FutureDAMS YWP training: Myanmar

FutureDAMS: Prof. Aung Ze Ya (YTU)
http://www.futuredams.org/
1

The FutureDAMS consortium is working to improve
the design, selection and operation of dams to
support sustainable development.
• More than 3,700 large dams planned or under construction, to service growing
demands for energy and irrigation. This new generation of dam schemes has the
potential to make a significant contribution towards achieving the Sustainable
Development Goals and Paris climate change commitments. But maximising the
benefits, while minimising the negative social and environmental impacts remains
a challenge.
• The FutureDAMS consortium is developing the knowledge base, tools and
approach to enable dam projects to support resilient and sustainable development
in a warming world.
• Led by The University of Manchester and the International Institute for
Environment & Development (IIED) this £8 million project runs until December
2021. A consortium of over 30 researchers is funded by RCUK as part of the Global
Challenges Research Fund.
http://www.futuredams.org/
2

New projects have the potential to
contribute to SDGs.
Poorly designed dam projects
exacerbate social and political
instability, environmental degradation.
A Global Boom in Hydro-Energy
Construction
Research Questions:
1. What’s happening
now?
2. What should be
improved?
3. How?
FutureDAMS: Prof. Aung Ze Ya (YTU) 3

Aim and Objectives
Rapid DAC (Development Assistance Committee)
country achievement of Sustainable Development
Goals by selecting, designing, financing and
managing dam systems to meet local, national and
regional development needs – Paris Agreement
• Understanding of nexus systems
• Long-term x-disciplinary network
• Building institutional capacity
Source: FutureDAMS ppt by Prof. David Hulme (GDI, UoM) at YTU (Dec, 2018)
4

Achieving Our Aim – 3 Pillars
1. Research
- World-leading
2. High impact applications
- Collaboration with established partners
3. Building capacity, capability and legacy
- Partnerships:
- Myanmar (YTU)
- Ghana (CSIR, UoG)
- Ethiopia (NRC)
- Int’l: IUCN, IHA, WB, TNC, IWMI
- Web-based open-source tools & training
- Co-production through case-studies
5

WP5: Case-studies
Ghana Middle
East
Myanmar
WP2:
Reviews
WP3:
Energy-WEFE
research
WP4: Integrated
Framework
Academic
Advisory
Board
Institutional
Advisory
Board
Science
Application
Capacity
Outputs
Outcomes
WP6: Academic capability building,
Practitioner training, Public engagement
Reviews, Journal Papers, Integrated framework,
Online training, Online open analysis tools,
Deeper understanding, Better policies, Skills,
New tools, SDG progress in case-study regions
Modelling & stakeholder assessment
WP1: Management

Governance,
& development
Finance &
Energy, climate policy
Climate,
hydrology &
hydro-ecology
Economic
analysis
Systems
engineering &
decision-making
under
uncertainty
Social impacts
(Researchers at Univ. Manchester if not specified)
FutureDAMS Researchers
• Prof Dale
Whittington
• Prof Kunal Sen
• Dr. Alvaro Calzadilla
Rivera (UCL)
• Dr Afzal Siddiqui
(UCL)
• Dr Ralitza Demova
• Prof Julien Harou
• Prof Pierluigi
Mancarella , Dr
Mathaios Panteli,
Dr Joseph Mutale,
• Dr Tim Foster
• Dr Tohid Erfani
(UCL)
• Prof Andy Norton (IIED)
• Jamie Skinner (IIED)
• Prof Nigel Gilbert (Surrey)
• Prof Jake Reynolds (Cambridge)
• Prof. Alice Larkin
• Clare Shakya (IIED)
• Prof. Kevin Anderson
• Prof Justin Sheffield
(Southampton
• Prof. Hayley Fowler
(Newcastle)
• Dr. Gwyn Rees, (CEH)
• David Weiburg (IWMI)
• Prof David Hulme
• Prof Bill Adams (Cambridge)
• Dr Tom Lavers
Volta basin Case-study
Dr Emmanule Obuobie
CSIR-Water
Myanmar Case-study
Prof Aung Ze Ya
Yangon Technological University
East Nile Case-study
Dr Abdulkarim Seid
NBI (Nile Basin Initiative)
P.I. : D. Hulme Capacity Development Lead: J. Skinner Research Lead: J. Harou

Example Case-study:
Volta River Basin
Transboundary river basin
Several new dams
proposed (e.g. Pwalugu,
World Bank)
Akasombo dam produces
30% of Ghana’s energy, it
needs re-operation to
mitigate devastating
livelihoods and ecosystems
impacts downstream

IUCN (International Union for the Conservation of Nature)
The World Bank
TNC (The Nature Conservancy)
IHA (International Hydropower Association)
Climate Bonds Initiative
IFC (International Finance Corporation)
EIB (European Investment Bank)
EBRD (European Bank for Reconstruction and Development)
UNECE (U.N. Economic Commission for Europe)
FAO (U.N. Food and Agriculture Organisation)
Institutional Advisory board for
Global Policy Impact
9

Research Questions
• What’s happening now? Who is selecting, designing, and financing
dams and systems of dams today? what approaches/tools do they
use? what shapes and incentivises decisions about dam selection &
operation?
• What should be improved? What technical and political knowledge is
required for new dams to maximise and appropriately allocate nexus
benefits, promote resilient and sustainable development, and
minimise conflict and socio-ecological loss? what participatory
decision processes need designing/improving?
• How? What skills, approaches, processes, tools and networks will
help create a new generation of engineers, social scientists and policy
analysts in the UK, case study countries & beyond to achieve our
mission?
10

• FutureDAMS’ is delighted to congratulate its Principal Investigator, Professor David
Hulme, who has been awarded with an OBE (Order of the British Empire) in the Queen’s
New Year Honours list for services to Research and International Development. Through a
distinguished 40 year career in development, Professor David Hulme’s research and
passionate commitment to creating positive change has helped to lift millions of people out
of extreme poverty. David’s passion and energy for tackling poverty is evident in the
FutureDAMS project and his leadership and input continues to improve the role of politics in
development, and the impacts of large dams.
Professor David Hulme awarded OBE
(Jan 2020)
http://www.futuredams.org/professor-david-hulme-awarded-obe/
11

http://www.futuredams.org/professor-david-hulme-outlines-the-futuredams-project/
Professor David Hulme outlines the FutureDAMS project
(Aug 2018)
14

Professor David Hulme interviewed on the
Global Dispatches podcast
(Sep 2019)
http://www.futuredams.org/david-hulme-interviewed-on-the-global-dispatches-podcast/
15

‘Building better dams: the past, present and future of dams’ –
University of Peking lecture by Professor David Hulme
(Oct 2019)
http://www.futuredams.org/peking-lecture-david-hulme/
16

http://www.futuredams.org/watch-prof-julien-harou-at-the-geneva-water-hub/
Professor Julien Harou’s Talk at the Geneva Water Hub
(Jun 2019)
• Professor Julien Harou was recently invite to give a 15 minute talk at the Geneva
Water Hub.
• The Water Talks series is an open-speech opportunity for researchers to expose
and explain their ideas, their stances on contemporary challenges linked to water
governance.
• Professor Harou’s presentation outlines the approach, progress and potential of
the FutureDAMS research agenda. It provides an excellent overview of the project,
demonstrating how improved design and assessment of water-energy-food-
environment mega-systems will support progress towards the SDGs.
17

FutureDAMS at YTU
Feb & Dec 2018
18

FutureDAMS (1st Workshop)
YTU 6th-7th June 2019

FutureDAMS MoU with Myanmar Rectors’ Committee
Official Signing Ceremony on June 17th 2019
http://www.futuredams.org/futuredams-formalises-partnerships-in-myanmar/
20

FutureDAMS (Second Workshop)
Mercure Kaba Aye Yangon 18th-19th June 2019

FutureDAMS (Annual Forum & Modeling Works)
Crowne Plaza Manchester (Oxford) 16th-20th Sep 2019
Ferranti Building, The University of Manchester 21st-28th Sep 2019

#HYDROPOWER SCENARIOS

Hydropower Installed Capacity Worldwide
Source: IHA 2019 Hydropower Status Report
24

Pumped Storage Hydropower Worldwide
25

Top 10 Hydropower Installed Capacity Worldwide (as of 2012)

Top Hydropower Capacity (as of 2015)

10-Year Trends in Renewable Energy
(Installed Capacity)
Source: IRENA.

Hydropower Growth through the Decades
29

Hydroelectric Generation Capacity (GW)
in SEA as of 2012 & planned to 2020

Hydro Power Resources
in Myanmar
Resources Total : 108,000.00 MW
Already Investigated : 46,330.55 MW (302 Nos)
< 10MW : 231.25 MW (210 Nos)
10 MW ~ 50 MW : 806.30 MW ( 32 Nos)
> 50 MW : 45,293.00 MW (6 0 Nos )
Already Installed : 3,231.50 MW
( 3% of Resources)
( 7% of Investigated)
Sr Region
Numbers of
Potentials Capacity
(MW)10~50 MW >50 MW
1 Kachin 5 14 18,744.5
2 Kayah 2 3 954.0
3 Kayin 1 8 7,064.0
4 Sagaing 2 4 2,830.0
5 Tanintharyi 5 1 711.0
6 Bago 4 4 538.0
7 Magway 2 3 359.0
8 Mandalay 3 6 1,555.0
9 Mon 1 1 290.0
10 Rakhine 3 3 764.5
11
Shan
East 1 3 719.8
South 3 5 7,569.5
North - 5 4,000.0
>10 MW 32 60 46,099.30
12 <10MW 210 231.25
Total 302 46,330.55
33
Source: MOEE (2018)

Top 20 Countries by Newly Installed Capacity
34

Top 20 Countries by Unutilized Hydropower Potential

#WHAT IS CLIMATE CHANGE ?
#HOW DO WE KNOW ?
#WHY ?

Components of the climate system, their processes, and interactions
Source: NSHD-M

#CLIMATE CHANGE
• Climate change is a change in the pattern of weather, and related
changes in oceans, land surfaces and ice sheets, occurring over time
scales of decades or longer.
• Climate is determined by many factors that influence flows of energy
through the climate system, including greenhouse gases.
• Changes in climate can occur through both natural and human-
induced causes.
• A disturbance to the climate system can trigger further changes that
amplify or damp the initial disturbance.
• Concentration of GHGs in earth’s atmosphere is directly linked to the
average global temperature on earth, rising steadily, is expected to go
up another 1.8°C to 4°C by 2100 if no action is taken.
38
Source: Australian Academy of Science (AU)

Indicators of Global Warming
39FutureDAMS: Prof. Aung Ze Ya (YTU)

40
Source: Australian Academy of Science (AU)
Energy enters the Earth system from the Sun…

41
Global Carbon Cycle

3 KEY CLIMATE-DRIVEN CHANGES
3 key climate-driven changes which can be expected in the water &
electricity sector potentially relevant in South East Asia, and the need of
the hydropower sector to adapt to such changes :
• changes in electricity demand (through increased cooling
requirements)
• changes in evaporation from reservoirs, which may make
reservoirs
less attractive
•changes in water demand (especially irrigation) which may reduce
water availability for hydropower
42
Source: NSHD-M

Fluxes and stocks of water
Source: NSHD-M

Hydropower Project Development Cycle

Prior to siting a hydropower project within a basin, typical questions
that must be considered include:
• How large is the overall expected power demand? Base load or peak load?
• Which reaches or tributaries have the best conditions (e.g., large and regular
flow, steep topography)?
• Are there any factors that exclude reaches or tributaries from consideration
(e.g., unstable geology, lack of access, protected areas)?
• Is there a choice between building one large station instead of several small
projects?
• Is there any logical sequence in which stations should be built?
• Can stations support each other in their operations?
• What other water uses besides hydropower exist in the area? How much
storage space do they require? Where are the locations with storage
capacities? Are their storage requirements going to be compatible with
hydropower operations?
Source: NSHD-M

Option A: Five stations located on the
mainstream and two tributaries. The
highlighted area shows that almost the entire
basin is upstream of the hydropower stations.
Possible issues include effects on fish passage
and sediment. However, this scheme enables
the control of flows or floods downstream.
Source: NSHD-M
Option B: Six stations located on one tributary.
Only one sub-basin is upstream of the stations.
This reduces the impact on sediment and fish
passage but also reduces the ability to control
flows/floods downstream. However, options
include operating cascades with reservoirs at the
top and run-of-river stations below the stations.
Also, the last reservoir could release close-to-
natural flows.

Engineering studies will progress from the conceptual level and
will be fed by subsequent site studies, which provide more data
about opportunities and constraints. Design engineering then
develops a project idea in more detail:
• What kind of dam (e.g., roller-compacted concrete, arch, rockfill)?
• How much live and dead storage capacity? Both for hydro and other
needs?
• How much capacity for spillways and other outlets? Where should
these be located?
• What kind or powerhouse (e.g., at foot of dam or further
downstream, above ground or underground, turbine type, and
number of turbines)?
• Can the project be developed in stages?
Source: NSHD-M

Additional considerations that should be addressed may be
influenced to various degrees by climate change:
• How large should the installed generation capacity be?
• What is the best balance between reservoir size, costs, and
environmental and social impacts?
• What is the design flood?
• Which layout of the civil works and which configuration of electro-
mechanical installations will minimize construction costs?
• How can the resource be used most efficiently?
• Will there be a need to manage sediment accumulation and water
quality in the reservoir?
• Will there be a need to mitigate environmental impacts on
downstream flows (e.g., timing, temperature, gas content) and
aquatic biodiversity?
Source: NSHD-M

Storage rule curves and average monthly inflows to Lake Madden, Panama
Climate change influences all currently operating projects, as well as the operations of potential
future hydropower projects. Operating projects are constrained in their choices by the
infrastructure and the rules that have already been established. New projects have a greater
degree of freedom in their choices. Whether existing or new, operational considerations include:
• How much water should be stored for dry periods and periods of high demand?
• How much water should be released in anticipation of the wet season or a flood?
• When should non-generation outlets be used? When should sediment be flushed?
• How should fluctuations of the reservoir level and of downstream flows be managed to
minimize environmental and social impacts?
Source: NSHD-M

Large infrastructure projects, such as hydropower stations, require the
development of a number of documents as steps in the planning
process; many of these may also be legal requirements:
• Masterplan
• Electricity generation expansion plan
• Strategic environmental assessment
• River basin development plan
• Project identification / pre-feasibility study
• Feasibility study (sometimes with multiple sub-studies, such as hydrological
studies)
• Detailed design
• Environmental and social impact assessment
• Environmental and social management plan
• Sustainability assessment
• Construction plan
• Operations plan
Source: NSHD-M

An early summary of water resource planning recommendations under climate change
still appears to include the most relevant recommendations:
• Interconnection of systems to provide additional backup for changing regional conditions.
• Incremental construction where possible and economically feasible (e.g., a number of small systems
rather than one large one) to allow for adaptation to changing circumstances.
• Choice of robust designs in which the chosen design will be fairly good under a wide range of
outcomes rather than optimal under one outcome.
• Postponement of irreversible (or very costly to reverse) decisions.
• Use of a range of formal decision techniques, including scenario analysis, sensitivity analysis, Monte
Carlo methods, and others.
• Designing for extreme conditions. Using historical or synthesized flows, the water resource planner
can suggest approaches that deal with extreme events (floods and droughts) rather than simply
maximizing the expected value of net benefits.
• Reallocation of storage. After projects are constructed, and circumstances change, storage can be
reallocated to improve project performance under changed climatic conditions.
• Reallocation of supply through the development of water markets.
• Development of non-structural measures such as warning systems. Flood and storm warning
systems (inland and coastal) can be used to adjust to the risks and uncertainties of flooding.
• Demand management measures. These measures, such as implementing pricing schemes, requiring
low-demand appliances, or formulating drought contingency plans, can be used to control demand
and thus provide a measure of safety in available supplies.
• Preservation of ecosystems. As an adjustment to uncertainty, areas can be reserved to protect
against the uncertain effects of climate change on ecosystems.
Source: Major (1998)

http://www.internationalrivers.org/campaigns/wrong-climate-for-damming-rivers

Main Parameters or factors affecting GHG production from Dam
• Carbon and nutrient loaded into the reservoir
• Rainfall
• Soil type and land use
• Biomass of plants, algae, bacteria and animals in the reservoir and in drawdown zone
• Water temperature
• Residence time
• Stratification of the reservoir body
• Reservoir age
• Drawdown zone exposure (changes in water depth)
• Wind speed and direction
• Presence of low level outlets
• Increased turbulence downstream of the dam associated with ancillary structures
(e.g. spillways and weirs)
• Reservoir shape (shoreline/surface ratio)
• Water depth

1st step must include the factors that determine the carbon supply.
•Organic material in the catchment
•Land use in the catchment
•Biomass in reservoir and drawdown areas
•Agricultural practices (that influence both carbon and nutrient stocks, and carbon and
nutrient ability)
•Rainfall
54
GHG Emissions from New Reservoirs:
2nd Step includes factors that impact the ability to create GHGs:
• Residence time
• Temperature
• Stratification
• Drawdown zone exposure
• Reservoir age
3rd Step includes factors that influence the ability to release GHGs :
• Wind
• Water depth
• Reservoir shape (surface/shoreline ratio)
• Low level outlets

2nd Step includes factors that impact the ability to create GHGs:
• Residence time
• Temperature
• Stratification
• Drawdown zone exposure
• Reservoir age
3rd Step includes factors that influence the ability to release GHGs :
• Wind
• Water depth
• Reservoir shape (surface/shoreline ratio)
• Low level outlets
GHG Emissions from New Reservoirs: 2nd & 3rd
Steps

Climate Change Mitigation
• A key questions are: how to decrease the impacts of climate
change on hydroelectric resource, and, how to promote long-
term sustainability of hydropower development?
• With the development of advanced technology, models,
methods and improved algorithms, more precise and accurate
planning and forecasting performance of climate change is
greatly enhanced.
• Increasing the proportion of hydropower generation
• Optimizing hydropower operation and management
56
Source: Impacts of climate change on hydropower development and sustainability: a review by J Shu (Global
Environment and Natural Resources Institute (GENRI), George Mason University, USA) et all

• Average Demand Shifting
• Variation of Peak and Off-Peak Demand
• Changing Generation Flexibility
• Changing Generation Mix
(Remark: not only Hydro)
57
Source: Climate change mitigation with integration of renewable energy resources in the electricity grid of new south wales,
Australia by Md Abu (Abdullah University of Wollongong) et all.
Climate Change Mitigation

Terminology of Adaptation to Climate Change: Sensitivity & Vulnerability
• Sensitivity: The degree to which a system is affected, either adversely or
beneficially, by climate variability or climate change. The effect may be direct (e.g., a
change in crop yield in response to a change in the mean, range, or variability of
temperature) or indirect (e.g., damages caused by an increase in the frequency of
coastal flooding due to sea level rise).
• Vulnerability: The degree to which a system is susceptible to, and unable to
cope with, adverse effects of climate change, including climate variability and
extremes. Vulnerability is a function of the character, magnitude, and rate of
climate change and variation to which a system is exposed, its sensitivity, and its
adaptive capacity.
58
Source: NSHD-M

• Adaptation: Initiatives and measures to reduce the vulnerability of natural and
human systems against actual or expected climate change effects. Various
types of adaptation exist, e.g. anticipatory and reactive, private and public, and
autonomous and planned. Examples are raising river or coastal dikes, the
substitution of more temperature-shock resistant plants for sensitive ones, etc.
• Adaptive capacity: The whole of capabilities, resources and institutions of a country
or region to implement effective adaptation measures.
• Resilience: The ability of a social or ecological system to absorb disturbances while
retaining the same basic structure and ways of functioning, the capacity for self-
organization, and the capacity to adapt to stress and change.
59
Terminology of Adaptation to Climate Change: Adaptation & Resilience
Source: NSHD-M

Hydropower as an Instrument for Adaptation
• Hydropower is just one among many industries that needs to adapt to climate
change. But it may be able, through the water storage it provides, to support
adaptation in other sectors.
• Hydropower is often the only major source of financial revenues from the use of
water resources, so that the initiative to build a reservoir may have to come from the
hydropower sector. Irrigation schemes often do not cover their costs; municipal water
supply needs are generally small; and other water uses that might benefit from
storage are either public goods or have low potential for revenues.
• Under some circumstances, hydropower projects can support adaptation in the
water resources sector. If properly designed and managed, their reservoirs can
provide additional storage to protect downstream populations from floods and
droughts. However, there are often alternative options to provide the same service.
60
Source: NSHD-M

Climate Change Adaptation: Water & Agriculture
Source: IPCC (2007) Fourth Assessment Report: Climate Change

Climate Change Adaptation: Infrastructure/Settlement & Human Health
62

Adaptation: Tourism & Transport

Adaptation: Energy

#Barriers and Enabling Factors for Hydropower Development
65
Source: NSHD-M

#Sustainability Issues should be considered for Hydropower Development
66
Source: IHA (2010)

Problem Tree
GHG Emissions
Can’t fulfil the
Electricity Needs
Fossil Fuels Utilization
may be Option
Least Harnessing of Myanmar’s
rich Hydropower Potential
Barriers of
Sustainable Hydropower Development
Poor Budget
Negative Point of
Views on
Hydropower
Recent floods Insufficient
Electricity
Generation
during Summer
EFFECTS
CAUSES
PROBLEM
Weak
Transparency,
Awareness
Climate Change
Weak in ESIA, RAP
Weak legislation,
Institutional
Framework, …
May lead to Delay the
Development of the CountryGlobal Warming
Technology/Design/O&M
Low RE
Utilization&
Slow Green
Growth
Deforestation
©Dr. Aung Ze Ya (Profesor, YTU)

Conclusions
• Key Actors Dialogue to be SHD
• System-scale Planning
• Comprehensive Research to promote SHD
• Enhancement of the Education for SHD in Myanmar
• Investigation of the Adverse Impacts of the Existing & New Hydropower Stations
• Implementation of the New Stations with the Least Negative Impacts
• Renovation of the Existing Hydropower Stations to be Efficient Operation
• Reviewing the Optimization of Myanmar’s Power System
• Applying the Innovative/Modernized Technologies to maximize the Benefits of
Hydropower
• Implementation of Pumped-Storage Hydropower Stations
• Integrating the Floating Solar PV Systems at the Appropriate Dams/Reservoirs
• Adopting the Guidelines & Plans for Grid Optimization
• Promoting the Power Quality of Grid
• Upgrading the Transmission Capacity
• Improving the Awareness & Transparency
• Building the Capacity & Trust to the Project Affected Local Communities
• Lessons Learnt from other ASEAN countries

Techno-economic Analysis of Energy Systems
Dr Eduardo Alejandro (Alex) Martínez Ceseña
Dr Mathaios Panteli
Prof Joseph Mutale
Prof Pierluigi Mancarella
Alex.MartinezCesena@manchester.ac.uk

Aim and objectives
Aim: Overview key technical and economic considerations
for the modelling of electricity power systems
• Objectives:
– Discuss the most economic operation of electricity generation
technologies using Economic Dispatch (ED)
– Highlight the technical constraints that the electricity power
network imposed on the economic operation of the system
– Present key considerations for the techno-economic modelling
of power systems using Optimal Power Flow (OPF)
• Why is this important?
– The core functionalities of the FutureDAMS energy model are
built on ED and OPF tools

Economic Dispatch (ED)

Main drivers of Power System Operation
• The electricity power system must be operated in a
secure and economic manner
• As a first stage, let us focus on the economics

Economic Dispatch (ED) – The context
• Assuming that the system is already secure, low
electricity costs are achieved by dispatching the most
economic electricity generation assets (generators)
• For this purpose, we will use the ED tool
• The aim of the ED is to
– Dispatch enough generators to meet electricity loads
– Minimise the cost of electricity generation

ED problem definition
• Several generators (e.g., A, B and C) serve the aggregated
load (L)
• We know that all units are available to produce electricity
(committed)
• Our task is to decide, using ED, how much each generator
has to produce to meet the load
A B C
L

ED – Generation costs
• In order to decide which generators to dispatch, we must first
understand the characteristics of generation costs
• There is a minimum (No-load cost) to maintain a generator
operating
• There are additional costs, based on fuel consumption, to increase
electricity generation

ED – Prices
• However, if the generators are already operating, we need to know
the price of electricity (also known as incremental or marginal
costs) to decide how much to generate
• The marginal/incremental costs represent cost of the next MWh to
be generated
• The next MWh to produce should be the cheapest available

Economic Dispatch (ED)
Objective:
• The ED consist on the use of generator (marginal) costs as a
means to dispatch the cheapest generators
• Constraints
– Generation must match the load (equality constraint
– Generators must operate within their lower and upper limits
(inequality constraints
A B C
L

Economic Dispatch (ED) – Power
World applications Example 01 and
Example 02

PowerWorld examples
“Economic Dispatch (ED) and Optimal Power Flow (OPF)”
General notes:
Make sure that “Run Mode” is selected; the top left corner of the PowerWorld window.
Issues: If you encounter an issue, most of the time it can be solved by closing and re-
opening the file.

Part 1: Economic Dispatch
• Notes:
• Load Example 01 in PowerWorld Simulator.
• Running ED: Select the "Add Ons" tab and click on the "Primal LP" button.
• Viewing generator data: Double click on the generator icons.
• Changing the load: Click on the arrows next to the load. By default, load
will vary in 10MW increments.

Example 02
• Observing the marginal costs for both generators
• Double click on a generator to open the "Generator Options" window.
Select the "Cost" tab and the "Output Cost Model" sub-tab. The marginal
costs can be found on the right side as a "Piece-wise Linear Cost Curve".
• Run ED under normal conditions
• Change the load at bus 3 in 10MW increments from 100MW to 300MW
• Every time you make a change, click on the "Primal LP" button to run an
ED algorithm.
• Run ED with piece-wise approximation
• Load Example 02, check the capacities and costs of the generators
• As above, run ED after changing the load at bus 3 in 10MW increments
from 100MW to 300MW
• Every time you make a change, click on the "Primal LP" button to run an
ED algorithm.

Optimal Power Flow (OPF)

Main drivers of Power System Operation
• Let us now also consider system security

• Is this picture realistic?
• Are large generators such as hydropower plants right
next to the loads?
A B C
L
Limitations of ED

Limitations of ED
Answer: No
– Generators loads are not all connected to the same bus (electricity network node); there is a
large network between most loads and generators
Implications:
– The economic dispatch may result in technically unfeasible network conditions, e.g.,
unacceptable flows or voltages
Network
A
B C
D

Example of Network Limitation
A
B
LBLA
Maximum flow on each line: 100MW
100£/MWh
PB
PB
MAX
BC
50 £/MWh
PA
PA
MAX
AC

Acceptable ED solution
• The solution of this (trivial) ED is:
A
B
LB
LA=100 MW
300 MW
LB=200 MW
100 MW
100 MW
0 MW
MW0
MW300


B
A
P
P
 The flows on the lines are below the limit
 The economic dispatch solution is acceptable
300 MW
0 MW

• Does the results show that the system in the example is
secure?
• Not necessarily, a single study is not sufficient to
guarantee system security as loads vary throughout the
day
Acceptable ED solution

Unacceptable ED solution
• The solution of this (trivial) economic dispatch is:
A
B
LB
LA=100 MW
500 MW
LB=400 MW
200 MW
200 MW
0 MW
MW0
MW500


B
A
P
P
 The resulting flows exceed their limit
 The economic dispatch solution is not acceptable
500 MW
0 MW

Modified ED solution
• In this simple case, the solution of the ED can be modified easily
to produce acceptable flows
• However, this is a daunting task for larger and more complex
networks
• We need a more general approach, e.g., OPF
A
B
LB
LA=100 MW
300 MW
LB=400 MW
100 MW
100 MW
200 MW

Optimal Power Flow (OPF) - Overview
• The OPF is also used to dispatch
generators in an economic manner
– Minimise the cost of generation
• However, unlike the ED, the OPF
considers the electricity network
– Power is balanced at each bus
(equality constraints)
– The technical limitations of the
network are modelled
 
GN
i
ii
u
PC
1
min
0),,( yuxG
0),,( yuxH

Mathematical formulation of the OPF
• To model the networks, we need to know the relevant
technical and economic information (Parameters)
– Known characteristics of the system
– Assumed constant
• Network topology
• Network parameters (R, X, B, flow and voltage limits)
• Generator cost functions
• Generator limits
• …
• Vector of parameters:
y

OPF problem formulation
• We can now use generators and network assets
(control variables) to facilitate the secure
operation of the network
– Active power output of the generating units, e.g.,
using hydropower to store surplus renewable energy
– Voltage at the generating units
– Amount of load disconnected
• Vector of control variables: u

• We can also capture the response of the
system to different control actions (State
variables)
– Power flows throughout the network
– Power losses
– Voltages at each bus
• Vector of state variables: x

• Can we do more than reducing electricity costs?
• Yes, we can also consider other criteria
– Maximise integration of renewable energy sources
(minimise spilling)
– Minimise technical loses
– Minimise load curtailment
– Minimise total generating cost
– Etc.

Complexity of the OPF
• Due to its complexity, the full (Alternate Current) formulation
of the OPF is generally used for operational analysis
– Snapshot or day ahead (24h) studies
    
    






N
i
ikkiikkiik
L
k
G
k
N
i
ikkiikkiik
L
k
G
k
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BGVVPP
Nk
1
1
cossin
sincos
,...,1

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0),,( yuxG
uuu 
ijij FF 
jjj VVV 
0),,( yuxH
Power balance
Technical limits

OPF Challenges
• Even when used for operational studies, the AC-
OPF can become too complex
– Large networks with 1000’s of lines, hundreds of
controls
– Problem is non-linear
– Problem is non-convex
• We need to simplify the problem, especially if
we are to use OPF for investment applications in
the context of FutureDAMS

Linearised OPF Formulation
• What is the fundamental difference between and
?
• Answer:
– can be solved directly:
– requires an iterative solution
• Solving linear or linearised optimisation problems is much easier
• Can we linearise the OPF problem?
0bax
0cos  xx
0bax
abx /0cos  xx

Linearising the OPF Problem
• Objective function
– Use linear or piecewise linear cost functions
• Equality constraints
– Use Direct Current (DC) power flow instead of AC power flow
• Inequality constraints
– DC power flow provides linear relations between injections (control
variables) and MW line flows

• Considering typical network characteristics and
operation:
– Network resistance is very low (G~0)
– Voltages are close to 1 (V~1)
– Voltage angle differences are low (cos(θk-θi)~0; sin(θk-θi)~θk-θi)
    
    






N
i
ikkiikkiik
L
k
G
k
N
i
ikkiikkiik
L
k
G
k
BGVVQQ
BGVVPP
Nk
1
1
cossin
sincos
,...,1


Linearising the OPF Problem

Trade-offs associated with DC OPF
Cons:
– The solution may be somewhat sub-optimal, unless the number
of lines used for the approximations (and complexity) is
increased
– The constraints may not be respected exactly; security margins
should be considered
Pros:
– It is a well established and mature technique
– Computational costs are greatly reduced
– The DC OPF can be solved using linear programming, some of
which are free, e.g., open source engines used in FutureDAMS

Example of DC-OPF
• Solving the full non-linear OPF problem by hand is too
difficult, even for small systems
• We will solve a linearised 3-bus examples by hand
• More complex examples will be solved using the
PowerWorld OPF package

Example
10 £/MWh
CA
PA
PA
MAX=390MW
20£/MWh
CB
PB
PB
MAX=150 MW
Economic dispatch:
MW60
MW390max


B
AA
P
PP
1 2
3
BA
450 MW

Flows from the Economic Dispatch
• Assuming that all the lines have the same reactance,
do these injection result in acceptable flows?
1 2
3
BA
450 MW
390 MW 60 MW
Fmax=200 MW
Fmax=200 MWFmax=260 MW

• No, the capacity of a line has been exceeded
• Let us solve the ED, and also the OPF using a graphical approach
Flows from the Economic Dispatch

Solving with graphical approach
• As we only have two generators, we can graphically represent how an
optimisation engine would solve the OPF
• For this purpose a search space is defined as potential combinations of GA and
GB outputs
GA output (MW)
GBoutput(MW)

Solving with graphical approach
• The objective of the optimisation problem is to minimise costs
• At this stage, the optimal solution would be to not generate any
electricity
GA output (MW)
GBoutput(MW)
Optimal solution?

Equality constraints
• However, we must meet the load
• If so, the optimisation would choose the cheapest generator
(GA)
Equality constraint:
Generation = Consumption
GA output (MW)
GBoutput(MW)
Optimal solution?

Inequality constraints
• However, GA has operational limits, thus we can only
use GA=390 MW, GB=60 MW
• This is now equivalent to the ED
GAlimit
GB limit
Optimal solution?
GA output (MW)
GBoutput(MW)

Network limits
• Finally, the network constraints are added
• Generation is revised to also meet network constraints:
GA=330 MW and GB=120 MW
OPF
solution
GA output (MW)
GBoutput(MW)

OPF solution
• The new solution is technically feasible
1 2
3
450 MW
120 MW330 MW
70 MW
GA output (MW)
GBoutput(MW)

Comments: what’s the cost of
the OPF solution?
• The OPF solution is more expensive than the ED solution
– CED = 10 x 390 + 20 x 60 = £5,100
– COPF = 10 x 330 + 20 x 120 = £5,700
• The difference is the cost of security
– Csecurity = COPF - CED = £600
• The constraint on the line flow is satisfied exactly
– Reducing the flow below the limit would cost more
…Is this enough to guarantee system security?

Optimal Power Flow (OPF) – Power World
applications Example 03 and Example 04

Part 2: Optimal Power Flow
• Notes:
– Load Example 03
• Running OPF, Select the "Add Ons" tab and click on the "Primal LP" button.
This is the same as running ED
• The difference between the OPF and ED formulations is that the OPF formulation
includes network constraints. To check this difference Click on the “Add Ons” tab of
the ribbon, click on the “OPF Options and Results” button.

Click on the “Constraint Options” to run ED tick the “Disable Line/Transformer MVA
Limit Enforcement” untick the box to run OPF.
ED vs OPF
Simulate the system (Add Ons/Primal LP) as you increase the load from 100MW to 300MW in
10MW increments.
Load Example 04 and repeat the previous step, i.e., increase the load from 100MW to 300MW.

Part 3: 7 bus SCOPF example
• Notes:
– Run traditional power flow study. A traditional power flow study, which does not
consider costs or technical limits can be used by clicking on “Single Solution – Full
Newton” in the “Tools” tab.
• Before running a security constrained OPF, a list of contingencies has to be defined. Click on
“Contingency Analysis” in the “Tools” tab.
• Click on the "Auto Insert" option to select all lines ("Single transmission line or transformer").

Click on ”Do Insert Contingency records”.
Click on “Yes” to accept the addition of the list of contingencies.

Security Constrained OPF (SCOPF)
• Remember that power system operation is about
“balancing profit maximisation and system security”
– Minimise the cost of running the system
– Make sure that the system can continue operating after
any credible contingency
• Conventional OPF only guarantees that the operating
constraints are satisfied under normal operating
conditions (e.g. all lines in service)
• This does not guarantee security under contingencies

Example: Base Case Solution of OPF
• Unacceptable because overload of line 1-3 could lead to
a cascade trip and a system collapse
1 2
3
BA
450 MW
330 MW 120 MW
0 MW
120 MW330 MW

Security Constrained OPF (SCOPF)
• To meet security considerations, the system
has to be operated in consideration of credible
contingencies
• Examples of SCOPF applications will be
illustrated with PowerWorld

Security constrained OPF PowerWorld Example 05

Security constrained OPF
Load Example 05
Run an OPF.
Disable a line by clicking on the switches (red squares) on one of its edges.
Run a traditional power flow analysis and check if a network limit has been exceeded.
Using the contingency analysis tool to define a list of single transmission line or
transformer contingencies.
Click on “Run Full Security Constrained OPF”.
Run a traditional power flow analysis and check if a network limit has been exceeded.

Primary LP OPF Solution

Tools  Contingency Analysis

Power Flow? Area Cost?

Perform a security constrained OPF by clicking on “SCOPF” in
the “Add Ons” tab.
Click on “Run Full Security Constrained OPF”.

Concluding remarks
• This lecture and tutorial provided an overview of ED, OPF and
SCOPF
• The characteristics of these studies make them suitable for
different applications, e.g., ED can be used after system
security has been guaranteed
• The energy engine developed as part of FutureDAMS is
flexible to perform customisable ED, OPF and SCOPF as
required by the study … This will be further discussed in the
next session

River basins as systems

River basins
River basins are complex systems of built and natural infrastructure
•Built infrastructure: dams, irrigation systems, transfer tunnels, diversion
weirs/barrages, hydropower plants, etc.
•Natural infrastructure: forests, ecosystems, floodplains, flood flows, flow regime, etc.
River basins benefits
A range of benefits are provided by this infrastructure
•Built infrastructure: hydroelectricity, irrigation, drinking water supply, industrial water supply,
reservoir fisheries, flood control, leisure activities on reservoirs
•Natural infrastructure: pastures for livestock grazing, floodplain fisheries, riverine fisheries,
coastal/estuarine fisheries, biodiversity tourism, flood recession agriculture, coastal
sediment/beach replenishment
Source: FutureDAMS: Modelling and software co-development session 1 ppt by Dr. Evgenii Matrosov (UoM) at YTU (June, 2019)

River basins challenges
• what the current balance of benefits is within a basin;
• whether the economic benefits from the basin are being maximised;
• whether the water available is being used efficiently;
• how changing conditions might affect the benefits from the system;
• how the different benefits from the system could be changed – how
does one benefit reduce when another is increased?
• what impacts new infrastructure, policies or operating rules might
have on the benefits from the system.

River basins modelling
Models can be:
•a series of linked formulae in a spreadsheet.
•developed in software licensed from companies who develop it
and provide user support.
•created in Open Source software, developed by a community
of software developers who may also provide support.

River basin system models:
•Use information from hydrological models and information about
built and natural infrastructure (physical characteristics and
operating rules).
•Simulate water management within a river basin system and the
various benefits achieved.
•Fast by avoiding hydrological/hydraulic calculations (i.e. water
balance only,– moving ’packets’ of water around).
•Can test many options, operating regimes and/or scenarios.
•Suitable for integration with other sectors’ system models (e.g.
power systems).
River basins system models

River basin models are usually built within specially designed software.
•A network of nodes (points) and links (connecting lines) is built.
•Nodes define points of interest:
•Reservoirs, demand centres, abstraction points, environmental
sensitive locations, estuaries and deltas.
•Nodes allow the modeller to apply constraints, rules or other
behaviour.
•Links are used to connect nodes together
•They define the flow pathway.
System as Network

System as Network: Figures

River basin simulation
FutureDAMS uses a network simulation model.
•During a model run a sequential number of time-steps are
performed.
•Time-steps are typically 1 day to 1 month depending on the system.
•A simulation can run over many decades of data.
•Scenario analysis allows
•different data to be evaluated against the same operating rules
(e.g. climate change impact assessment), or
•new operating rules to be compared with existing behaviour, or
•a combination of both.

Water resource allocation
During each time-step water resource allocation decisions must be
made.
•How much water should be stored or released?
•How much hydropower to generate?
•Which demands have a higher priority during times of drought?
•Allocation behaviour must be defined by the modeller.
•The most interesting behaviour emerges from rules that depend on
model state(s) E.g. current reservoir volume determines release
rates.

Optimisation based allocation
The FutureDAMS river basin modelling tool uses linear programming.
•Linear programming is a widely used and understood mathematical tool for optimising
allocation problems.
•It minimises (or maximises) a cost-benefit function by adjusting a set of variables.
•E.g. releases from dams, irrigation supply
•The range of values that can be assigned to variables is constrained. E.g. to limit the
release from a dam.
The FutureDAMS river basin modelling tool uses optimisation based water resource
allocation.
•Each time-step the current river basin network is translated in to a linear programme.
•The linear programme optimises allocation.
•To achieve the lowest cost (i.e. minimisation) within the constraints provided.
•The river basin network is updated ready for the next time-step. i.e. changes to
reservoir volumes and node flow rates.

Group Work I
• What are the challenges of the Future Hydropower Development in
Myanmar and how can we address?
• What is the nexus systems?
• Why do we use PowerWorld and how can apply it?
• What is your understandings about the River Basin and its modelling?
NWRC: HIC 7th Jan 2020

• How can we mitigate the negative impacts and the causes of the unharnessed
Hydropower potential in Myanmar?
• What assessments should be carried out before the implementation of
Hydropower Project?
• What are the criteria of the SHD (Sustainable Hydropower Development) in
Myanmar?
• What is more important: the construction of new Hydropower plant vs. renovation
of the existing Hydropower plant for more efficient and optimal operation?
• What are urgently needed to improve the nexus systems in Myanmar?
Group Work II
NWRC: HIC 8th Jan 2020

Browser-based modelling tool

What is Hydra Platform?
• Hydra Platform (referred to here-on as ‘Hydra’ for brevity) is a model
platform, and aims to provide data support for network-based
models.
• The software stores inputs and outputs for models which use a
network structure including data, metadata and model topology.
• Models which use network structure include for example the
simulation or optimisation of water, energy, transport, and trading
systems.
Source: S. Knox et al., 2019

• Designed to provide a standardised means to store and access data for network-based
modelling and to allow data from multiple models, model types and modelling
disciplines to be stored and accessed from the same place.
• Two main design principles:
 Decouple data storage and management from application logic
 Provide support for a wide range of applications.
• To achieve the first, an architecture is required which contains minimal application logic,
and is flexible enough to store different types of data. To achieve the second, a
programming interface (API) is required which links to the data storage and provides
logical and standardised protocols for accessing data.
• Supporting a wide range of applications requires a design which understands the scope
of possible applications while maximising its utility within that scope. In the case of
Hydra, this is network-based resource modelling. Within this broad application area,
Hydra should be useable by any modeller from different disciplines equally — it should
be designed from the outset to not favour any one.
• This principle relies on a generalised design, where application-specific content can be
entered as data, rather than requiring changes to Hydra’s source code or architecture.
What is the Principle of Hydra?

Overview of the Hydra Architecture
• Data storage and management (left) are separated from the application of that
data (right). Data within Hydra can be used within three broad categories —
importing and exporting to generic data formats such as JSON or Excel, translating
to and from specific model instances, written in different languages such as GAMS
or Matlab, and visualised and managed data using a UI.

Methodology of the Hydra
• Hydra uses a high-level representation of networks to support storing a range of network types, and employs a
‘templating’ system, which mimics object-oriented programming by allowing users to define user-configurable
network types, and then create instances of those types.
• The user can define types of nodes and links, with properties relevant to specific modelling problems. Instances
of those nodes and link classes, along with their datasets are the inputs for modelling.
• A range of dataset formats are supported, along with ability to define custom data formats for non-standard
values. The customizability of the system facilitates integration with a range of models, so long as they use a
network (nodes and links) structure.
• Hydra is a software library, written in Python, that provides an application programming interface (API). The
API contains high-level operations to create and edit networks, manage scenarios, and share networks with
other users.
• The user has no direct interaction with the database (i.e. they must use the API to perform queries on the
database). This separation of the logical layer from the storage allows the database implementation to be
upgraded or altered while data operations remain unchanged.
• A web API (an additional Python library) connects to the core Python library to allow remote data
management. Using a web API allows multiple users to use and access the same data, limiting duplication in
collaborative modelling.
• Remote access through a web API also allows software developers to build applications in any mainstream
programming language. Separating database from logical operations, and providing a web API all serve to
broaden the application areas in which Hydra can operate, from a simple desktop installation to a large-scale,
cloud-enabled service.

Usages of the Hydra
1. Importing data from a 3rd party file format, for example output of a model
run.
2. Exporting data to a 3rd party file format, for example for use in a model run.
3. Running a model with data stored in Hydra. This involves exporting data to
the appropriate file format, running a model instance, then importing
results back to Hydra.
4. Using a User Interface, i.e, a graphical front-end so non developers can
interact with the data.

Hydra’s web architecture using example clients
The client makes remote procedure calls to hydra server to import and
export data, to general file formats like Excel, to applied systems like a
simulator, and to a GUI which visualises the network and data.

Clients communicate directly with Hydra Base on the same physical machine. This
approach gives greater data storage efficiency, when internet access is unreliable,
and/or where a multi-user environment is not needed. The clients require little or no
alteration to work with this local deployment and with Hydra Server.
Hydra’s architecture when a server is not deployed

Let’s Go to: Hydra.org.uk
https://www.hydra.org.uk/

Register on Hydra Platform
https://www.hydra.org.uk/register

Login to Hydra Platform
https://www.hydra.org.uk/login

Hydra: Creating a new project
https://www.hydra.org.uk/projects
‘Click ‘ + Create a new project

Hydra: Add Project
https://www.hydra.org.uk/projects
‘Give ‘ Name
‘Fill ‘ Description

Hydra: Add new model network
https://www.hydra.org.uk/project/651
‘Click + ‘ Add a new model network to new project

Hydra: Add new model network
‘Give ‘ Name
‘Fill ‘ Description
‘Select ‘ Pywr
HydroPower Simple
‘Click‘ Submit

Hydra: Open the network editor
Click on the network title to open the network editor

Hydra: Build the network
Click to open the ‘Build’ palette to be ready to build a network

Hydra: Pywr Nodes
output
link
reservoir
linear storage
release control
turbine
monthly
catchment
proportional input
monthly
output

Hydra: Pywr Links
output
link
reservoir
linear storage
release control
turbine
monthly
catchment
proportional input
monthly
output

Hydra: Details of Pywr Nodes
expand &
condense
Drag & Double click to view the Details of the Nodes

Hydra: Run a Model

Hydra: Clone a Scenario

“Thank You Very Much for Your Kind Attention!”
DR. AUNG ZE YA
(PROFESSOR, YTU)

FutureDAMS YWP training: Myanmar

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