I. INTRODUCTION
Demand response (DR) programs have emerged as one of the most important
ways for energy grid operators to reduce energy shortages or excesses
and, as a result, enhance system dependability. 1 More crucially, with
the increasing prevalence of Renewable Energy Sources (RES),2 Energy
Storage Systems (ESS [1]), and even Electric Vehicles (EV3), extra
flexible assets are easily accessible to provide new avenues for
profitability, maximize current ones, and reduce overall risks. The
economic benefits of this new era are not restricted to essential energy
players such as grid operators and merchants. End-customers, or
individual units, and the different types of Distributed Energy
Resources (DERs) placed on their premises are, in fact, playing an
increasingly important role, which may be linked to the development of
new business models centered on aggregation and virtual power plants
(VPP4). Aside from the aforementioned economic benefits, coordinating
such a diverse landscape of DERs enables exploiting the underutilized
flexibility accessible at lower scales. Indeed, residential and tertiary
customers have been recognized as substantial sources of flexibility
[2, 3], made even more so by the arrival of prosumers, or consumers
who also create energy. For example, in 2016, the EU member states
produced more than 33GW
of home solar photovoltaics (PV), 53% of which was transferred to the
grid [3]. This is likely to rise as a result of the EU’s strong
green energy policy, which aim to attain a 32% proportion of RES by
2030. 5 However, because to the lack of a scalable Information and
Communications Technology (ICT) infrastructure capable of managing the
sheer bulk of small to medium-scale clients, disaster recovery (DR)
programs are currently primarily supplied to big industrial customers.
In this context, DR proposals are typically designed to work in a closed
world in which new data sources are not expected to appear, and thus
they do not consider the necessity of integrating new data sources that
rely on different formats, models, or protocols to exchange and consume
data with them. Numerous DR data models have been suggested, but only a
handful are ontologies that allow for the provision of a semantic
interoperable layer for data sharing [4]. Similarly, proposals rely
on non-semantic models [5-12], while there has recently been a
movement toward building proposals using ontologies [13-18].
However, the majority of these ideas lack natural means for integrating
additional data sources, necessitating a significant data harmonisation
effort to incorporate new data sources. Furthermore, the DR plans rely
on a diverse set of protocols, some of which need infrastructures to
openly give their data (HTTP) or broadcast their information in
low-security environments (MQTT); in fact, security is a component that
is frequently overlooked or ignored in most proposals. The CIM
middleware is introduced in this article to handle the practical issues
that arise in real- world DR systems. The CIM’s major purpose is to
create a private and secure peer-to-peer cloud network enabling disaster
recovery systems and data infrastructures to transparently interchange
and consume data, despite the fact that the systems and infrastructures
use various formats or models. To that purpose, the CIM employs semantic
interoperability modules, which enable bidirectional data translation
mechanisms to convert data represented in disparate forms or models into
a semantically compatible version based on RDF
[19] and an ontology [20], and vice versa. The CIM tool was
created in the framework of the European DELTA project, in which
semantic interoperable data adheres to the DELTA ontology [21],
although it may be used with any ontology. Furthermore, anytime a data
payload is transferred, the CIM performs semantic validation of data
based on W3C SHACL shapes [22], verifying its validity and coherence
with the ontology. The CIM is an Open Source tool6 that implements its
semantic interoperability layer [23] using well-established IoT
techniques that have been adapted to the DR and the decentralized cloud
context. However, the CIM may be utilized in a variety of application
domains; it is not limited to ontologies relating to the energy sector.
The CIM has been utilized as middleware in the context of DELTA for
communications across DR systems and data infrastructure using an
edge-cloud architecture. The CIM, on the other hand, goes beyond
encouraging the decentralization of DR systems and the integration of
distributed data sources by providing them with a distributed, scalable,
secure, and end-to-end privacy- preserving peer-to-peer (P2P) network
that can be hosted across multiple cloud providers and ensures service
liveness even in the face of failures. The CIM enables the unification
of discrete real-world corporate DR systems without needing them to
disclose any data through public channels. A careful experiment was put
out to demonstrate the features of the CIM. First, the accuracy of the
CIMs’ semantic compatibility has been established. The capacity of the
CIMs to respond to concurrent requests while exchanging ordinary
payloads or payloads that must be translated in order to be semantically
compatible. Finally, the ability of CIMs to exchange larger payloads has
been proven.
The remainder of this article is organized as follows: Section 2
analyzes current DR proposals in the literature, Section 3 elicits
practical challenges that DR proposals must address, Section 4 reports
the CIM tool, providing an insight into its architecture and
functionalities, Section 5 explains the experiments performed to
validate the CIM, as well as the results obtained, and Section 6
summarizes our findings and conclusions.