Video Demo

I've created a short video of my centralised demo. It doesn't have any audio or captions explaining what's going on because I would normally use it during presentations where I explain what's happening.Here's an explanation: To start with the network is working normally, loads and output are balanced and no transmission lines are overloaded. The green node is a generator that has been nominated to follow the load within the network and will ramp up/down accordingly. As the red nodes's load increases the nominated generator will ramp up its output to compensate, however this overloads two transmission lines on the network. In order to reduce the overload, the generators must be coordinated such that they reconfigure there outputs. The objective function is minimising carbon emissions but it is also making sure that the distance between a generators current output and the output it is changing to is minimal.

One step forward, two steps back

Today I had a supervisor meeting with Alex and Gopal. In it I explained my simple decentralised message passing algorithm for determining which generators need to ramp up in order to reduce an overloaded line. As in the scenario I described in my previous blog post. However Alex pointed out that the way in which I allocate power does not take into account when a load overloads a line but the load is not directly connected to the line that gets overloaded. In this case a node nearer the load may be able to satisfy it however in my model I had assumed that load was situated at the root of the tree.

Therefore I have turned my attention to solving the decentralised power flow in a tree using dynamic programming. Is there an allocation of generator outputs that satisfy the thermal limits of the transmission lines and satisfy the loads within the tree using a decentralised dynamic programming algorithm. As a starting point I am going to read the following paper which does a similar thing for sending information around a tree using sensors:

Kho J, Tran-Thanh L, Rogers A, Jennings NR. An Agent-Based Distributed Coordination Mechanism for Wireless Visual Sensor Nodes Using Dynamic Programming. The Computer Journal. 2010;53(8):1277.

Initially from the dynamic programming explanations I think each generator will represent a stage and the state of each stage will be a continuous variable representing the generators output. The decision variable for each stage will represent the optimal output for each generator such that carbon emissions are minimised, the flow within the network is satisfied and the transmission lines are not overloaded.

Decentralised

I have come up with a simple strategy that coordinates generators in a decentralised way. The strategy will be applied to trees and works as follows:

Line A agent detects that it is overloaded, therefore it sends a message to the node receiving its flow that they need to increase their power output by x, and sends a message to the node sending their flow that they need to decrease their generation by x amount. In doing this the amount of power flowing down line A will be reduced by x and thus the line will no longer be overloaded.

Each subsequent node simply forwards the amount of power that is asked of them from a transmission line to all children.

A transmission line agent checks that the power required by one node from the other will not actually overload its transmission line, if it will then the transmission line changes the required power to be capped at the (maximumCapacity - currentFlow)

(i.e. Node 1 sends a message to Node 2 that it requires an extra 300kW, however the transmission line in between can only handle an extra 200kW therefore Node 2 will recieve a request for 200kW instead. )

Once the required power has been propagated down to the leaf nodes the nodes then send messages back to the root saying how much power they can produce and at what carbon intensity.

Finally messages are propagated back down to the leaf nodes starting from the root. At the root node it calculates how much power it needs to ask from each of its neighbours nodes in order to minimise carbon emissions, it only asks for the required power and doesn't care how it is broken down further between generators. Subsequent nodes repeat the process until all leaf nodes have received a message saying how much power is required from them. They all increase by the required amount.

Similarly the subtree that is required to decrease its power output by x amount perform the same steps however the transmission lines do not need to intervene in changing the required decrease in power.

This Week

This week I will be focusing on researching ways to implement a decentralised approach to the problem of generator coordination within a network. I will also be polishing my demo so that it can be displayed at the next energy meeting where I will be giving a presentation on my work so far.

Java implementation of CPLEX

I have written some code in Java which uses CPLEX to solve the same problem that I specified in my last post. The reason for using Java and CPLEX to specify the problem is that an actual simulation can be conducted that uses multiple CPLEX models to demonstrate the centralised approach to generator coordination. The simulation scenario is as follows:

1. Network is balanced, all thermal limits are satisfied. Carbon emissions are minimised. One generator/Node is nominated to follow loads within the network.

2. Load at a substation increases/line failure

3. This causes nominated generator to balance the demand

4. However in doing so this overloads a line.

5. Run the CPLEX code to optimise the thermal constraints within the network and also reduce the distance between the new power output of a generator and its previous power output when the network was overloaded.

6. network solution is achieved, network is restored back to fully working order.

This benchmark will be used to test the centralised, and eventually, decentralised approach.

Programming

Last week I took 'time out' from my PhD to code a BAE Logistics simulator. I made some quick skeleton code with basic functionality to show BAE so that they could come up with a proper set of requirements. I will finish the coding in the coming weeks but will only be working on it part time.

Since the coding I have been working on my PhD again. I am currently testing some CPLEX code I have written. It works out the generator output that minimises the carbon emissions of the generators, subject to thermal limits of the power lines. The plan is to code in some sort of ordering on the generators such that generators that have been connected to the power grid the longest are curtailed last. I am also looking at refining my mathematical model of the problem, however I am having difficulties with specifying an ordering on a set in mathematical form. I have taken a few abstract set theory books out of the library, so hopefully they will help

Back in Southampton

Back in Southampton now here is a summery of what I have been doing:
I have been focusing my attention on achieving a good understanding of how the electricity system works by reading a variety of books including 

1. Galvin R, Yeager K, Stuller J. Perfect power: how the microgrid revolution will unleash cleaner, greener, and more abundant energy. 1st ed. McGraw-Hill Professional; 2008.

2. Weedy BM, Cory BJ. Electric Power Systems. 4th ed. John Wiley & Sons; 2004.

3. Gonen T. Electric power distribution system engineering. 2nd ed. McGraw-Hill New York; 2007.

4. Chard FDLC. Electricity Supply: Transmission and Distribution. 1st ed. Longman Group Limited; 1976.

I have also been studying the paper:

1. Davidson EM, Dolan MJ, McArthur SDJ, Ault GW. The use of constraint programming for the autonomous management of power flows. In: 15th International Conference on Intelligent System Applications to Power Systems (ISAP 2009).; 2009:1-7.

with the intention of first recreating the results found in the paper and then applying a decentralised way to calculate power flow analysis. By the end of January the goal is to have a mathematical model of the various constraints and equations used and a set of experiments and results written up in a small two column style paper.

Research Methodology Assignment: Introduction

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 electricity infrastructure. Therefore a smart grid capable of incorporating an increasing number of generators is required.

These generators need to be coordinated efficiently in order to supply loads within the network whilst satisfying thermal limits of the transmission lines. The proposed solution is to model the network as a constraint satisfaction problem and then solve using constraint programming. However this is a centralised approach and therefore will have scaling issues when applied to a larger network.

To address these shortcomings we propose a decentralised message passing algorithm, max-sum, which scales well with the size of the network; since the size and number of messages sent, is only dependant on a local neighbourhood. Max-sum has been extended to incorporate thermal constraint satisfaction and to give priority to generators that use renewable resources while curtailing non-renewable generators.

The rest of this paper is organised as follows...

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.