Difference between revisions of "Projects:2019s2-25501 Allocation of Storage Resources"

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==== Supervisors ====
 
==== Supervisors ====
 
* David Vowles
 
* David Vowles
* A/Prof Soong Wen Liang
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* Dr. Ali Pourmousavi Kani
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==== Advisors ====
 
==== Advisors ====
 
*
 
*

Revision as of 12:12, 9 June 2020

Abstract here

Introduction

Intermittent renewable sources such as wind and solar are highly competitive with fossil fueled generations in South Australia(SA). Currently, Renewable Energy makes up about over 50% of total generations in SA. However, this introduces many issues on balancing sources within the grid due to meet power demand. These balancing sources can be in the form of responsive gas turbines, demand respond and grid storage which consists of hydro-pump storage and battery storage. There are many pros and cons for each storage that will be discussed throughout this project. Solar PV generations installed by houses, industries, consumers, will also have impacts on the grid as it is estimated that next few years, the solar PV generations installation will be increased. The impacts and effects of solar PV generations will be included as it will effect the power demand of the grid in SA. It is important that as the intermittent renewable sources increasing, balanced sources are allocated in a coordinated and economically efficient way. This investigation will be based on the simplified model of the SA grid and recordings or the output from intermittent sources in SA.


Project team

Project students

  • Aiman Arif Bin Amir
  • Nur Amira Batrisyia binti Rozmizan
  • Xinzhou Cao

Supervisors

  • David Vowles
  • Dr. Ali Pourmousavi Kani

Advisors

Objectives

  • To investigate the techniques for optimizing the allocation of balancing sources within a grid.
  • To minimize the need for network augmentation.
  • To minimize the amount storage and reserve generations.
  • To minimize prioritize the use of demand respond.

Background

Demand in SA

The main background of this project is demand, which is measured in kilowatts (kW) and represents the rate of electricity needed to the grid for the consumers. This topic needs to be understood as this is one of the factors that can be used to optimize the allocation and the amount of energy resources. In Figure 4, the figure indicates the latest network in SA, which consists of 5,600 circuit kilometres of transmission lines and underground cables that has 91 substations and switchyards. The transmission lines are operated at 132 kV and 275 kV while three existing substations are operated at 66 kV, 132 kV and 275 kV. It can be seen in SA that there are quite lots of wind generators, two main solar generators, two battery power reserves and three power stations.

In SA Transmission Annual Planning Report 2017-2018, it states that there are nine different conditions for the power system to remains its system strength that includes from very high to low demand.

a. Very high summer demand

This condition represents 10% of Propagation of Error (POE) condition where the peak typically occurs late in the day, the output from solar generators is low and output from the wind generator is quite low.

b. High summer demand

This condition is modelling the high demand on a hot summer’s day, with a high level of wind and a moderate level of solar generation.

c. High summer noon demand

This condition has a high level of wind generation and a very high level of solar generation that occurs at noon on a hot summer day.

d. High winter demand

This condition represents a high demand on a windy winter day where the output for wind generators is the highest while there is no output for the solar generators.

e. Medium demand (sunny and still)

This is when it is a sunny and still day with high level of solar generation and a quite low level of wind generation.

f. Medium demand (windy and overcast)

This condition represents a day of windy and overcast where the wind generators produce a high level of power while a low level of solar generation. Under this system condition, the new generation will be limited due to a maximum of about 150 MW due to existing limits on export capability.

g. Low overnight demand

This is when there is no solar generation but has a high level of wind generation. The interconnector has the highest amount of export power due to the existing limits on the export capability.

h. Very low daytime demand

This condition is considered to be very low demand which is only 660MW where it usually occurs during mild, sunny weekend or public holiday.

i. Very high demand

This condition in SA representing as “Condition a” as it is assumed that generators within regional networks could be out of service. Plus, its output from solar generators is low and output from the wind generator is quite low.

In 2016-17, SA has experienced its minimum operational demand lower than expected. These values for the minimum demand are expected to be reduced years by years. The causes in reduced values of minimum demand are due to the increment of rooftop PV installations by consumers, the lower demand from the large industries sectors and most importantly is the weather itself in SA. In fact, in the next decade, it is predicted by the AEMO, there will be negative demand during summer when forecasting the minimum operational demand in SA. The consequence of this event, SA will be the region that would act as the main exporter of electricity to other regions. Apart from that, this will open many opportunities to develop more energy storage systems to be more-efficient on optimizing the renewable energy sources.

SA Elecicity report by AEMO

From the AEMO’s report in 2017-2018, it was found there are two average daily demand profiles (summer and winter) from 2013 to 2018 that represent in megawatt (MW) with a one-hour interval for a day. These demand profiles are analysed by the workdays, weekend and public holidays are excluded.

In the summer season, there is not enough depth at mid-day throughout the five years, which differs from the understanding of the increment on the installation of rooftop PV. During the mid-day in the summer days, it is believed that the rooftop PV of consumers generates more energy compared to the supplying energy from the grid. The line graphs should have more depth at mid-day. However, it can be seen there is slightly different in-depth during mid-day between the five years, where the line graph in 2017-18 has more depth compared to in 2013-14 during the mid-day. Besides, there are peak graphs at 11.00 pm throughout the five years, which is due to the controlled switching of electric hot water storage systems. It is believed that the sudden peak demand during mid-night, which is the hot water systems should be turned on away from the mid-night time to during mid-day instead. This will lower the peak during mid-night and increase the drop of demand at mid-day.

In the winter season, the line graphs have reduced demand during mid-day hours because the consumers use their output of rooftop PV. The peak at evening hours is mainly used for the heater as it is winter season. Consumers tend to switch on the heater when it is getting cold at night, and once it is quite warm at their homes, they will switch off the heater as it can be seen the line graphs are decreasing until mid-night. Then, there are sudden peak at mid-night in the five years, same as summer’s demand profile where the hot water storage systems are switched on. For a stable demand profile, the peak demand for hot water systems during the mid-night can be change to a few hours later. However, this might concern the consumers as they think it will be less efficient for them. For instance, if the consumers have to start work early at specific days, and they want to take shower at 4 am, but the hot water in their houses still not yet hot due to the late on switching on the hot water storage systems. Therefore, the right time for the hot water storage systems are still on investigation.

Transmision Interconnectors

In SA, there are two interconnectors for imports and exports power which are Heywood and Murraylink interconnectors [2 page 36]. The Heywood interconnector is between the Heywood substation in Victoria and South East substation in SA. This interconnector is 275 kilovolts (kV) with a limit up to 650 MW. Meanwhile, for the Murraylink interconnector, it is between Red Cliffs in Victoria and Monash in South Australia with 220 MW direct current (DC) cable.

in 2008-09 to 2016-17, the total interconnector imports from Victoria to SA increased gradually and it is then dropped more than half in 2017-18. This change is due to the increasing installation of renewable energy generations in SA and the closure of coal-fired generation which is the Hazelwood Power Station in Victoria at the end of March 2017. Apart from that, the total interconnector exports from SA to Victoria fluctuated from 2008-09 to 2010-11 and then decreased slowly until 2016-17.

In the year of 2017-18, it is the highest export over the past 10 years with 1.3 gigawatt hours (GWh). Rather than export a huge amount of power, it is recommended that the huge amount of power is used to store in the energy resources in SA. Although it is one of the ways to make money by exporting the power into the interconnectors, it is also beneficial that the power can be stored to support the reliability of intermittent sources in SA.

Method

Power flow equation are being done as to express the electric power flow in the interconnected system as been mention. The aspects of power flow equation focus on simplified notations and focus on several aspects of AC power parameters, which includes voltages, voltages angles, reactive power and real power. The voltages regarding some of the nodes are being regulated by generators, storage and controlled loads which are denoted by V_σ, vector of control voltages. Voltages of the remaining nodes are uncontrolled (V_σ ̃ ) and can be solved by the load flow. This power flow equation is being done as to determine the best operating of an existing system. The main information regarding this power flow equation are both the phase angle and magnitude at each bus and both the real and reactive power flows on each line. The power flow equation is as table 21 below: Real Power Õ=fp(P_GT,P_L ,E_ST,V_σ,V_σ ̃ ,Ɵ) Reactive Power Õ=fq(Q_GT,Q_L,V_σ,V_σ ̃ ,Ɵ)

Results

The optimization problem is to minimize the total system cost which is subjected to the balancing of relation between power supply and power demand at all times which is within the network and security constraints. The allocations of storage factor have been briefly explained on sections 4.7.1 and 4.7.2 where this allocation contributed towards the minimization of network cost and enable the power balancing between both supply and demand. The idea is to minimize the total cost as equation (8) below: min⁡∑_(t=1)^T▒∑_(i=1)^I▒〖C_i ((P_(GT,t) ) ̃ )+ 〗 ∑_(t=1)^T▒∑_(i=1)^nt▒〖C_j (F_t ) 〗 The equation above illustrates the summarization of the whole factor of power sources. Equation above which is the total cost of the system is being subject to the satisfy network equations of the power flow such as : Real Power Õ=fp(P_GT,P_L ,E_ST,V_σ,V_σ ̃ ,Ɵ) Reactive Power Õ=fq(Q_GT,Q_L,V_σ,V_σ ̃ ,Ɵ)

This power flow network equation comprises the interconnection of the transmission line and transformers and they are connected to the sources of the supply demand. By having this power flow equations, the voltages on the network will be satisfy and solve. The power flow equation depends highly on the network constrainsts of the power systems variable.

Conclusion

For the conclusion, the traditional linear energy system where it starts with the generation, transmission, distribution, retailer and consumers meanwhile in the future, it is predicted that the energy system becomes more modern and flexible which there are additional parameters such as energy storage, rooftop solar, battery storage in the houses and others. With the new modern flexible energy system, the solar farms can be built in order to increase the production of energy from renewable energy generations. With both solar and wind generations, it is estimated by AEMO who did an hourly energy balance analysis in 100% renewable energy scenario in Australia which its excellent renewable resources.

For the next future work, there is a need for designation in the analytical tool of software that can optimize the allocation of the storage resources and the amount of energy needed for the storage resources.

References

[1] a, b, c, "Simple page", In Proceedings of the Conference of Simpleness, 2010.

[2] Project:2018s1-140 Energy Storage Requirements for the SA Grid

[3] Australia Energy Market Operator Annual Report (AEMO), 2017, 2018

[4] South Australian Transmission Annual Planning Report (ElectraNET), 2018

[5] Aurecon Hornsdale Power Reserve Impact Study ,2018

[6] Snowy 2.0 Overview , 2018

[7] An Atlas of Pumped Hydro Energy Storage, Australia National University,2017