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Research Methods and Challenges in Demand Side Management

The smart grid requires proper methods for balancing the power supply and demand. Equally, it requires an appropriate way to simultaneously address contrasting demands from both the power utility and the customer. Direct load control—a contract between the utility and the customer—is one possible solution.

Most researchers agree that power utility companies should adopt an appropriate demand load management scheme to cost-effectively balance the power supply and demand, so as to help smooth power use distribution over time. Different methods exist. One involves shifting non-urgent power demands from regular peak time to off-peak time. Another allows utility operators to set energy prices in a dynamic or real-time fashion. For example, during peak hours, the operator sets the energy price higher, giving customers a choice between paying a hefty price for energy consumed and transferring part of their demand to off-peak periods. A third method exploits distributed power generation sources or energy storage to alleviate peak load.

These methods, however, usually only consider either the interest of the utility company or the customer, which can be diametrically opposed. Typically, a power company wants to maximize its revenue while its customers aim to reduce their energy costs; so, for example, a program that prices peak electricity up and discourages consumption might actually deprive the utility of revenue. This case is just one of many in which contrasting demands make it difficult to design a joint demand side management technique that takes into account the interests of the utility company and its customers simultaneously.

Game theory has recently emerged as an attractive solution. Various game-based operator-customer interaction strategies have been proposed that often try to balance the power use over time while adjusting the cost of energy so both the company and the users may benefit. Game theoretic approaches, however, usually let the customers negotiate with the power company until they reach a “consensus” or a point of equilibrium where all the customers feel they have made the best out of the negotiation. Such a consensus may take a long time to reach or it may not exist at all in some cases.

Another potential solution is direct load control (DLC), which is based on a contract between the power company and its customers. According to this contract, the company may control the residential appliances’ operations and energy consumption remotely during peak hours to balance the grid’s power consumption levels. DLC poses privacy concerns for users, however, and it is questionable whether the customers would give such control to the company willingly.

To solve this privacy issue, additional polices need to be integrated. Instead of controlling all residential equipment, the power company could be given access to control only a few power-intensive, non-urgent appliances; that would imply differentiating various types of appliances with different grades of energy usage requirements. A completely transparent policy will offer the customer a clear choice between using or not using DLC. Thus, if a customer wants to reduce his or her energy bill, he or she can give the utility company permission to apply it.

Before we can think of constructing DLC-compliant power usage grades for differentiating appliance-demand requests, we must know whether a residential device is non-schedulable or schedulable. A non-schedulable device, such as a light-bulb, television or personal computer, needs to be operable at any time. A schedulable device, on the other hand, can be planned to start at some time to come.

Schedulable appliances can be further categorized as interruptible and non-interruptible. An interruptible device, such as an air-conditioner, heater or plug-in hybrid vehicle battery, can be paused and re-activated. Non-interruptible equipment, such as washing machines and dish washers, need to be operable continuously once started and do not fall into the scope of DLC-based policy. Each of the schedulable (both interruptible and non-interruptible) appliances can be programmed to request energy from the power company.

Each device is also supposed to have a number of attributes, such as a power requirement (is it extremely power hungry device?), the power company’s preference (does the company prefer activating washing machines to air-conditioners during peak hours?) and operation time (how long may the device run once started?). By combining an appliance’s different attribute values, it is possible to construct the notion of power usage grade.

A device that belongs to a particular power usage grade can request a certain level of power from the company. In other words, the power company can map the device’s requests into a comprehensible maximum and minimum power requirement level depending on its attributes, and manage its power flow based on its level of priority. If the current usage level plus the requested energy is below the company’s total available power threshold, the control station allows the request and activates the appliance immediately. Otherwise, the request is placed in a queue.

A queued request is handled according to the priority of its power usage grade. The earliest arriving request with the lowest power usage grades has the highest priority. Other requests in the queue are then gradually served according to their priority. When an interruptible appliance is activated, it is allowed to remain active without any interruption within a window of time. When the time window expires, the appliance’s request is queued again and assigned a relatively lower priority than appliances activated fewer times.

The direct load control method allows for power usage grades to be easily constructed, and its ability to reduce power distribution demand during peak hours has been demonstrated. DLC enables the power company to balance power use distribution over time and customers to decide when to run their appliances. In the future, more intelligent control policies may be developed for direct load control, which would ensure more sophisticated scheduling for electrical appliances.

Contributor

  • Zubair Md. FadlullahZubair Md. Fadlullah, a member of IEEE, is an assistant professor in the Graduate School of Information Sciences, Tohoku University, Japan. He obtained a Ph.D. from the university in 2011 and a master’s degree in in 2008, both degrees in applied information science.

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  • Nei KatoNei Kato, an IEEE Fellow, has been a full professor at the Graduate School of Information Sciences at Tohoku University since 2003. His research has been in satellite communications, computer networking, wireless mobile communications, the smart grid, image processing and pattern recognition.

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About the Smart Grid Newsletter

A monthly publication, the IEEE Smart Grid Newsletter features practical and timely technical information and forward-looking commentary on smart grid developments and deployments around the world. Designed to foster greater understanding and collaboration between diverse stakeholders, the newsletter brings together experts, thought-leaders, and decision-makers to exchange information and discuss issues affecting the evolution of the smart grid.

Contributors

Joseph PaladinoJoseph Paladino is Senior Advisor in the Department of Energy’s Office of Electricity Delivery and Energy Reliability.

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Shalinee KishoreShalinee Kishore is an associate professor in the Department of Electrical and Computer Engineering at Lehigh University in Bethlehem, Pa.
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Larry SnyderLarry Snyder is an associate professor of industrial and systems engineering at Lehigh University in Bethlehem, Pa.

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Parth PradhanParth Pradhan obtained his B.Tech degree in electronics and instrumentation engineering from the National Institute of Technology, Rourkela, India.
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Zubair Md. FadlullahZubair Md. Fadlullah, a member of IEEE, is an assistant professor in the Graduate School of Information Sciences, Tohoku University, Japan.
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Nei KatoNei Kato , an IEEE Fellow, has been a full professor at the Graduate School of Information Sciences at Tohoku University since 2003.
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Davor Svetinovic Davor Svetinovic is an assistant professor at Masdar Institute of Science and Technology in the United Arab Emirates.

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