem. A metaheuristic achieves dynamicdispatch among gas turbines according to their individual characteristics. Results showthat the system is capable of operating the system properly while reducing costs and emissions.The computing and communication requirements of the system, resulting from theselected architecture, are also evaluated.With other demand-side management techniques, demand response enables reducingload during a given duration, for example in case of a congestion on the transmissionsystem. A demand response system is proposed and relies on the use of the assets ofresidential customers to curtail and shift local loads (hybrid electric vehicles, air conditioning,and water heaters) so that the total system load remains under a given threshold.Aggregators act as interfaces between grid operators and a demand response market. Asimulator is also developed to evaluate the performance of the proposed system. Resultsshow that the system manages to maintain the total load under a threshold by usingavailable resources, without compromising the steady-state stability of the distributionsystem.