ABSTRACT
Smart grid in the future generation of power system has the characteristic for the power flow in grid-connected mode, but one of the major issues encountered is the unpredictable power supply from renewable energy sources (RESs). In the noncooperative https://www.w3.org/1998/Math/MathML"> H ∞ https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9780429432941/63df2486-ef31-4a70-9449-3488247ff9a8/content/inline13_1.tif" xmlns:xlink="https://www.w3.org/1999/xlink"/> management strategy, the microgrids are involved in pursuing their own interests with different energy cost consideration which may partly conflict with others in a smart grid network. Recently, it still lacks analytical or computational methods to efficiently solve the complex noncooperative power flow management problem for the smart grid with unpredictable power supply from RESs. In this chapter, we formulate a novel utility function by which each manager could design his/her noncooperative https://www.w3.org/1998/Math/MathML"> H ∞ https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9780429432941/63df2486-ef31-4a70-9449-3488247ff9a8/content/inline13_2.tif" xmlns:xlink="https://www.w3.org/1999/xlink"/> management strategy according to their own consideration and the possible effect of other competitive managers’ strategies. Since it is not easy to solve the noncooperative https://www.w3.org/1998/Math/MathML"> H ∞ https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9780429432941/63df2486-ef31-4a70-9449-3488247ff9a8/content/inline13_3.tif" xmlns:xlink="https://www.w3.org/1999/xlink"/> management strategy directly, by the proposed indirect method, we can transform the noncooperative https://www.w3.org/1998/Math/MathML"> H ∞ https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9780429432941/63df2486-ef31-4a70-9449-3488247ff9a8/content/inline13_4.tif" xmlns:xlink="https://www.w3.org/1999/xlink"/> management strategy design problem in a smart grid network to a linear matrix inequalities (LMIs)-constrained multiobjective optimization problem (MOP). Because the LMIs-constrained MOP still has difficulty to solve directly by the conventional multiobjective evolution algorithm (MOEA) for its Pareto optimal solutions, we develop an LMIs-constrained MOEA to obtain the multiperson noncooperative https://www.w3.org/1998/Math/MathML"> H ∞ https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9780429432941/63df2486-ef31-4a70-9449-3488247ff9a8/content/inline13_5.tif" xmlns:xlink="https://www.w3.org/1999/xlink"/> management strategy for a smart grid network efficiently. For the cooperative https://www.w3.org/1998/Math/MathML"> H ∞ https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9780429432941/63df2486-ef31-4a70-9449-3488247ff9a8/content/inline13_6.tif" xmlns:xlink="https://www.w3.org/1999/xlink"/> management strategy design problem of a smart grid network, we also transform it into a LMIs-constrained single-objective problem (SOP) to guarantee a robust https://www.w3.org/1998/Math/MathML"> H ∞ https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9780429432941/63df2486-ef31-4a70-9449-3488247ff9a8/content/inline13_7.tif" xmlns:xlink="https://www.w3.org/1999/xlink"/> target regulation performance. The simulation results of these two https://www.w3.org/1998/Math/MathML"> H ∞ https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9780429432941/63df2486-ef31-4a70-9449-3488247ff9a8/content/inline13_8.tif" xmlns:xlink="https://www.w3.org/1999/xlink"/> management strategies are given to illustrate the design procedure and validate the performance of the proposed method in the smart grid network.
