From the Book:
“Clean code that works” is Ron Jeffries’ pithy phrase. The goal is clean code that works, and for a whole bunch of reasons:
Clean code that works is a predictable way to develop. You know when you are finished, without having to worry about a long bug trail.Clean code that works gives you a chance to learn all the lessons that the code has to teach you. If you only ever slap together the first thing you think of, you never have time to think of a second, better, thing. Clean code that works improves the lives of users of our software.Clean code that works lets your teammates count on you, and you on them.Writing clean code that works feels good.But how do you get to clean code that works? Many forces drive you away from clean code, and even code that works. Without taking too much counsel of our fears, here’s what we do—drive development with automated tests, a style of development called “Test-Driven Development” (TDD for short). 
In Test-Driven Development, you:
Write new code only if you first have a failing automated test.Eliminate duplication.

Two simple rules, but they generate complex individual and group behavior. Some of the technical implications are:You must design organically, with running code providing feedback between decisionsYou must write your own tests, since you can’t wait twenty times a day for someone else to write a testYour development environment must provide rapid response to small changesYour designs must consist of many highly cohesive, loosely coupled components, just to make testing easy

The two rules imply an order to the tasks ofprogramming:
1. Red—write a little test that doesn’t work, perhaps doesn’t even compile at first
2. Green—make the test work quickly, committing whatever sins necessary in the process
3. Refactor—eliminate all the duplication created in just getting the test to work


Red/green/refactor. The TDD’s mantra.

Assuming for the moment that such a style is possible, it might be possible to dramatically reduce the defect density of code and make the subject of work crystal clear to all involved. If so, writing only code demanded by failing tests also has social implications:
If the defect density can be reduced enough, QA can shift from reactive to pro-active workIf the number of nasty surprises can be reduced enough, project managers can estimate accurately enough to involve real customers in daily developmentIf the topics of technical conversations can be made clear enough, programmers can work in minute-by-minute collaboration instead of daily or weekly collaborationAgain, if the defect density can be reduced enough, we can have shippable software with new functionality every day, leading to new business relationships with customers


So, the concept is simple, but what’s my motivation? Why would a programmer take on the additional work of writing automated tests? Why would a programmer work in tiny little steps when their mind is capable of great soaring swoops of design? Courage.
Courage
Test-driven development is a way of managing fear during programming. I don’t mean fear in a bad way, pow widdle prwogwammew needs a pacifiew, but fear in the legitimate, this-is-a-hard-problem-and-I-can’t-see-the-end-from-the-beginning sense. If pain is nature’s way of saying “Stop!”, fear is nature’s way of saying “Be careful.” Being careful is good, but fear has a host of other effects:
Makes you tentativeMakes you want to communicate lessMakes you shy from feedbackMakes you grumpy

None of these effects are helpful when programming, especially when programming something hard. So, how can you face a difficult situation and:
Instead of being tentative, begin learning concretely as quickly as possible.Instead of clamming up, communicate more clearly.Instead of avoiding feedback, search out helpful, concrete feedback.(You’ll have to work on grumpiness on your own.) 

Imagine programming as turning a crank to pull a bucket of water from a well. When the bucket is small, a free-spinning crank is fine. When the bucket is big and full of water, you’re going to get tired before the bucket is all the way up. You need a ratchet mechanism to enable you to rest between bouts of cranking. The heavier the bucket, the closer the teeth need to be on the ratchet.

The tests in test-driven development are the teeth of the ratchet. Once you get one test working, you know it is working, now and forever. You are one step closer to having everything working than you were when the test was broken. Now get the next one working, and the next, and the next. By analogy, the tougher the programming problem, the less ground should be covered by each test.

Readers of Extreme Programming Explained will notice a difference in tone between XP and TDD. TDD isn’t an absolute like Extreme Programming. XP says, “Here are things you must be able to do to be prepared to evolve further.” TDD is a little fuzzier. TDD is an awareness of the gap between decision and feedback during programming, and techniques to control that gap. “What if I do a paper design for a week, then test-drive the code? Is that TDD?” Sure, it’s TDD. You were aware of the gap between decision and feedback and you controlled the gap deliberately.

That said, most people who learn TDD find their programming practice changed for good. “Test Infected” is the phrase Erich Gamma coined to describe this shift. You might find yourself writing more tests earlier, and working in smaller steps than you ever dreamed would be sensible. On the other hand, some programmers learn TDD and go back to their earlier practices, reserving TDD for special occasions when ordinary programming isn’t making progress.

There are certainly programming tasks that can’t be driven solely by tests (or at least, not yet). Security software and concurrency, for example, are two topics where TDD is not sufficient to mechanically demonstrate that the goals of the software have been met. Security relies on essentially defect-free code, true, but also on human judgement about the methods used to secure the software. Subtle concurrency problems can’t be reliably duplicated by running the code.

Once you are finished reading this book, you should be ready to:
Start simplyWrite automated testsRefactor to add design decisions one at a time

This book is organized into three sections.
An example of writing typical model code using TDD. The example is one I got from Ward Cunningham years ago, and have used many times since, multi-currency arithmetic. In it you will learn to write tests before code and grow a design organically.An example of testing more complicated logic, including reflection and exceptions, by developing a framework for automated testing. This example also serves to introduce you to the xUnit architecture that is at the heart of many programmer-oriented testing tools. In the second example you will learn to work in even smaller steps than in the first example, including the kind of self-referential hooha beloved of computer scientists.Patterns for TDD. Included are patterns for the deciding what tests to write, how to write tests using xUnit, and a greatest hits selection of the design patterns and refactorings used in the examples.

I wrote the examples imagining a pair programming session. If you like looking at the map before wandering around, you may want to go straight to the patterns in Section 3 and use the examples as illustrations. If you prefer just wandering around and then looking at the map to see where you’ve been, try reading the examples through and refering to the patterns when you want more detail about a technique, then using the patterns as a reference.

Several reviewers have commented they got the most out of the examples when they started up a programming environment and entered the code and ran the tests as they read.

A note about the examples. Both examples, multi-currency calculation and a testing framework, appear simple. There are (and I have seen) complicated, ugly, messy ways of solving the same problems. I could have chosen one of those complicated, ugly, messy solutions to give the book an air of “reality.” However, my goal, and I hope your goal, is to write clean code that works. Before teeing off on the examples as being too simple, spend 15 seconds imagining a programming world in which all code was this clear and direct, where there were no complicated solutions, only apparently complicated problems begging for careful thought. TDD is a practice that can help you lead yourself to exactly that careful thought.

Test Driven Development: By Example

This is the first part of a two-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part I of this paper examines the potential value of MAS technology to the power industry. In terms of contribution, it describes fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications. As well as presenting a comprehensive review of the meaningful power engineering applications for which MAS are being investigated, it also defines the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part II of this paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented.

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Multi-Agent Systems for Power Engineering Applications—Part I: Concepts, Approaches, and Technical Challenges

This paper proposes a secondary voltage control of microgrids based on the distributed cooperative control of multi-agent systems. The proposed secondary control is fully distributed; each distributed generator only requires its own information and the information of some neighbors. The distributed structure obviates the requirements for a central controller and complex communication network which, in turn, improves the system reliability. Input-output feedback linearization is used to convert the secondary voltage control to a linear second-order tracker synchronization problem. The control parameters can be tuned to obtain a desired response speed. The effectiveness of the proposed control methodology is verified by the simulation of a microgrid test system.

/pdf/distributed-cooperative-secondary-control-of-microgrids-2g3woto403.pdf

Distributed Cooperative Secondary Control of Microgrids Using Feedback Linearization

The objective of this paper is to discuss the design and implementation of a multi-agent system that provides intelligence to a distributed smart grid — a smart grid located at a distribution level. A multi-agent application development will be discussed that involves agent specification, application analysis, application design and application realization. The message exchange in the proposed multi-agent system is designed to be compatible with an IP-based network (IP = Internet Protocol) which is based on the IEEE standard on Foundation for Intelligent Physical Agent (FIPA). The paper demonstrates the use of multi-agent systems to control a distributed smart grid in a simulated environment. The simulation results indicate that the proposed multi-agent system can facilitate the seamless transition from grid connected to an island mode when upstream outages are detected. This denotes the capability of a multi-agent system as a technology for managing the microgrid operation.

Multi-agent systems in a distributed smart grid: Design and implementation

The objective of this paper is to provide a review of distributed control and management strategies for the next generation power system in the context of microgrids. This paper also identifies future research directions. The next generation power system, also referred to as the smart grid, is distinct from the existing power system due to its extensive use of integrated communication, advanced components such as power electronics, sensing, and measurement, and advanced control technologies. At the same time, the need for increased number of small distributed and renewable energy resources can exceed the capabilities of an available computational resource. Therefore, the recent literature has seen a significant research effort on dividing the control task among different units, which gives rise to the development of several distributed techniques. This paper discusses features and characteristics of these techniques, and identifies challenges and opportunities ahead. The paper also discusses the relationship between distributed control and hierarchical control.

Distributed Control Techniques in Microgrids

The growth of distributed generation in the UK has led to the increase in the number of flexible Distributed Energy Resource (DER) connections and services required at distribution level. In some parts of the UK, flexible connections are offered to Distributed Generators (DG) via the use of Active Network Management (ANM) systems. In exchange for a cheaper, quicker connection to the network, DG may be subject to curtailment when the network is constrained. This paper presents a Virtual Private Wire (VPW) solution that can allow generation and demand within the same ANM system to be linked in association with specific network constraints. Smarter Grid Solutions developed a demonstration system in order to show the effect of a VPW arrangement in the SP Energy Networks Accelerating Renewable Connections (ARC) trial area. This paper draws conclusions on both commercial and technical issues as well as solutions for local network balancing introduced by the VPW concept. The future direction for control architectures required to support VPW and multiple other new DER management applications is also set out, along with the wider market and system implications of coordinated DER control and local system balancing.

Supporting DER customer participation in active distribution networks and local markets

The introduction of Active Network Management (ANM) has enabled managed connections as a business-as-usual option. These provide generation developers with access to network capacity, while avoiding costly network reinforcement. The managed nature of such connections introduces the prospect of energy curtailment during periods of network constraint. It is necessary to perform curtailment analysis in order to estimate the potential curtailment which a generator may expect to experience. The assumptions and limitations of these curtailment assessments must be understood both by those performing the studies and by those who must review outcomes and understand implications for commercial viability of a generation development. The process of agreeing and, consequently, procuring the necessary input data often introduces a source of commercial challenge and possible delay into the curtailment assessment process. There is, therefore, a requirement for standardisation of data inputs for the curtailment assessment process. This will ensure that the analysis provides a representative estimate of curtailment that is based on appropriate network models and data. This paper explores the requirement for standardisation and recommends a standardised approach for the provision of network data and presentation of outcomes.

Standardisation of curtailment analysis and the implications for distribution network operators and generators

A large volume of data is generated following a fault in the electrical power system. As a result of extensive research, hybrid intelligent systems have been identified as a method of supporting the online analysis and interpretation of the data. Hybrid intelligent systems employ multiple artificial intelligence techniques to perform various analytical and interpretation functions in order to generate a comprehensive diagnosis and meaningful information for the engineers to act upon. The authors have developed a multi-agent architecture as a platform for hybrid intelligent systems within the electrical power system. This paper will discuss the design of the architecture, and how it is being used to provide online power system protection analysis for ScottishPower’s engineers. The paper will also discuss the integration of a novel model based diagnosis agent within the overall multi-agent architecture. In addition, it will consider the engineering issues surrounding the application.

A Multi-Agent Intelligent Interpretation System for Power System Disturbance Diagnosis

com ABSTRACT This paper describes the development of distribution state estimation for trial on the Orkney Isles in the north of Scotland. The paper discusses the techniques selected for the trial based on the requirements of the distribution network operator. The techniques include the use of a novel error estimation technique that calculates an error interval for data derived via distribution state estimation.

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Requirements-driven distribution state estimation

This paper presents and discusses fault classification and fault accommodating controls for a notional Medium Voltage AC (MVAC) integrated shipboard power system (IPS). The goals of the proposed methodology are to differentiate amongst operating conditions, predict, detect, and locate faults, and to design a flexible distributed fault accommodating control scheme that is able to maintain system stability during control operations. Simulation studies of the proposed architecture, including simulated line-to-line faults, and details of fault classification algorithms employed are presented.

Euan Davidson

Papers

Supporting DER customer participation in active distribution networks and local markets

Standardisation of curtailment analysis and the implications for distribution network operators and generators

A Multi-Agent Intelligent Interpretation System for Power System Disturbance Diagnosis

Requirements-driven distribution state estimation

Fault classification and accommodation in shipboard power systems