智能微电网研究（Python&Matlab代码实现）

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本文目录如下：

目录

1 概述

2 运行结果

3 参考文献

4 Python&Matlab代码、数据、文章讲解

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1 概述

摘要：随着小型可再生能源发电机的出现，将我们的能源系统从分级设计转变为一组几乎独立的微电网的想法变得可行。智能微电网概念在针对负荷平衡、定价、消费者集成和家庭自动化的研究和工程中面临着若干挑战。在本文中，我们首先概述了这些挑战，并提出了针对所发现问题的方法。虽然针对特定领域存在有前景的算法，但发现缺少专门针对智能微电网的集成。因此，本文提出了一种体系结构，该体系结构集成了所提出的方法，并定义了识别的组件（如发电机、存储、智能设备）之间的接口。

文献来源：

  分布式可再生能源生产的趋势带来了新的挑战。可再生能源通常依赖于天气，因此导致难以管理的可变能源生产。然而，它们是未来智能电网的一个重要组成部分，因此有很多努力使这些资源更高效。未来的智能电网不仅必须整合分布式可再生能源，还必须整合信息和通信技术（ICT）进行管理和控制。目前，信息和通信技术的集成是通过安装智能电表完成的，这将打开一个新的应用领域。

                                               图3：联网智能微电网展望 

2 运行结果

 

 

 

部分代码：

% One Week
figure('Position', [100, 100, 600, 400]);
plot(ideal_solar(1:168), 'g.-');
hold on;
plot(real_solar(1:168), 'r.-');
title({'Comparision of Ideal and Real PV Value',' (1991-01-01~1991-01-07)'});
ylabel('Energy (Wh)');
xlabel('Hour');
legend('ideal PV', 'real PV', 'Location', 'southoutside','Orientation','horizontal');
% One Year
figure('Position', [100, 100, 600, 400]);
plot(ideal_solar, 'g.');
hold on;
plot(real_solar, 'r.');
title({'Comparision of Ideal and Real PV Value',' (1991-01-01~1991-12-31)'});
ylabel('Energy (Wh)');
xlabel('Hour');
legend('ideal PV', 'real PV', 'Location', 'southoutside','Orientation','horizontal');

% -------------------------------------------------------------------------
% Plot Different Models
% -------------------------------------------------------------------------
% 3 Scenarios
figure('Position', [100, 100, 600, 400]);
plot(cumsum(sp3_2), 'g.');
hold on;
plot(cumsum(sp8_2), 'r.');
title({'Comparision of 3 Scenarios and 8 Scenarios'});
ylabel('Cumulative Sum of Year-long Energy (Wh)');
xlabel('Hours');
legend('3S', '8S', 'Location', 'southoutside','Orientation','horizontal');

figure('Position', [100, 100, 600, 400]);
plot(cumsum(sp3_2), 'g.');
hold on;
plot(cumsum(sp3_3), 'r.');
title({'Comparision of 3 Scenarios with Repeating Second Stage or Not'});
ylabel('Cumulative Sum of Year-long Energy (Wh)');
xlabel('Hours');
legend('Repeat', 'Non-Repeat', 'Location', 'southoutside','Orientation','horizontal');
% plot difference
plot((sp3_2-sp3_3));

% plot with look day ahead
figure('Position', [100, 100, 600, 400]);
plot(cumsum(sp3_24), 'g.');
hold on;
plot(cumsum(sp3_2), 'r.');
title({'Comparision 1-hour Ahead and 24-hour Ahead in 3 scenarios'});
ylabel('Cumulative Sum of Year-long Energy (Wh)');
xlabel('Hours');
legend('24-hour Ahead', '1-hour Ahead', 'Location', 'southoutside','Orientation','horizontal');

plot((sp3_24-sp3_2));

3 参考文献

部分理论来源于网络，如有侵权请联系删除。

[1] C. Avery, A. Beggs, S. Bikhchandani, and S. Board. A Survey of Auction Theory.
Economic Theory, 13(3):227—-286, February 2004.
[2] M.H. Albadi and E.F. El-Saadany. A summary of demand response in electricity mar-
kets. Electric Power Systems Research, 78(11):1989–1996, November 2008.
[3] T. Basar. Dynamic noncooperative game theory. Academic Press, 1995.
[Cle96] S. Clearwater, editor. Market-Based Control: A Paradigm for Distributed Resource
Allocation. World Scientific Publishing Company, 1996.
[4] R. Duan and G. Deconinck. Multi-agent model and interoperability of a market mecha-
nism of the Smart Grids. 2010 IEEE/IFIP Network Operations and Management Sym-
posium Workshops, pages 312–315, 2010.
[5] R. Duan and G. Deconinck. Market Mechanism of Smart Grids: Multi-agent Model
and Interoperability. In International Conference on Networking, Sensing and Control,
volume 0, pages 8–13, 2011.
[6] A.L. Dimeas and N.D. Hatziargyriou. Operation of a Multiagent System for Microgrid
Control. IEEE Transactions on Power Systems, 20(3):1447–1455, August 2005.
[7] R. Das, J.E. Hanson, J.O. Kephart, and G. Tesauro. Agent-human interactions in the
continuous double auction. In Proceedings of the International Joint Conferences on
Artificial Intelligence, 2001. 

4 Python&Matlab代码、数据、文章讲解