Robust self‐scheduling of a virtual multi‐energy plant in thermal and electricity markets in the presence of multi‐energy flexible technologies

Abstract

Virtual power plant (VPP) aggregates a diverse set of renewable energy sources, energy storages, and flexible loads to operate them as a single power plant in the energy markets. With the advent of multi-energy systems, the operation and mechanism of traditional VPPs are enhanced for acting in multiple markets and providing more flexibility for energy management systems.In this work, a robust optimization method is proposed for optimal selfscheduling of a new VPP termed as a virtual multi-energy plant (VMEP) in electricity and thermal markets in the presence of different energy storages.The proposed VMEP consists of wind turbines, heat-only units, combined heat and power units, photovoltaic panels, electrical energy storages, and thermal storages. Also, thermal and electrical price-based demand response (DR) programs are employed to shift the multi-carrier energy demand of the VMEP's consumers into low price periods. In addition, a robust decisionmaking model is implemented to deal with the uncertainty of electricity market prices in the optimal self-scheduling of the VMEP without the need for a probability distribution function. The simulation results demonstrate that the implementation of multi-energy DR programs can increase the VMEP's daily profit by about 8.5%. Additionally, the economic performance of the VMEP in multi-energy markets is enhanced up to 10.5% by utilizing multi-carrier energy storages. K E Y W O R D Scombined heat and power systems, multi-carrier energy storage, multi-energy demand response, robust optimization, virtual power plant

Optimal operation of active distribution networks hosting hybrid hydrogen‐electricity refuelling stations considering water demand under a stochastic‐IGDT approach

“…In other words, all of the hydrogen demand of HVs comes from the electrolysis process. The available hydrogen produced by P2H technology is given in (30). Furthermore, the upper and lower limitation of the electricity consumption of P2H is represented in (31).…”

“…Therefore, utilizing IGDT instead of scenario‐based stochastic programming for modelling an uncertain parameter enhances the computational time of optimization problems [29]. In robust optimization approach, it is essential to have the upper and lower level of uncertain parameter [30]. However, in IGDT approach, the robustness region is obtained after solving the problem.…”

Risk-Averse Decentralized Optimal Scheduling of a Virtual Energy Hub Plant Equipped with Multi Energy Conversion Facilities in Energy Markets

“…For instance, a self-scheduling risk-constrained method based on information gap decision theory (IGDT) is introduced in [7] to maximize the profit of a VEH in local heat and power markets. Study [8] has proposed a robust scheduling for a multi-energy VPP equipped with storage systems as well as demand response program to optimally participate in power and heat markets. A two-stage robust-stochastic optimization method is also presented in [9] for optimal participation of a VEH in energy markets with multiple industrial EHs to minimize the operational costs and risk associated with existing uncertainties.…”