۱- Introduction

۲- Smart-building model

۳- Smart-buildings community framework

۴- Case study and discussion

۵- Conclusion

References

Abstract

Demand Response (DR) is progressively moving from a centralized, unidirectional structure to a set of advanced decentralized mechanisms that better balance distributed supply and demand. This paper presents a decentralized cooperative DR framework to manage the daily energy exchanges within a community of Smart-Buildings, in the presence of local Renewable Energy Sources (RES). The proposed algorithm taps into the flexibility of the participants to let them decide of a day-ahead community power profile, and subsequently ensures the forecast tracking during the next day. In practice, the algorithm is fully decentralized by the Blockchain technology, that enables a trusted communication medium among the participants and enforces autonomous monitoring and billing via Smart-Contracts. With such an energy management framework, participating Smart-Buildings can together aim at a common objective, such as carbon-free resources usage or aggregated grid services, without depending on a centralized aggregator/utility. Simulations on realistic Swiss building models demonstrate that nearly all the renewable production resources could be harnessed locally through the presented framework, compared to selfish individual optimization. Under a quadratic cost of grid electricity, the considered community profile could dramatically be flattened, hence avoiding costly peaks at the grid interface. A scalability analysis shows that, considering the current public Ethereum Blockchain, the framework could handle a community size of up to 100 Smart-Buildings.

Introduction

The increasing world demand in electricity is envisioned to be entirely supplied by sustainable Renewable Energy Source (RES) in order to counteract the global climate change. However, intrinsic volatility and uncontrollably of decentralized RES production pose severe challenges to the current electrical grid, that must ensure stability at any time. Progressively, the centralized grid sees a change in paradigms, transitioning from a system dispatching a production portfolio following the electrical demand to a Smart-Grid that handles a portfolio of controllable demand to match an uncontrollable supply [1]. To assist this transition, Smart-Buildings have recently emerged as a solution to leverage the flexibility offered by the various entities commonly found in buildings. Equipped with the right hardware and Information and Communication Technology (ICT) they can provide active Demand Response (DR) to the electrical grid [2,3]. DR regroups a set of mechanisms divided into incentive and price-based programs, that specify various signals to be exchanged between the grid and the consumers in order to shape the power profile of the latter. Many works have tackled the problem at individual building level, demonstrating their capability to adapt their power consumption to grid signals while ensuring occupant comfort [4–۷]. Beyond individual building optimization, there is a need of handling the problem at the community level. By doing so, local resources such as Photovoltaic (PV) production can optimally be harnessed while reducing the overall peak power demand. Many optimization frameworks have emerged to collectively manage the energy of multiple users [8–۱۲]. Nevertheless, these solutions require a central agent that collects user information to subsequently dispatch optimal set points to each of them. Even though generic simplified models can be used to represent buildings flexibility [9,12], centralized solutions still face issues of privacy, single point of failure, scalability, and market entry of small prosumers.