Artificial Immune System in the Management of Complex Small Scale Cogeneration Systems

Artificial Immune System in the Management of Complex Small Scale Cogeneration Systems

Fabio Freschi (Politecnico di Torino, Italy) and Maurizio Repetto (Politecnico di Torino, Italy)
DOI: 10.4018/978-1-60566-310-4.ch007
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Abstract

The increasing cost of energy and the introduction of micro-generation facilities and the changes in energy production systems require new strategies to reach their optimal exploitation. Artificial Immune System (AIS) metaphor can be used to reach this aim. In this kind of management, the energy system can be seen as a living body which must react to external stimuli (cost of fuel, energy prices, fares, etc.) fulfilling its internal requirements (user loads, technical constraints, etc.). In this chapter, a developed procedure based on AIS is described and applied to this problem. Its performance is compared with the mixed integer linear programming on the same test. The result shows that AIS based method obtained better results, in terms of computational efficiency, compared with classical energy production management procedures based on Mixed Integer Linear Programming.
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Introduction

Energy costs and concerns about its availability are an important issue at present. Recently, a growing interest in problems concerning energy distributed generation has emerged. This fact can be explained with two reasons: failures of centralized power grids with events of power delivery interruption, not always short, involving a large number of users (black-outs in USA and in Europe), and impact of energy market deregulation for industrial and civil uses. At the same time, the attention to a larger energy efficiency and to the environment contributes to the diffusion of renewable or combined energy sources to be used together with the energy furnished by the power stations connected to the grid has grown.

At present, many American and European institutions (U. S. Department of Energy, 2000, 2001; EU, 2003) are suggesting the realization of small distributed energy networks, usually called micro-grids, aiming at supplying, partially or totally, a small number of users. Thus they are able to operate in grid connected and stand alone modes. Such a twofold possibility allows to be disconnected from the grid if power peaks requirements occur or, if an excess of energy is produced by the sources connected to the micro-grid, to sell this power to the network.

Designing and optimizing of the energy local network are quite different from those of the classical energy grid, because the micro grid includes both sources and loads, so it is active in nature. A large number of small or medium size generators are often present in the grid: this makes their intermitted working during the day possible.

Another problem occurring in the micro-grid design stage is the sizing and sitting of the generators with respect to the loads, in order to reduce transmission losses and improve the dynamic response of the grid with respect to load power requirements. Moreover, very often loads need both electric and thermal power, so that the micro-grid must be of Combined Heat and Power (CHP) type.

One of the main peculiarities of these networks is that it often combines production of electric with thermal energy using in a positive way. The thermal energy is wasted in the thermodynamic cycle for thermal loads both in domestic or industrials. Since heat cannot be efficiently moved over long distances, its source must be located close to the load. In order to meet the specific needs of loads, the following factors, including generators with different nominal powers, reliability and pollution levels, the presence of storage units in conjunction with fuel cells and super-capacitors, must be taken into account in the micro-grid. These devices must be optimally controlled. And they add more degrees of freedom in the micro grid management.

The high complexity of the micro grid structure, the heterogeneity of sources, loads and backup units require an advanced management system. The use of new network strategies, for instance, can accumulate part of the energy produced in a given time instant. It needs to be exploited when favorable cost/price conditions are needed. Experience has taught us that decision making procedures, driven by low standards criteria, can lead to sub-optimal solutions both on the energy and operational standpoint.

The aim of maximizing performance indicators can be pursued by putting at the heart of the system. An energy manager (Energy Management System, EMS) which can optimally manage power flows inside the system and toward an external energy network. This management must be carried out by considering in each instant, the satisfaction of load requests, prices/costs of energy, operational constraints of the power units and optimizing different indicators, such as minimizing costs, minimizing emissions, keeping each power unit work at its best.

The interest of the research community toward this kind of problems is testified by several publications (Lasseter, 2002; Lasseter & Paigi, 2004; Georgakis, Papathanassiou, Hatziargyriod, Engle, & Hardt, 2004, Hernandez-Aramburo, Green, & Mugniot, 2005).

Key Terms in this Chapter

Minimum On Time (MOT) Constraint: Minimum on time constraint is the minimum time interval during which CHP must be on when it is switched on. This constraint is set because the switching on of the machine has some costs on its own and so it is justified only if the working time interval is sufficiently long. In addition, some technologies, like micro-turbines of aeronautical derivation, have a limited number of switching on before maintenance must be applied.

Ramp Limit Constraint: Inner dynamics of power generator does not always allow to change instantaneously power production level and so a maximum rate of change of produced power must be set. Usually generators with larger rated power have more strict ramp limits.

Combined Heat and Power (CHP) Generation: Combined Heat and Power (CHP) is a highly fuel-efficient energy technology, which puts to use waste heat produced as a by-product of the electricity generation process. CHP can increase the overall efficiency of fuel utilization to more than 75% Gross Calorific Value – compared with around 40% achieved by fossil fuel electricity generation plants in operation today, and up to 50% from modern Combined Cycle Gas Turbines – and has the potential to save substantially on energy bills. CHP is the simultaneous generation of usable heat and power (usually electricity) in a single process. Most new CHP schemes use natural gas, but a significant proportion burn alternative, renewable fuels and some, such as those bio-fuels that are suitable for use, qualify for additional support (e.g. under the Renewable Obligation). CHP is not only more efficient through utilization of heat, but it also avoids transmission and distribution losses and can provide important network services such as “black start”, improvements to power quality, and the ability to operate in “island mode” if the distribution network goes down.

Minimum Shutdown Time (MST) Constraint: Minimum on shutdown constraint is the minimum time interval which CHP must be off since it was turned off (see Minimum On Time (MOT) constraint).

Energy Management System (EMS): An energy management system is usually a collection of computer-aided tools used by operators of electric facilities to monitor, control, and optimize the performance of the generation and/or transmission system. Different computer aided tools are implemented from short time control modules to scheduling or commitment of power production units on a day/week basis. EMS has the objective of maximizing system performances by monitoring and control functions which require a centralized system of data collection and a decision making procedure.

Cogeneration: Cogeneration is the production of electricity and useful thermal energy simultaneously from a common fuel source. The rejected heat from industrial processes can be used to power an electric generator. Surplus heat from an electric generator can be used for industrial processes, or for heating purposes.

Primary Fuel Cost: The cost of fuel used by the primal engine moving the electrical generator. Usually this fuel is natural gas but could be also oil. The fuel cost is taken into account inside the production cost coefficient and it is expressed in €/kWh or $/kWh.

Distributed Generation: Distributed Generation (DG) is a new trend in electric power generation. The basic concept sees an electricity “consumer”, who is generating electricity for his/her own needs, to send surplus electrical power back into the power grid. An example of DG are factories, offices and especially hospitals which require extremely reliable sources of electricity and heating for air conditioning and hot water. To safeguard their supply and reduce costs, some have installed cogeneration or CHP facilities, often using waste material, such as wood waste, or surplus heat from an industrial process to generate electricity. In some cases electricity is generated from a locally supplied fuel such as natural gas or diesel oil and the waste heat from the generator’s thermal energy source is then used to provide hot water and industrial heating as well. It is often economic to have a co-generation plant when an industrial process requires large amounts of heat that are generated from non-electric sources such as fossil fuels or biomass.

Selling and Purchasing Energy Costs: Electrical energy is exchanged with the power grid, operated by an external utility, with different purchasing and selling costs set by Authorities and Facilities. Due to the different request of electrical power by user, these cost coefficients, expressed in €/kWh or $/kWh, are changing through the day and during the week. Dynamics of energy costs is ruled by free energy market which originated by deregulation process started during ‘90s.

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Table of Contents
Foreword
Lipo Wang
Preface
Hongwei Mo
Chapter 1
Fabio Freschi, Carlos A. Coello Coello, Maurizio Repetto
This chapter aims to review the state of the art in algorithms of multiobjective optimization with artificial immune systems (MOAIS). As it will be... Sample PDF
Multiobjective Optimization and Artificial Immune Systems: A Review
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Chapter 2
Jun Chen, Mahdi Mahfouf
The primary objective of this chapter is to introduce Artificial Immune Systems (AIS) as a relatively new bio-inspired optimization technique and to... Sample PDF
Artificial Immune Systems as a Bio-Inspired Optimization Technique and Its Engineering Applications
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Chapter 3
Licheng Jiao, Maoguo Gong, Wenping Ma
Many immue-inspired algorithms are based on the abstractions of one or several immunology theories, such as clonal selection, negative selection... Sample PDF
An Artificial Immune Dynamical System for Optimization
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Chapter 4
Malgorzata Lucinska, Slawomir T. Wierzchon
Multi-agent systems (MAS), consist of a number of autonomous agents, which interact with one-another. To make such interactions successful, they... Sample PDF
An Immune Inspired Algorithm for Learning Strategies in a Pursuit-Evasion Game
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Chapter 5
Luis Fernando Niño Vasquez, Fredy Fernando Muñoz Mopan, Camilo Eduardo Prieto Salazar, José Guillermo Guarnizo Marín
Artificial Immune Systems (AIS) have been widely used in different fields such as robotics, computer science, and multi-agent systems with high... Sample PDF
Applications of Artificial Immune Systems in Agents
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Chapter 6
Xingquan Zuo
Inspired from the robust control principle, a robust scheduling method is proposed to solve uncertain scheduling problems. The uncertain scheduling... Sample PDF
An Immune Algorithm Based Robust Scheduling Methods
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Chapter 7
Fabio Freschi, Maurizio Repetto
The increasing cost of energy and the introduction of micro-generation facilities and the changes in energy production systems require new... Sample PDF
Artificial Immune System in the Management of Complex Small Scale Cogeneration Systems
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Chapter 8
Krzysztof Ciesielski, Mieczyslaw A. Klopotek, Slawomir T. Wierzchon
In this chapter the authors discuss an application of an immune-based algorithm for extraction and visualization of clusters structure in large... Sample PDF
Applying the Immunological Network Concept to Clustering Document Collections
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Chapter 9
Xiangrong Zhang, Fang Liu
The problem of feature selection is fundamental in various tasks like classification, data mining, image processing, conceptual learning, and so on.... Sample PDF
Feature Selection Based on Clonal Selection Algorithm: Evaluation and Application
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Chapter 10
Yong-Sheng Ding, Xiang-Feng Zhang, Li-Hong Ren
Future Internet should be capable of extensibility, survivability, mobility, and adaptability to the changes of different users and network... Sample PDF
Immune Based Bio-Network Architecture and its Simulation Platform for Future Internet
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Chapter 11
Tao Gong
Static Web immune system is an important applicatiion of artificial immune system, and it is also a good platform to develop new immune computing... Sample PDF
A Static Web Immune System and Its Robustness Analysis
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Chapter 12
Alexander O. Tarakanov
Based on mathematical models of immunocomputing, this chapter describes an approach to spatio-temporal forecast (STF) by intelligent signal... Sample PDF
Immunocomputing for Spatio-Temporal Forecast
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Chapter 13
Fu Dongmei
In engineering application, the characteristics of the control system are entirely determined by the system controller once the controlled object... Sample PDF
Research of Immune Controllers
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Chapter 14
Xiaojun Bi
In fact, image segmentation can be regarded as a constrained optimization problem, and a series of optimization strategies can be used to complete... Sample PDF
Immune Programming Applications in Image Segmentation
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Chapter 15
Xin Wang, Wenjian Luo, Zhifang Li, Xufa Wang
A hardware immune system for the error detection of MC8051 IP core is designed in this chapter. The binary string to be detected by the hardware... Sample PDF
A Hardware Immune System for MC8051 IP Core
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Chapter 16
Mark Burgin, Eugene Eberbach
There are different models of evolutionary computations: genetic algorithms, genetic programming, etc. This chapter presents mathematical... Sample PDF
On Foundations of Evolutionary Computation: An Evolutionary Automata Approach
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Chapter 17
Terrence P. Fries
Path planning is an essential component in the control software for an autonomous mobile robot. Evolutionary strategies are employed to determine... Sample PDF
Evolutionary Path Planning for Robot Navigation Under Varying Terrain Conditions
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Chapter 18
Konstantinos Konstantinidis, Georgios Ch. Sirakoulis, Ioannis Andreadis
The aim of this chapter is to provide the reader with a Content Based Image Retrieval (CBIR) system which incorporates AI through ant colony... Sample PDF
Ant Colony Optimization for Use in Content Based Image Retrieval
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Chapter 19
Miroslav Bursa, Lenka Lhotska
The chapter concentrates on the use of swarm intelligence in data mining. It focuses on the problem of medical data clustering. Clustering is a... Sample PDF
Ant Colonies and Data Mining
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Chapter 20
Bo-Suk Yang
This chapter describes a hybrid artificial life optimization algorithm (ALRT) based on emergent colonization to compute the solutions of global... Sample PDF
Artificial Life Optimization Algorithm and Applications
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Chapter 21
Martin Macaš, Lenka Lhotská
A novel binary optimization technique is introduced called Social Impact Theory based Optimizer (SITO), which is based on social psychology model of... Sample PDF
Optimizing Society: The Social Impact Theory Based Optimizer
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Chapter 22
James F. Peters, Shabnam Shahfar
The problem considered in this chapter is how to use the observed behavior of organisms as a basis for machine learning. The proposed approach for... Sample PDF
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Chapter 23
Dingju Zhu
Parallel computing is more and more important for science and engineering, but it is not used so widely as serial computing. People are used to... Sample PDF
Nature Inspired Parallel Computing
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Chapter 24
Tang Mo, Wang Kejun, Zhang Jianmin, Zheng Liying
An understanding of the human brain’s local function has improved in recent years. But the cognition of human brain’s working process as a whole is... Sample PDF
Fuzzy Chaotic Neural Networks
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