**Modeling and Analysis**

[26] UNDP (1995). Human Development Report 1995. UNDP; New York

2 Billion People. Washington, DC, USA. www.worldbank.org

the fight against poverty, 2004, www.unido.org

itiative 62(3): 282–292.

98 New Developments in Renewable Energy

Health Organization).

(PPI), www.worldbank.org

[27] UNIDO (2004). Industrial Development Report 2004: Industrialization, Environment and the Millennium Development Goals in Sub-Saharan Africa, The new frontier on

[28] Wallace, Bill (2005). Becoming part of the solution: the engineer's guide to sustaina‐ ble development. Washington, DC: American Council of Engineering Companies. In‐

[29] WHO (2002) "Fuel for Life: Household Energy and Health" — report by the World

[30] World Bank (1996). Rural Energy and Development: Improving Energy Supplies for

[31] World Bank (2001). World Bank Database on Private Participation in Infrastructure

**Chapter 5**

**Improved Stochastic Modeling: An Essential**

Nowadays, governments are developing ambitious goals toward the future green and sus‐ tainable sources of energy. In the U.S., the penetration level of wind energy is expected to be 20% by the year 2030 [1]. Several European countries already exhibit the adoption level in the range of 5%–20% of the entire annual demand. Also with further developments in the solar cells technology and lower manufacturing costs, the outlook is that the photovoltaic (PV) power will possess a larger share of electric power generation in the near future. Gridconnected PV is ranked as the fastest-growing power generation technology [2]. PV gener‐ ates pollution-free and very cost-effective power which relies on a free and abundant source

Due to the increasing wind and solar penetrations in power systems, the impact of system variability has been receiving increasing research focus from market participants, regulators, system operators and planners with the aim to improve the controllability and predictability of the available power from the uncertain resources. The produced power from these re‐ sources is often treated as non-dispatchable and takes the highest priority of meeting de‐ mand, leaving conventional units to meet the remaining or net demand. This issue makes the optimum scheduling of power plants in power system cumbersome as embeds the sto‐ chastic parameters into the problem to be handled. The unpredictability along with poten‐ tial sudden changes in the net demand, may face operators with technical challenges such as

> © 2013 Abedi et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

© 2013 Abedi et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

distribution, and reproduction in any medium, provided the original work is properly cited.

ramp up and down adaptation and reserve requirement problems [3-4].

**Tool for Power System Scheduling in the**

**Presence of Uncertain Renewables**

Seyed Hossein Hosseinian and Mehdi Farhadkhani

Additional information is available at the end of the chapter

Sajjad Abedi, Gholam Hossein Riahy,

http://dx.doi.org/10.5772/45849

**1. Introduction**

of energy.
