A Self-Governed Online Energy Management and Trading for Smart Micro/Nano-Grids

Abstract

Joint energy consumption and trading management is still a major challenge in smart (micro-) grids. The main goal of solving such problems is to flatten the aggregate power consumption-generation curve and increase the local direct power trading among the participants as much as possible. Here, an inclusive formulation for energy management and trading of a Micro/Nano-grid (M/NG) is proposed. Subsequently, a holistic solution to jointly optimizing the internal energy consumption management and external local energy trading for a smart grid including several M/NGs is provided. As the problem is computationally intractable, the proposed approach involves three hierarchical stages. Firstly, a game-theoretic online stochastic energy management model is provided with a reinforcement learning solution by which the M/NGs can schedule their power consumptions. Secondly, an effective incentive-compatible doubleauction is formulated by which the M/NGs can directly trade with each other. Thirdly, the central controller develops an optimal power allocation program to reduce the power transmission loss and the destructive effects of local energy trading. The simulation results validate the efficiency of the proposed framework.

A Multi-Criteria Computer Package-Based Energy Management System for a Grid-Connected AC Nanogrid

“…The NG under study can produce renewable energy for its own usage and sell excess to the main grid. Besides the aim of obtaining a friendly bi-directional operation between the NG and the utility, maximization of revenues is interesting to incentive the NG user in this business model, establishing negotiations with the utility or any aggregator [19]. At the same time, the HESS can store the energy surplus to be delivered during high-price time periods.…”

“…Once the NG topology has been designed and the main objectives to be met by the NG have been determined, the EMS can be implemented by different manners and techniques The techniques used for the EMS implementation in NG applications found in the literature can be based on: fuzzy logic control [10,[13][14][15][16], linear programming [4,5], integer programming [17], dynamic programing [8], neural networks [10], a finite state machine [18], game theory approaches [7,19] and model predictive control [20]. The EMS for NG based on linear programming approaches defines a feasible region which is used to find the optimal solution considering a set of linear inequalities [4,5].…”

“…The main objectives were to provide ancillary services to the grid and to guarantee a coordination with the distribution system operation. From another side, some EMS for NG determines the operation mode by means of the Nash equilibrium point of the game [7,19]. This kind of method highlights because the motivation and incentives they generate among the users.…”

A Robust Scalable Demand-Side Management Based on Diffusion-ADMM Strategy for Smart Grid

“…A real-time decentralized DSM was also developed to adjust the realtime residential load to follow a pre-planned day-ahead energy generation by micro-grid [22]. A holistic formulation for energy management and trading of a Micro/Nano-grid (M/NG) with several potential components was developed in [23] to jointly optimize the internal energy consumption management and external local energy trading for a system including several M/NGs.…”

“…whereφ h bl, is the intermediate estimate of customer that is available at customer k and b ,k is its weight (see [51] for detailed analysis about the accuracy of this approximation). Minimizing problem (23) for estimatingp h bl at customer k denoted byp h,i bl,k using a traditional iterative steepest-descent solution takes the following form [52]:…”