Direction of research: Distributed Optimal Power Flow

I have just read the following papers on distributed optimal power flow:

1. Kim BH, Baldick R. Coarse-grained distributed optimal power flow. IEEE Transactions on Power Systems. 1997:932-939.

This paper provides a distributed approach to optimal power flow based on an objective function. It involves splitting the network into a variety of regions each with its own set of buses and transmission lines. Regions which have a transmission line between them share variable values by using a dummy bus. The variables are then duplicated for that dummy bus and each region uses their version of the variables to solve their power flow. For each iteration, regions exchange their shared variables and update them until convergence is experienced.

The distributed equations involve using lagrangians to solve and update the shared variables. This paper provides some very good reasons for decentralised power flow and promising test results. As future work it suggests a number of possibilities which could be used. For example make use of previous iterations to avoid having to refactorise certain equations.

2. Baldick R, Kim BH, Chase C, Luo Y. A fast distributed implementation of optimal power flow. IEEE Transactions on Power Systems. 1999:858-864.

This paper is a fast implementation of [1] using techniques from:

3. Wu Y-C, Debs AS, Marsten RE. A direct nonlinear predictor-corrector primal-dual interior point algorithm for optimal power flows. IEEE Transactions on Power Systems. 1994:876-883.

This is one direction that my research is heading. I am very interested in distributed optimal power flow and how this can be used in conjunction with reducing overloaded lines within a network whilst minimising carbon emissions.

Need to get back into the habit of blogging!

I have been familiarising myself with a few techniques that are used in the paper "The use of constraint programming for the autonomous management of power flows":

• back tracking search - Search the tree by starting at the root and traversing in a depth first manner. At a particular node C, checks whether a valid solution can be computed from C, if not whole subtree is pruned. Obviously the hard part using a good approximation function that can calculate whether a valid solution exists.
• best first - explores the tree by expanding the most promising node chose according to a specific rule. A* search is an example.

I have implemented the DC power flow equations in matlab, they use considerably less code then the java implementation. Hopefully this matlab implementation will be used by CPLEX, a constraint optimisation solver, so that it can calculate the flow, however I am still working on expressing the problem in CPLEX language. 

In parallel to this I have been describing the model and the problem in mathematical terms, I have written a one page mathematical explanation of the constraints, the network and the variables defining their domains and bounds. This in theory will allow me to then translate it into any coding environment in order to implement it.

A selection of papers that I have read:

1. Dam KH van, Houwing M, Lukszo Z, Bouwmans I. Agent-based control of distributed electricity generation with micro combined heat and power: cross-sectoral learning for process and infrastructure engineers. Computers & Chemical Engineering. 2008;32(1-2):205-217. 

2. Taylor P, Xu T, McArthur S, et al., others. Integrating voltage control and power flow management in AuRA-NMS. In: SmartGrids for Distribution, 2008. IET-CIRED. CIRED Seminar.; 2008:1-4.

3. Pipattanasomporn M, Feroze H, Rahman S. Multi-agent systems in a distributed smart grid: Design and implementation. In: Power Systems Conference and Exposition (PSCE 2009).; 2009:1-8. 

4. Dolan MJ, Davidson EM, Ault GW, McDonald JR. Techniques for managing power flows in active distribution networks within thermal constraints. In: Electricity Distribution-Part 1, 2009. CIRED 2009. 20th International Conference and Exhibition on.; 2009:1-4.

I have implemented the same network as the one in the paper "The use of constraint programming for the autonomous management of power flows". However it does not specify any of the loads, and therefore I will not be able to recreate the exact experiments without them.

Next week I am putting the PhD on a one week hold to code full time on a project for BAE systems.

Electricity

This week I will be learning all things to do with electricity. I have also written an introduction for my Research Methodology course, here it is:


By the year 2050 the UK government has made it mandatory that we reduce our carbon emissions by 80%. In order to meet this target, lower carbon technologies such as the electrification of heating and transport will need to be introduced. Unfortunately this rising demand for electricity cannot be sustained with the current national electricity structure. A more dynamic two-way electricity network is needed that incorporates decentralised generators, renewable resources and storage devices managed in an efficient manner.

However incorporating these devices will be difficult without revised management techniques. The proposed solution is to split the national electricity structure into multi-agent managed micro-grids which are able to run as isolated electricity networks capable of meeting electrical demand efficiently. One aspect of the management system involves decentralised generator coordination to determine the output of each generator needed to satisfy the loads on the network when thermal constraints apply.

Previous work in this area has used constraint optimisation techniques to find an optimal solution; however this is in a centralised manner, therefore posing potential security and reliability issues due to the central point of failure. DPOP, a coordination algorithm that uses message passing, has been applied to this setting and is almost completely decentralised. However the size of the messages increases exponentially with the size of the network and thus this technique does not scale well.

We propose an extension of the novel algorithm called MAX-SUM which uses message passing to coordinate renewable and non-renewable generators within a micro-grid. Specifically the MAX-SUM algorithm has been extended to incorporate thermal constraint satisfaction and to give priority to generators that use renewable resources while curtailing non-renewable generators. The extended MAX-SUM algorithm coordinates in a decentralised manner, with the size and the number of messages dependant only on a local neighbourhood therefore scaling well.

4 day training reflection

The four day intensive course has given me a number of useful skills that I will be using for the next three years, and probably beyond. The things that were really useful to me was the chance to give three presentations and get feedback. Other helpful areas were how to get the most out of your supervisors and how to make my routine of how I read and analyse papers even more useful.

Doing this course has allowed me to network with a number of other PhD students across a range of disciplines. I even have an idea about a possible collaboration with a civil engineer called Andrew Hamilton who is looking at traffic control using emerging technologies. One of his emerging technologies involved cars talking to each other. Immediately I talked to him about agents and how they could play a role in this for coordinating traffic. We swapped emails and will be talking soon about how to solve this problem when we have both researched our respective areas more thoroughly.

However being on this course has meant that this week I have achieved nothing for my PhD. TODO (next week): Implement the MAX-SUM algorithm and clustering variables on a factor graph.

First monthly report

This month I have been conducting lots of reading in the area of energy, smart grids and agent based computing. I have been having weekly meetings with my supervisors where we discuss relevant areas I should be researching and directions that I should be taking. I have made a Gantt Chart of my expected work load from now until the hand-in date of my 9 month report. In the most recent meeting with my supervisors, we established a goal to work towards for the 9 month report: re-implement the work conducted in "The use of Constraint Programming for the Autonomous Management of Power Flows" using C-PLEX and try to make it decentralised by using some sort of coordination algorithm such as MAX-SUM. Work for the next month will consist of implementing the MAX-SUM algorithm and variable clustering to get an understanding of how the algorithm works, Researching and calculating DC power flow in electricity systems and investigating more thoroughly multi-agent based computing and micro-rids.

This week I am on the 4day intensive course that develops my skills in research and presentation.

A and E

Last night I took one one of my mates to A and E, nothing serious. Since there was a long waiting time, it gave me an opportunity to read quite a bit of hot air. It seems that nuclear power is the future for us if we want to have a 'sustainable' energy consumption for the next 1000 years. There are obviously a lot of problems to overcome first before but it is not totally infeasible. Over the next decades we sill see a rising demand for electricity. This will be due because of a shift to cleaner energy. Electricity is a high grade form of energy which if generated in the right way can be very clean. Therefore a shift to the electrification of certain service such as public transport and heating will be experienced. The book talks about the joys of heat pumps compared to conventional gas fired boilers. They are much more efficient at producing energy (in the form of heat) then gas or combined heat and power (CHP) and thus should be used in the future to provide living spaces with heating (and in the summer with air conditioning) Therefore I will be researching ways in which these micro generators can be incorporated into microgrids.

BAE Systems Report

Today I read the BAE Systems report on smart grids, here is a summery:

This report gives a detailed overview of smart grids and there applications. It begins with defining what a smart grid is and what it consists of. It then goes on to define how it could be used to manage the electricity in a static or deployable army base.

It identifies three aspects of the system that it would implement: demand side management (DSM), supply side managment (SSM) and Network management (NM). DSM is concerned with understanding and manageing consumer power demands in order to minimise peak power across many different consumers.SSM is Resposible for ensuring there is sufficient electrical power available to meet the total real-time consumers electrical power requirements. Needs to balance the generating of power across different generators and facilities. Uses predicted power estimates. NM manages the transmission and distribution of electrical power from the electricity generators to the consumers. This report provides the advantages of using a smart gird to manage the electricy on an army base.

Reading this report gave me some interesting areas that I could look into:

power dispatch and scheduling
network load balancing

All in all I want to look at power flow management and how this could be achieved in a microgrid environment.

interesting stuff

Read a few papers today and had some possible ideas about what to investigate: one paper Coalition formation in transmission expansion planning uses coalition formation to determine the optimal number of transmission lines to add to an existing network in order to supply forecasted load. This same technique could be applied to mircorgenerators to determine how many should be added to the network and in what locations in order to meet various flows. This could be based on certain factors like average power output or efficiency or something. I also talked to Harry in the labs today, he suggested Power Matcher (which I already new about) and also IDAPS which I had forgotten about, so will look at the various papers produced on these implementations in due course.

interesting lecture

Had a lecture on power systems analysis today. It was quite interesting and gave me a few ideas of what could be done. For instance load factor (average power / peak power) could be taken into consideration when controlling a microgrid. The idea is that you want the load factor to be as close to 1 as possible, that way the fluctuations between supply and demand is reduced meaning that high cost generators, which allows peak demand to be satisfied, do not have to be used as much! This is difficult to achieve in a microgrid that contains alot of renewable generators and many loads, but this could be solved using agents. I am interested in this and will look into this area of agent based microgrid management. TODO: Print off some more papers to read, read one paper and make notes, explore load factor and load loss factor, read notes on power systems analysis, go to lecture at 12:00 (sustainable energy), read some hot air (for background knowledge).

the start of the week

Tried to go to the conventional generation technologies lecture today at 9am but the lecture was full and there were no chairs. Sorted out my ID card and now in the office getting ready to read a bunch of stuff. TODO : Read some more hot air, Read one paper and make notes, Go to the lecture at 14:00. Over the weekend I have been thinking about what sort of area of smart grid I want to look at. I definitely want to look at integration of microgenerators in a microgrid environment. I need to find the papers that I read for my 4th year research project and make notes on them.

an interesting fact

One for the people against wind turbines because of bird deaths -
Annual average number of birds killed by wind turbines in denmark = 30,000
Annual average number of birds killed by cats in Britain = 55,000,000
Anyway I digress:
Today I read two papers on the AuRA-NMS which is currently being developed by a number of different Universities and companies. They have come up with a framework that can control a number of things using agents such as power flow and voltage levels, and integration of generators. I have also read a large amount of the book I mentioned previously, it is very interesting. From what I gather so far, the most feasible microgenerators that could be installed on every home and actually produce enough electricity to be worthwhile is photovoltaic panels. Also thermal panels which heat water are another viable way to decrease demands for electricity due to heating. Ways in which these technologies could be integrated and controlled by agents in a distributed system is of some interest to me.