Bi‐level optimization of the integrated energy systems in the deregulated energy markets considering the prediction of uncertain parameters and price‐based demand response program

Abstract

The proliferation of multienergy systems (MESs) in recent years has led to the higher efficiency of energy consumption in different sectors. MESs are generally studied in the context of energy hubs (EHs). Most of the previous works in the field of EH operation and scheduling are at the scale of residential households or communities. There is a research gap in proposing a framework in which the EH actively interacts with different energy networks. This article, however, is focused on the optimal operation and scheduling of the EH and two integrated energy grids such as power and heat distribution systems. The EH consists of combined heat and power (CHP), electric heat pump (EHP), thermal and electric energy storage systems, which are operated by an independent entity. The proposed optimization problem is based on the bi-level optimization method in which the EH is the leader and two networks are the followers. Nonlinear variables are also linearized using binary expansion and Big M methods. As another research novelty, uncertainties of load demand and photovoltaic (PV) systems' output power are taken into account using a mixed machine learning (ML)-based forecasting method. To enhance the flexibility of the system, a price-based demand response (DR) program is proposed based on the predicted load pattern of the mixed ML method. Simulation results show the efficiency of the optimization model. Also, the DR program has a significant impact on the system's cost by 40% improvement in the profit of the system.

Coordinated planning of the distribution system and regional energy network in the presence of responsive loads

“…pvi wtj at the loadlevel L i and the energy stored in the previous period. In Equation (20), the maximum power injected and absorbed by the EES device is modeled, and the two binary variables b Ch EES and b Disch EES are defined to identify the charge and discharge status of the energy storage, respectively. Equation (21) indicates that the presence impossibility of an energy storage device in both charging and discharging modes at the load-level L i , simultaneously.…”

“…The existence of variable and uncontrollable production resources such as PVs and WTs 6 in addition to controllable resources such as microturbines, CHP units, 7 and responsive loads with adjustable and shiftable consumption necessitates the need for optimal energy management and planning 8 . Smart microgrids planning with different objectives such as microgrid type, 9 reducing losses, 10 costs, 11 and improving reliability due to uncertainties related to load demand, 12,13 renewable resources power generation, 14,15 thermal 16 and hydrogen demand, 17 heat storage, 18 demand response (DR), 19,20 and energy price 21,22 have been researched. With the expansion of the energy portfolio and the use of different energies such as electricity and natural gas, various definitions of energy systems including multienergy systems, 23 energy hubs (EHs), 24 and regional energy networks (RENs) have been formed.…”

Multi-Time-Scale Optimal Scheduling of Integrated Energy System Considering Demand Response

“…According to the load demand characteristics of users, it can be divided into transferable load and interruptible load. According to the response form, it can be divided into price demand response (PDR) and incentive demand response (IDR) [5][6]. Ref.…”

Investigating the onset and steady-state conditions of a diaphragm thermoacoustic stirling engine

“…Stirling engines as external combustion engines can be employed to achieve this aim. 5,6 One can classify Stirling engines into two classes, i.e. kinematic and dynamic ones.…”