The future smart grid is envisioned as a large-scale cyber-physical system encompassing advanced power, communications, control, and computing technologies. In order to accommodate these technologies, it will have to build on solid mathematical tools that can ensure an efficient operation of such heterogeneous systems [1]. Game theory as an analytical powerful tool can use for addressing relevant challenging problems in various aspects of smart grid. We work in the following fields of smart grid in our group.

Current Projects:

1. Data Center Demand Response

The use of cloud-based services has significantly increased in recent years due to its various advantages ranging from seamless connectivity to almost unlimited storage. To accommodate this growing trend, cloud providers (CPs) are incentivized to build multiple large-scale data centers (DCs) to serve their clients. Each data center includes hundreds to thousands of servers, storage equipment, cooling facilities, and power supplies. The energy consumption of DCs is very significant. This amount of power consumption has a major impact on the power grid. On the other hand, the huge power consumption of DCs increases the CPs cost. Therefore, it is necessary for CPs to cut their electricity bill.

Despite this side effect, DCs are considered as a good target for participation in the smart grid demand response (DR). DR which is an important feature of the smart grid seeks to provide suitable incentives to induce dynamic demand management of customers’ load in response to grid conditions. Moreover, the participation of DCs in DR is beneficial for DCs operators as they can reduce their paid for electricity bill as well as gain a profit by contribution in some programs. But. this participation needs the design of the suitable DR programs that consider the special conditions and requirements of CPs, and this is a challenging problem. In this work, we try to address this issue and help the CPs to better scrimmage with this problem. 

Finished projects:

1. Key Management Scheme for Smart Grid Network

Reliable communication and security issues are considered as one of the main challenges in the Smart Grid (SG) establishment. Security and privacy policy in future smart grid will have an absolutely significant impact on their implementation. What is done and required in the current electricity grid has a significant difference with telecom and Internet. As an example, the ordinary meters of electricity or gas in Advanced Measuring Infrastructure (AMI) are devices with low functionality, using personal wireless network technology (e.g.IEEE 802.15.4).

We need to design a safe protocol with small computational overhead. Authors in [2] proposed a secure key management scheme which uses the collected information from human body sensors (e.g. heart electrocardiography (ECG) sensors) for generating the symmetric cryptographic key for a Wireless Body Area Network (WBAN). Due to the similarity between requirements of SG and WBAN; similarly, we can use the ”consumers load pattern” for the key generation in the SG.

2. Electric Vehicle Owner Behaviour Analysis in Smart grid

Electric vehicles (EVs) play a potentially crucial role in smart grids and can strongly affect power networks. If EVs’ effect is neglected in power networks, it may cause severe problems in providing service. In contrast, if smart grids analyze this effect and establish policies to manage it, then EVs can be regarded as assets: occasional producers instead of perpetual consumers. The smart grid can manage load balancing by the assistance of EVs. Moreover, EVs may save money or even make money in the process. In fact, this collaboration is beneficial for both EVs and smart grid. Our research in this field has been focused on creating policies to regulate prices in a way that incentivize EVs to cooperate with the smart grid.

[1] Saad, W., Han, Z., Poor, H. V., & Başar, T. (2012). Game-theoretic methods for the smart grid: An overview of microgrid systems, demand-side management, and smart grid communications. Signal Processing Magazine, IEEE, 29(5), 86-105.

[2] Mana, M., Feham, M., and Bensaber, B. A. Trust Key Management Scheme for Wireless Body Area Networks. IJ Network Security, 12(2), pp.75-83, 2011.


  • Dr.  Mohammad Hossein Manshaei
  • Monireh Mohebbi Moghaddam
  • Mohammad Sadegh Nourbakhsh
  • Roya Vaezzadeh
Reseach Status: Active