動態定價是一種非常重要的管理策略以幫助我們穩定在智慧電網中不平衡的能量。每一個微電網的電力需求將會考慮平均電力需求行為（由於價格策略而產生的交互作用），以及受到智慧電網中的隨機擾動干擾。因此，我們對平均場隨機智慧電網系統（之電力需求）建模以描述每一個微電網（在價格策略而產生的交互作用）之電力需求的交互作用。而對於智慧電網的運作來說，智慧電網管理者期望利用節省的定價管理策略使得微電網的儲能水平達到一定的運作標準，而且可以抑制外來干擾對儲能性能的影響來自於斷斷續續、不可預測的可再生能源 (RESs)。在本篇研究，一個多目標最佳化 (MO) H_2/H_∞的動態定價策略被提出同時達到H_2最佳化之追蹤期望的參考儲能水平性能以及H_∞最佳化之抑制外部擾動對於追蹤效能之影響，藉由基於積分器的狀態回授控制設計在平均場隨機智慧電網系統。由於直接求解多目標最佳化問題 (MOP)，一個間接法被提出來，而多目標H_2/H_∞的動態定價策略問題被轉換成一組線性矩陣不等式限制下之多目標最佳化問題 (LMIs-constrained MOP)。然而使用傳統多目標演化演算法 (MOEA) 仍然不容易直接地求解LMIs-constrained MOP。因此LMIs-constrained MOEA 被提出以有效地解決動態定價在平均場隨機智慧電網之LMIs-constrained MOP。最後，展示平均場智慧電網系統之模擬結果證明所提出的MO H_2/H_∞的動態定價策略之有效性。

Dynamic pricing is one of the most important managemant policy to help stabilize imbalaence energy in the smart grid network. The power demands of each mircogrid will interact among each other through the price policy, which is influenced by the mean (collective) behavior and random fluctuation of the smart grid network. Therefore, we model mean field stochastic smart grid network system to describe the interation with each microgrid by the mean field stochastic theory. For the market operation of smart grid system, managers of smart grid network expect the dynamic pricing policy to achieve their desired energy stored working level not only with parsimonious price but also can attenuate effect of external disturbance due to unpredictable intermittent renewable energy sources (RESs). In this study, a multiobjective optimal (MO) H_2/H_∞ dynamic pricing policy is proposed to achieve H₂ optimal desired reference energy storage tracking and H_∞optimal attenuation of the effect of external disturbance by an integration-based state feedback control in the mean field stochastic smart grid network system. Since it is diffcult to slove the multiobjective optimization problem (MOP), an indirect method is proposed to transform the MO H_2/H_∞ dynamic pricing policy problem in mean field stochastic smart grid network system to a linear matrix inequalities (LMIs)-constrained MOP. Since the LMIs-constrained MOP is still not easily solved directly by the conventional multiobjective evolution algorithm (MOEA). Therefore, an LMIs-constrained MOEA is proposed to solve the LMIs-constrained MOP of the dynamic pricing policy in the management of mean field stochastic smart grid network efficiently. The simulation results of the mean field stochastic smart grid network system are also provided to illustrate and validate the proposed multiobjective H_2/H_∞ dynamic pricing management.

Abstract ---------------------------------------------------------------------------------------- i
Content ---------------------------------------------------------------------------------------- ii
I.Introduction -------------------------------------------------------------------------- 1
II.System Description and Problem Formulation----------------------------------- 3
III.Multiobjective H_2/H_∞ Dymanic Pricing Policy Design For Mean Field Stochastic Smart Grid System---------------------------------------------------- 8
IV.The LMIs-Constrained MOEA For Multiobjective H_2/H_∞ Dynamic Pricing Policy of Mean Field Stochastic Smart Grid System ------------------------- 11
V.Simulation Results ----------------------------------------------------------------- 12
VI.Conclusions--------------------------------------------------------------------------16
Appendix A ---------------------------------------------------------------------------------- 17
Appendix B ---------------------------------------------------------------------------------- 20
References ----------------------------------------------------------------------------------- 22

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