Virtual power plants (VPP) play a crucial role in balancing the electricity smart grid. VPPs aggregate energy from decentralized sources, for example, biogas, solar panels, or hydropower, to generate and consume electricity on demand. We study the management of electric vehicle (EV) fleets organized in VPPs as a way to address the challenges posed by the inflexible energy supply of renewable sources. In particular, we analyze the potential of parked EVs to absorb electricity from the grid, and provide electricity back to the grid when needed. A fleet owner can either charge, discharge for renting, discharge to the grid, or keep an EV idle. A unique property of our mixed rental‐trading strategy is that decisions are made between making an EV available for rental, where the location within the city matters (drivers want a car to be close to their place of departure or arrival) and for discharging it to the grid, where location does not matter (vehicles can discharge to the grid from any capable charging point). We study the feasibility of VPPs for a fleet of 1500 real EVs on the “Nord Pool Spot,” a North European electricity spot market. A Fourier series approach captures the demand patterns of carsharing vehicles accurately, especially when our weighted objective function with asymmetric payoffs is applied. We show that the VPP can be profitable to fleet owners, ecologically advantageous through reductions in wind power curtailment, and beneficial to consumers by reducing energy expenses.
We empirically analyze twelve electricity tariffs for residential microgrids. • We calculate that tariffs with volumetric rates would encourage grid destabilization. • We show that capacity charges would moderate the impact of time-varying rates. • We find that a mix of capacity and customer charges would benefit all stakeholders.
Private households are increasingly taking cooperative action to change their energy consumption patterns in pursuit of green, social, and economic objectives. Cooperative demand response (DR) programs can contribute to these common goals in several ways. To quantify their potential, we use detailed energy consumption and production data collected from 201 households in Austin (Texas) over the year 2014 as well as historic real-time prices from the Austin wholesale market. To simulate cooperative DR, we adapt a load-scheduling algorithm to support both real-time retail prices and a capacity-pricing component (two-part pricing schemes). Our results suggest that cooperative DR results in higher cost savings for households than individual DR. Whereas cooperative DR that is based on real-time pricing alone leads to an increase in peak demand, we show that adding a capacity-pricing component is able to counteract this effect. The capacity-pricing component successfully reduces the cooperative’s peak demand and also increases the cost savings potential. Effective peak shaving is furthermore only possible in a cooperative setting. We conclude that cooperative DR programs are not only beneficial to customers but also to energy providers. The use of appropriate tariffs allows consumers and suppliers to share these benefits fairly
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