VPP Self-Scheduling Strategy Using Multi-Horizon IGDT, Enhanced Normalized Normal Constraint, and Bi-Directional Decision-Making Approach

Abstract

including the URL of the record and the reason for the withdrawal request. Abstract1-This paper presents a new robust self-scheduling strategy for virtual power plants (VPPs) considering the uncertainty sources of electricity prices, wind generations, and loads. Multi-horizon information-gap decision theory (MH-IGDT) as a non-deterministic and non-probabilistic uncertainty modeling framework is proposed here to specifically model the uncertainty sources considering their various uncertainty horizons. Since each uncertain parameter tends to optimize its uncertainty horizon competitively for a particular value of the uncertainty budget, the proposed MH-IGDT model is formulated as a multi-objective optimization problem. To solve this multi-objective problem, enhanced normalized normal constraint (ENNC) method is presented, which can obtain efficient uniformly-distributed Pareto optimal solutions. The proposed ENNC includes augmented normalized normal constraint method and lexicographic optimization technique to enhance the search performance in the objective space. To address the unsolved issue of being risk-averse or riskseeker for a VPP in the market, a bi-directional decision-making approach is presented. This decision maker comprises an ex-ante performance evaluation method and a forward-backward dynamic programming approach to hourly find the best Pareto solution within the generated risk-averse and risk-seeker Pareto frontiers. Simulation results of the proposed self-scheduling strategy are presented for a VPP including dispatchable/non-dispatchable units, storages, and loads.

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

“…The VPP trades energy and DR internally with consumers in its territory based on demand curtailment requests. Authors of [16] proposed a self-scheduling strategy using a multi-horizon information-gap decision theory model instead of assuming a probability distribution function or confidence interval for uncertain parameters, and ensure that a predefined minimum allowable benefit is achieved. In [17], deterministic price based unit commitment model that takes into account balance and security constraints of VPP participating in the joint market of the energy and spinning reserve service has been proposed.…”

Contingency‐constrained operation optimization of microgrid with wind and solar generations: A decision‐driven stochastic adaptive‐robust approach

“…In (14) and 15, active and reactive grid powers are restricted to be within the allowable ranges. The charge and discharge rates of batteries are limited in (16) and (17). The energy limits of batteries are given in (18).…”

“…In [13], a multi‐objective IGDT approach using weighted sum technique has been introduced for the unit commitment and optimal power flow (OPF) of islanding MGs. Other multi‐objective IGDT approaches based on enhanced directed search domain method [14], augmented ɛ‐constraint method [15], and augmented normalized normal constraint method [16] have been presented for AC OPF, multi‐year transmission expansion planning, and virtual power plant self‐scheduling, respectively.…”

A refined decision-making method for orderly power consumption in virtual power plants considering load characteristics

“…Demand response is an important resource for virtual power plants to carry out internal regulation, which is of great significance in assisting the grid in cutting peaks, filling valleys, and consuming new energy sources [15]. In order to improve the stability of virtual power plants and the utilization rate of resources, it is particularly important to study the refined assessment and optimal scheduling of virtual power plant resources based on the consideration of load characteristics [16]. This paper proposes a refined decision-making method for orderly power usage, which makes the decision-making process more scientific and reasonable by aggregating load resources through virtual power plants.…”