Energy Sharing in Smart Community Microgrids: Pricing Strategy

This report focuses on the development of an optimum pricing strategy for energy sharing in a smart community microgrid, addressing the increasing demand for energy and the need for modernizing conventional power grids. It explores the concept of smart grids and microgrids, highlighting the benefits of energy sharing such as cost savings and direct transactions between prosumers and consumers. The report reviews energy sharing scenarios, analyzes auction-based pricing strategies, and discusses the role of Information and Communication Technology (ICT) in facilitating efficient energy distribution. It further investigates the economic advantages and transparent business practices within microgrids, providing insights into optimizing power generation, consumption, and distribution in sustainable smart communities. Desklib provides access to this and other solved assignments for students.

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ELECTRICAL ENGINEERING
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Table of Contents

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Executive summary...............................................................................................................................................................................3
CHAPTER 1.........................................................................................................................................................................................4
Introduction............................................................................................................................................................................................4
1.1 Background......................................................................................................................................................................................4
1.2 Study objectives...............................................................................................................................................................................5
1.3 Scope of the project.........................................................................................................................................................................6
1.4 Organisation of the report................................................................................................................................................................6
CHAPTER 2.........................................................................................................................................................................................8
Literature review....................................................................................................................................................................................8
2.1 Smart Grids......................................................................................................................................................................................8
2.2 Microgrids......................................................................................................................................................................................10
2.3 Energy sharing scenario in microgrid............................................................................................................................................13
2.4 Benefits of energy sharing in smart microgrid..............................................................................................................................14
2.5 Auction based pricing strategy for energy sharing........................................................................................................................14

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CHAPTER 3.......................................................................................................................................................................................17
Methodology........................................................................................................................................................................................17
3.1 Case studies...................................................................................................................................................................................17
3.2 Secondary sources.........................................................................................................................................................................18
3.3 Game Theory.................................................................................................................................................................................18
3.4 System Model................................................................................................................................................................................19
CHAPTER 4.......................................................................................................................................................................................21
Results & Discussions.........................................................................................................................................................................21
CHAPTER 5.......................................................................................................................................................................................27
Concluding remarks.............................................................................................................................................................................27
CHAPTER 6.......................................................................................................................................................................................29
References............................................................................................................................................................................................29
Appendices..........................................................................................................................................................................................31

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Executive summary
With the rapid increase in installation and demand of energy, many power consumers have been forced to become prosumers who
can both consume and generate energy. This study primary focuses on the Development of optimum pricing strategy for energy
sharing in a smart community Micro grid. Targets such as energy conservation, demand response, reduction of carbon footprint,
improved reliability, high penetration of renewable sources of energy and improving efficiency cannot be solved by using the
existing conventional electricity grid, this has called for the modernization of the convention power grid system to the modern
smart grid systems where ICT plays a significant role. Microgrid refers to a set of loads for households, distributed energy
resources (e.g. solar panels), and possibly an Energy storage System such as batteries, operating as a single controlled system
which provides power energy to its local area. The main advantages of sharing energy are: Saves money for those who buy the
excess energy from the prosumers, the tariff rates of the prosumers tends to be much lower as compared to those of main grid.
There are no middlemen who are involved in the trade, thus the consumers and prosumers can be able to carry out their business
on their own terms.

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CHAPTER 1
Introduction
1.1 Background
Increasing demands of energy, diminishing of fossil fuels and climate change have led to essential changes in the production,
distribution and consumption of electricity. This has called for the modernization of the convention power grid system to the
modern smart grid systems where Information and Communication Technology plays a significant role.
The Smartgrid is considered as an intelligent power grid and it is expected to solve the major challenges which are experienced in
the conventional grid. It is designed in a way to ensure a two-way flow of information and electricity between the power plants
and the appliances. It is required to enable resilient and adaptive operations at the same time and Self-healing.
The smart grid should offer full pervasive and visibility control over the system functions and components. They are considered as
the major component of sustainable smart communities and cities, thus opening a wide spectrum of modern technologies and
business models in order to improve energy efficiency and reduce the climate impact.
With the rapid increase installation of distribution generation at the demand side, many of the power consumers have been forced
to become prosumers who can both consume and generate energy. The high level of penetration by the renewable energies in the
power market may cause severe problems to the power systems (Alpcan, 2017, p. 76). Hence, to facilitate the self-consumption

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of the locally generated energy, the export price at which the electricity is sold by the prosumers to the utility grid should be
designed to be lower than the retail price at which they buy the electricity. This offers the major motivation for the prosumers to
share their surplus energy with the neighbour rather than feeding the electricity back to the utility grid. The tariff rates of the feed-
in in many countries are very low which strengthens the incentives. Encouraged by the demand, many projects have been initiated
by manufactures, utilities and high tech start-ups in various parts of the world such as Vandebron in Netherlands, Puclo in United
Kingdom, Mosaic and Yahola in the United States and Sonnen Community in Germany.
Established regional and national platforms which support energy sharing between the prosumers have developed projects like
Smart Watts and Peer Energy Cloud in Germany mainly to focus on the communications and information technologies which
support the incentives of sharing energy (Basar, 2016, p. 136). Also there are some projects like Trans Active grid in the United
States which creates decentralised energy sharing platforms based on the block chain technology.
1.2 Study objectives
The main aim of this study was to develop an energy sharing platform for a residential neighbourhood with different energy
portfolio in order to strengthen the incentive of energy sharing in various parts of the world. In order to achieve the aim of study, a
number of study objectives were set and they included:
1. To evaluate the economic benefits associate with energy sharing
2. To review the energy sharing scenarios in a smart community microgrid

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3. To come up with a pricing technique for energy sharing
1.3 Scope of the project
This study primary focuses on the Development of optimum pricing strategy for energy sharing in a smart community Micro grid.
The study will involve the literature review of the of the energy sharing in smart community micro grid, Reviewing the various
existing theories ,models and working principles, regarding to the energy sharing. Recording the findings from the models and
theories which were analysed for discussions. The materials which will be reviewed will include Books, Journals and other
publications containing relevant information regarding the study topic. The study will be restricted to the study of energy sharing
in a smart community Micro grid and therefore any other information outside the study topic will be irrelevant.
The study will involve:
 Developing an energy sharing platform for a residential neighbourhood with different energy portfolio.
 Develop pricing techniques for the energy sharing
 Analysis economic properties of energy sharing.
 Analysis the energy sharing scenarios in a smart community microgrid

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1.4 Organisation of the report
This study report contains the following sections:
Chapter 1: Introductions, which contains the background information about energy sharing in a smart community microgrid.
Chapter 2: Literature review, contains the summary of previous studies which have been conducted
Chapter 3: Methodology, discusses the working principles of energy sharing, mathematical models and other sources of data.
Chapter 4: Results, in this section the finding of the study are outlined and well presented
Chapter 5: Discussion, in this section the results are discussed and analysed to come up with a conclusion of the study.
Chapter 6: Concluding remarks, in this section the summary of the entire project is given and the recommendation for the future
project are also discussed.
Chapter 7: References, this section contains all the materials which date was referred from and they include books, Journals and
other publications.

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CHAPTER 2
Literature review
2.1 Smart Grids
The term grid is used to refer to electric power network, which is liable on the electricity generation, distribution, control and
transmission. Targets such as energy conservation, demand response, reduction of carbon footprint, improved reliability, high
penetration of renewable sources of energy and improving efficiency cannot be solved by using the existing conventional
electricity grid (Bhave, 2013, p. 563). Due to that there is need to modernise the existing power grid, in which ICT will be the
major player. Smart grid replaces the transition of electricity from the convention electricity power grids, where the flow of
electricity is one way from the generation point to the consumers, to flexible and interconnected grids which ensures a
bidirectional flow of information and electricity between power plants and appliances including all the points in between.
Smart grids are intelligently integrated technological and operational systems for the purpose of optimizing power, generation,
consumption and also distribution, and is therefore is considered as a major component of the smart grids. Also they provide
utility companies with full pervasive and visibility control over their services and assets, which opens up for a wide spectrum of
modern technologies and other business models to reduce the impact on climate and improve the energy efficiency (Vermesa,
2013, p. 411). From another point of view, they empower clients to interact with the energy management systems in order to

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reduce their demand costs and adjust accordingly their power consumption. The table below shows the comparison between the
smart grids and the conventional grids.
Smart Grids Conventional grids
Two-way communication One way communication
Digital Electromechanical
Distributed generation Centralised generation
Self-monitoring Manual monitoring
Sensors throughout Few sensors
Islanding and Adaptive Blackouts and failures
Many customer choices Few customer choices
Pervasive control Limited control
Table 1: comparison between smart and convention power grids

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Fig 1: The major components of smart grid infrastructure
2.2 Microgrids
The concept of microgrid refers to a set of loads for households, distributed energy resources (e.g. solar panels), and possibly an
Energy storage System such as batteries, operation as a single controlled system which provides power energy to its local area.
The Microgrid are considered to be intelligent distribution systems having two different modes of operations: The grid connected
mode and Island mode. They incorporate power plants that are capable of meeting local demands and submitting the surplus
energy to the main grid. The microgrid incorporates smart meters and sensors which are capable of monitoring and measuring a
number of parameters such as current, voltage and power among other. Apart from, a communication infrastructure is important
for a microgrid to enable the components of the system to command reliably and securely and exchange information (Billinton,
2014, p. 287). In addition it incorporates appliances capable of accepting commands and communicating their statues and smart

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terminations to control and adjust their services and performance level based on utility or user requirements. The figure below
show the microgrid topography (Chicco, 2015, p. 149).
Fig 2: A schematic diagram illustrating microgrid topography

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Fig 3: A schematic diagram illustrating microgrid

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2.3 Energy sharing scenario in microgrid
Further enhancement of the architecture of the smart grids, the microgrid is defined as a network which is interconnected of
Distributed energy resources, loads and storages, which can be able to work either connected or disconnected from the main
electricity grids. To the utility the smart microgrid can be thought as cell of power system that is controlled. To the
customers/clients, the microgrid can be designed in such a way to meet the unique needs of the customer, such as to improve the
local reliability, increase efficiency and to minimise feed losses among many others The microgrid are seen as the basic structure
of the smart grid. The figure below illustrates a simple microgrid scenario (Crossley, 2011, p. 63).
Figure 4: A microgrid scenario.

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2.4 Benefits of energy sharing in smart microgrid
In the smart community microgrid the consumers who are able to produce the excess energy then their demand are able to sell to
other consumers. The main advantages of sharing energy are:
 Save money for those who buy the excess energy from the prosumers. The tariff rates of the prosumers tends to be much
lower as compared to the min grid rates thus they save a lot money.
 There are no middlemen who are involved in the trade. Thus the consumers and prosumers can be able to carry out their
business on their own terms.
 The business is very transparent has it involves dealing directly with other consumers.
 The business is respected as much as any other large business.
2.5 Auction based pricing strategy for energy sharing
Auction based pricing techniques uses an auction market for the local generation and demand. Each of the household in the smart
microgrid plays an active role in providing offers or bids for their generation or demand (Farhangi, 2013, p. 410). These bids and
offers are put together and then managed by using a pre-defined rules to define and allocate the price strategy. The process of
energy sharing in a smart community microgrid pricing by use of Auction based pricing strategy is as shown in figure 3 below.

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Fig 5: Auction based pricing strategy

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At a given time each of the household in the smart community grid announces an approximated clearing price based on the
offering/bidding and the clearing prices at the previous times by use of a recursive least square technique. The role of each
household regarding the selling and buying is determined by their net load. If for instance the solar panel generation is smaller than
the demand, then automatically the household is a buyer (Turunen, 2011, p. 182). The household will be required to offer bids with
the electricity amount it has to buy, and the bidding price is the household estimated clearing price. If the solar panel generation is
higher than the demand, then the household is automatically a seller. The household will required to offer bids with the electricity
amount it can sell, and the bidding price is the household estimated clearing price. The n the offers/bids are arranged in an
ascending order, then the clearing price will be determined by the actual generation and demand in the smart community
microgrid. Next the absolute value of the difference between the clearing price and the biding price (Hatziargyriou, 2015, p. 23)
Clearing price is determined by the actual demand and generation in the community. Next, the absolute value of the difference
between buyers biding prices and the clearing price is computed. The obtained value determines the corresponding client’s priority
to obtain the cheaper offers. The smaller the absolute value the higher the priority. Then the final actual selling and buying prices
for time t is determined (Huston, 2013, p. 56). The entire process then runs for the next time instant. A recursive least square
technique was applied for the households to approximate the clearing price .The clearing price id established by the availability
generation and demand.

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CHAPTER 3
Methodology
3.1 Case studies
Energy sharing in a smart community microgrid
Fig 6: An example community microgrid

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The figure 3 above is an illustration of a community smart microgrid having 10 households, 5 of the housed have solar panels
installed. The utility meter is used to connect the community grid to the main grid. Daily solar generation and demand profiles
with 1-min resolution were obtained using the Centre for Renewable Energy System Technology tools (Pardalos, 2013, p. 65). The
load of each household is modelled considering factors such as: occupancy and the associated application of electrical appliances
with the human activities randomness. It is assumed that all the solar panels are having same generation profiles, because of the
small are of smart microgrid. The generation peak is selected randomly from a range of 2.0 to 3.5 KWP.
3.2 Secondary sources
Secondary sources such as books, newspapers and journals which contain the information related to sharing of energy in smart
community microgrid were reviewed. Lot of relevant date regarding to this topic was obtain from the sources which was later
analysed. Most of the books and journals used in this study were the once which were published in the recent past and they were
than 10 years old.
3.3 Game Theory
Game theory is a branch of applied mathematics which was developed more than years ago. Game theory has been researched and
applied in a wide range of fields from politics, sociology, economics, and communication networks and more recently in the smart
grid applications. Game theory offers analytical tools to model interactions among the entities which have configuring interests

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(Radosavljevic, 2016, p. 67). The Theory has been used to address various challenges which occurs in the smart grids. There are a
lot of studies which were review that applied the Game theory model in smart grids.
In this reach paper a scheduling game for the energy consumption was formulated. Where by the individual households are the key
players of the game and their day to day schedules of their appliances are the strategies.
3.4 System Model
A generic microgrid was considered in this project which was consisting of a set of households N= (1…….N), where N= number
of households, with a small-scale on site Distribution energy resources. The householders were connected to the main grid through
AC power lines. In addition it was assumed that the power demands of the households might vary both in in time and quantity and
they can predetermine their demands in future.
Time was divided into periods such as days and the periods was divided into slots e.g. hours which was a representation the time
instants at which a given event occur in the system in the system. The electricity consumption patterns of the households does not
overlap with each other this can be exploited to reduce the need of purchasing electricity from the main grid. This is achievable by
allowing the households to share their renewable able energies (Schewe, 2013, p. 54). At a given time each household can be a
supplier of power and be able to share the excess generated energy.

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Fig7: The proposed microgrid scheme

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CHAPTER 4
Results & Discussions
From the study which was carried out it was found out that. In order to share energy in smart community microgrid the following
requirements must be met;
 There must be a prosumers with surplus renewable energy to share with other local consumers.
 The in-feed tariffs for feeding the excess energy into the main grid must be very low so as to discourage the prosumers
from feeding the surplus power into the main grid and instead to share with the local Consumers.
 There must be local consumers with controllable devices, this will enable them to come up with a strategy on how to share
excess energy produced.
After the study was carried out there were two options of frameworks of energy sharing in residential neighbourhood with a
different energy portfolio.
Option 1: ‘central’ administration and coordination energy sharing framework.
In this frame working which was developed members of the neighbourhood interacts to each other through a common centralized
administrator.IT is upon the administrator to give electric power to the members according to their requirements. Some of the

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members who might be having surplus energy than their demands will sell to others as they are guided by the central
administrator. The schematic figure below illustrates how the central framework of sharing energy works (Hatziargyriou, 2015).
Fig 8: The central energy sharing framework.

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Option 2:
Consumer to consumer energy sharing framework (‘truly’)
In this framework of energy sharing in a residential neighbourhood there is no specific coordinator to coordinate on how the
energy is shared. Those residents who have excess energy they sell to the other residents whose supply of electricity does not meet
their demands. The consumers have developed a grid through which they share the power. The figure below is an illustration on
how the consumer to consumer share energy without any trusted middleman.

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Fig 9: The customer to customer energy sharing framework.

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After the analysis was carried out on the Auction based pricing techniques which was developed regarding on how the pricing of
the shared electricity power energy in a smart community microgrid. Each of the household in the smart microgrid plays an active
role in providing offers or bids for their generation or demand. These bids and offers are put together and then managed by using a
pre-defined rules to define and allocate the price strategy. The three scenarios which were developed were presented as follows:
1) In the situation The PV is equal to the Demand. The clearing price will be equal to the highest offer among the all the bids
which were provided by the sellers as shown in the figure below.
2) The PV generation is higher than the demand. In this scenario the excess is sold to the main grid at the selling price in order
to ensure that the clearing price is equal to the t price.as shown in the figure below (Sioshansi, 2016, p. 43).

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3) In the scenarios where the Generation is lower than the demand. The residual demand is usually supplied by the main grid at
the buying price rate .as shown in the figure below.

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CHAPTER 5
Concluding remarks
In conclusion, Sustainability has become a very essential requirement on many systems and in fracture of the current societies with
the environmental deterioration and impending energy crisis. Traditional power grids are being transformed into smart grids
having advanced information, communication and sensors technologies. In the smart grids which are developed in the modern
times the flow of energy is a two way between the consumers and the grid having renewable energy generations, which will be
controlled and monitored by smart meters, sensors, analytical tools and digital.
The term grid is used to refer to electric power network, which is liable on the electricity generation, distribution, control and
transmission. Targets such as energy conservation, demand response, reduction of carbon footprint, improved reliability, high
penetration of renewable sources of energy and improving efficiency cannot be solved by using the existing conventional
electricity grid
Controls. All the above strategies which have been put in place is to ensure that energy is conserved.
The microgrid is defined as a network which is interconnected of Distributed energy resources, loads and storages, which can be
able to work either connected or disconnected from the main electricity grids. To the utility the smart microgrid can be thought as
cell of power system that is controlled. To the customers/clients, the microgrid can be designed in such a way to meet the unique

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needs of the customer, such as to improve the local reliability, increase efficiency and to minimise feed losses among many others
The microgrid are seen as the basic structure of the smart grid (Bhave, 2013, p. 67).
Smart grids are intelligently integrated technological and operational systems for the purpose of optimizing power, generation,
consumption and also distribution, and is therefore is considered as a major component of the smart grids. Also they provide
utility companies with full pervasive and visibility control over their services and assets, which opens up for a wide spectrum of
modern technologies and other business models to reduce the impact on climate and improve the energy efficiency.

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CHAPTER 6
References
Alpcan, T., 2017. Mechanisms and Games for Dynamic Spectrum Allocation. 4th ed. London: Cambridge University Press.
Basar, T., 2016. Dynamic Noncooperative Game Theory. 4th ed. Auckland: SIAM.
Bhave, M., 2013. The Microgrid Revolution: Business Strategies for Next-Generation Electricity. 5th ed. Berlin: ABC-CLIO.
Bianco, V., 2013. Analysis of Energy Systems: Management, Planning and Policy. 3rd ed. Chicago: CRC Press.
Billinton, R., 2014. Reliability Evaluation of Engineering Systems: Concepts and Techniques. 6th ed. London: Springer Science &
Business Media.
Chicco, G., 2015. Distributed Multi-generation Systems: Energy Models and Analyses. 4th ed. Sydney: Nova Science Publishers.
Crossley, C. P., 2011. Microgrids and Active Distribution Networks. 6th ed. Paris: Institution of Engineering and Technology,.
Farhangi, H., 2013. Smart Microgrids: Lessons from Campus Microgrid Design and Implementation. 1st ed. London: CRC Press.
Hatziargyriou, N., 2015. Microgrids: Architectures and Control. 7th ed. Sydney: John Wiley & Sons.
Huston, S., 2013. Smart Urban Regeneration: Visions, Institutions and Mechanisms for Real Estate. 1st ed. Texas: Routledge.
Pardalos, P. M., 2013. Sustainable Interdependent Networks: From Theory to Application. 2nd ed. Auckland: Springer.

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Radosavljevic, J., 2016. Metaheuristic Optimization in Power Engineering. 4th ed. Auckland: Institution of Engineering &
Technology.
Schewe, P. F., 2013. The Grid: A Journey Through the Heart of Our Electrified World. 4th ed. London: National Academies Press.
Sioshansi, F. P., 2016. Smart Grid: Integrating Renewable, Distributed & Efficient Energy. 3rd ed. Texas: Academic Press.
Srivastava, M., 2014. Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings. 5th ed.
texas: Association for Computing Machinery,.
Turunen, I., 2011. 23rd European Symposium on Computer Aided Process Engineering. 1st ed. Sydney: Elsevier,.
Vermesa, O., 2013. Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems. 7th ed.
Texas: River Publishers.

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Appendices

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