Microgrid modelling and online management

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Faculty of Electronics, Communications and Automation | Doctoral thesis (monograph)
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Date

2008-03-07

Department

Department of Automation and Systems Technology
Automaatio- ja systeemitekniikan laitos

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Mcode

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Language

en

Pages

143

Series

Report / Helsinki University of Technology, Control Engineering Laboratory, 154

Abstract

Modern power network owners have to respond to a number of challenges such as significant load changes and growth in the geographical distribution of the customers. On the other hand, the environmental policy and economic requirements from the market are constantly growing. The presence of these problems has led to an increased interest in the local renewable energy generation at the distribution level. The Microgrid (MG) concept assumes a cluster of loads and microsources operating as a single controllable system that provides both power and heat to its local area. Not much is known about Microgrid behavior as a whole system. Some models exist which describe the components of the Microgrid. This thesis aims to model Microgrids at steady state and study their transient responses to changing inputs. Currently models of a Diesel Engine, a Fuel Cell, a Microturbine, a Windturbine, a Photovoltaic cell, and Battery storage have been developed. In this thesis, a generalized formulation is introduced to determine the optimal operating strategy, the goal to minimize the operating costs as well as the reduction of the emission costs and level for a MicroGrid. To solve such a management problem it is first formulated as a nonlinear constrained cost optimization problem. Since the management problem poses a number of simultaneous objectives and constraints a Multiobjective optimization problem is formulated by considering the emission level reduction. A daily income from sold power and cost to be paid to the utility of the purchased power is added to the problem. The model takes also into consideration the reduction of emissions caused by NOx, SO2 and CO2. The optimization is aimed to minimize the operating costs of the system, while constraints are adjusted to meet the customer demand and the safety of the system. Different optimization techniques are applied to solve the problem, such as Mesh Adaptive Direct Search, Sequential Quadratic Programming, Genetic Algorithms, and Game Theory. Test cases provide comparison and evidence of the efficiency of the proposed methods.

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microgrid, diesel engine, fuel cell, microturbine, windturbine, photovoltaic, battery storage, optimization, emission level

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https://urn.fi/urn:nbn:fi:tkk-011374

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[diss] Sähkötekniikan korkeakoulu / ELEC