Jun 11, 2019 08:36 AM EDT
People view the power system as a large-scale complex system with uncertainty because the output of renewable energy such as photovoltaic generation tends to fluctuate. There is a need for an energy management system to control this issue to stabilize the balance of supply and demand of electricity.
In recent years researchers have been making ongoing studies on energy management system against the background of the liberalization of power and the spread of smart meters that visualize the power consumption. An associate professor at Hokkaido University, Koichi Kobayashi, Shun-ichi Azuma, professor at Nagoya University, and an associate professor at Tokyo University of Science, Nobuyuki Yamaguchi, developed demand response analysis and control technologies focusing on time-varying power generation costs.
One of the techniques in energy management systems is demand response. The definition of demand response is when the supply-demand balance is tight, consumers conserve the power consumption and change the power consumption pattern according to the setting of the electricity price or the payment of incentives (rewards). There is no clarification to the cost-effectiveness.
There has been much attention to the introduction of the "aggregator" that controls the power consumption of consumers. In this framework, aggregators trade between electric power companies and consumers, instead of direct trade between consumers and electric companies. Aggregators manage hundreds of consumers and control their power consumption in response to requests from electric companies. The control of the whole power system becomes more manageable with the introduction of aggregators.
In a day, the cost-effectiveness of demand response fluctuates depending on the demand and supply of electricity. The expectation of this fluctuation becomes more significant by the spread of renewable energy. The demand response aims to maintain the balance between supply and demand, and the focus is its cost-effectiveness.
In the future, however, it will be essential to evaluate the economic value of demand response, focusing on the power generation cost and the adjustment cost (the cost required to adjust power consumption) at each time. Furthermore, it is necessary to develop control strategies that maximize the economic value of demand response.
The unit price of power generation costs needs to fluctuate significantly during the day for the demand response to produce the economic value. If the difference between the highest and lowest generation costs is massive compared to the adjustment costs, then the demand response provides the financial benefit.
More specifically, the authors of this research have derived the condition that "demand response produces the economic value if the difference between the highest price and the lowest price is more than twice the adjustment cost." Since it is a simple condition, it can also be used as a guide to calculate the rewards to consumers.
A control method for demand response is developed to maximize the economic value based on model predictive control in which the optimal control strategy is discovered by the prediction via a mathematical model.
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