A virtual microgrid platform for the efficient orchestration of multiple energy prosumers

Abstract

The Smart Energy Grid concept aims to exploit Information and Communication Technologies (ICT) towards making the energy sector more secure, reliable and efficient, while the electricity markets are rapidly becoming more liberalized with new business actors/models being introduced. In particular, passive energy consumers are being transformed into active energy prosumers (i.e. both producers and consumers), while energy aggregation/services companies are emerging as intermediaries in the so called "Internet of Energy" arena. Prosumers need to have their energy assets efficiently managed and participate in the market independently of their size and negotiating power, while aggregators aim at maximizing prosumers' benefits by representing them as a single big power entity in the wholesale energy market. This paper introduces the Virtual MicroGrid (VMG) concept, in which multiple energy prosumers are orchestrated into bigger associations towards optimizing the association's benefits. An innovative decision support system platform is presented showcasing that the management of aggregated energy resources can outperform state-of-the-art solutions that manage resources at the individual prosumer's level. The platform's implementation is based on virtualization techniques and a wide range of functionalities are described, tested and validated. Datasets from 37 real-life prosumers are used and results of various decision-making algorithms show that under different system operation contexts, dynamic formation of prosumers' groups (clusterings) can provide remarkable energy savings and monetary profits to the end users.

Energy Peer-to-Peer Trading in Virtual Microgrids in Smart Grids: A Game-Theoretic Approach

“…We introduce a game theory-based approach to optimize energy trading costs in a single VMG. This important area has not been reported in other studies investigating VMG such as [3], [4], [32], [33].…”

“…ij , 2: α ji , µ i , λ i τ , T consumer, prosumer and grid parameters 3: for τ = 1 to T do 4: Solve (32) to realize λ i τ as in (33) at τ th trading interval 5: Using the result in (32), solve (29) and (30), respectively 6: Obtain e ii from the solution of (29). producer energy consumption from the Stackelberg game.…”

“…if the stopping criteria is met: then 15: break the loop of iteration 16: end if 17: end if 18: end for At first, we follow the time-varying pricing update model in (33) to obtain the optimal price using Algorithm 1. The loop is iterated over τ = 1, · · · , T trading periods.…”

“…The loop is iterated over τ = 1, · · · , T trading periods. The stopping criteria is established as the point where the slack variable in (33) is less than the best amount of energy consumed by the producer, i.e. (E (g)…”

A Novel Prosumer-Based Energy Sharing and Management (PESM) Approach for Cooperative Demand Side Management (DSM) in Smart Grid

“…Mamounakis et al [20] present a prosumer energy management platform based on dynamic community creation and organization. Aggregator companies are at the center of this idea.…”