This paper formulates an applied methodology for an agile demand response using mathematical micromodels. The optimal strategy chosen by an aggregator is the maximization of social welfare derived from demand flexibility. The notion of complex demand bidding is already given in the literature, however heretofore it is formulated as the relationship of price with both demand elasticity and marginal cost along with temporal and profit constraints. Although the planning of flexible demand is already handled by using advance learning techniques in literature, herein simple Q-learning technique in a decentralized fashion is proposed. Moreover, trade-offs between the proposed complex bidding rules are explored in a day-ahead market context. Due to the given complex bidding rules and principle of learning, the methodology can be easily applied in active distribution network. Several number of houses, equipped with the proposed complex bidding mechanism and decentralized learning capability, has been simulated, thus illustrating the application of methodology formulated herein.

Effective Energy Management with an active Demand Response (DR) is crucial for future smart energy system. Increasing number of Distributed Energy Resources (DER), local microgrids and prosumers have an essential and real influence on present power distribution system and generate new challenges in power, energy and demand management. A relatively new paradigm in this field is transactive energy (TE), with its value and market-based economic and technical mechanisms to control energy flows. Due to a distributed structure of present and future power system, the Internet of Things (IoT) environment is needed to fully explore flexibility potential from the end-users and prosumers, to offer a bid to involved actors of the smart energy system. In this paper, new approach to connect the market-driven (bottom-up) DR program with current demand-driven (top-down) energy management system (EMS) is presented. Authors consider multi-agent system (MAS) to realize the approach and introduce a concept and standardize the design of new Energy Flexometer. It is proposed as a fundamental agent in the method. Three different functional blocks have been designed and presented as an IoT platform logical interface according to the LonWorks technology. An evaluation study has been performed as well. Results presented in the paper prove the proposed concept and design.

Optimizing the operation of power generation systems is one of the core objectives of Smart Grid. The area of Smart Grid focuses on this issue is Demand Response (DR). DR is an essential tool to limit the demand to flatten spikes. This can reduce the need of peak power generation units which normally are less efficient and operational for only very short time interval. Eliminating the peak power generation units will cut the price of electricity by a significant amount. This paper discusses about the implementation of variable price structure and incentive based methods for the implementation of DR. An actual day consumption pattern of Riyadh region from the last year is taken as initial demand curve for the implementation of developed model.