Abstract
The concept of virtual power plant (VPP) has been proposed to facilitate the integration of distributed renewable energy. VPP behaves similar to a single entity that aggregates a collection of distributed energy resources (DERs) such as distributed generators, storage devices, flexible loads, etc., so that the aggregated power outputs can be flexibly dispatched and traded in electricity markets. This paper presents an optimal scheduling model for VPP participating in day-ahead (DA) and real-time (RT) markets. In the DA market, VPP aims to maximize the expected profit and reduce the risk in relation to uncertainties. The risk is measured by a risk factor based on the mean-variance Markowitz theory. In the RT market, VPP aims to minimize the imbalance cost and wind power curtailment by adjusting the scheduling of DERs in its portfolio. In case studies, the benefits (e.g., surplus profit and reduced wind power curtailment) of aggregated VPP operation are assessed. Moreover, we have investigated how these benefits are affected by different risk-aversion levels and uncertainty levels. According to the simulation results, the aggregated VPP scheduling approach can effectively help the integration of wind power, mitigate the impact of uncertainties, and reduce the cost of risk-aversion.
Highlights
Virtual power plant (VPP) is termed as a single entity that integrates the portfolio of different types of distributed energy resources (DERs) [1,2]
The operational cost of battery energy storage system (BESS) CitBESS at time t bus i generally refers to maintenance cost [16], which can be modeled by a linear function as: CitBESS = β iBESS · PitBESS ∆t + β iBESS EitBESS η L ∆t where PitBESS denote the charged or discharged BESS power at time t bus i; EitBESS denotes energy stored in BESS; ∆t denotes a factor that converts power to energy, i.e., time duration; η L denote leakage loss factor of BESS; and β iBESS is the cost coefficient of the BESS lifetime depression, which is calculated as [23]: IC BESS
distributed generation (DG) can refer to any kind of DERs though, in this paper DG is only termed as distributed thermal units, in order to distinguish it from wind power units
Summary
Virtual power plant (VPP) is termed as a single entity that integrates the portfolio of different types of distributed energy resources (DERs) [1,2]. The profit variability in electricity markets becomes the major concern and VPP should optimize the scheduling of DERs. Energies 2017, 10, 965 and bid/offer more strategically [11]. Reference [10] has proposed an optimal operation model for VPP participating in both day-ahead (DA) and balancing markets. In [12], the optimal offering of VPP in the DA and balancing markets is modeled as an MILP problem, aiming to maximize the expected profit. Some references have not well addressed the risks for VPP participating in pool markets or failed to characterize the correlations between uncertainties (e.g., load, price or wind power availability). In this paper, we have proposed an optimal scheduling model for a price-taker VPP participating in the DA and RT balancing markets.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
