Short-Term Bidding Strategy for a Price-Maker Virtual Power Plant Based on Interval Optimization

Abstract

A virtual power plant is proposed to aggregate various distributed renewable resources with controllable resources to overcome the uncertainty and volatility of the renewables so as to improve market involvement. As the virtual power plant capacity becomes remarkable, it behaves as a strategic price maker rather than price taker in the market for higher profit. In this work, a two-stage bi-level bidding and scheduling model is proposed to study the virtual power plant strategic behaviors as a price maker. A mathematical problem with an equilibrium constraints-based method is applied to solve the problem by transforming the two level problem into a single level multi-integer linear problem. Considering the deficiency of computational burden and implausible assumptions of conventional stochastic optimization, we introduce interval numbers to represent the predicted output of uncertainty resources in a real-time stage. The pessimism degree-based method is utilized to order the preferences of profit intervals and tradeoff between expected profit and uncertainty. An imbalance cost mitigation mechanism is proposed in this pessimism degree-based interval optimization manner. Results show that the bidding price directly affects the cleared day ahead of the locational marginal price for higher profit. Interior conventional generators, energy storage and interruptible loads are comprehensively optimized to cover potential power shortage or profit from market. Moreover, controllable resources can decrease or even wipe out the uncertainty through the imbalance cost mitigation mechanism when the negative deviation charge is high. Finally, a sensitivity analysis reveals the effect of interval parameter setting upon optimization results. Moreover, a virtual power plant operator with a higher pessimism degree pursues higher profit with higher uncertainty.

Optimal Control of a Virtual Power Plant by Maximizing Conditional Value-at-Risk

“…The weather data used are from 1 July 2018 to 21 July 2018 and 1 December 2018 to 31 December 2018. The time horizon chosen was one day of 24 h. By using LSSVM, the wind speed and global radiation were forecast simultaneously, where the power output of WT/PV was calculated using Equations ( 6) and (7). The number of data samples for CVAR was 50.…”

“…Kardakos et al [6] proposed a bidding strategy for VPPs in the power market, where a bi-level program with equilibrium constraints was presented for modeling the behavior of each producer. Based on the concept of aggregators, a bidding strategy for the dispatch of a VPP was proposed to obtain maximal profit [7]. VPP architecture was proposed in [8,9] based on a smart producer to establish an energy management platform by integrating distributed power and energy storage with demand response.…”

Optimal Operation Strategy of Virtual Power Plant Considering Real-Time Dispatch Uncertainty of Distributed Energy Resource Aggregation

“…Authors in [12] have presented a hybrid optimization model including grid search algorithm and a proprietary derivative-free algorithm for DGs, ESSs, and MGs in VPP. A DAM bidding strategy for a VPP as a price maker to minimize RTM penalty has been studied through uncertain resources modeling using interval interpretation [13]. A strategy for a VPP to purchase DR services to reduce penalty cost has been presented in [14].…”

Virtual Power Plant’s Optimal Scheduling Strategy in Day-Ahead and Balancing Markets Considering Reserve Provision Model of Energy Storage System

“…Depending on the type of market the VPP participates in, different optimal scheduling models have been launched to optimally coordinate the VPP's components and maximize the VPP's profits. For instance, studies [14][15][16] only consider the role of VPP in the day-ahead (DA) market, while in studies [17,18], the authors assume that their VPPs participate in the joint DA and intraday (ID) markets. Some more recent studies [19][20][21][22][23][24] consider the VPP's trading strategy in both DA and balancing markets.…”

“…Equation ( 14) ensures that the ESS reserve energy provision does not affect its scheduling in the energy market. Besides, Equation (15) shows that the ESS energy level should be limited by its rated capacity at any time.…”