RESIDENTIAL COMMUNITY MICRO GRID
LOAD SCHEDULING AND MANAGEMENT
SYSTEM USING COOPERATIVE GAME
THEORY
Sania Khaskheli
Master’s Student, Electronic System Engineering, Institute of Information and
Communication Technologies, Mehran UET. Jamshoro (Pakistan)
E–mail: sania.14es.01.muet@gmail.com
Irfan Ahmed Halepoto
Department of Electronic Engineering, Mehran UET. Jamshoro (Pakistan)
E–mail: irfan.halepoto@gmail.com
Ayesha Khalid
Master’s Student, Information Technology, Institute of Information and
Communication Technologies, Mehran UET. Jamshoro (Pakistan)
E–mail: ayesha87khalid@gmail.com
Recepción: 05/03/2019 Aceptación: 15/03/2019 Publicación: 17/05/2019
Citación sugerida:
Khaskheli, S., Halepoto, I. A. y Khalid, A. (2019). Residential Community Micro
Grid Load Scheduling and Management System Using Cooperative Game Theory. 3C
Tecnología. Glosas de innovación aplicadas a la pyme. Edición Especial, Mayo 2019, pp. 534–551.
doi: http://dx.doi.org/10.17993/3ctecno.2019.specialissue2.534–551
Suggested citation:
Khaskheli, S., Halepoto, I. A. & Khalid, A. (2019). Residential Community Micro
Grid Load Scheduling and Management System Using Cooperative Game Theory. 3C
Tecnología. Glosas de innovación aplicadas a la pyme. Special Issue, May 2019, pp. 534–551. doi:
http://dx.doi.org/10.17993/3ctecno.2019.specialissue2.534–551
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
536
ABSTRACT
This paper proposes a residential community based microgrid using cooperative
game theory to share excessive energy within a community’s neighbor homes
for optimal load scheduling and management. The proposed model is a grid
connected residential community where smart homes are connected through
central energy management system (EMS) to share the benets of excessive
distributed energy resources (DERs) from solar PV or wind turbine by selling to
other community residents at a price lower than the utility gird but higher than
the feed–in tari. The community smart homes are categorized as Externally
Importing Homes, Internally Exporting Homes and Externally Exporting Homes
which are further classied as passive consumers, active prosumers and proactive
prosumers based on the facilities they possess in form of DERs and battery storage
(BS). With the cooperative energy transaction mechanism, the smart community
homes after fullling their own load requirements can place the excessive energy
on community poll using decentralized or centralized approach through peer to
peer trading or smart community manager (SCM) respectively. The excessive
energy can be sold or purchased to and from other community homes as per
some dened preferences and priorities. This will benet the entire community in
terms of cost compared to the utility grid’s Time of Use (ToU) pricing. Proposed
system will not only share, schedule and manage the community load optimally
but will reduce the overall energy cost, system operational stress, improves system
operational eciency and reduces carbon emission.
KEYWORDS
Residential Microgrid, Distributed Energy Resources, Cooperative Game
Theory, Load Scheduling, Energy Management System.
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1. INTRODUCTION
Considering the scarcity of conventional energy sources, increasing environmental
carbon emission and requirement of improved operational eciency needs
diverse, and smarter solutions for meeting energy eciency and conservations
at the same time (Halepoto, Uqaili, & Chowdhry, 2014). Residential sector
contributes almost one third share of energy consumption (Sahito, et al., 2015)
and unfortunately this sector mostly relies on conventional energy sources. There
is a strong need to shift the residential load by utilizing small DERs to minimize
the concerns about polluted carbon emission and to meet the ever–growing
energy requirements and operational stability (Basu, Chowdhury, Chowdhury
& Paul, 2011). Recently, the electric power industry has seen the acceleration in
DERs installation and utilization. Microgrids are the most complex and dynamic
form of DERs. A microgrid is setup by integrating the interconnected electric
loads and DERs acting as a single controllable entity with respect to the grid
(Planas, Gil–de–Muro, Andreu, Kortabarria & de Alegría, 2013). In recent
years, microgrids have evolved from a growing concept to a signicant source of
opportunity however it is still in the developing phase as there isn’t one–size–ts–
in all microgrid development system. In the microgrid industry, the immense focus
has been given to the institutional campus, commercial or industrial segments, but
now there is a growing trend to expand these applications to serve broader needs.
Residential communities can serve this purpose as it is broadly accepted that the
electric utility future is only sustainable and reliable with resilient communities to
supplement the existing energy infrastructure with microgrids.
Since DERs are internment in nature and their availability is subject to natural
concerns and climatically variations, these resources must be operated in a
coordinated manner. For the interactive operation of DER in a coordinated
approach, the introduction of multi–agent system (MAS) can provide
hierarchical control architecture for the optimization of resources and to avoid
any operational uncertainty (Halepoto, Sahito, Uqaili, Chowdhry & Riaz,
2015). This can framework an ecient load sharing, load scheduling and EMS
especially for residential sector by developing a community based residential
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microgrid where each resident is cooperative with each other in game theory
concept. In a game theory, instead of each user utilizing the DERs individually
for its own load usage and management, a better approach can be to use DERs as
cooperative load management scheme (Parisio, Wiezorek, Kyntäjä, Elo, Strunz
& Johansson, 2017). The community microgrid can potentially serve the needs
of both community residents and utility grids by selling or purchasing the excess
amount of energy optimally, as every residential community home consumer can
be a prosumer (producer and consumer) at the same time.
The reminder of paper is organized as follows. In section 2 a residential smart
home system model is proposed and discussed being the mandatory requirement
of community microgrid. A residential smart home community based microgrid
developed in Section 3 which categorizes three types of community homes on
base of facilities they possess. In Section 4 a cooperative game theory based
energy management system for community microgrid is proposed using EMS.
A prosumer–centric residential community microgrid system using decentralized
and centralized design is proposed and analyzed in Section 5. Section 6 concludes
the work and point to the future work directions.
2. RESIDENTIAL SMART HOME SYSTEM MODEL
The residential community microgrid is strongly dependent on residential smart
home system (RSH) as proposed in Figure 1. The smart homes are utility grid
connected and are equipped with RESs (PV solar system, and wind turbine), solar
charge controller, wind charge controller; advance metering infrastructure (AMI)
based smart meter, energy scheduling unit (ESU), energy management controller
(EMC), DC/AC inverters, battery storage (BS) and home appliances. Through
smart meter, not only the bi–directional communication between utility grid and
consumers can be established but the consumer can get real time information
about load demand, energy consumption data and electricity prices especially
ToU pricing. The solar PV system generates the electricity in DC form which is
then converted into AC form via DC/AC converter. The BS is utilized for both
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source and sink purpose to store DERs produced energy. The ESU is responsible
to generate and schedule the appliance energy consumption data and send the
scheduling patterns to the EMC. According to the generated schedules by ESU,
the EMC controls the BS and appliance’s operation.
Figure 1. Residential Smart Home System Model.
The home appliances are categorized into three types by end consumers
according to schedulability, exibility and interruptibility; (i) Partially Flexible
Appliances (PFA), (ii) Totally Flexibly Interruptible Appliances (TFA), (iii) Always
Running Appliances (ARA). With PFA, the consumers are partially exible to
shift or schedule the appliances load to another time slots. The starting and
nishing time slots are dened already with mostly one hour time interval. Once
any appliance is started, it will complete the one hour operating time slot; after
that, the consumer will follow the utility request to schedule or curtail the load
for next time slot. With TFA, as per dened agreement between the utility and
consumers, the end consumer must cuto, curtail or schedule the electric load as
per utility request at any time. The ARA is most inexible type as the consumer’s
home appliances are not non–interruptible, non–deferrable and non–shiftable.
These types of appliance are always run type of appliances.
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3. RESIDENTIAL COMMUNITY HOMES BASED
MICROGRID
This paper aims to model a residential community based microgrid to generate,
utilize and serve multiple residential home prosumers in cooperate way to share
or sell excessive DERs energy to the other community residents or even to main
grid at a price lower than the utility gird’s price but higher than the feed–in
tari. The community homes are grid connected and are classied into three
types; Externally Importing Homes, Internally Exporting Homes and Externally
Exporting Homes . The considered model homes and their parameters are shown
in Figure 2.
(a) Externally Importing
Home
(b) Internally Exporting
Home
(c) Externally Exporting
Home
Figure 2. The different types of homes in a community microgrid.
Externally Importing Homes: The residents of does not possess any DERs
(either cannot aord DERs or not willing to install) and are totally dependent
to utility grid supply and on community homes which one oers low electricity
prices. Such residents are a greedy and passive consumer whose only focus is low
electricity prices, even not on the energy availability.
Internally Exporting Homes: The residents of are active prosumers. They
have DERs installed to meet their energy demands but they do not possess any
battery storage. After meeting their energy demands, the owners become part of
residential community through a SCM to sell excessive energy to their neighbor
homes especially to external importing homes on priority. In case, the neighboring
homes do not require energy at that time since these homes do not have any
backup battery system, they try to sell energy to the grid rather than wasting it.
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Externally Exporting Homes: The residents of are proactive prosumers.
These are ideal community homes possessing both the DERs and BS. Such
homes after lling their own energy requirements store the additionally available
energy through BS. After that, they try to share or sell the excessive energy to the
neighbors especially to through the smart community manager.
As externally importing homes are without DERs so they always need energy
either to be purchased from the utility grid or from other community homes,
depending which one is oering lowest prices. The oered price from utility will
base on ToU price, so for case of high peak price periods, they can purchase
energy form community homes through SCM using cooperative game theory.
Being the part of community microgrid, they may get the energy at a price
lower than the utility gird price but still higher than the feed–in tari. On the
other hand, the internally exporting homes will put their excessive energy into
community poll for sell through the SCM at a very low feed–in tari during the
day. Since such homes do not have BS, so during nights they may also need to
purchase the energy either from externally exporting homes or from the utility
grids depending on the ToU pricing periods. As a special case, since externally
exporting homes have both DERs and BS, so they can easily store the energy
which can be utilized during nights. Even with storage, if they sell out the stored
energy to community homes at some earlier time of the day, they may also face
power shortage during night’s occasionally.
4. COOPERATIVE GAME THEORY BASED ENERGY
MANAGEMENT SYSTEM
Game theory is a multi–agent based concept where dierent autonomous
rational players interact with each other for mutual benets (Nguyen, Kling &
Ribeiro, 2013). This game theory concept can be very eective to energy related
applications especially in community based micro grids for optimizing the energy
resources (Mei, Chen, Wang & Kirtley, 2019). The game theory is classied
into cooperative and non–cooperative game theory (Stevanoni, Grève, Vallée &
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Deblecker, 2019). In non–cooperative game theory, dierent players which are
the part of the system, partially interact with each other to achieve their own
individual objective(s) which can be contradictory to overall system objectives.
On the other hand, cooperative game theory is based on a coalitional game
theory, where all the set of players are always ready to cooperate, coordinate and
communicate with each other without any conict of interests to achieve one
common goal. Figure 3 illustrate the community based microgrid conguration
connectivity of dierent homes using EMS.
Figure 3. Residential Community Microgrid Connectivity Conguration using central EMS.
The prime objective of cooperative game in a community microgrid is to
schedule and optimize the electricity load requirements within the community
smart homes. Being the prosumer, the locally generated electricity from DERs
is used by homes to ll their load demands and after meeting the requirements
the excess energy is either sold back to the community resident or to utility grid.
Although this is an attractive solution for both utility and community prosumers,
this can be more eective if managed optimally through community microgrid
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manager using MAS to develop the communication at dierent layers. Figure
4 shows the MAS conguration for a central EMS which comprises of three
communication levels; primary, secondary, and tertiary EMS.
Figure 4. MAS Conguration for a central EMS in Community Microgrid.
The community smart homes are considered as the primary EMSs. At the
primary level, every smart community home having its own EMS, communicates
its ongoing energy status with the secondary EMS, which on receiving the
information shares the energy status and places an excess amount of energy on
community poll for other community homes less than the utility grid’s prices
according to priorities dened by community homes for sell or purchase. Finally,
the tertiary EMS being the overall decision maker, based on secondary EMS
information, accumulates the overall energy status and decide(s) for buying or
selling energy to and from the utility grid.
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5. PROPOSED PROSUMER–CENTRIC RESIDENTIAL
COMMUNITY MICROGRID
In this paper, we have proposed two stage prosumer–centric residential cooperative
community microgrid; decentralized cooperative community microgrid design
and centralized cooperative community microgrid design. We have molded
six community homes, two from each category being passive consumer, active
prosumer and proactive prosumers.
Decentralized Cooperative Community Microgrid Design: This is a decentralized
or distributed agent based design approach where the community homes’
residents can directly negotiate with each other in form of grouping or peer to
peer (P2P) to make energy transactions (selling or purchasing) without involving
any centralized supervisory mechanism like smart community manager, as shown
in Figure 5.
Figure 5. Decentralized Cooperative Community Microgrid Design.
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Figure 5 illustrates all the possible combination of P2P trading patterns between all
community homes. The trading patterns are shown vice versa form where everyone
can trade (sell or purchase) with other one. The information is only shared to those
community homes that are willing to trade the energy (sell or purchase) in P2P form.
The price of energy transaction is kept in secrecy. Figure 6 illustrates the model
example microgrid consisting of 6 community homes trading in P2P. The peers 1
and 2 are passive consumers; peers 3 and 4 are active prosumers while peers 5 and 6
are proactive prosumers. For the illustration purpose we have chosen prosumer 3 is
one community home which has some excessive amount of energy. Since it equipped
only with DERs but does not possess ant battery back, so he desperately wants to
sell the excessive energy. Since this is a decentralized approach so community home
by itself tries to nd out the target homes. This example illustration of prosumer
3 is shown in Figure 6. Consumers have prosumers as energy transaction trading
partners and while prosumers have both consumers and producers as trading
partners. Considering the negotiation process, the pricing priorities for bilateral trade
can be dened and may vary accordingly for trade between the peers. Considering
the prosumer 3 case scenario, the bilateral trading between four prosumers can be
made by using reciprocity property as shown in Figure 6.
Figure 6. Decentralized Cooperative Community Microgrid Design Trading in Peer to Peer.
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This conrms that all four prosumers have equal opportunities of balance trade
during bilateral trading but with opposite sign. This design is truly a consumers’
preference centric where the owner has total freedom to whom energy transaction
is to be made. At the same time the negotiation process (price, time horizon)
scalability and limitations are the main issues. The negotiation process can
become more complex for the scenarios where a greater number of community
homes are involved, as both the seller and the buyer are unaware about the
requirements and priorities of each other.
Centralized Cooperative Community Microgrid Design:
This is centralized poll based design approach where all community homes put
their excessive energy on the community energy poll and is managed, decided
and shared through a CSM which acts as an intermediator between all the
community homes. This system design is shown in Figure 7. Based on three
community homes categories; a centralized cooperation through SCM can be
made based on dierent priorities or preferences mainly on electricity prices and
available time horizon dened by community homes owners. Based on dened
priorities, the SCM is responsible to decide to whom the excess energy is to be
sold to or purchased from on the bases of auction schemes where the energy
seller’s and buyer’s demands are met. It is not necessary for the seller and the
buyer to know each other as energy transactions are handled through the SCM.
This design is more structured and optimized where most of the community
members can not only be in a social relationship by helping other community
residents but can also earn good revenues as energy can also be sold to utility
grid at higher prices when the utility grid is under system stress through smart
community manager. This scenario can be more realistic if through SCM an
aggregated energy is sold to the utility grid and total collected revenue is shared
(e.g., in a logical proportional way) midst all community members. At the same
time, SCM has the strong responsibility of being fair and unbiased so that every
community home gets equal opportunities of energy transaction.
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Figure 7. Centralized Cooperative Community Microgrid Design through Smart Community Manager.
6. CONCLUSION
In this paper, a grid connected residential community based microgrid is proposed
using cooperative game theory to share, manage and schedule the excessive amount
of energy generated through DERs with the community neighboring homes. In
this work, three types of community home residents’ passive consumers, active
prosumers and proactive prosumers are used as the main agents which makes
the energy trading and transaction to other community residents after fullling
their own load requirements. This energy trading and transaction is based on
two stage prosumer–centric residential cooperative approach in centralized or
decentralized way to sell or purchase the energy from community neighboring
homes at a price lower than the utility gird but higher than the feed–in tari.
With decentralized approach community residents can directly negotiate with
each other in form of grouping P2P to make energy transactions; whereas in
centralized approach all community homes put their excessive energy on the
community energy poll and is managed, decided and shared through a smart
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community manager which acts as an intermediator between all the community
homes. It is concluded that the proposed system will not only share, schedule
and manage the community load optimally but reduces the overall energy cost,
system operational stress, improves the system operational eciency and reduces
the carbon emission. This work can be extended further to develop the rural
electrication using community microgrid especially in energy decit countries
like Pakistan to avoid grid system stress.
ACKNOWLEDGMENTS
Authors are highly grateful to Mehran University of Engineering and Technology,
Jamshoro, Pakistan, for the necessary support, technical laboratory facilities and
comfortable research environment.
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