Participation of Energy Storage-Based Flexible Hubs in Day-Ahead Reserve Regulation and Energy Markets Based on a Coordinated Energy Management Strategy

Abstract

In order to increase energy efficiency, the energy hub is considered as a form of aggregator and coordinator of various resources and storage. With the optimal performance of resources and storage generators based on a proper energy management system, it is expected that hubs can gain financial benefits from energy markets and ancillary services. So, the paper presents the participation of networked energy hubs in day-ahead (DA) reserve regulation and energy markets, where the hub operator incorporates a coordinated energy management (CEM) strategy to manage power sources and energy storage devices within the hub. Hence, this problem maximizes the total profit of hubs in the DA energy and up and down reserve markets. Also, the problem is constrained by optimal power flow (OPF) constraints in gas, electricity, and thermal networks, reserve limits, and hub constraints, including the model of the combined heat and power (CHP), renewable energy source (RES), electrical/thermal storage, parking lots of electric vehicles (EVs), and boiler. Following that, a linear format is obtained for the nonlinear equation using traditional linearization methods so that an optimal solution is found in less time considering less computational error. Eventually, a standard case system is used to test the strategy, and thus, the capabilities of the approach are investigated. The obtained findings validate the potential of the proposed design in enhancing the economic situation of power sources and storage in hub form, which can enhance operation indices by optimal management of the hub so that the energy management of resources and storage in the form of a hub based on CEM compared to their independent management plan has been able to increase the profit of these elements in energy and up and down reserve markets by about 17%, 28%, and 15%, respectively. Regarding technical indices of energy networks, the proposed scheme by creating low energy losses in the gas network and providing pressure drop, overvoltage, and overtemperature within their permissible limits succeeded in reducing the energy losses in electricity and heat networks by about 83% and 38%, respectively, compared to power flow studies. Also, in these conditions, it has reduced the maximum voltage and temperature drop by 45% and 39%, respectively.

Decentralized restoration of distribution systems with coupling neighboring microgrids

“…B) CNMG overloading management : To examine CNMG overloading management, two EV charging schemes based on [ [28] , [29] , [30] , [31] , [32] , [60] , [61] , [66] , [67] , [68] ] are presented:…”

“…Models that are linear, non-linear, mixed integer linear, or mixed integer non-linear are the constraints [ 58 ]. Intelligent devices are required to apply the optimization model on a network or hybrid system [ [59] , [60] , [61] , [62] , [63] , [64] , [65] ]. The foundation of smart systems is telecommunications technology.…”

A resilience‐motivated restoration scheme for integrated electricity and natural gas distribution systems using adaptable microgrid formation

“…x n,t ⋅ 𝛼 31) and ( 32) represent the state-of-charge (SOC) of ESSs at each time instant, given the initial level of energy stored in ESSs [46].…”

“…Equations (28) and (29) indicate storage units' charging/discharging constraints. Maximum and minimum bounds of SOC are restricted in Equation (30) while Equations (31) and (32) represent the state‐of‐charge (SOC) of ESSs at each time instant, given the initial level of energy stored in ESSs [46]. $$$$\begin{equation}P_{m,t}^{{\mathrm{ESS,ch}}} \le \gamma _{m,t}^{{\mathrm{ESS}}} \times \overline {RT_m^{{\mathrm{ch}}}} \,\,\,\,\,\,\,\,\,\forall m \in \Upsilon ,t \in T\end{equation}$$$$ $$$$\begin{equation}P_{m,t}^{{\mathrm{ESS,dch}}} \le \left( {1 - \gamma _{m,t}^{{\mathrm{ES}}}} \right) \times \overline {RT_m^{{\mathrm{dch}}}} \,\,\,\,\,\,\,\,\,\forall m \in \Upsilon ,t \in T\end{equation}$$$$ $$$$\begin{equation}\underline {P_m^{{\mathrm{SOC}}}} \le P_{m,t}^{{\mathrm{SOC}}} \le \,\overline {P_m^{{\mathrm{SOC}}}} \,\,\,\forall m \in \Upsilon ,t \in T\end{equation}$$$$ …”

Resilience-Oriented Scheduling of Shared Autonomous Electric Vehicles: A Cooperation Framework for Electrical Distribution Networks and Transportation Sector

“…Like conventional EVs, an important potential of SAEVs is the ability to deliver the electrical energy stored in their batteries to the power grid, called V2G capability. Tis feature of SAEVs can provide the power system with various ancillary services such as peak shaving [40][41][42], demand response [43,44], mitigating renewables generation variability and intermittency [45,46], voltage, and frequency regulation [47][48][49][50][51][52]. In addition, SAVEs can help distribution networks in emergency conditions to make the grid more resilient.…”